Manufacturing Operations Management Talk

Three Dimensions Converge on Smart Manufacturing

- Smart Factory, Digital Thread, and Value Chain Management

There is a myriad of new technologies coming into the manufacturing arena, each with tempting value propositions. How does an organization know that they are investing in the right areas to stay competitive? If your organization is confused about where to start or where to focus investments on the journey to a future highly connected, orchestrated, and optimized Smart Manufacturing enterprise, you are not alone.

A clear roadmap to Smart Manufacturing is of the utmost importance for each organization, but not easily realized because of the complexity of different organizational perspectives, data models, and business processes that converge at the manufacturing shop floor—processes that get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner.

This article discusses how to organize the convergence of processes supporting Smart Manufacturing into three dimensions or perspectives: (i) smart factory, (ii) digital thread, and (iii) value chain management. These different dimensions relate to different perspectives and systems coming from engineering, operations, and business management disciplines and help explain why the Smart Manufacturing endeavor requires collaboration among many different stakeholders in the organization.

This three-dimensional model also explains the intersection of several manufacturing improvement initiatives included under the scope of Smart Manufacturing: IIoT (Industrial Internet of Things), Model-Based Manufacturing, and Connected Enterprise. The Digital Thread dimension aligns with the goals of Model-Based Manufacturing, he Smart Factory dimension aligns with the goals of IIoT, and the Value Chain Management dimension aligns with the goals of orchestrating and optimizing the entire value chain in the Connected Enterprise.

The Smart Factory Dimension

From a Smart Factory perspective, we are interested in connecting equipment, resources and personnel in order to acquire real-time data through automated methods, analyze it, and leverage that information to (a) provide complete real-time visibility of factory processes, (b) optimize process control, and (c) provide insights to where we can further improve performance.

For example, an assembly line with Smart Manufacturing automated and semi-automated processes may do the following:

• Monitor production flow in real-time to eliminate constraints, dispatch automated material handling, and eliminate wasted idle time
• Auto-identify parts going down the line to automatically load programs and materials for each different product configuration
• Automatically aggregate product data, analyze and identify constraints and required adjustments or improvements
• Manage equipment remotely using sensors to conserve energy, reduce downtime and trigger preventive maintenance

To achieve these levels of automation, through which products, parts, and equipment interact among themselves with enhanced communication mechanisms, we will need resources and industrial automation equipment with communication standards to acquire and publish data to higher levels of processes in the Smart Factory stack including operations management and intelligence applications.

 

Figure 1: The Smart Factory Dimension

The Smart Factory dimension illustrated in Figure 1 includes the following connected processes and systems flowing from equipment and resources up to higher levels of process control, analytics, and intelligence.

• Smart Machines, Sensors, Tooling, and Workforce interact with each other via structured communications and integrated systems providing real-time data about their status and the processes they are executing
• Smart Apps, Controllers, OT-IT Bridges like the Manufacturing Service Bus provide the communication bridge between Operations Technology (OT) exchanging data directly with machines and tooling, and Information Technology (IT) systems and apps where personnel interface to execute supervision, production, inspection, and maintenance tasks at the shop floor
• Operations Management System optimizes the flow of products through production processes and orchestrates the allocation of resources
• Connected Enterprise Systems including PLM, ERP and SCM are maintained in real-time sync through A2A data exchanges with production process status, resources used, and products produced
• Business Intelligence System receives periodic updates of aggregated data for performance analysis and business metrics

The Smart Factory dimension is aligned with the goals of the IIoT (Industrial Internet of Things). The IIoT takes the concepts of ease of equipment connectivity, data acquisition and advanced analysis via cloud services from the Internet of Things (IoT) initiative in consumer markets and applies them to the next generation of automation for the factory floor.

An enabler behind the IIoT is that it is becoming easier to connect and mine data directly from smarter machines. The IIoT can monitor, collect, exchange, analyze, and deliver valuable new insights. Mined data is more accurate, consistent, near real-time, and enables organizations to sense inefficiencies and problems sooner, saving time, money, and driving smarter, faster business decisions for industrial companies.

A major issue slowing down the IIoT is interoperability between older devices and machines that use different protocols and have different architectures. In contrast to the automation achieved in the last few decades, the connectivity methods targeted under IIoT and Smart Manufacturing need to be open, standards based, and able to facilitate publish-subscribe methods over the internet.

