Manufacturing Operations Management Talk

Eliminating the Hateful Eight Types of Disconnectivity in Smart Manufacturing

“For hundreds of years, humans and machines have worked together. But something peculiar has happened recently: the machines are now speaking to us—and to one another”
                                                              -- Booz Allen, Vice President at Sedar LaBarre [1]

That would be a great quote from Booz Allen, if it weren’t slightly ahead of its time. New machines want to speak to us and want to be part of orchestrated processes in the factory, but we are not there yet. The mission ahead for Smart Manufacturing initiatives is to make it happen and realize the enormous potential from new levels of synchronization spanning from product design to order, realization, and delivery.

How Smart Manufacturing complements Lean Manufacturing efforts

Smart Manufacturing can be viewed as a process improvement movement complementary to the Lean Manufacturing movement of the past two decades. Lean Manufacturing initiatives design out the overburden, inconsistencies and waste to achieve enhanced processes that deliver products smoothly and consistently. In contrast, Smart Manufacturing initiatives are bringing the physical and digital worlds together designing in connectivity and orchestration to achieve enhanced processes that deliver products and their data smoothly and consistently to customers. Going forward, both Lean and Smart Manufacturing will be interrelated improvement efforts in the manufacturing value chain.

Eliminating the Hateful Eight Types of Disconnectivity

There are eight types of disconnectivity in the manufacturing value chain that need to be eliminated as organizations embark on the Smart Manufacturing journey to synchronize, automate and optimize the physical and digital processes.
Each of the communication and process participants listed in this slideshow need to be systematically connected to achieve the desired Smart Manufacturing goals, which we discussed in the prior article titled: “On the Journey to a Smart Manufacturing Revolution.” [2]

#1 Equipment

Machines and equipment are getting smarter with more onboard computer processing power and more connectivity to the Internet. This advancement in connectivity is referred to as the Internet of Things (IoT). For example, at home, I can set up my printer to directly order ink when it is low and have it shipped to my house. At the factory, we refer to this type of internet connectivity direct from machines as the Industrial Internet of Things (IIoT).

Equipment, machines, robots and advanced sensors can be connected to systems that automatically adjust or switch operations based on sensing the product, diagnostic or environmental conditions. IIoT connectivity needs to be orchestrated in the overall Smart Manufacturing architecture to, for example, avoid excessive downtime at the factory, because each of our machines has auto-requested maintenance a few days apart instead of coordinating a single service day of downtime for the entire line.

According to a research report from the analyst firm Berg Insight, the installed base of wireless IIoT devices reached 10 million in 2014 and will grow at an annual growth rate of 27% to reach over 43 million by 2020. [3] Yet, many organizations are struggling to reap efficiencies and insights from the connectivity and data because they haven’t created the proper technical and decision making structures to organize connected processes and analyze data for real business value.

#2 Workforce

Yes, people are an integral part of Smart Manufacturing.

People are resources that need to be connected to the processes orchestrated and data produced by the Smart Manufacturing system. We might appreciate disconnectivty when we are on vacation, but definitely not at work. We want to know what is happening at our operations as soon as possible. We want to be able to see when customers raise issues, when the production line is not running smoothly, and when problems are not being resolved in a timely manner.

Technicians, inspectors and material movers need function-specific applications that dispatch them to prioritized tasks and enable them to update status and document issues immediately. Some functions are performed at a desktop, other functions are performed on-the-go on a mobile device—function specific applications connect people at the place where they need to perform the function.
Some workforce resources might be external to the organization. As an example, smaller manufacturers can’t afford to keep every specialized skill as a full-time employee, and might need to route tasks to third-party contract technicians.

Managers need dashboards that display status, trends and alerts to focus their attention on where it is needed most. However, the old passive information model of aggregating data and providing metrics to managers to digest and take action is not enough to achieve new levels of efficiency.
New active manufacturing systems need to automate routine decisions and trigger action based exceptions and unpredictable patterns that require experts to manually analyze for root-cause. There are always plenty of exceptions to manage in daily operations, so don’t worry, people will always be needed.

Legacy paper-based procedures promoted departmental silos by creating paperwork between people in different disciplines, departments and companies in the manufacturing value chain. Social communication and collaborative work tools can connect teams and communities in new ways breaking down old silo boundaries and creating new levels of efficiency in processing and trouble shooting procedures.

As the manufacturing enterprise becomes more connected, it creates the need for a new breed of talent—one that embraces computer skills as much as mechanical skills. The next generation of manufacturing talent must be able to collaborate across multidisciplinary walls, connect digital factories, interpret results from analytics, and protect supply chains from cyber intrusions. Luckily, millennials are tech savvy, learn fast and embrace teamwork. Manufacturers will face strong competition from high-tech companies in recruiting this talent and will need to elevate the importance of strategies to recruit, develop and retain the workforce of tomorrow.

#3 Design

There are currently many manual steps and translations between the virtual product and process designs modeled in 3D and the programming and configuration of physical manufacturing and inspection processes. This type of connectivity is referred to as the “digital thread” spanning from design into supply chain, manufacturing, inspection, and product service.

One of the first applications of the digital thread is between product design and 3D printing processes. A few more of the enhanced processes targeted for the digital thread include: (a) simulation of the virtual production processes and validation of designs in collaboration with partners before physical processes are ever changed, (b) flow of revised specifications into the supply chain to ensure subassemblies are received to the correct design revision level, and (c) automation of translations between design specifications and instructions loaded into automated production and inspection machines.

#4 Parts

One of the goals of Smart Manufacturing is to create flexible manufacturing processes that can adapt to build different products and support mass customization business models. Connected auto-identified parts and materials can help achieve that type of flexibility.

Accuracy of product genealogy becomes more difficult to achieve as product variances and changes increase in a multi-tier supply chain. Accuracy can only be guaranteed if identifiers are read directly from components and materials with automatic identification (auto-ID) tags, like RFID, either embedded in the component or in the packaging. Common applications for RFID include tracking goods in the supply chain, reusable containers, high-value tools assets, parts on inventory shelves, and parts issued to manufacturing.

Flexible manufacturing processes need to (a) recognize the configuration of each product as it traverses the line, (b) load the right parts and programs to machines, and (c) compare component parts as they are loaded into machines and assembled into product in order to verify the correct bill of material configuration.

RFID tags can contain more than part and serial numbers, they can have information about hazardous warnings, expiration date or storage requirements that could trigger special handling processes at the shop floor. For example, automated processes could alert staff if material has expired due to the time it spent outside of the required refrigeration temperature.

#5 Processes

Business processes are chains of tasks that produce a specific service or product for specific customers. Tasks in a business process can span across organizational departments and system boundaries. In order to minimize delays throughout the business process, task outputs need to be connected as inputs to successor tasks. Communication and data processing among tasks should avoid manual data input and translation errors whenever possible.

Legacy business processes where designed around the sequential handover of paper documents between different departments. Modern business processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams and parallel tasks across production and supply chain. Consider the benefits of processes where utilities auto-adjust based on environmental sensor data, where machines take corrective action to avoid damages, where part shelves report usage and are automatically replenished by suppliers, where correction tasks for non-conformances are routed in parallel to multiple departments including Engineering, Procurement and Inventory Control.

Automated processes are standardized processes, and standardization promotes best practices and reduces overhead tied to maintenance of multiple ways of doing similar functions in the organization.

#6 Suppliers

To optimize today’s highly outsourced manufacturing value chain, supply chain management has to switch off the traditional procurement function managed by paper documents, emails and phone calls. Suppliers need to connect digitally with the companies they supply via web portals, supply chain hubs, and business-to-business (B2B) communication exchanges.

A demand driven supply chain can reduce inventories and schedule performance. Automated demand triggers can be achieved to replenishment and move material based on sensing part installation or empty shelves.

The orchestration of multi-tier supply chains for complex products like aircraft or medical devices require additional attention to connected change management processes for product design information.

#7 Customers

Manufacturers have traditionally faced challenges in collecting reliable data to understand and adjust to changing customer needs. Data collected via surveys is unreliable, expensive and burdens the customer.

Today’s data analytics capabilities represent a new frontier for customer satisfaction. The ability to collect and analyze customer usage data in real time allows manufacturers to make quicker adjustments. For example, remotely collecting usage data on farms from deployed windmills can reveal shifting wind conditions and immediately allow a remote operator to optimize the degree of effort that specific windmills are exerting within those farms. This type of data visibility direct from customers allows manufacturers to not only fix current products, but also anticipate the need for new features before customers ask for them.

Customer involvement in the middle of some business processes can lead to better customer experiences. Customers want more visibility of progress on long projects, and online processes to coordinate orders, deliveries, audits, warranty claims and returns. Customers should be able to remotely approve changes like deviations from specifications, late deliveries, and corrective actions.

#8 Records

As more machines are connected, it would be easy to fall into data hoarding practices. Data should be stored for a specific period of time as part of a connected system strategy, and not just collected because it might be useful one day.

Data hoarding practices can be expensive and make it hard to find real productivity improvement areas among all the noise.
Different documentation and data records have varying degrees of precision, control and time persistence requirements in support of different business processes in the organization. Some data can be of a temporary nature and only needed during the execution of a specific business process, but some data has persistence requirements.

It is important that systems of record are well connected as sources and destinations for data in processes orchestrated by Smart Manufacturing systems. Regulated industries require detailed production history records. As an example, the FDA's quality system regulation (21 CFR Part 820) requires medical device manufacturers to establish and maintain device history records (DHRs) for each batch, lot and unit they produce. The DHR must include as-built history that can be validated against the respective product design (as-designed) and applicable processing and labeling standards.

Pulling it Together With Security and Communication Standards

Security and communication standards are two important considerations that apply to each of the eight areas of connectivity for smart manufacturing systems.

The connected nature of future Smart Manufacturing systems provide great opportunities for optimization but also increase the exposure to potential cyber threats. A strong cybersecurity program spanning people, processes and technologies is necessary to protect information assets and the trusted relationships with suppliers and customers.

A key missing capability to achieve Smart Manufacturing is the ability to practically and affordably knit together a threaded orchestrated business processes across varied equipment, systems, suppliers and customers.

Today, vendors provide custom one-off solutions that are complicated, expensive, require enormous amounts of time to implement, and are built on proprietary communications. To achieve affordable plug-and-play capabilities, next-generation hardware and software technologies need to work together through common standardized communications.

