About Accountants and Production Managers: ERP vs. MES

This is a rewrite of a whitepaper that I published in 2004 based on a long and frustrating MES selection process where the "can I use my ERP as MES" misunderstanding went rampant. I find that the discussion is still very relevant today and the topic gets even more confusing with some of the emerging digital technologies in this space. So this is an attempt to bring more clarity...

About ERP and MES

With today’s increasingly accelerating manufacturing technology innovation, digital transformation is critical for staying competitive. Among the key systems that have traditionally driven manufacturing operations are Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems. The myriad of opinions and discussion on these concept in the context of digital transformation and therefore understanding the differences between these two systems is crucial. In addition, as digital technologies continue to advance, the lines between MES and ERP are increasingly blurring, especially with the advent of No-Code democratization and Frontline Operations Platforms. I have found that and effective ways to conceptualize this is through the analogy of a production manager and a company accountant.

The Accountant: ERP’s Role in Manufacturing

In the last decade ERP systems have seen massive proliferation into many businesses, including manufacturing businesses. These businesses have invested heavily in ERP systems and today struggle to realize payback from these investments. In the case of manufacturing businesses, realizing ROI is logically focused on the production floor, an area in which ERPs are traditionally considered weak.

As the ERP market becomes increasingly saturated, vendors are looking for ways to increase revenue and expand their footprint. ERP vendors have turned their attention to shop floor management and manufacturing execution systems (MES). By adding MES functionality, they can increase license revenue. 

Consider the role of an accountant in a manufacturing company. The accountant manages financial records, oversees budgets, handles payroll, and ensures that all financial transactions comply with regulations. Their work involves high-level data analysis, financial forecasting, and strategic decision-making that influences the entire organization. This is akin to the role of an ERP system.

At their core, ERP systems are advanced accounting information management systems, they are enterprise-wide management tools designed to integrate various functions across a business. In manufacturing, ERP systems handle tasks such as procurement, inventory management, finance, human resources, and supply chain operations. They provide planning tools like Material Requirements Planning (MRP) and Manufacturing Resource Planning (MRP II), which help companies predict future resource needs based on historical data and forecasts.

However, despite their comprehensive nature, ERP systems are not designed to manage the real-time, dynamic environment of the production floor. They excel at providing a broad, strategic view but lack the granular control needed to manage the intricacies of manufacturing processes. Just as an accountant isn’t equipped to manage the day-to-day operations on the production floor, an ERP system isn’t designed to handle the real-time demands of production management.

The Production Manager: MES’s Role on the Shop Floor

MESs have evolved to address the inherently complex production management functions. An MES is a specialized system focused on the shop floor, where it manages real-time production activities. It coordinates equipment, workers, materials, and processes to ensure that production is carried out according to plan. Unlike ERP systems, MES operates in real-time, responding instantly to changes and ensuring that production goals are met. It tracks production data minute by minute, making it possible to identify and correct issues as they arise.

Imagine the role of a production manager. This person is in the thick of things, ensuring that production runs smoothly and efficiently. They manage workers, monitor machines, and make real-time decisions to keep everything on track. The production manager is intimately familiar with the production process, knows when to adjust schedules, and reacts quickly to any disruptions. This role exemplifies what an MES does in a manufacturing environment.

While ERP provides a high-level overview of production schedules and resources, MES is concerned with execution ensuring that production is executed as planned. MES is deeply integrated with the physical aspects of manufacturing, enabling it to manage the nuances of the production process that ERP systems cannot.

Differences Between MES and ERP

Aspect	ERP  (Enterprise Resource Planning)	MES  (Manufacturing Execution System)
Scope and Focus	Covers a wide range of business functions across the entire enterprise. Designed for strategic planning and resource management across departments.	Specifically focused on the production floor, with deeper engagement in executing production processes, equipment monitoring, and labor management.
Data and Time Frame	Deals with high-level, aggregated data, often historical or forecast-based and financially biased. Works on a broader timeframe for long-term planning and decision-making.	Operates in real-time, handling detailed, granular data from the shop floor, responding immediately to production needs.
Integration and Flexibility	Integrates various business functions but often lacks the flexibility needed for real-time adjustments on the production floor.	Highly flexible and adaptable to the dynamic environment of manufacturing. Integrates with machinery, sensors, and other shop floor systems.
Decision-Making	Supports strategic, long-term decision-making at the corporate level, focusing on overall financial business performance and resource allocation.	Highly flexible and adaptable to the dynamic environment of manufacturing. Integrates with machinery, sensors, and other shop floor systems.

Blurring the Lines: How Digital Technologies Are Redefining MES and ERP

As digital transformation continues to reshape manufacturing, the era of traditional monolithic MES may be coming to an end. New technologies and platforms are presenting a different way to solve the shop floor management coordination challenge. Based on the foundations of MES these new solution incorporate advanced digital technologies such as No-Code, IIoT (Industrial Internet of Things), machine learning, AI-driven analytics, Generative AI and enhanced user interfaces. They offer a more holistic view of manufacturing operations, providing real-time insights that empower workers on the shop floor to make data-driven decisions.

