David Westrom
Dave Westrom is VP of Business Development at MachineMetrics. Dave has spent much of his career in executive team roles at innovative IIoT companies. He has led business development organizations and driven strategy at three IIoT start-ups that experienced successful exits, including most recently ThingWorx. Dave was GM of the MES business in the early 2000’s for Wonderware Invensys (now Aveva) following the acquisition by Wonderware of an MES start-up of which he was a Principal.
Executive Summary
MES (Manufacturing Execution Systems) has been around forever. The level of success in implementing and deploying MES solutions is still a topic of debate. The challenge lies in the variability associated with plant production processes and the approach of the MES vendor offerings and the customers that consume them. At MachineMetrics, we believe there is a more effective and impactful approach, one that establishes asset data capture and transformation, along with descriptive analytics, as the foundation for successful manufacturing initiatives. We have applied our solution as a cloud-based service with great success across many discrete manufacturing customers and partners.
The MES Conundrum
What is MES? It seems like a basic question but when you research it, you get a broad range of definitions. Wikipedia defines it as ‘computerized systems used in manufacturing, to track and document the transformation of raw materials to finished goods.’ Other definitions include:
- Information system that connects, monitors and controls complex manufacturing systems and data flows on the plant floor to improve manufacturing operations
- Systems that provide workflow visibility and insight into improving manufacturing operations
- System that drives the execution of manufacturing operations, monitoring the execution of the production processes and the materials used
According to the ISA-95 ‘standard’ for MES, core functions of an MES system include:
- Data acquisition
- Production Scheduling
- Personnel Management
- Resource Management
- Production Tracking and Dispatching
- Traceability and Genealogy
- Quality Control
- Process Management
- Performance Analysis
- Document Management
- Maintenance management
This covers a lot of ground and might lead one to conclude that MES solves everything short of world hunger.
What is clear in this MES mess is the following:
- The approach is process and material centric. Everything is viewed from the standpoint of the manufacturing process, the steps in the process workflows, and the materials flowing through the process
- The variables are many and this is no accident. When an opportunity or problem can not be concisely defined or articulated, it logically follows that a solution will be riddled with variables and difficult to package
Solutions that can’t be structured or packaged are often expensive, time consuming, and almost impossible to deploy across an enterprise. I have experienced this with MES systems throughout my career. While this may be bad news for the customer, it is not necessarily bad news for the MES industry and the many companies promoting the space and providing the tools and technology. If you search MES, hundreds of companies come up in what you might think is a hot, emerging space. But it isn’t, MES has been around for 40+ years. How come there hasn’t been consolidation or the emergence of a leader or leaders in this space? How many successful ‘MES’ companies are there?
The larger players in the industry who power the MES ecosystem and promote MES do very well. But many MES initiatives become one off custom development and system integration projects enabled by various tools, technologies, and consultants. They take a long time, cost a lot of money, and are very difficult to deploy and sustain across the multiple machines, lines, and plants that make up a company’s enterprise. The difficulty in replicating and effectively deploying a solution dampens the value of an MES initiative.
The challenge for MES lies in the variability of manufacturing processes and the approach. When your context is based on the processes, and the materials and products flowing through the assets in a plant, the enormous number of variables limit your ability to standardize, package, and achieve enterprise wide objectives. This doesn’t change even if an MES vendor uses fancy marketing terms such as ‘configurable’, ‘scaleable’, and ‘codeless’, to name a few, to promote the perception that an offering is easy to use and deploy.
A Next Generation Approach
For discrete manufacturers, there is now a new approach that provides a step change in value. MachineMetrics believes that any manufacturing production improvement initiative should be asset data-centric versus process centric. We believe that effective connectivity, capture and transformation of machine, product, and people data lays the foundation for rapid, iterative value creation through analytics that can be packaged and deployed across an enterprise. Development of an automated data collection and transformation infrastructure is the first and most important step in an Industry 4.0 journey. Core functions of MES, along with various other functions and technology offerings, layer on top of this infrastructure, if and when they are required. In the past, this approach was challenging in discrete manufacturing plants due to the range of complex machines that were difficult to connect and extract data from. MachineMetrics has solved this problem and has packaged an infrastructure in a manner where discrete manufacturers can connect and transform data from complex machines and begin realizing value from descriptive, diagnostic, prescriptive and predictive analytics in a matter of hours. The MachineMetrics approach also drives value in areas that can’t be obtained through custom one-off MES projects. Those benefits include baselining and comparative analysis across machines, lines, operators, and plants, driving best practices and continuous improvement.
Where’s the Value?
For discrete manufacturers, an asset-centric data collection and transformation infrastructure provides, through out of the box descriptive analytic solutions, much of the functional coverage of a custom MES solution with far greater value achieved faster and at lower cost. MachineMetrics has generated short term 20%-50% improvements in asset utilization, machine downtime reduction, standards optimization, and equipment service improvements. Furthermore, the infrastructure and solutions are easily supplemented by diagnostic, predictive, and prescriptive analytics which drive continuous improvement over time. And as mentioned previously, the solutions are deployable across the enterprise, enabling customers to compare performance and capture best practices across a range of assets and locations. Customers also have the option of baselining and comparing the performance of their assets to industry standards overall.
Enterprise Optimization
The MachineMetrics approach also opens a new world of opportunity for customers who wish to drive innovation through the creation of new business processes that cut across traditional organizational and functional boundaries. A few brief examples being implemented by MachineMetrics customers are as follows:
- Descriptive analytics on machine and operator performance can be leveraged by HR to improve performance reviews. Our customers are integrating machine and operator insights into their HR systems to achieve multiple HR objectives.
- Measuring and tracking machine runtime enables true predictive maintenance. Our customers are integrating machine data into their computerized maintenance management software (CMMS) to fundamentally change how they manage the servicing of their assets.
What’s Left for MES?
When you implement the MachineMetrics data collection and transformation infrastructure along with the complementary descriptive analytic solutions, and then consider the functional benefits of integrating to various back office and enterprise systems such as those mentioned earlier, the obvious question becomes: what’s left for MES?
The added challenge for the IT organizations of enterprise customers is supporting and maintaining yet another system, in this case an MES system, when most, if not all, of the value can be delivered by MachineMetrics and integration of MachineMetrics to various enterprise systems that already exist.
Conclusion
For discrete manufacturers, an Industry 4.0 journey starts with an asset data collection and transformation infrastructure. MachineMetrics provides such an infrastructure with analytics to many customers who are rapidly realizing incremental value and transforming their businesses. MES offerings should be considered once this infrastructure is in place only if required functionality cannot be achieved through integration of the infrastructure with other existing enterprise systems.
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