The list of disconnectivity areas above serves as a roadmap for identifying gaps and opportunities towards new levels of connectivity for future Smart Manufacturing systems. They are all of equal importance on the journey to future Smart Manufacturing excellence.

References
[1] “Capitalizing on Connectivity: The Top 5 Priorities for Industrial Manufacturers”, Booz Allen Hamilton Inc., January 2015

[2] “On the Journey to a Smart Manufacturing Revolution”, Conrad Leiva, IndustryWeek IdeaExchange, December 2015.

[3] “The installed base of wireless IoT devices in Industrial Automation reached 10.3 million in 2014”, Berg Insight blog post, May 2015

[4] “5 Signs you are a Big Data Hoarder”, Bernard Marr, Data-Informed.com, August 2015

[5] “MESA White Paper #52: Smart Manufacturing – The Landscape Explained”, Smart Manufacturing Working Group, MESA.org, January 2016

This article was originally published as "Eliminating the Eight Types of Disconnectivity in Smart Manufacturing" at IndustryWeek.com, Feb 2016

 

What is Smart Manufacturing? What is Ahead?

This presentation recording from MESA.org provides an overview of insights assembled by the MESA Smart Manufacturing Working Group.

Smart Manufacturing is an initiative to bring about a revolution in manufacturing business strategy, turning traditional factories from cost centers into profitable innovation centers. Smart Manufacturing is fueled by a convergence of technologies and process improvement initiatives with the potential to radically improve the way manufacturers thread processes and systems in the entire product value chain in order to deliver more options and new service models to customers. Technologies fueling Smart Manufacturing include the integration of smart machines, Industrial Internet of Things (IIoT), and information technology (IT) including cloud services, 3D models, mobile computing, intelligence, analytics and integration platforms.

"Smart Manufacturing – The intelligent, real time orchestration and optimization of business, physical and digital processes within factories and across the entire value chain.”   MESA Smart Manufacturing Working Group

This presentation discusses the drivers, goals, building blocks and roadmap to Smart Manufacturing. Link to recorded presentation:

"Smart Manufacturing - The Journey Ahead"

 

 

My New Year Wish – Less Hype for Big Data Analytics, More Buzz for Smart Manufacturing

I know of several good use cases for big data related technology in manufacturing operations management and I am sure there are a few more out there but sorry to break it to you…it is not a magic bullet for manufacturing improvement!

Instead, I hope that Smart Manufacturing gets the attention in 2016 that it deserves as a new way to look at connectivity, manufacturing platforms and linkages in the manufacturing enterprise and extended product value chain.

The real value of connectivity of manufacturing equipment and systems is not found by trying to mine a stream of sensor data emanating from machines in the plant in the hope of finding some pearl of wisdom. Wisdom that must have been escaping our shop floor managers’ imagination for years because it was buried in the data. I don’t think so.

The real value of connectivity is found in being able to orchestrate and streamline business processes from desktops to machines, across department walls, across tiers of manufacturing operations management, and across tiers of suppliers— layers of production support processes that have been relegated to email, spreadsheet and other manual cumbersome and error prone methods far too long. Of course, orchestration requires data and metrics, but manufacturing processes and systems need to be designed to acquire certain data with the intention of doing something with it. It is planned data usage, transformation, integration and aggregation driving real innovation. The mining of all machine data in hopes of finding meaningful patterns in the future is not a solid innovation strategy.

In one article Gartner research mentions that 70% of captured manufacturing data goes unused today. [1] The question should not be “What can I do with this data?” the question should be “Why am I capturing data I don’t need”. The solution to that problem is to stop capturing data that does not have a designed use as part of a monitoring or control process.

An LNS article in 2015 titled “Analytics: The New Frontier in Manufacturing Operations and Business Performance” boasts analytics as the new frontier for manufacturing systems. [2] However, looking at the data in that article we can come to a totally different conclusion. LNS found that manufacturer’s top objectives were:

1. Ensuring consistent quality of products
2. Responsiveness to customer order demands
3. Increasing production capacity and capabilities
4. Getting new products to market faster

Then LNS finds that the top challenges for meeting the above are:

1. Lack of collaboration across different departments
2. Disparate systems and data sources
3. ROI justifications for improvement investments
4. Difficulty coordinating across the supply and demand chains

And, finally, the LNS survey lists the following as the most critical areas of software for manufacturers:

1. Production Execution Management
2. Quality Management
3. Real-time Performance Visibility
4. Data Collection and Reporting
5. Supply and Demand Response
6. Overall Equipment Effectiveness
7. Analytics

Analytics was listed as #7, yet it somehow got the headline of the article. Does that make sense? Instead note what did get a lot of attention: “lack of collaboration across departments” and “difficulty coordinating across supply and demand chain”. Those are the real opportunities for improvement in manufacturing business processes. Instead of more articles pushing the hype agenda for big data and analytics, in 2016, it would be great to see more emphasis on manufacturing process improvement enabled by integration standards that connect machines, processes, systems  and the entire Smart Manufacturing value chain of the future.

There is hope in the horizon, one article from Gartner in 2015 suggested that Big Data might be passing the “peak of inflated expectations” and heading for the “trough of disillusionment” in the hype cycle. As manufacturers discover the real use cases for big data technologies they will be able to properly prioritize the importance of its usage within innovation initiatives and stop looking at the technology itself as the innovation.

More on Smart Manufacturing in this article: On the Journey to aSmart Manufacturing Revolution

References:

[1] "Predicts 2016: Opportunities Abound for the Factory to Reach Its Potential", Gartner, 2015 

[2] "Analytics: The New Frontier in Manufacturing Operations and Business Performance", LNS Research, 2015

[3] "Hype Cycles for 2015: Five Megatrends Shift Computing Landscape", Gartner, 2015

 

 

MOM Cures Three Legacy Ailments in Operations

Manufacturers are picking up the pace of new product introductions to satisfy the customer demand for products that incorporate the latest technology innovations. The need for faster innovation adoption into product lines fuels a need for more efficient change management procedures throughout the organization. Many are asking themselves, "Is it possible to handle higher levels of product and process change with small patches to old practices?" Probably not. Strategic level process improvements are needed to increase the capability of the organization to handle higher rates of product updates and new product introduction.

Engineers are using the latest CAD technology to make better, more accurate,  3D models faster leveraging reusable components. 3D models include more product and manufacturing information (PMI) for use downstream in Model-Based Manufacturing, to simulate and accelerate production process innovation, in Manufacturing Operations Management (MOM) to develop shop floor for work instructions, and create automated inspection and test procedures. So why are some organizations still working with 2D drawings, spreadsheets, and paper work instructions at the shop floor? We have found three ailments that hold back many organizations from significant improvement in their change management speed and we have one prescription that helps all three.

Significant strategic process re-engineering can only happen when organizations face head on the following ailments brought upon the organization by a legacy of paper-based practices.

Dependence on Manual Processes is Holding Manufacturers Back

With the technology available today, any manufacturing organization should not be relying on paper-based or spreadsheet-based processes at the shop floor to collect data, track progress, and prepare product or service documents for the customer. Yet many companies still depend on paper today. 

Even traditional Lean Manufacturing practitioners like Toyota are embracing information technology to raise the organization’s performance to a new level. Toyota US CIO, Tim Platt, spoke about "Lean's High Tech Makeover" [1] at the 2014 Industry Week conference. If an organization like Toyota can move beyond the manual Lean processes they promoted for years, other organizations can change too and embrace automated Lean processes supported by information technology.

With a manufacturing operations management (MOM) system the organization moves into a completely paperless system for managing operations. Before MOM, operations would work on piles of paper work orders filled and assembled manually into massive build books. Many labor hours wasted on moving paper, organizing paper, fixing missing information and mistakes. When work orders, instructions and records are digital the paper handling waste is eliminated and processes are hugely streamlined. The mechanic does not have to search through paper or online drawings or books to find the information he needs. The online work instructions will have quick shortcuts and links to documents

Poorly Defined Business Processes are Hiding Inefficiency

It is very difficult to see opportunities for improvement without a good understanding of current business processes. The implementation of a MOM system forces the organization to a better understanding of the as-is and to-be business processes. Many times these processes are not documented and very few employees understand the overall value chain across the organization. Value stream mapping exercises across functional departments like engineering, production, and quality can be very eye opening. Departments are usually focused on their daily routines and do not spend much time thinking about how their work is affecting downstream activities.

Even if procedures are defined in a book it is often difficult to enforce policies at the shop floor. A MOM system installs a work, documentation and change management discipline on operations. It is much easier to implement best practices.

Inaccurate Delayed Information Leads to Sub-optimal Decisions

In order to make good decisions, management needs good data. It is important to know that products are delivered on time and bills are paid, but management also needs to know details about engineering and production to improve those processes. Data is needed beyond delivered product quality, inspection results and defect counts, details are needed about defect and cause classification to tackle the root cause of problems.

Without the proper data, it is difficult to do proper root-cause analysis and easy to always end up finding someone to blame for the problem instead of finding the true system weakness. In order to go beyond the typical blames of: (a) lack of training, (b) personnel carelessness, and (c) human error, organizations need data that will point to any of the potential resource causing problems including personnel, equipment, processes or product design. Information maintained manually in different departmental databases or spreadsheets (information silos) is likely not consistent and possibly leading to different conclusions and priorities in different departments.

With MOM, real-time dashboards provide status of in-process work access to quality data on any work order with the single press of a button. Quality management can be more proactive, focusing on process improvement instead of focusing on manual documentation. There is complete visibility and accountability of who is performing each task, what resources are causing constraints, and the level of engineering changes impacting operations. MOM systems can also provide manufacturing intelligence and scorecards capabilities to monitor the organization’s achievement of performance goals and help quickly identify areas for improvement.

In Summary

Strategic investments in new information technology and new business processes can look like big cultural and budget challenges, but strategic investments can also lead to big contract wins, big margin gains, and the development of new agile, flexible and lean production operation.

A MOM system helps to eliminate some legacy ailments in operations by providing (a) a platform for agile paperless business processes, (b) a systematic control of manufacturing business practices, and (c) an accurate real-time information system for better performance and continuous improvement management.