Unlike traditional monolithic MES, which focused solely on production execution, the new breed of technologies leading with the Frontline Operations Platforms encompass a broader range of activities, including quality control, maintenance, lab operations, inventory management, and workforce training. This transformation is a direct response to the growing need for systems that not only manage production but also integrate seamlessly with other digital tools and platforms to enhance overall operational efficiency. It also aligns with the broader digital paradigm, where the goal is not just to automate existing processes but to create a more connected, intelligent, and responsive manufacturing environment. The integration capabilities of Frontline Operations Platforms enable a seamless flow of information between the shop floor and the enterprise level, blurring the traditional lines between MES and ERP.

Several key trends are driving this convergence:

1. IIoT and Real-Time Data Integration:

The proliferation of IIoT devices on the shop floor allows for the real-time collection and analysis of data. This data can be fed into both MES and ERP systems, enabling more informed decision-making across all levels of the organization. For instance, real-time production data captured by IIoT sensors can be used by the ERP system to adjust supply chain logistics or by the MES to optimize production schedules on the fly.

2. Advanced Analytics and AI:

Machine learning and AI are increasingly being used to analyze the vast amounts of data generated by manufacturing processes. These technologies enable predictive maintenance, demand forecasting, and process optimization, functions that traditionally belonged to either MES or ERP. The use of advanced analytics allows these systems to overlap, as both can now contribute to strategic and operational decision-making.

3. Human Centric Platforms:

The new no-code platforms take a human centric approach that break down the traditional process centric solution. They allow to build solution that can be used across manufacturing modalities and also allow to combine MES and ERP functionalities blurring the lines between the two. The new solutions provide a democratized platform for managing all operational process. Workers on the shop floor, managers, and executives can all access the same platform, though with different levels of detail and control, depending on their role.

4. Cloud Computing and Edge Computing:

The shift towards cloud-based solutions and edge computing is enabling greater integration and scalability of MES and ERP systems. Cloud computing allows for centralized data management, making it easier to integrate MES and ERP data. Edge computing, on the other hand, brings computational power closer to the production site, enabling real-time data processing and decision-making that benefits both MES and ERP functions.

5. Interoperability and Open Standards:

Increasingly, manufacturers are adopting interoperable systems that can communicate with each other through open standards. This trend is making it easier to integrate MES and ERP systems, allowing for a more seamless exchange of data and better collaboration between different departments.

The Future: A Converged System for Manufacturing Excellence

The convergence of MES and ERP functionalities into more integrated platforms represents the future of manufacturing. As these systems continue to evolve, they will offer manufacturers the ability to manage both high-level strategic planning and detailed operational execution through a single, cohesive platform. This convergence will enable a more agile and responsive manufacturing process, better equipped to meet the demands of the modern market. The systems are working together more closely than ever, driven by advancements in digital technology that empower manufacturers to achieve new levels of efficiency, flexibility, and innovation.

In Summary...

In the rapidly changing landscape of manufacturing, understanding the distinct yet increasingly interconnected roles of ERP and MES systems is crucial. As digital technologies continue to advance, these systems are evolving and converging, offering manufacturers a powerful toolset for driving operational excellence. The transformation of MES into Frontline Operations Platforms exemplifies this convergence, blurring the lines between strategic planning and operational execution. By embracing these integrated platforms, manufacturers can unlock new opportunities for efficiency, agility, and competitiveness, setting the stage for a new era of manufacturing excellence in the digital age.

Yet, it is naive and risky to assume that one of these systems can be extended to effectively do the other’s job. Similarly, one would not assign an accountant to be a production manager, or vice versa. Each might be an expert in his own field, yet it takes a completely different set of skills, expertise and knowledge to effectively tackle each task.

Hour by Hour Boards in the Digital World

What does an Hour by Hour board look like in a digital world? This Lean visual management tool that is a common and useful method to drive performance in manufacturing. But what do they look like in the digital paradigm? I find that the go-to is to just digitize them, meaning replicate the whiteboard without much thought about what they can be and how we can impact outcomes, ie drive performance increase. 

If we are to really gain order of magnitude productivity increases when introducing digital technology we have to go past that "just digitize the board" mentality. Let's start by considering why its hour by hour (or some standard period). Its to provide a tangible target to aim performance at with a given frequency. Its also because the operators or person who is supposed to update the board does it at that frequency. 

However if we introduce digital tools then we also introduce means to capture the required data at higher frequencies and also varying frequencies. This can be done a simple screen to quickly quantities, to interactive capture of quantities through buttons, input devices and sensors and ultimately through advanced sensory devices such as vision.  With that in mind it becomes a bit trivial to just do it every hour! In addition we can also capture a lot of context about the data. From the obvious such as operators, stations, orders, etc. but also things like environmental data, events, materials used, stock levels, etc. 