In the past, organizations depended on custom integration, vendor-proprietary interfaces and separate network protocols for integration and automation at the factory. Moving forward with IIoT, organizations want to embrace open standards and Internet protocols to facilitate an easier swap and mix of multi-vendor equipment and software, which might be on-premise or in the cloud.

A big promoter of the IIoT is the Industrial Internet Consortium (IIC) which adopted the term, and promotes the move from older automation protocols to newer Internet-enabled IIoT protocols for industrial equipment.

The Operations Management system optimizes the flow of products through production processes and orchestrates the allocation of resources. It executes programs for processes like cutting, machining or 3D printing equipment, and collects data from operators or directly out of equipment for inspection, test, pick-and-place, or packaging processes. Data is collected in a structured form that allows distribution to multiple subscribing functions in the Smart Manufacturing system like quality verification and parts traceability.

Enterprise Systems manage all kinds of business processes in the organization from receiving orders from customers to scheduling production, planning deliveries, ordering materials, invoicing, receiving payment, and paying suppliers. The timely performance of these activities depends on real-time data from the connected Operations Management system through A2A (application-to-application) data exchanges

Business Intelligence systems aggregates and organize data into actionable metrics and Key Performance Indicators (KPIs) the represent the organization’s strategic goals. In the digitally connected Smart Manufacturing organization, management is automatically alerted of areas not performing to plans and expectations. Management must be able to drill down from metrics into causal analysis, and depending on analytical capabilities, systems might even be able to suggest areas for improvement.

The Digital Thread Dimension 

The Digital Thread dimension of Smart Manufacturing starts with the engineering design definition of the product and follows the product lifecycle through its sourcing, production and service life ensuring that the digital definition of each product unit is aligned with the physical product. The digital data for each product includes every incorporated revision to the engineering definition and any deviations from the design specifications approved and executed on the product during its lifecycle. The flow of processes in the Digital Thread dimension is depicted in Figure 2 including:

• Specifications Management for design of product and processes including definition of 3D models and recipes, product variations and configurations, and engineering change management practices
• Operations Management which includes production and verification processes including programs and work instructions for automated 3D printing, machining, and verification against engineering specifications
• Product Services Management for maintenance of the product during its service life with data collected on product performance, modifications, and replacement of components.

Figure 2: The Digital Thread Dimension

In discrete manufacturing, the Digital Thread perspective is aligned with the goals of Model-Based Manufacturing and Model-Based Enterprise initiatives. The Digital Thread initiative aims for seamless threads of structured communications and data exchanges throughout the value chain that are accessible to all stakeholders across the extended ecosystem to ensure complete visibility and traceability of the digital and physical product from design through sourcing, production, and ultimately to the end user or customer.

The Digital Thread begins with a 3D model-based or recipe-based definition of the product from design teams flows into the Operations Management and the Supply Chain Management via standards of integration for pervasive distribution of the data throughout the connected Smart Manufacturing enterprise. Examples of communications in the Digital Thread include product and process specifications, test results, conformance issues, asset maintenance requirements, and details and approvals for deviations from standard specifications.

Digitally connected model-based design and manufacturing help connect previously disconnected functional departments through a logical thread of integrated data and processes which aids in (a) faster design revision distribution and new product introduction (NPI), (b) accurate translation of product to process specifications, (c) smarter business decisions with visibility of product performance during its lifecycle, and (d) quicker resolution of issues requiring engineering design changes.

The product design engineer states the materials, form, and fit requirements for the components in 3D models for discrete manufacturing, or the chemistry and physical transformations in a recipe for process industries. The manufacturability of a product is dependent on the particulars of design parameters and tolerances. The production and inspection process definition is a repeatable structured means of conveying the engineering intent to Operations Management. There should also be a design feedback loop between the design of the product and the design of the manufacturing processes, tooling design and inspection capabilities. There is a need for better ways to communicate specifications, capture the relation to actual measurements, and leverage that information for resolving issues on non-conformance to the requirements in a well-defined formal way.

As an example, in discrete manufacturing today there is a lot of manual interpretation, transformation, and translation of data between engineering and manufacturing systems. In addition to being inefficient, each time data is manually converted from one format to another, it introduces a chance for misinterpretation and error. For example, in current processes, CAD models need to be manually converted to (a) computer numerical control (CNC) programs for machining, (b) coordinate measurement machine (CMM) programs for inspection, and (c) manufacturing execution system (MES) illustrated work instructions for assembly. During these manual processes, the associativity to objects in the CAD model is usually lost. When a CAD model revision comes down the pipe, the engineers and programmers must do a thorough review of the entire model to avoid missing anything instead of concentrating with confidence on a few highlighted revised areas. In future processes, with structured digital handoffs, systems will be able to easily highlight revisions, do impact analysis on downstream programs and instructions, and facilitate the automated incorporation of changes.