References

[1] “Toyota’s Lean High-Tech Makeover”, a presentation by Tim Platt, VP Information Systems, Toyota US, May 2014  

 

Clarifying the New Manufacturing IT Lingo

- Smart Manufacturing vs Industrie 4 vs Digital Manufacturing vs IIoT

I had the pleasure of facilitating a workshop on Smart Manufacturing at the Industry Week conference with over 120 professionals involved in different aspects of manufacturing information technology (IT) in a wide range of industries. After we briefly introduced Smart Manufacturing and shared a few results from a MESA-SCMWorld survey, I casually surveyed the group on clarity
among the new terms in manufacturing IT and adoption for the “plug and play” paradigms promoted by Industrial Internet of Things (IIoT), Smart Manufacturing, Digital Manufacturing, and Industrie 4.0 initiatives. Some of my observations from this interesting discussion follow.

QUESTION: Third or Fourth Industrial Revolution?

SURVEY SAYS: 4TH Industrial Revolution. The audience was pretty confident with this response. There was an overwhelming show of hands in favor of 4th versus 3rd.

Some consulting groups have favored calling this era the start of the third industrial revolution and others are calling it the fourth industrial revolution. Everyone agrees that the era of mass production is behind us. An era that was enabled by revolutionary advances like the assembly line and electrical standards. But many groups closer to manufacturing automation want to recognize that there was a third industrial revolution that started in the 1970’s fueled by advances in industrial automation and software for personal computers (PCs).

There is agreement that the next industrial revolution is starting now, and it is fueled by advances in model-based manufacturing, additive manufacturing, robotics, and the Internet of Things (IoT). New technologies are allowing manufacturers to move from mass production make-to-stock practices towards make-to-order mass customization business models. Based on popular opinion, we should stick to calling this the 4th Industrial Revolution.

QUESTION: IoT and IIoT side by side or one under the other?

Do we view the Industrial Internet of Things (IIoT) as a subset of the Internet of Things (IoT) with similar standards and methods of connecting manufacturing, home and office devices to cloud applications and smart phones? Or do we see IoT and IIoT as having similar goals but using different standards and infrastructure?

SURVEY SAYS: IIoT should be subset of IoT. The majority of manufacturers want to see industrial automation use similar standards and mechanism to home and office equipment integration. They would like to see apps on their phones with the ability to view, interact and control the shop just like they have apps today controlling their home or car. IIoT standards and methods should be a subset of IoT methods and standards.

One automation vendor warned that this view of IIoT and IoT might be an oversimplified view on the subject. Point taken, but the survey was more about confirming the expectations of these initiatives and the desires of the end users—similar type of “plug and play” capabilities at the factory and at home.

Another voice wanted to claim that many companies are already doing IIoT at their plants. We went ahead and tested this claim with the audience.

QUESTION: How many companies in this audience currently have integration from
machines to enterprise systems?

SURVEY SAYS: Around 80% of hands were up.

QUESTION: How many of these companies are using open standards for integration
and could simply swap out one automation or software vendor for another?

SURVEY SAYS: Only one timid person in the audience raised their hand half way
up.

These answers point to a big difference between IIoT and prior manufacturing automation integration methods. For the last few decades we depended on custom integration, proprietary interfaces and separate protocols for integration and automation at the factory. Moving forward with IIoT we want to embrace open standards and internet protocols so we can easily swap and mix multi-vendor equipment and software which might be on-premise or in the cloud.

QUESTION: Are Smart Manufacturing and Industrie 4.0 the same thing?

SURVEY SAYS: Yes. The consensus was that in general they are close enough in scope and purpose. However, Industrie 4.0 is perceived as a very specific German initiative so the majority of participants preferred the term Smart Manufacturing.

It is important to note that if initiatives from different countries do not coordinate among each other, we could end up with different standards supported by different countries. Similar to how we ended up with slightly different electrical standards in different countries. Nevertheless, better off with a few standards than none at all and we can always use converters to plug equipment to
different standards.

I noticed that the general voting participation was lower on this topic so I paused the survey for a few clarifications.

QUESTION: Would you like more clarification on the scope of Smart Manufacturing?

SURVEY SAYS: Yes.

Smart Manufacturing is about creating smart products, smart factories, smart manufacturing processes and smart enterprise procedures that link the entire product value chain with a digital thread.

Smart factories are the foundation of Smart Manufacturing. Smart machines and robots in smart factories are capable of managing complexity, are less prone to disruption and are able to manufacture goods more efficiently. In the smart factory, human beings, machines and resources communicate with each other as naturally as in a social network.

Smart products know about their configuration, details of how they were manufactured including critical components and how they are intended to be used. They actively support the anufacturing process, answering questions such as “which parameters should be used to process me?”, “when was I made?”, “for which customer?”, and “when do I need to be serviced?”

QUESTION: Should we view Digital Manufacturing as part of Smart Manufacturing or should we view Digital and Smart as separate initiatives?

SURVEY SAYS: Not clear. So instead we further discussed the difference and relationship between smart and digital manufacturing.

The digital thread that starts with the 3D model-based definition of the product from engineering flows to smart manufacturing and to the smart supply chain linked via standard integration interfaces that connect to web applications, mobile devices and cloud services. The network of connected devices, resources systems, partners and suppliers will result in the transformation of
conventional value chains and the emergence of new business models.

Several industries have used the term Model-Based Enterprise for the initiative of creating a continuous digital thread from design 3D models and specifications (a.k.a. Model-Based Engineering) flowing downstream into the supply chain, manufacturing, inspection and aftermarket services of the product.

As stated earlier, Smart Manufacturing includes smart products and smart manufacturing processes. The digital thread must be interpreted by smart product and smart manufacturing processes in order to build to the right product specifications and configuration. Therefore, we see the digital thread as a requirement for smart manufacturing. We also agree that these two initiatives could be worked side by side if the intersection areas and integration is coordinated among the two initiatives and well defined.

QUESTION: Are IIoT and Smart Manufacturing hype or substance?

SURVEY SAYS: Hype with opportunity behind it. The participants believe there is a bit of hype in the media right now but they also believe there is opportunity for big improvements in manufacturing based on the adoption of new technologies and standards into new manufacturing business models. The intent of these initiatives resonate with the audience.

Did I clarify things? Are we smarter now? (Trick question)

Thanks to everyone that participated in the workshop, especially to those who “volunteered” to participate : )

Do We Have Manufacturing Problems Looking for Smart Solutions?

If you are responsible for manufacturing operations improvements, you can probably dream of a few solutions that would make some hard production problems go away or take your operations to a new level of efficiency. When you hear titles like “Smart Manufacturing”, it is easy to hope that these initiatives will yield some of those dream solutions.

I was in brainstorming workshops a few weeks ago discussing the next generation of platforms and integrated systems for Smart Manufacturing. The discussions around potential new enhanced processes were very fructiferous, so I am sharing a few ideas I personally found very interesting.

• Manufacturing machines could set up themselves and select the right configuration files based on auto identifying the product’s configuration from an RFID tag. 
• More defects could be detected in-process through robotic CMM that read the product configuration RFID, read the latest product definition directly out of CAD files, and automatically measure critical dimensions as the material is cut or product is assembled. 
• Processes that depend on final product testing to ensure product quality can cause a lot of scrap waste when the final product does not meet specifications. It would be more efficient to have improved sensors that would monitor the product recipe as ingredients are added and transformed with feedback processes that would adjust the process automatically to account for material, process or environment variables. 
• Variation within control specs/limits of raw material properties causes shifts in final product quality. We want to auto adjust our process or recipe to accommodate variations in materials instead of having to demand costly higher accuracy in material specifications. Material would come with actual measured properties instead of us assuming that they meet a specification. 
• Employee safety could be improved by using sensors and automating test procedures involving hazardous materials or processes.

It is easy to see that these types of processes would yield many benefits including:

• Quality consistency of realized products with less waste/scrap and rework
• Less labor of non-value added tasks like setup, testing, and fixing 
• Lower cost for manufacturing one custom configured piece at a time
• Improved personnel safety

But is it possible to reduce the cost of achieving these levels of automation? Will “Smart Manufacturing” ideas and new technologies get us there? Or will it always be easier to design human sensing and decision making into these processes? Some of the challenges
to achieving these levels of automation include:

• High cost of sensor and analysis technology 
• Lack of integration standards between hardware, network and software tiers 
• Lack of models that link available measurements/inputs to desired output properties
• Lack of trained resources in the factory to implement new technology

Can we can make these technologies more affordable and easier to implement. If we can, what problems would you want to address in your factory with the new generation of Smart Manufacturing technologies and integration standards?

 

Future Smart Manufacturing and IIoT Systems will Leverage Cloud Services

A little background on cloud services

Before diving into the manufacturing system specific topic, it is necessary to clarify the term “cloud
services”. There are multiple cloud platform models in use today but this article will focus on the
opportunities provided by leveraging new commercial cloud component services in a mix with on-premise software versus looking at moving the entire information system to the “cloud”.

Types of cloud services:
• Infrastructure-as-a-Service (IaaS) – Virtual machine services accessed over the network providing
computational and storage capabilities
• Software-as-a-Service (SaaS) – Software applications (such as PLM, ERP, Inventory Control, or CRM)
provided as a service from the cloud
• Platform-as-a-Service (PaaS) - Platform software services (including application server, database
server, web user interface framework, workflow orchestration services, and enterprise service bus
middleware) on which to build custom web service-based applications for the enterprise
• Component-as-a-Service (CaaS) - Commercial cloud delivered technology, data and connectivity services (such as identity verification, app marketplace, supply chain data exchanges, analytical engines and document storage) that can be incorporated into on-premise or cloud-based custom enterprise applications. In contrast to regular web sites focused on interacting with human users, these cloud component services provide APIs to exchange data directly with applications.

From a financial point of view these cloud services can be very exciting because they are provided
under term and usage pricing models. But in addition to the new pricing models that are making new technology adoption more affordable, we should also get excited about the new functionality and integration options enabled.

The benefits of using SaaS and IaaS have been covered in many general information technology articles, but I haven’t seen much discussion on the potential for PaaS and CaaS for manufacturing systems. I also want to propose that a mixed model leveraging a combination of distributed on-premise applications and cloud component services should provide the best framework for future manufacturing systems.

