I was fortunate to participate at several industry conferences this spring listening and speaking with many leaders and practitioners working on innovation initiatives for their manufacturing companies under the general theme of Smart and Digital Manufacturing transformation. A few noteworthy observations are summarized below.
The digital transformation trend that started a few years ago continues stronger than ever. The digital business strategy for these companies has two main goals: (i) digital business optimization with goals of improved customer experience and improved productivity, and (ii) digital business transformation with goals of new business models and increased revenue through product and service offerings that leverage new levels of digital product data in this era of IoT.
Gartner states that around 65% of manufacturers surveyed are working on their digital business strategy and roadmap. These manufacturers expect that 50% of their production data and information will be automated by 2020.
MESA International’s survey, performed with Industry Week, shows that among U.S. manufacturers, the preferred term for this digital transformation is Smart Manufacturing (around 50%) followed by Connected Enterprise, Digital Manufacturing, and Industry 4.0 (each around 10-15%).
MESA also reports that 62% of manufacturers have already started with projects towards Smart Manufacturing.  Their survey is very consistent with the Gartner survey. Both reports are good benchmarks on industry-wide progress towards Smart Manufacturing goals.
There are a lot of new technologies we can apply to help us realize the Smart Manufacturing vision and manufacturers are currently trying many of them. Connected supply chain, Manufacturing Execution Systems (MES), and Robotics were on the top of manufacturers list of current projects related to Smart Manufacturing.
Gartner shows us the latest on their hype-cycle for these technologies. It is worthwhile noting that IIoT, cloud manufacturing systems and predictive analytics are heading down into the “trough of disillusionment” on the cycle which means that manufacturers have experimented with these technologies and are now resetting their expectations of value and practical use cases.
The Gartner survey also points out that manufacturers are currently focused on getting the foundational pieces in place including master data management, supply chain collaboration, and manufacturing execution system (MES) before they start implementing the next wave of innovation with IIoT, big data, and advanced analytics.
Edge computing devices are helping connect manufacturing equipment to the cloud. They can handle high frequency data acquisition on one side with analog, digital or serial input, they can perform computations and translations using controllers like Arduino, Beaglebone, or Raspberry Pi, and communicate on the other side with enterprise systems using standards understood by enterprise systems like MTConnect or OPC UA on XML or JSON.
In a recent report , Gartner warns that manufacturers are not paying enough attention to the intersection of plant operations management systems and supply chain management systems. 71% of manufacturers are working these initiatives independently in parallel and 85% report that integration of plant and supply chain is seen as a current challenge. Gartner predicts that manufacturers tackling this connection will likely see big benefits as new Smart Manufacturing ecosystems evolve in the next few years.
We are finally talking about robots and artificial intelligence (AI) beyond the simple use cases for replacing humans on brute strength, repetitive, or unsafe tasks. We are starting to see more use cases where robots and AI are working side by side collaborating and assisting the workforce with functions like feature detection, image analysis, natural language interfaces, and smart advisors.
One of the areas of AI assistance is computer vision that will monitor the work area and automatically guide or warn the technician by performing (a) visual inspection on things like cracks or scratches, (b) parts tracking that detects correct or incorrect parts as they are installed, (c) process monitoring that can detect out of sequence steps or detect safe practices.
There is progress with machine learning (ML) tools where humans assist in training the machine and can adjust for bias and unusual scenarios as they come up in the collected data. We are still in experimentation stages with deep learning where machines can train their own neural networks based on image recognition and historical data.
Yet, much of the AI talk is still aspirational. Examples of what visionaries are looking for include the following types of tasks:
- Scheduling production based on maximum machine performance and least impactful changeovers;
- Assigning available staff with the greatest efficacy for a process and product;
- Managing maintenance schedules for least downtime impact;
- Forecasting and optimizing material inventory based on the latest actual completion dates;
- Coordinating delivery routings with awareness of truck locations, schedule, and traffic conditions;
- Identifying sales trends that haven’t yet been identified by humans and recommend production volume changes
Indeed, we are seeing a lot of exciting technology advances but how do we put it all together and thread it into a new Smart Manufacturing enterprise? MESA International reports that many have started pilots and initiatives tied to trying out these technologies but Gartner reports that 2/3 of manufacturers are working out their strategies for Smart Manufacturing. There is still much to define to get from vision to reality.
Manufacturers need tools to help them navigate the ocean of technologies, systems, platforms and connection alternatives. One recent assessment tool is the Singapore Smart Industry Readiness Index  which helps companies assess at a high level their readiness in dimensions of process, technology and organizational structure. MESA International has several tools for assessment and is working on more of these types of tools to help manufacturing IT staff. Their current tools include: MESA Manufacturing Operations Management (MOM) Capability Maturity Model , Metrics Maturity Framework , and the MESA Smart Manufacturing Page  which is periodically updated with new resources and many are open to non-members. I encourage you to check out the resource references listed below for more information on advancing your Smart Manufacturing initiatives.
 Research Report: Seeking Common Ground for Smart Manufacturing, MESA International, 2018
 Harvest the Value of Smart Manufacturing in the Supply Chain - Not the Factory, S. Jacobson, Gartner, 2018
 Four Best Practices to Manage the Strategic Vision for the Internet of Things in Manufacturing, S. Jacobson, Gartner, 2016
 Webcast: Smart Manufacturing: Continuous Improvement or Strategic Transformation, MESA International, 2018
 Managing Disruption Requires Supply Chains to Foster Innovation and Scale the Digital Supply Chain: A Gartner Trend Insight Report, M. Griswold, Gartner, 2018
 Singapore Smart Industry Readiness Index, Singapore Economic Development Board, 2018
 MESA Manufacturing Operations Management (MOM) Capability Maturity Model, MESA International, 2016 (available to MESA members only)
 Metrics Maturity Framework - A Guide to Assessment and a Roadmap to Increased Performance, MESA International, 2016 (available to MESA members only)
 Seeking Common Ground for Smart Manufacturing – Research Report, MESA International, 2018
 MESA’s Smart Manufacturing Page (periodically updated with new resources and many for non-members)
 Smart Manufacturing: Continuous Improvement or Strategic Transformation? – Recorded Webcast, MESA International, 2018