The Digital Thread will provide a formal framework for the controlled and automated interplay of authoritative technical and as-built data with the ability to access, integrate, transform and analyze data among disparate systems throughout the product lifecycle. In the Digital Thread, the product’s data “travels” along with the physical product and evolves through data collected at each step of its manufacturing process. By “travel” we mean that the data needs to be easily accessible at any time during production and referenceable to each product’s lot or serial number. The scope of data includes as-designed requirements, validation and inspection records, as-built records with part genealogy traceability, and as-tested data. The Digital Thread needs to be able to deliver the digital product data along with the physical product to the end customer. For some products, the thread of the product data will continue into Product Service to maintain the product during its entire service life.

The Value Chain Management Dimension

An important dimension to achieving a fully connected extended enterprise in Smart Manufacturing is the Value Chain Management perspective. Value Chain Management focuses on minimizing resources and accessing value at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products, and enhanced customer satisfaction.

The scope of Value Chain Management spans from managing suppliers of materials and parts, to managing the handover of information through internal departments including the production shop floor, and all the way to managing the delivery of the product to the end customer. It encompasses the procedures, forms, and data handoffs that link these organizational entities into a value chain that delivers a final product and services for that product to the end customer.

The standardization of IT practices that ERP started decades ago for cash-to-order processes within the organization—covering activities like contracts, procurement, receiving, invoicing, purchase orders, delivery, and payment—must be extended now across the entire value chain with an emphasis on open data exchange standards that enable publish/subscribe connections across the internet and cloud services. Configurable repeatable patterns of orchestrated activities across the value chain will enable highly automated, efficient, and agile business processes.

As illustrated in Figure 3, the Value Chain Management dimension includes processes for:

• Customer Management with online interaction with customers for quicker custom product configurations, order in-process visibility, and approvals for changes, deviations or delays
• Compliance Management maintaining organizational guidelines, coordinates audits and monitors compliance performance with internal departments and external regulatory agencies
• Operations Management delivering real-time information from production processes to other business management functions and orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time
• Resource Management of personnel and equipment required to make the product, provide product services, and maintain the equipment up and running with the required capabilities and certifications
• Supplier Management with functions from identifying and establishing the supply chain with the right partners to monitoring, synchronizing, and maintaining the required quality levels.

Figure 3: The Value Chain Management Dimension

The new Smart Manufacturing ecosystem aims to create closer relations and interactions with customers in processes and services. Customer Management includes functions for customizing orders to customer preferences, providing more visibility to in-process order status, coordination of deliveries, download of data for each product shipment, known issue alerts for purchased products, warranty claims and issue resolution, approval for changes and deviations to contract specifications, and coordination of service subscriptions and service orders. Some organizations have a Program Management function that closely works to achieve program goals with the customer organization and the supply chain.

Since the Value Chain Management dimension encompasses procedures that link the enterprise departments into a connected value chain, it is necessary to have a Compliance Management function which maintains organizational guidelines, coordinates audits, monitors compliance among internal departments, and coordinates with external industry and government regulatory agencies. The Compliance Management function maintains the brand’s quality reputation.

Compliance Management functions include the business processes that (a) document and control standard practices, (b) record history for reporting and auditing purposes, (b) handle resolution of issues and tracking of corrective action and continuous improvement efforts. Compliance Management ensures that corporate procedures are aligned with industry regulations for safety, environmental protection, risk mitigation, and quality management as documented industry standards such as ISO9001, ISO14000, ISO45001, and ISO31000.

Operations Management touches every dimension in Smart Manufacturing performing a very critical coordination function. Operations Management orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time. It provides demand signals for resources and delivers real-time information from production processes that includes the context of orders, specifications, and resources. Good data from Operations Management enables confident decision-making in all parts of an organization including production, quality, maintenance, sales, and engineering. This information is essential to allow management to drill down from corporate Key Performance Indicators (KPIs) into causal analysis to uncover areas for improvement.