Cloud services and new standards evolving around the Industrial Internet of Things (IIoT) are rendering the old division between automation, IT and business obsolete. The availability of quality cloud component services that are easy to assemble into a custom application will change the future of the Information Technology (IT) department. Other enterprise departments will be able to assemble applications they need themselves on a UI, workflow and integration framework. The IT department will change from focus on developing, installing and supporting software applications, to focus on providing guidelines and a framework for assembling integrated enterprise applications. The framework provided by the IT department will include security schemes, brokering of the mix of commercial IaaS, SaaS, PaaS and CaaS, and integration standards that the enterprise will use.

The question should not be whether or not our manufacturing system will be on the cloud, but instead figuring out how much of our manufacturing system will be on the cloud to maximize the benefits to our organizations and customers. Cloud computing is here to stay providing new software delivery models and information technology options. A mix of on-premise plus on-cloud solutions will most likely provide the best landscape for future manufacturing systems, and in fact some software vendors are already moving in that direction. Business critical data, and speed/availability sensitive applications can be maintained on-premise. Integrated enterprise systems can leverage a layered and distributed landscape of on-premise applications and on-cloud components integrated via SOA (service-oriented architecture) interfaces. The new IT options enabled by cloud services will become more affordable and easier to maintain which will be of great interest to smaller organizations with a smaller IT staff.

A hybrid on-premise and on-cloud model for manufacturing systems

What would this mixed on-premise and on-cloud model look like? I could see it having: (a) an enterprise level business process management (BPM) and workflow orchestration engine, (b) integration mechanisms to enable receiving and sending information between the BPM layer and other layers from machine to plant to supply chain, (c) several persistent layers of data with different precision and time horizons for data needed at the controller level versus business level, and (d) a combination of role specific and enterprise system applications designed to optimize the job of each user role and function in the overall integrated enterprise system.

 

Manufacturing systems orchestrate interdepartmental processes

The Manufacturing Operations department cannot effectively be managed as an island isolated from other enterprise departments including Engineering, Supplier Management, Quality Management, Human Resources, Facilities Management and Financial Management. We need effective ways to create information threads for complete business processes across departments that do not depend on manual translation of information. We currently run many interdepartmental business process via paper, email, and with many manual interpretations and translations of data inputs to outputs along the way. These manual interdepartmental business processes are error prone and cannot scale to handle higher volume of transactions.

Business Process Management (BPM) and Workflow tools can be used to string together business processes that span across enterprise departments. For example, managing the release of a revision to a product design from engineering to supply chain to revise component parts, to quality to revise inspection requirements, to fabrication to revise NC programs, and to production to revise assembly procedures. All these processes need to be orchestrated so that they cut in at the right time so the right version of the components are available for production with the right machine and inspection instructions.

An ideal manufacturing system platform would facilitate (a) integration throughout different functional layers including automation control, operations management, equipment maintenance, supply logistics, and business management layers, and (b) integration throughout parallel management systems including physical, human, information, sustainability, economic, social, and regulatory systems.

Cloud component services for manufacturing systems

Types of cloud component services that could be leveraged in a manufacturing system include:
• Data Services including Data Storage, Data Format Translation, Document Repository
• Analysis Services including statistical correlation or predictive analysis that does not have to run
real-time along with the run of a machine process
• Specialized Apps including Document Authoring and File Viewers for specific CAD formats
• Industry Market Places to acquire parts and materials more effectively
• Supply Chain Data Exchanges for controlled communications tied to contracts between multiple tiers of suppliers in the product value chain
• Weather Services to optimize energy utilization settings at the factory
• Product Transportation and Distribution Hubs to get the product to the distribution channel and
customer in the most efficient manner
• Identity Verification Services to make sure the person or machine trying to exchange information is authorized to access the information requested.
• Printing Services in case you must still print something at the corner printing store : )

Distributed layered manufacturing system between business and machines

Could we just move the whole manufacturing system to the cloud? Perhaps for some cases, however at some plants more intimate controller-machine interactions should probably remain on-premise near the machine. For a more distributed and layered system, the general MOM (Manufacturing Operations Management) functions of Work Order Management, WIP Management, Quality, Maintenance, etc. could be provided by applications hosted in a cloud service that would interface to multiple lower level “Smart Machine Controller Appliances” at the shop floor.

The Internet of Things (IoT) interconnects “smart” devices via standard Internet Protocol Suite
(TCP/IP). The Industrial Internet of Things (IIoT) has to strive for the same goal for a new generation of “smart” machines. New IIoT standards should not maintain the old networking standards (Profibus, Fieldbus, etc) used for older machine automation controls. To achieve this goal we might need an intermediary application layer that will translate “smart” communications to older “not as smart” controllers, machines and sensors that do not speak the new integration language standards directly.

In the IIoT, the proliferation of connected smart machines, devices, and sensors can result in an
explosion of data. A distributed layered system is a way to contend with this explosion and organize
people-to-people connections, people-to-machine, and machine-to-machine communications. If
organizations embrace this opportunity to organize these automated communications and leverage all the resulting data at different layers, the visibility and analysis enabled will lead to new levels of
visibility, capabilities, accuracy, and control.

A Smart Machine Controller Appliance would be placed on-premise closer to the machines it controls for any of the following reasons:

(a) The controller function needs high-availability and high-speed connectivity
(b) The controller has large bandwidth requirements for the amount of data that needs to be analyzed and aggregated for immediate feedback back to the running machine process
(c) The controller appliance needs to aggregate data locally during the processing of a machine job 
because the higher level systems do not need to know each little data transaction generated by the 
machine during the job. The higher level MOM functions only need summary data like job start, job end, quantity complete and quantity scrapped. This approach minimizes the requirements on bandwidth due to unnecessary levels of details traveling into the cloud systems.

The requirement for real-time control of machine processes is a very good reason for a local controller appliance. If connectivity is lost between the MOM user and the MOM application for a few minutes, it might be a bit frustrating for the user, but it will not cause a machine process to fail causing loss of invested labor and material for in-process product. The controller appliance would have a cache memory to store a full machine program before it starts a machine run or similar to how video streaming is handled for movies on demand. If connectivity is lost between the MOM application and the controller appliance for a few minutes, the appliance should be able to continue and start transmitting again when the connection is regained.

For example, in industries where the same product is made in large quantities every day for a long
period of time and only summarized information is required at the MOM level, it would be easy to see the MOM functionality hosted in a cloud service. If we only need to know at the MOM level that the machines produced 100,000 units in the current run, it would be wasted network bandwidth and application processing to send 100,000 transactions to the MOM level.

The appliance approach also provides a way to support older machines and controllers in the new Smart Manufacturing systems landscape as newer smarter machines are introduced to the factories. The Smart Machine Controller Appliance would be able to talk “smart” up to the central MOM layer using standards like OAGIS, MIMOSA, ISA88, and ISA95, and talk down to the older machines using protocol standards like OPC UA, MTConnect, EthernetIP CIP, Profinet, Modbus, MQ Telemetry Transport (MQTT).

The distributed layered model also extends into higher systems levels that operate between global
locations, into the supply chain, or into customer systems. There can be a layer of systems on top of
the MOM layer that only needs data aggregated at higher levels. At higher levels of the organizations
and business processes, we might only need work order, or project, or daily numbers. We might not need to know the details of each machine job when we are looking at performance metrics for the
organization.

Smarter machines are coming

As the standards evolve for the IIoT, newer “smarter” machines will enter the factories with the
capabilities of the Smart Machine Controller Appliance already built in. The detailed machine
transactions will be gathered in real-time from intelligent sensors and other monitoring technology
that will provide current machine conditions, the state of the production process in the work cell,
state of automated material handling, and local analysis of data for SPC control, alerts, and
programmed automated self-adjustments. Smart machines might also be equipped with sensors that verify product quality during the production run instead of waiting until the entire run is complete to do manual sampled inspection.

When machines become smarter with on-board controllers and diagnostics, the traditional functions of the SCADA system are handled by a combination of functions at the smart machine and at the MOM layer. For example, a MOM function can provide a view of the manufacturing process status or manufacturing assets’ status on a plant layout diagram.

Apps become smaller and focused

Today’s enterprise systems landscape is full of monolithic software applications that have been loosely interconnected at the database level, operate inconsistently, and are not optimized for any one function. If interoperability standards are embraced for integration of future smart manufacturing systems, not only inside the plant, but among global locations and the supply chain, I can see a future where smaller specialized apps (short for applications) are easy to incorporate into the manufacturing systems landscape. Smaller apps would be optimized for specific functions and specific users and specific hardware platforms (phone, tablet, work station or wall mounted monitor).

References:

• “Lessons Learned From Cloud in Manufacturing Industries”, Hagemeyer, Koslowski, Halpern, Scheibenreif, and Shanler, Gartner, 2014
• “The Evolution of Manufacturing Software Platofrms: Past, Present, Future”, Mark Davidson, LNS Research, 2013
• “The White Book of Cloud Adoption”, Ian Mitchell, Stephen Isherwood, and Marc Silvester, Fujitsu, 2011

 

 

Your Next Enterprise App? Survey Says: Manufacturing Operations Management

A few weeks before Gartner Research and MESA.org published their annual MES/MOM survey results [1], LNS Research released some survey results from their report, “The Global State of Manufacturing Operations Management (MOM) Software,” [2] with consistent positive trends for the adoption of MOM systems in manufacturing.

#1 - MOM is an Enterprise Level System

Why have MOM systems moved up to enterprise level importance? Manufacturers have realized that manufacturing processes are important after all. It is not enough to engineer a product and rely on outsourcing manufacturing processes. The brand owner becomes too dependent on the contract manufacturers and suppliers can become competitors after a few years of experience in the market. There is also an understanding that the old paper-based procedures of the old factory will not cut it in the new manufacturing marketplace. The path to a digital enterprise with a digital thread from engineering to production and supply chain starts with replacing the old paper-based procedures.

In the LNS survey, 68% of manufacturers are viewing MOM as part of an enterprise strategy versus 32% that see it as a plant level decision. The MESA survey also showed that 77% of manufacturers where budgeting and approving MOM systems at the enterprise level. MOM is viewed as a key bridge to realize the digital thread in the enterprise closing the gap left between engineering, shop floor, procurement and financial systems.

#2 - MOM has Real Return on Investment

Leading companies are sharing their MOM success stories in surveys, at conferences and press articles, and the word is getting out. The LNS report shows that implementers of MOM/MES systems have improved Total Cost Per unit by 22.5%, Net Profit Margins by 19.4%, and On-Time Delivery by 22%. Improvements for MOM users were generally double the average among all respondents.

Areas of improvement include:

• Common user interface across the enterprise
• Elimination of duplication of input
• Integrated interdepartmental information
• Platform of interdepartmental collaboration and workflow
• Closed-loop quality management

#3 MOM has Fast Return on Investment

MOM projects are achieving quick returns. Return on Investment (ROI) is achieved in less than 1 year by 47%, and less than 2 years by 81% of respondents. These numbers are consistent with the number of respondents finding more out-of-the-box (OOB) MOM solutions that fit their needs. Over 30% of manufacturers were able to support 80% of their requirements with OOB functions in their selected MOM solutions. This is consistent with the findings in the MESA report which showed a big decrease in the amount of customization performed in MOM software implementations.

#4 - MOM Adoption is Driven by Competitive Pressures 

Perhaps because the surveys asked slightly different questions, it is interesting that the top drivers and challenges listed by LNS are not the same as those reported by MESA. In the MESA survey the top three benefits from MOM were: increasing product quality, reducing operational cost, and improving operations visibility. In the LNS survey the top drivers included: increased number and complexity of new products, lack of collaboration across departments, inconsistent data from disparate systems, and lack of coordination across the supply chain.

A general theme among all the listed drivers is that competitive pressures are making manufacturers take notice that systems like MOM are needed to complete the digital thread in the enterprise. The capabilities gap between the Have-MES and the Have-No-MES companies will continue to grow. Companies that adopt and integrate an MES/MOM system will have a significant competitive advantage moving forward.

For more information on the LNS Research or MESA/Gartner reports check out the reference below.

References:

[1] “Four Good Trends for Manufacturing Execution System Adoption”, manufacturing-operations-management.com, 2015

http://www.manufacturing-operations-management.com/manufacturing/2015/01/four-good-trends-for-manufacturing-execution-system-adoption.html

[2] “The Global State of Manufacturing Operations Management Software - Weaving the Digital Thread Across Industrial Value Chains”, LNS Research, 2015

http://blog.lnsresearch.com/blog/bid/202211/Weaving-the-Digital-Thread-Across-Industrial-Value-Chains-INFOGRAPHIC

[3] “Has MES Come of Age?”, Recorded webcast by Julie Fraser of IYNO and Rick Franzosa of Gartner, MESA.org, 2014

https://services.mesa.org/ResourceLibrary/ShowResource/47eff093-b4f9-417e-9aa1-1ddbf3e82757

 

Four Good Trends for Manufacturing Execution System Adoption

The Gartner research group and MESA (Manufacturing Enterprise Systems Association) recently completed their annual survey of manufacturers (over 100) on their usage of manufacturing systems. Thanks to several years of data, they are able to look at trends in the manufacturing industry and it was generally good news in 2014 for the Manufacturing Execution System (MES) market—for both users and vendors.

#1. MES is finally Accepted as part of Enterprise IT Landscape

MES projects have a proven track record of benefits and are now viewed at the same strategic and enterprise level of importance than PLM and ERP projects. Most companies are not putting MES projects in queue behind PLM and ERP projects anymore. Manufacturing competency is recognized as an important competitive differentiator.

#2. MES Benefits Expectations focused on Improving Product Quality

More companies view quality as an integral part of their manufacturing systems strategy and are seeing the benefits of integrating quality versus keeping quality as a separate process and system.

#3. MES Barriers Move from Credibility to Agreement

As MES is now viewed as critical part of the enterprise systems platform, the entire enterprise needs to participate in the requirements definition, selection process and rollout plan. The shift from local to corporate level project can slow down projects and the achievement of early benefits.

#4. MES Implementations Move Away from Heavy Customization

The increased availability of industry specific mature MES solutions means that companies are looking at more out-of-the-box implementations and should expect less need for expensive custom extensions to commercial products. To benefit from OOB functionality, it is important that companies do their homework and look for MES products that fit their needs instead of assuming that customization is a required part of a project with any MES product. In today’s market, heavy customization is usually a symptom of selecting a product that does not fit the industry or manufacturing type.

There are many more valuable insights in the recorded webcast than the four listed above, so if you are interested in these subjects, I recommend the whole webcast and presentation which is found for members at MESA.org (see References below). I also recommend joining MESA.org for full access to this and other valuable reference resources.

Reference:

“Has MES Come of Age?”, Recorded webcast by Julie Fraser of IYNO and Rick Franzosa of Gartner, MESA.org, 2014   https://services.mesa.org/ResourceLibrary/ShowResource/47eff093-b4f9-417e-9aa1-1ddbf3e82757

 

Eight Potential Barriers to Strategic Manufacturing Process Improvements

The tune at leading companies has changed from cost cutting to product and service innovation. The need to incorporate technology innovation faster into product lines is fueling an executive push for higher levels of process improvements. Can we scale our old practices to handle higher levels of product and process change? Probably not. We probably need to rethink processes at a strategic level to enable new business paradigms and increase speed for product updates and new product
introduction.

Engineers are using the latest CAD technology to make better 3D models faster with more reusable components, more product and manufacturing information (PMI). Model-Based Manufacturing practices are leveraging 3D models to simulate and accelerate production process innovation, develop shop floor work instructions, and create automated inspection and test procedures. So what is holding back our organizations from these types of process innovations? Why are some organizations still working with 2D drawings, spreadsheets, and paper work instructions at the shop floor?

Is IT investment cost holding us back?

U.S. aerospace and defense companies have been generating large amounts of cash reserves for quite some time. The largest aerospace and defense contractors have tens of billions of dollars in cash available to them. [2] There is still uncertainty that the U.S. will pass a defense budget in the
near future but companies have to move forward with process innovation or be left behind by the competitors that are gearing up with strategic investments. The cash reserves are there in many companies to make the required investments. However, the tight budgeting processes, that organizations put in place to control the wallet over the last few years, could be holding the organization back from large strategic investments.

Is commercial software maturity holding us back?

Based on Gartner’s 2014 Hype Cycle for Discrete Manufacturing and PLM information technology [3], commercial applications for Manufacturing Execution Systems (MES) and Enterprise Quality Management (EQMS) are in the “slope of enlightenment” moving to a stable maturity state. Applications for Model-Based Manufacturing (MBM), Supplier Quality Management, Manufacturing Integration Standards, and Operations Intelligence are beyond the hype peak for early adopters and are expected to fully mature within 5 to 10 years in the market.

Even companies that are industry followers versus leaders and very conservative in technology adoption, should probably be investing in these technologies which are ready for the pragmatic majority of the market. Some technologies like Model-based Manufacturing can be implemented in a pilot area to gear up organizations for the next program or product initiative.

Are we complacent with small continuous improvements?

Organizations with a proven record of continuous process improvement through localized short term Kaizen and Six Sigma efforts might have a hard time tackling the bigger strategic projects. “Good enough” can be the enemy of excellence.

Kaizen and Six Sigma efforts look to identify and eliminate areas of waste and variability. But
focusing solely on those efforts can lead to organizations overlooking initiatives like Model-Based
Manufacturing that can enable new business paradigms and revenue opportunities. The organization can fall behind in the changing competitive landscape.

It is not always possible to achieve higher levels of performance through small incremental
localized efforts—efforts that slightly improve on old business processes. That is why some
organizations follow the Lean practice of an annual Hoshin Kanri (business strategy improvement)
effort in which the organization makes an assessment of what resources and capabilities it needs to
stay competitive, win new business and grow.

What else could be holding us back?

Significant strategic process re-engineering can only happen when organizations face head on the
following potential barriers.

Dependence on Manual Processes

With the technology available today, the organization should not be relying on paper-based or spreadsheet-based processes at the shop floor to collect data, track progress, and prepare product or service documents for the customer. Yet, a recent survey [4] by IYNO Advisors found that over 50% of the companies surveyed were depending on homegrown or spreadsheet systems for their production systems. Many other companies are still depending today on paper-based processes at the shop floor.

Even traditional Lean Manufacturing practitioners like Toyota are now embracing information technology to raise the organization’s performance to a new level. This was the topic of a presentation by Toyota US CIO, Tim Platt, who spoke about "Lean's High Tech Makeover" [1] at the 2014 Industry Week conference. If an organization like Toyota can move beyond the manual Lean processes they promoted for years, your organization can change too and embrace automated Lean processes supported by information technology.

Poorly Defined Business Processes

It can be difficult to see the opportunities for improvement right in front of us if we don’t have a
good understanding of the current business processes. I have been lucky to participate in very eye
opening value stream mapping activities with experts from production, quality and engineering
departments. I have seen how different departments can often be so focused on their own daily duties that they do not give much thought to how their work is affecting downstream activities. We expect someone else in the organization is looking at the performance of the entire system. But the reality is that many organizations do not dedicate a team to enterprise level improvement and do not spend enough time cross training personnel on business processes that cross departmental walls. Many times enterprise level processes are not documented and often very few employees understand the overall value chain across the organization. These type of value mapping exercises across departments can uncover hidden gold.

If we don’t have a good understanding of key business processes across the organization documented, it is difficult to perform a proper evaluation and optimization of the entire value stream. Tools like value stream maps, business process activity diagrams, BPMN diagrams or SIPOC diagrams for each department are very useful tools in identifying processes that might not scale to handle a lot of innovation volume and speed in our product lines.

Insufficient Accurate Information 

To justify bigger investments in process, product or equipment changes we need good data backing up the justification. We need better data to justify a new MES system which we need in order to collect accurate data to make better informed decisions in the future. Are we are caught in a Catch-22 situation?

It is important to know that we are delivering on time and paying the bills, but we also need to know more details about engineering and production processes if we want to improve those processes. It is important to know our delivered product quality, inspection results and defect counts but also more details about defect and cause classification to help us tackle the root cause of the problems.

Without the detail data, it is easy to always end up finding someone to blame for the problem
instead of taking a more methodical view to problem solving and finding the true system weaknesses. If we find that we keep blaming all problems on (a) lack of training, (b) personnel carelessness, and (c) human error, we should pause and figure out if we are jumping to conclusions because we do not have good data to identify other sources of potential issues. We want data that will point to any of the potential resources causing the problem which could be personnel related but could also be related to equipment, processes or product design.