To ensure the operational outcomes desired we must properly train personnel on required skills and keep track of the required certifications for specialty jobs. Resource Management includes Workforce, Facilities, and Equipment Management. Equipment Management includes the care of the plant, environmental controls, machines, equipment and tools handling trouble-call tasks as well as scheduled preventive maintenance and calibration service tasks.

Workforce Management includes maintaining the right level of workforce with the right level of skills and certifications to perform the required production and inspection tasks. It includes tracking attendance and labor costs in concert with the Operations Management system. Human Resources has the challenge to attract the next generation of the Smart Manufacturing workforce with the required new skills for the job.

Supplier Management includes the activities for sourcing materials and components to suppliers, coordinating the proper production of those components at the supplier site including supplier qualifying and auditing, negotiating contracts, scheduling deliveries, managing warehouse and stockroom, receiving and inspecting incoming materials and parts, and handling of warranty issues, returns, and corrective actions with suppliers. Product design changes must be carefully coordinated with impacted suppliers. In the connected smart supply chain, digital data about materials and components must be delivered by suppliers along with the physical units in a way that allows easy roll up of the data to higher levels of assembly by the Operations Management system for full parts genealogy traceability. Communications into the supply chain is performed via the internet through B2B (business-to-business) data exechanges.

A Convergence on Smart Manufacturing

Smart Manufacturing strives for higher levels of connectivity, orchestration, and optimization of processes in the manufacturing value chain. To reach this goal, the organization must (i) align the intersection of the different organizational perspectives into a cohesive set of functional roles, data models, and business processes that converge to get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner, and (ii) figure out how to leverage the many technology building blocks available into a flexible architecture of systems that helps automate the activities as much as possible, pass data seamlessly, and enable new levels of optimization, predictive, and prescriptive analysis in enterprise processes.

The organization can start trying out new technology but cannot complete the analysis of the required technology building blocks until the functional requirements to support the new connected enterprise are fully understood.  The three-dimensional model for Smart Manufacturing in Figure 4 provides a functional framework to start laying out business processes in the industrial ecosystem, the interactions required between activities, and the data exchanges required to support those interactions. Operations Management is a common central function in these three dimensions and has the critical role of coordinating the convergence of the digital, physical, and business process dimensions.

Figure 4: Three Dimensions Meet for Smart Manufacturing

The flow of information in the typical legacy manufacturing environment is, at best, full of manual information handoffs with a lot of human data interpretation and transformation along the way. Legacy processes were commonly designed around the sequential handover of paper documents between different departments. Within major enterprise systems such as ERP or PLM most processes are based or focused on departmental issues; the processes are rarely cross-functional. 

Modern processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams, and more 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 and request maintenance to avoid costly damage, 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, Inventory Control, and into the supply chain.

To minimize delays and communication errors among intra-departmental processes, process outputs need to be connected as inputs to successor processes. Communication and data processing among activities should avoid manual data input and translation errors whenever possible. Publish and subscribe data services must connect enterprise systems, web applications, mobile devices, and cloud services in a system of systems to ensure the pervasive distribution of the data.

There will be some technical challenges along the way to create the Smart Manufacturing connected enterprise. For example, data exchange standards will need to evolve and be adopted by hardware and software vendors, and security concerns will need to be addressed at all levels of enterprise communications. But the biggest challenges ahead are cultural. How do all involved embrace new business models, new processes, and new levels of transparency among departments and partners in the ecosystem?

Organizations will soon overcome these barriers and realize a network of connected partners, systems, and resources that will result in the transformation of conventional value chains and the emergence of new manufacturing practices and business models that leverage the higher levels of connectivity to achieve new levels of orchestration, optimization, and customer service.

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

 

 

Manufacturing’s Evolution to the Digital Business World is Well on Its Way

-  What I learned at the 2015 Gartner Supply Chain Conference

There is no denial that the manufacturing industry is in a period of evolution to support a new digital business world. New companies are popping up with new business models, some companies will slowly be left behind, and a few companies will figure out how to transform and adapt to take advantage of the new markets and business opportunities. Companies that do not evolve will not be able to compete against new business models like mass customization and product-as-a-service. Examples of companies on this evolutionary path were presented at this year’s Gartner Supply Chain Conference in Phoenix, Arizona.

In this new digital manufacturing world, a Manufacturing Execution System (MES) is a requirement, not an option. An integrated MES system is needed for a digital thread into the plant floor and the supply chain in order to streamline and automate procedures to support use cases like single unit work orders where each order in the sequence builds a different custom product configuration. MES users are reporting more than cost and quality improvements. They are experiencing improvement in employee engagement and flexibility.