Information maintained manually in different departmental databases or spreadsheets (information silos) is likely not consistent and could possibly lead to different conclusions and priorities in different departments. This information inconsistency can be tackled by implementing MES/EQMS information systems and integration standards that keep information synchronized between these production systems and other enterprise systems including PLM and ERP.

Lack of Flexibility to Change Processes

Are we evaluating software solutions based on our organization’s needs for growth and improvement or based on “fit” to our old legacy processes? It would be natural for the IT department to have a bias towards maintaining and improving the old custom systems that they developed versus looking to adopt new commercial software solutions. Commercial software benefits from the input and investment of many customers into one solution. If instead of looking at commercial solutions, the team keeps developing custom IT solutions and justifying the ongoing investment because the company is “different” than the competition, it might be good to get a second opinion from a neutral third party consultant.

It is also natural for engineering, manufacturing engineering, production control and quality
departments to be comfortable with the processes they have used for decades. It is good to expose
the team periodically to new ideas through trade conferences where they can see how other companies are innovating their business processes.

The cost of rebuilding custom integrations for new systems can also be a constraint. Integration
interfaces between enterprise applications based on integration standards like OAGIS make it easier
to replace functional modules in the enterprise system and stay up-to-date with best-in-class
functionality for different departments.

Fear of Too Much Change At One Time

Organizations might have a concern with making big changes all at once that will end up adversely
affecting production cost and schedule for even a few months. The risk associated with this learning
curve on new technology and business processes can be mitigated by implementing first in a pilot
area or “teaching plant” that can serve as an education platform for the rest of the organization
and proof point on the benefits and gains of implementing the new business processes.

Summary

The reality for most organizations include a mix of several of the barriers listed above. It is
important not to get put off by these challenges. Focus on how investments in new information
technology and new business processes can contribute to more contract wins, margin gains, and the
development of new agile, flexible and lean production operations. Expect production operations
remodeled based on the insights gained in your pilot implementation programs.

References:

[1] “Toyota Talks about Lean Manufacturing’s High-Tech Makeover”, manufacturing-operations-
management.com, 2014
http://www.manufacturing-operations-management.com/manufacturing/2014/05/toyota-talks-about-lean-manufacturings-high-tech-makeover.html

[2] “Aerospace and Defense Industry: Navigating a Global Future”, Trade & Industry Development, Jim Kemp, 2014
http://www.tradeandindustrydev.com/industry/aerospace-defense/aerospace-and-defense-industry-navigating-global-f-9187

[3] “Hype Cycle for Discrete Manufacturing and PLM, 2014”, Gartner, Halpern/Jacobson/Suleski, 2014
https://www.gartner.com/doc/2808322/hype-cycle-discrete-manufacturing-plm

[4] “Profitable Projects Webinar: Transforming Project-based Operations”, IYNO and Deltek,
Fraser/Kestel/Leiva/White, 2014
http://more.deltek.com/LP=3958?sourceid=49&utm_source=deltek-com-generic&utm_medium=deltek- com&utm_campaign=generic-Deltek-com&cmp=deltek-com_deltek-com-generic_generic-Deltek-com

 

Smart Manufacturing needs a Real-time Integrated Enterprise

The smart manufacturing factory employs 21st century technology to fabricate and assemble a mix of configurable products in response to immediate customer demands. It implements advanced processes that can incorporate technology innovation as fast as scientists and engineers develop it. It marries information, technology and human ingenuity to bring about a rapid revolution in product design, development, manufacturing, distribution, sales and service. It delivers these process improvements while also improving worker safety, minimizing environmental
impact, and improving product quality.

The smart manufacturing factory is at the intersection of a few technology initiatives including the development of smart manufacturing devices that are part of “The Internet of Things” (IoT), 3D model based manufacturing, and a re-architecting of enterprise systems around services and integration standards to improve communication speed and accuracy not only within the organization, but also with the entire partner and supplier network.

Smart Manufacturing Automation Devices

Manufacturing machines are getting smarter and are equipped with their own computers that control and coordinate a myriad of microprocessor chips on every sensor, motor, and actuator. Robots have been working behind fences automating many of the dirty, dangerous and dull work in the factory, and work requiring high levels of precision and consistent repetition. A new generation of easily configured and set up robots are being developed to assist human personnel side by side. For example, a prototype robot named Baxter (by Rethink Robotics, http://www.rethinkrobotics.com/baxter/) is taught his task through a demonstration by the operator on how to move its arm and hands to accomplish the task.

In the past, each advanced manufacturing center was managed as a process island within the plant operated by a few experts with its own special procedures and controls. The new generation of smart manufacturing equipment connects to the wireless network as easily as our cell phones and
computerized glasses (wearable technology in general), and will be ready to receive input and broadcast feedback. This creates an opportunity to standardize on new equipment and application data exchanges—machine-2-machine (M2M) and machine-2-application (M2A) —that eliminate the information silos within the plant.

For example, our espresso machines can already broadcast when they need coffee beans and when they need to be cleaned via a display at the machine. What if the machine also broadcasted that information to our home computer? An application on our computer could be tracking the inventory level of coffee in the pantry, the weekly usage pattern from our coffee machine, and could use this information to forecast when we would run out of coffee and even automatically put coffee on our shopping list. These are some of the promised concepts at home from the “Internet of Things” (IoT) (http://www.iot-a.eu/).

IoT concepts extend to the factory floor and the next generation of smart machines. There are efforts for a Common Industrial Protocol (CIP) to facilitate these type of data exchanges in the factory between machines, computers and personal devices.

The German government refers to this next generation of connected manufacturing machines, robots and information systems as “Industry 4.0” (http://www.bmbf.de/en/19955.php) and looks at it as the fourth industrial revolution. But to make Industry 4.0 a reality, equipment manufacturers will need to embed intelligence and communication capabilities into their products and we will need broadly accepted standards for communicating, collecting data and interacting with the equipment.

In the United States, NIST’s Smart Manufacturing Operations Planning and Control Program (http://www.nist.gov/el/msid/syseng/smopc.cfm) aims to enhance U.S. innovation and industrial competitiveness by facilitating the adoption of smart manufacturing systems (fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs). This program will enable smart manufacturing based on efficient networked sensing and control, prognostics and health management (including diagnostics and maintenance), integrated wireless platforms, industrial control security, efficient information exchange and interoperability of system components.

Real-time Integrated Processes and Information Systems

IoT deployments will generate large quantities of data that need to be processed and analyzed as close to real time as possible. If a system can’t keep up with the volume of data, it will increasingly fall behind and its analysis and processing of old data will quickly be obsolete and of no value.

The recent trends in enterprise information technology (IT) have been to centralize applications to reduce costs. However, organizations will need to rethink that IT strategy or risk creating a bottleneck in an IoT world. The processing of data streams from smart devices will need to be
distributed and layers of systems will aggregate and filter the data from lower level systems close to machines, to layers of systems managing business processes (internally and into the supply chain) and all the way up to enterprise systems with centralized financial information for the business.

This might sound similar to having a SCADA or historian system collect machine information and pass filtered and aggregate data up to a Manufacturing Operations Management (MOM) or ERP system, but it is different because the smart machines can do much more processing and
filtering of the data themselves and will be communicating with the same protocols that our cell phones and TVs use. For these smarter devices, we can probably skip the traditional layers 0-2 in the old ISA95 and ISA88 models. The machines might broadcast straight to a cloud service which in turn can broadcast to our personal device which has been registered via the company’s portal to receive specific types of messages.

From the product design side, especially in Aerospace and Defense industry, the “Model-Based Enterprise” (MBE) (http://model-based-enterprise.org/) is redefining how manufacturers work with engineering and suppliers in the digital 3D world. Product definitions in 3D formats need to be communicated through the supply chain and through our own internal systems for procurement, production and quality control. Change management practices in the 3D world set the bar higher than what could ever be achieved via 2D drawings and paper forms. The goal is full associativity between engineering, inspection, manufacturing, and service definitions to facilitate change and configuration management practices among multiple tiers in the supply chain.

NIST has a digital thread for Smart Manufacturing that picks up the work from MBE and takes it downstream and links it to the Smart Manufacturing infrastructure (http://www.manufacturing.gov/docs/DMDI_overview.pdf)

Over 50% of the actual manufacturing of a product happens in the supply chain and over 50% of the data exchanged with partners and suppliers still travels today over email, phone and fax rather than flowing directly between business applications via B2B (business-2-business) integration in
structured XML. This low adoption level of B2B integration is surprising given that the first EDI systems were introduced four decades ago and XML standards have been around for decades also. There has been a lack of a concerted effort to tackle this integration arena. Perhaps because
organizations have been plenty busy trying to orchestrate A2A (application-2-application) integrations within their four walls. However, with such a big percent of the success of the company riding on the reliability of these communications, it is time to start improving B2B
integration techniques with our supplier network. 

MESA (http://www.mesa.org) and the Open Applications Group (OAGi, http://www.oagi.org/) have recently signed a collaboration agreement. OAGi is a standards organization managing XML integration standards for A2A and B2B communications and is widely used in the discrete manufacturing arena.

APICS has recently joined with the Supply Chain Council (https://www.gartner.com/doc/2728128/supply-chain-councilapics-merger- help) and they have the SCOR framework for supply chain management.

The SCOR model addresses business practices but do not translate to B2B integration requirements. OAGi is working on mapping SCOR to B2B integration standards. NIST is also working with OAGi on integration standards that span into the supply chain.

Manufacturing organizations that want to lead the next generation of supply chain communications should join these organizations and participate in these efforts.

Benefits of a new real-time integrated enterprise platform for smart manufacturing include:
• Accelerate time to market for innovative products
• Future-proof the IT investment with flexibility to easily switch best-in-class functional modules
• Reduce total cost of ownership with simplified management across the platform by leveraging plug and play integration
• Ensure tight integration between enterprise applications and operational systems at the shop floor
• Increase speed and accuracy throughout the extended value chain
• Gain insight you can act on with metrics acquired directly from transacting the business process model

 

 

The Dangers of OEE as KPI for Manufacturing Operations Management

There is much hype in some circles about OEE (Overall Equipment Effectiveness) as a KPI (Key Performance Indicator)  for manufacturing companies, and as we know from experience,  we must always approach hype with some level of skepticism. This article discusses three reasons why OEE is a potentially dangerous KPI for a manufacturing company to rely on for business decisions and operations management.