 The Industrial Internet of Things (IIoT) is real, not only within plants but also into the supply chain. Gartner is predicting 30% more connected devices every year with more open standards. Current IIoT examples are merely providing a glimpse into the future potential of the Smart Factory. The roadmap for the Internet of Everything (IoE) in the connected supply chain includes increased visibility, proactive issue resolution, enhanced customer experience, data anytime and anywhere, and multi-tier value chain collaboration. 

 

The complexity of mass customization requires tighter integration of Product Lifecycle Management (PLM), MES and Supply Chain Management (SCM) systems. Starting with product design and manufacturing simulations, taking advantage of 3D printing and robotics and smart manufacturing machines and processes that can setup themselves to each product’s custom configuration. The factory of the future will be focused on capability rather than capacity. The next level of integration in the digital thread will require more CAD vendor openness and support for standards like ISO10303.

Many examples were given showing great strides in the connected supply chain for some industries for demand sensing and inventory visibility, but Quality is currently a missing link in supply chain management. There is a great opportunity to improve the digital thread for managing quality, not just for transmitting requirements and data collection but also for handling issues and corrective actions more efficiently in a multi-tier supply chain network.

Companies that want to transform will need more “Mode 2” versus “Mode 1” type of business investments.

* Mode 1: Reduce cost, increase capacity and utilization, plan driven, setup for batch production, proprietary integrations
* Mode 2: Focused on agility, capability, demand driven, order qty 1, integration of IT and OT  

 

 

 

 Organizations must appoint change champions and start working on the following types of initiatives:

• Transforming the workforce to embrace learning new skills, flexible work skills and performance transparency across departments and divisions of the organization and partner network
• Moving from custom and proprietary integration to open standards to support the Industrial Internet of Things and Smart Machines
• Convergence of IT and OT (Operations Technology) integrating plant floor, enterprise systems, mobile, cloud and social technologies
• Smart products that can store and exchange information with smart machines and smart manufacturing processes about their configuration and state
• Taking business and manufacturing intelligence to the next level developing predictive and prescriptive analytic capabilities

If you have access to the Gartner presentations, the following is a list of my favorite presentations from the conference:

• “Operational Strategies for a Digital Business World,” Dwight Klappich, William McNeil “Product Innovation Platforms: Managing Complex Product Life Cycles,” Marc Halpern
• “The Emperor’s New Clothes and the Factory of the Future,” Simon Jacobson
• “How to Avoid the Pitfalls of MES Implementations,” Rick Franzosa
• “Quality: The Missing Link in Your Supply Chain Strategy”, Simon Jacobson, Gartner SCC 2015 Conference Presentation
• “Supplier Quality 2017: The Multitier Quality Frontier,” Ray Barger
• “Leading Supply Chain in a Digital Business,” Mike Burkett
• “Internet of Things in Supply Chain: What’s Real Now?” Mike Burkett

 

Are Your Manufacturing Operations Ready for Transparency?

What does transparency mean in the context of manufacturing operations? Literally it means we can see through the manufacturing organization into its internal workings. Practically it means full availability and access to information required for collaboration and collective management decision making. It means full disclosure of detailed historical records for root-cause investigation purposes to uncover areas for continuous improvement. It means being open to share our best practices along with our past mistakes so they serve as learning experiences.

Transparency is an essential condition for a free and open exchange and evaluation of priorities for process improvements. It is essential to creating a culture of trust and teamwork in the organization.

Transparency through metrics

The deployment of a performance measurement system with standardized manufacturing metrics is a key ingredient to providing unbiased visibility and transparency within the organization.

In addition to providing a mechanism for transparency, carefully selected metrics can help an organization achieve its strategic goals. Work groups can become more motivated and engaged when their performance is measured according to well selected numeric metrics. Personnel must understand the relation of their metrics to the corporate goals and feel that the targets are achievable. Work group level metrics are considered a better practice than individual employee goals to promote teamwork in the organization. Wouldn’t it be great if all employees understood the corporate goals and how they could make a difference?

Personnel must trust the accuracy of the data and the calculation of the metrics. Manually calculated metrics in spreadsheets can lead to introduction of bias, manipulation of the data and general distrust of the reported numbers. Having a manufacturing system that automatically collects data and rolls it up into metrics avoids these types of concerns and greatly improves accuracy.