What is OEE?

OEE is a measure of how well equipment in a plant are utilized in relation to their full potential. It was conceived to quickly identify areas contributing to big productivity losses including breakdowns, long setups, frequent stops, and defective output.

OEE = Availability x Performance x Quality. Availability, performance, and quality are good solid metrics, and the OEE formula is simple, so why not use OEE as a KPI for the organization? What dangers could be lurking under such simplicity?

Danger #1: OEE does not relate to the company’s true business goals

If the company gets paid to run machines at full capacity all day, OEE might be a decent metric for the business. For example, if you run an electric power company or a chemical processing plant, you might find that OEE relates to your bottom line. However, that is not the reality for many manufacturers. Companies that manufacture discrete products to fulfill customer orders have business goals related to factors that influence customer buying decisions: Schedule, Price/Cost, and Quality. Their KPIs should align with their business goals and OEE does not.

Danger #2: OEE does not address the real constraints to production 

From studying Eliyahu Goldratt’s “The Goal” and his Theory of Constraints (TOC) principles, we understand that the most important considerations in manufacturing operations are to keep the plant running to a “drum beat” and to mitigate the risks of any constraints that can affect the plant rhythm and choke the production rate. The Theory of Constraints is a holistic view that takes the entire plant into account. OEE is focused on local optimization of each work center, but the goal is optimization of the entire production system. OEE assumes that the goal is to keep each work center busy and producing at 100% capacity all the time. However, in the context of the entire production system, it might be acceptable to have areas of low utilization.

The goal is not to keep every work center and piece of equipment busy all the time; the real goal is to get product out on time to match demand, at a low cost, and with high quality. The organization’s metrics should be directly related to the real business goals that lead to the ultimate goal of most manufacturers: higher profits.

The Theory of Constraints is used by manufacturers to identify their production bottlenecks and then work to improve and eventually eliminate them. Not all resources in a plant are potential bottlenecks, so only resources that are (or have potential to become) constraints to production should be closely monitored and optimized for the company to achieve its real manufacturing goals. If the organization focuses on “fixing” work centers with the lowest OEE numbers, it might be under optimizing the overall macro manufacturing process. 

Danger #3: OEE is an aggregate metric that obfuscates instead of clarify areas for improvement

Aggregate measures like OEE have the risk of hiding underlying issues. Each component of OEE in and of itself (availability, performance and quality) provides better visibility into the organization’s performance. When the sub-metrics are multiplied by each other, as is done with OEE, the resulting number can end up hiding the areas that have the most problems. For example, an area might have high availability and utilization numbers, but a low quality number, but because all of the numbers are multiplied together, the low quality number is hidden and therefore, not addressed.

Not only does OEE hide underlying issues, but it also muddies the waters when it comes to determining areas for improvement. OEE assumes that each of the sub-metrics have equal importance, but for many organizations, a 1% labor performance loss is not as important as a 1% quality loss. For example, it is easy to increase quality by increasing cost. The trick is to increase quality while reducing cost. An area with 90% quality and 70% performance has a different problem than an area with 70% quality and 90% performance, but they can both have the same OEE rate. 

In Summary

Considering the three dangers stated above, do OEE numbers really tell us anything important or useful about our business? Will they lead to more sales and profit? Or do they potentially misguide improvement prioritization efforts?

In addition to the dangers listed above, using OEE as a way to benchmark the business against others is not wise unless we are comparing across very similar types of businesses. The idea that you can have a benchmark goal for OEE of 85% across the industry is unsound. Perhaps it should be 95% for one type of process, and perhaps 70% is okay for another type of process.

If OEE is not the silver bullet metric for manufacturers, which one is? We shall explore that topic in a future blog post. What metrics should an organization look at to monitor profit, schedule and quality goals? What metrics help identify areas for improvement and areas of efficiency loss due to poor quality, low speed, equipment downtime or supplier issues? We should explore some Lean Six Sigma metrics like Cycle Time Efficiency and methods of identifying the underlying causes behind the loss of efficiency. Stay tuned. 

More information on OEE definition in this older post: 

“Overall Equipment Effectiveness (OEE) Or Overall Resource Effectiveness?”
http://www.manufacturing-operations-management.com/manufacturing/2009/11/overall-equipment- effectiveness-oee-or-overall-resource-effectiveness.html

 

Toyota talks about Lean Manufacturing and Digital Technology

I was at Industry Week’s annual conference last week and had the pleasure to listen to one of Toyota’s VP of Information Systems, Tim Platt, talk about "Lean's High Tech Makeover".  Mr. Platt shared how Toyota is leveraging information technology in their current Lean Manufacturing efforts to gain more efficiencies. He showed examples of how information technology is supporting the Lean principles of Jidoka, just-in-time and standardization.

Three case studies were presented for US plants totaling around $3.5M per year in hard savings from implementing information systems to achieve the Lean principles.

The following are some examples of how information technology is helping achieve new levels of
performance with these Lean principles:

Jidoka Lean principles include the ability to stop a process when an issue is found and
immediately alert the supervisor and support personnel that can assist in solving the problem. Old school Andon boards are replaced by visual dashboards that provide the same visibility within the work center but also provide visibility from any computer in the plant to any work center Andon board. Old school Andon lights used to indicate that the line was stopped are replaced by event triggered alerts that are broadcasted via email to the respective support department to assist in clearing the issue as soon as possible.

Standardization principles are enforced via a manufacturing execution system (MES) that provides
work instructions to encourage process repeatability and reduction of variance.

Pokayoke tooling and mechanical methods are complemented with sequence and data collection
enforcement by an MES to make sure the correct process is followed every time.

Just-in-time principles can be applied with system generated Kanban triggers. Heijunka type work
leveling to achieve smooth flow can be assisted by the use of special scheduling software that
incorporates Lean principles and similar principles like drum-buffer-rope.

It was very refreshing to hear a Toyota executive talking about leveraging information systems
to implement Lean principles after years of hearing old school Lean practitioners taking the
hard line against information technology.

Some of us have talked for years about the information flow being a critical part of the value
chain that requires waste reduction to achieve a Lean Information Value Chain.

The examples from Toyota, one of the companies that taught us Lean Thinking, continue to
reinforce that information technology and Lean Manufacturing are not mutually exclusive. Another source for examples leveraging information technology to achieve Lean is MESA’s “Lean
Manufacturing Strategic Guidebook”; a resource available to MESA premium members at mesa.org.

References:
“Toyota’s Lean High-Tech Makeover”, a presentation by Tim Platt, VP Information Systems, Toyota US, May 2014
“Lean Manufacturing Strategic Guidebook”, MESA International, 2010

Was my Smart Phone a Smart Purchase? What about my MES Purchase?

When was the last time you purchased an item for home based on return on investment (ROI)? Most of the time, we make up our minds on what we are going to buy without doing any numbers and we just go shopping based on price and value. But for an example of an ROI analysis, I will look back at the purchase of my current smartphone.

I used to carry around a regular cell phone which I loved because it was small, light and had a long battery life. So when I went to buy a new phone I was not sure about getting a smart phone. As any industrial engineer would do, I did an ROI analysis to see if it made sense.

My approx. labor time
= $100,000/year / (50weeks x 40 hours/week) = $50/hour (very rounded numbers)
My extra time working thanks to smartphone
= 10 trips/year x (2 hours usually idle at airport/trip
+ (1 hour work recovered between meetings x 3 days/trip))
= 50 hours/year
Savings /year = 50 hours/year x $50/hour = $2500/year

Investment Cost = $200 for the phone
Recurring Cost/year = $700 data plan + $100 IT support = $800/year

Payback is less than one year !

The ROI calculation would have been the smart thing to do, but who does that? Not even my IT department. The justification for smart phones is obvious and does not really need this type of analysis. The intangible benefits alone are even greater than the tangible ones listed above.

The intangible benefits of the smartphone include:
• Email anywhere. What is the cost of missing an opportunity because I didn’t get the email on time or upsetting a customer who was waiting for a reply to his urgent email.
• GPS. I will never get lost in an unknown city again. There is also a tangible cost savings here for not having to rent the GPS with my car rental.
• Web access anywhere. I can look for a nearby restaurant using Urbanspoon, or Google that trivia that came up during dinner conversation.
• Music player. I don’t have to carry around an additional device like an MP3 player anymore.
• Apps. I cannot live without the flight status application from the airline. I have used it to determine when I was going to miss connections and rebooked right from my phone. Priceless!

I have been wondering if the same buying patterns apply to some purchases at work including purchases for some information systems. Were the purchases made based on ROI? Perhaps ROI should not be the only criteria.

In the report titled “Insufficiently Defined Business Cases and Application Strategies Are Holding Back the Value of MES,” Gartner states that the lack of a clear ROI is one of the biggest obstacles for Manufacturing Execution System (MES) projects listed by manufacturing Operations management executives. However, the same month Gartner’s report titled “Extend the Value of MES Beyond the Plant” lists that manufacturers have actually reported many greater intangible significant values for MES and most of them realized after the first year of implementation. Perhaps we shouldn’t need an ROI study to get MES projects approved. The intangible values identified include (a) using data from MES as the basis for continuous improvement, (b) increased product quality, (c) standardization of processes, (d) reduction of cycle time for resolution of issues, and (e) easier new product introductions. These Gartner reports have a lot more details and are worth downloading if you have access to them via Gartner or MESA. MESA is also publishing a new ROI Guidebook for manufacturing systems in the next few weeks.

Users at companies that have successful MES implementations say that they do not want to go back to the old paper-based forms. I was at a plant that had implemented a new paperless process with MES at one building for a new program and those employees dreaded going on loan to the other building. Just like I would dread going back to my old plain cell phone. I do not want to lose all the functionality I have in my new smartphone.

Of course, if you just use your smartphone to play Angry Birds, then there is no ROI. The same applies to the MES purchase. If your company doesn’t use the MES to its full extent, then the company will not realize the full expected value until it is fully implemented.

Do we really need to do the numbers when the decision is that clear? Or do we need to make these decisions the same way we make our smart phone decisions? Did the same companies, holding up MES projects, do the ROI for their ERP systems? Or was that decision obvious to the CFO and did not require the same level of justification? With all the documented success stories for MES, the only debate companies should be having is which MES fits the business better and who should help implement the MES, not whether or not there is a need for MES in the factory.

What do you think?

 

References:

• “Insufficiently Defined Business Cases and Application Strategies Are Holding Back the Value of MES”, Jacobson and Dornan, Gartner, 2014
• “Extend the Value of MES Beyond the Plant”, S. Jacobson, Gartner, 2014

MES versus MOM Terminology Confusion – Manufacturing Execution System or Manufacturing Operations Management

It is 2014 and the MESA (www.mesa.org) organization is wrestling with an important decision. Does it keep using the term MES (Manufacturing Execution System) or move to the term MOM (Manufacturing Operations Management) to describe manufacturing systems? The debate was very active in LinkedIn’s MESA International group forum and also at a recent MESA webcast panel discussion.

Is it time to change the term “MES” to another term just when it is starting to gain recognition and acceptance? Is there any good reason for the change?

First some historical perspective

The terminology timeline shows the introduction of acronyms for manufacturing systems over the last few decades in three major areas: engineering, inventory planning, and production shop floor. We can see in the diagram that just about every 10-12 years, the software industry gets an itch to introduce a new term. These new terms usually signal an evolution in function or footprint for a software category. It has been one of the ways the software industry defines “progress”.

For example, PLM (Product Lifecycle Management), was introduced in 1995 as an evolutionary vision above the definition of PDM (Product Data Management). Software vendors working on solutions for the engineering departments soon jumped to use the term and fulfill that vision. The bar was set high for the PLM vision and vendors and manufacturing companies are still working on fulfilling that vision.

The term ERP (Enterprise Resource Planning) stopped evolving in the mid 90’s. Did that signal a maturity for that category of solutions? There were minor attempts to introduce an ERP 2.0 term around 2007 and 2010 but they didn’t succeed. The term MES (Manufacturing Execution System) has been around since the early 90’s when it was introduced by AMR Research to be the successor of the term CIM (Computer Integrated Manufacturing).

The CIM term was introduced in 1973 by Joseph Harrington and published in his book “Computer Integrated Manufacturing” in 1979. The term gained popularity around 1984. In the CIM wheel published by SME (Society of Manufacturing Engineering) we start seeing functional blocks for CAD Design, Scheduling, Tooling, Production Control, Quality Control, Maintenance, Material, Fault Diagnosis, Material, and Document management. Building blocks that we will later see in the MES definition.

After the MES term was introduced by AMR Research, the MESA organization embraced it and started writing on the subject and educating manufacturing companies on the importance of creating a platform for managing operations at the shop floor. Michael McClellan also published a book in 1997, “Applying Manufacturing Execution Systems” which further defined the term and the functionality scope.

From page 2 of McClellan’s MES book: “MES is the step of integrating all of the activities between the planning layer and the automation layer as components of a proactive integrated on-line system providing a synergistic process that is greater than the sum of the parts.”

With the definition of MES by MESA we moved from “wheels” to “gears”. Note that there are gears in MES for everything you will later find in the MOM definition and more. Also note that McClellan had introduced a layer of Production Planning on top of MES and a layer of Automation Control under MES. The MESA papers also explained that MES would integrate with ERP, Supply Chain Management and Engineering Systems.

Areas of MES include from the following from its inception:

• Resource Allocation and Status
• Operations/Detail Scheduling
• Dispatching Production Units
• Document Control
• Data Collection/Acquisition
• Labor Management
• Quality Management (real-time analysis of measurements, problem identification)
• Process Management (monitoring)
• Maintenance Management
• Product Tracking and Genealogy
• Performance Analysis

It is also important to note that McClellan’s book also referenced several industry leading companies that were doing MES. This was not just an academic wish list. Discrete manufacturing companies were developing these MES applications.

In 2005, companies in process and batch manufacturing working within ISA95 decided to make their own diagrams and moved from “gears” to “oval eggs”.

The ANSI/ISA9-5 Standard for Enterprise-Control System Integration, made some nice clarifications and numbered the layers between machines and enterprise systems. For example, Layer 1 collects data in seconds, Layer 3 in hours, and Layer 4 in days.

However, they decided to rename the MES layer, Layer 3, to Manufacturing Operations Management (MOM) because they liked the word “Management” better than the word “Execution”. It was a naming preference, not a marker for an evolutionary step in manufacturing systems. It did not raise the bar for manufacturing systems; it was thought to be a better name.

You might actually see a few things in MES definitions which you don’t see in the MOM definition. The ISA95 standard was created by people working in the process and batch manufacturing industry where the manufacturing process is very equipment centric. In those industries, the process design is often the entire manufacturing plant or a fixed manufacturing cell with a flow designed to produce a recipe for a batch of product. In contrast, in discrete manufacturing the process design is more work order centric. The process is often designed for flexible manufacturing cells that can manufacture different types of configurations of product units for each work order. This is why there has always been a difference in how to model the different types of manufacturing. A major problem I have with the ISA95 standard is treating Quality as a separate silo. Quality is really part of every process. But I digress… back to discussions of the terms MES and MOM.

On the lighter side

I do think that the ISA95 team members underestimated how much we love acronyms! Because they might have thought twice about introducing the acronym “MOM”.

In this illustration you see an email I received about a week after the LinkedIn discussion had started with the title “MOMs email list”.

At first I thought, wow… I usually get a lot of unsolicited email offering PLM and ERP users email lists but this time it seemed like someone had an email list of MOM users. Sounded interesting so I opened the email to find out they were actually talking about email lists of Moms, Dads, Parents, Gamblers, Golfers, etc. I chuckled and pressed the delete button. This email illustrates a problem with the acronym MOM. Are we really going to ask the next generation to replace their old MOM and look for a new MOM? I find the MOM term a little hard to write about and use seriously in conversation about manufacturing systems.

A reality check

Is it worth it to transition to the term MOM to help align the discrete and process/batch camps? Even when the discrete manufacturing industries have done so much progress on the adoption of MES and establishment of COTS (commercial off the shelf) solutions for MES? Should the process/batch industries embrace MES even when they don’t feel they are “Executing” something? Google searches for “Manufacturing Execution System” were around 160 per month in 2008 and are around 480 per month in 2014. “Manufacturing Operations Management” searches are only at around 110 per month in 2014.

The move to a new term might be a setback for solutions focused at improving manufacturing operations. Two terms do not make it easier for companies to find the solutions they need. Confusion in the marketplace will only benefit the PLM and ERP software vendors that are trying to penetrate the manufacturing systems arena with broad and thin functionality. Perhaps these software giants are behind the move to change terms. Machiavellian tactics? Conspiracy paranoia?

Is there a third alternative?

Some are suggesting that we limit the use of the MOM term to discussions around the ISA95 model, discussions on “business processes” for Operations, and keep the MES term in relation to discussions about IT “systems” that help us support and execute those processes. Of course, that might start debates on how good or bad the ISA95 models are to represent discrete manufacturing processes versus process/batch manufacturing industries. That might be the topic of another article ; )

When MESA changed the “E” in MESA from “Execution” to “Enterprise” they were on to something. In discrete manufacturing, we are now developing manufacturing software for the extended enterprise. Driven by the reality that over half of our products’ subassemblies and components are manufactured by partners and suppliers.

Perhaps instead of focusing on the MES-MOM debate, we should focus on new terms for the future. Maybe change the “E” in MES and define the next generation of “Manufacturing Enterprise Systems” that will support better supplier collaboration and orchestration. Is it time for a four letter acronym? “eMES” for “Extended Manufacturing Enterprise Systems”? Some software vendors are already working on these solutions. It would be nice to have a market definition that supports a broader solution footprint for the broader problems manufacturers are trying to solve today.

Perhaps the term PPM (Production Process Management) or the term Collaborative Production Management (CPM used by ARC Group) will stick. I recommend everyone read the the paper by Michael McClellan titled “Improving Manufacturing Excellence by Managing Production Process across the Value Chain”. It is an example of how we are now looking for tools to manage across the entire value chain.

My vote is to embrace a new term when we are ready to raise the bar for the software category. There is a need to define a new footprint that extends into the supply chain, not at a separate Layer 4, but as an integrated part of the manufacturing process. We should focus as architects of manufacturing systems on this next generation and what we are going to call it. We should look ahead, not back.

References

• “Computer Integrated Manufacturing”, Krieger Pub, Joseph Harrington, 1979.
• “Computer Integrated Manufacturing” by Cheng WU, Yushun Fan, Deyun Xiao, 1999
• “MES Explained – A High Level Vision”, MESA International, 1997
• “Applying Manufacturing Execution Systems” Michael McClellan, CRC Press/APICS, 1997
• ANSI/ISA9-5 “Standard for Enterprise-Control System Integration”, ISA, 2005
• “Improving Manufacturing Excellence by Managing Production Process across the Value Chain”, Michael McClellan, 2012

 

Did You Know? People in Manufacturing Love their Jobs!

In a recent IndustryWeek Salary Survey, 85% of subscribers reported they were satisfied to very satisfied with manufacturing as a career. The high level of career satisfaction was not a product of a good economy or pay raises.

As it turns out, IW subscribers have remained steadily positive about manufacturing as a career choice even during difficult economic times and no pay raises. People involved in manufacturing love the sense of accomplishment that comes with the job. There is always something new going on at the factory. Manufacturing plants have to keep up with the latest technologies to stay competitive.

So why aren’t more people entering manufacturing careers? It seems we are not doing a good job of spreading the word. There is a need for more skilled labor in US manufacturing and people in manufacturing like their jobs.

Spread the word!

References:

2013 IW Salary Survey: Salaries Remain Upward Bound
http://www.industryweek.com/salary-survey/2013-iw-salary-survey-salaries-remain-upward-bound?page=3

There Really is A Talent Gap in Manufacturing
http://www.manufacturing-operations-management.com/manufacturing/2012/07/there-really-is-a-talent-gap-in-the-workforce.html

Motivating the Next Generation of Engineers and Manufacturers
http://www.manufacturing-operations-management.com/manufacturing/2010/07/motivating-the-next-generation-of-engineers-and-manufacturers-.html