Metrics selection

The selection of good metrics and the involvement of key stakeholders can greatly help with the trust and adoption of the standardized measurement system.

Generally we should be able to relate the metric to (a) strategic corporate goals, and (b) to desired outcomes for the customer. If the relation between the metric and a corporate goal or key performance indicator (KPI) is indirect, we should explain and publish this relation so it is very clear to all stakeholders.

It should be fairly straightforward to understand how the metric is derived. This will make it easier for everyone to visualize, trust and relate to the metric.

The consequences of bad performance of the metric should be easy to see on the shop floor. For example, for longer than planned cycle times we should start seeing work orders and planned inventory queuing up somewhere in the work cell. For an increase in cost of poor quality, we should see an increase of rework orders at the shop floor.

The metric should be actionable. It should be easy to derive the types of actions that could be taken to improve performance in relation to the metric. For example, a metric should not combine too many measures into one number because the possible corrective action becomes very unclear.

Metrics should be consistent and independent of subjective bias. A good metric is consistent in what it measures. The source of data, the equations used, and the weighting criteria applied should be consistent but might also evolve over time. When it does change, it should be communicated to all stakeholders. It is good practice to avoid measures where a subjective evaluation is introduced in the calculation. Different people might apply the subjective criteria differently leading to inconsistent results for that metric in the organization.

Accountability and fairness

The organization can set goals to achieve specific target levels on key metrics within a certain timeframe to promote motivation, create urgency, and establish commitments for the team. Accountability in the organization is driven by how performance to the goals is tied to a performance reward system and the actions taken by the management team about poor performance.

For accountability to work, personnel must understand and trust the metrics, perceive the targets to be achievable, and feel that they are empowered to improve performance on the metrics selected for goals. For example, manufacturing throughput rates are constrained by the number of purchase orders brought in from Sales. Increasing the number of purchase orders is out of the control of Operations so it would be better to set performance goals for Operations on something they have more control on like improving cycle times.

In order to successfully set goals and reward systems based on metrics, they must be fair across the organization. But some types of work are harder and more prone to error than others. To be fair with metrics used to compare and benchmark across the organization, it is important to have a method to weigh the difference in difficulty for achieving similar results across the different types of work centers. An example of a weighted metric is the Six Sigma metric, Defects Per Million Opportunities (DPMO). Since DPMO weighs defects by the number of opportunities for defects in each manufacturing operation, we can use the metric to compare performance across different work centers.

Constructive scrutiny

Are you ready for peers looking over your shoulder? Questioning your numbers? Are you used to constructive feedback? The executive management team must be open to bad news and reward the messenger of bad news. Instead of punishing the manager that comes forward, the organization should provide additional budget to that department to help fix the problems. Other managers will get the idea and embrace honest reporting as part of the organization’s continuous improvement processes.

I heard Allan Mulally a few years ago (CEO at Ford at the time) talking about establishing a culture of teamwork and rewarding bad news in management meetings.  Alan celebrated finding improvement opportunities. When he started at Ford, he would ask “How can all the lights in the dashboards be all green when we just announced big losses?” He applauded the manager that brought that first issue up in a meeting. Instead of frustration, he showed enthusiasm and asked, "How can we help you solve that problem?" The following week, he was presented with a rainbow of colors in the charts.

The anxiety can be high in management meetings when there is so much visibility and transparency. It is important to keep the meetings positive with a spirit to collectively address problems and improve the overall organization.

Team buy in

Often it is easier to select a set of metrics than to get employees to buy into the performance measurement system. We listed some important considerations above to improve stakeholder acceptance and trust. Personnel that is not used to being evaluated based on metrics will offer a natural resistance to this new process. The executive management team must express a compelling case for how this new system is linked to the organization achieving its strategic goals.

A few key departments can be selected to pilot the new system. Change champions should be enlisted in these departments and early success in those departments with the new system should have a high visibility celebration.

Evolution over time

Just as a company’s goals and objectives evolve over time so should the set of performance metrics be revisited periodically and changed over time.

For example, for the last two years the strategic goals might have been to reduce cost by 10% and metrics were selected to reduce cost of poor quality and improve labor efficiency. This year the corporation might have a growth strategy and it wants to focus on reducing cycle time and time to introduce engineering changes into production.

Deploying a standard performance measurement system is no simple task but it is a tool to help your organization move up to a new level of business process maturity. It can become a differentiator for your business, and lead to major improvements to the bottom line.

 

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 can obfuscate 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

 

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