Lets take an example of a digital solution that essentially is capturing good and bad quantities of parts produced with some context such as time stamp, operator, station, shift, product and optionally some comments. However unlike the manual boards the operators can enter data at any given frequency and much more frequent than hour by hour. The system can also prompt and even require that he enter data at a given frequency. This is of course, if we only we rely on manual entries, if we supplement with sensory devices we can get much more granular and frequent data.

Now that we have all this data we can of course display hour by hour board with total quantities. Great but let's think about what else we can derive from this data? First of all we can calculate throughput and output rates, for example:

• Throughputs: Good parts per hour, Good parts per shift, etc.
• Output: Total bad parts by day, total good parts by line

And then include simple predictions of performance, for example:

• Predicted good parts by end of shift, predicted total bad parts by line, etc.

But it doesn't end there! With this data you can also visualize trends of performance for example:

• Trend good parts by line by shift. 
• Daily performance trend 

We can also do comparison by a multitude of dimensions for example:

• Bad parts by vs good parts by operator, or by day 
• Good parts by product for different shifts or operators 

We can take actions on deviations and critical scenarios, for example:

• Send a text when Good parts per hour is below 10

With all this digital data there is just so much insight to gain from just a simple capture of quantities. We now have a continuous feed of information and the hour by hour transforms into a wealth of information and importantly insights. This information can be displayed in numerous, dare I say infinite, formats for use by the operator, supervisor, leadership and any function in the operation. 

And it doesn't end there. With enough volume of data we can start applying more advanced analytics (read AI/ML) and gain insights that we did not uncover. Then operationalize these insights by proactively doing something about the performance predictively and intelligently.

Remember we started by simply capturing good and bad quantities of parts produced in a digital form. This took us from just looking at quantities and performance against a target to the ability to look at performance trends, patterns historically, performance in the context of different dimensions. Then to insights based on human intelligence, taking proactive actions, then to predictive analytics with AI/ML ending with deep insights into our operation. This is where and how digital transformation offers order of magnitude productivity increases.  

Let me end with a favorite quote from Dr. Seuss:

"Think left and think right and think low and think high. Oh, the things you can think up if only you try!"

So do "think high", digital is much more than digitizing, re. the hour by hour board in this example. The "things that you can think" once you have digital data is where the value is.

To Data Model or not to Data Model Part II - What to Data Model

The previous post about Data Models included a bit of a long winded discussion of why strict monolithic data models are not the alternative in the new paradigm. The main conclusion was that rather than focus on design of a strict data model for all of manufacturing lets step back and understand the problem that needs to be solved. The discussion is a bit theoretic and that is why I am compelled to go one level of detail deeper in an attempt to clarify some of the concepts. 

What do we need to help us in the transformation journey to maturity, how can we achieve Visibility, Transparency, Predictive Capacity and Adaptability? We need to shift the thinking from "what is the correct data model?" to "what do we need to become  predictive, and adaptable?". First of all we need more data, start digitizing your operation - the majority of the data we need is still on paper and diverse electronic documents and spreadsheets. Second, and this is the topic of this post, understand the informational elements of your operation and define a loose data dictionary that supports your digitization initiatives and citizen developers. With that and modern and emerging technologies for data analysis including AI/ML you will be able to gain the required insights and intelligence without a strict standardized monolithic relational data model. This will allow freedom within an organization for people to capture data without having to spend immense efforts in curating and micro managing how the data is stored and structured. Remember democratization and citizen development are a key enabler of digital transformation, their creative abilities with no-code technologies is the fastest way to digitize and instrument the operations. Get more digital data fast, its more important than how its structured and don't forget variety, multi media etc.

In my close to 30 years of studying the manufacturing domain it has become clear that there are just a few main and critical informational elements to a manufacturing operation. With that in mind I recommend an approach that uses generalization to help create transparency and interpretability but still allow for flexibility for specific use cases and varying degrees of complexity. The following generalization allows for a top down perspective into the complexity of a manufacturing operation. 

With this thinking, data about the artifacts represents the current status of each artifact, a single unique set of data (e.g. a row in a table). The processes that impact the artifact are captured in a historical record, a set of data for each significant transaction that transformed the state of the artifact (e.g. a running log). This results in a data set that represents real world artifact in a one to one relationship while everything that has happened to this artifact is captured in logs.  

If you create simple templates that allow contextualization of data at the source based on these simple rules you can with modern analytics tools rapidly get the insights that you need to mature digitally. I find that it works for both human driven analytics, from charting and graphing in Excel to Tableau, Sigma or whatever tool you prefer. Taking this even further you can super charge that with AI/ML driven analytics. I urge you to try, the good and easy thing is that the effort to build and use something like this with modern operational platforms is minimal compared to a building and using a complex relational data model. 

I also find that this model and generalization is a helpful tool to rapidly gain an understanding of a specific manufacturing operation. In fact I use it as a mental model when I do plant walk-thrus (Gemba walk) after which digital improvement ideas to observed operational challenges can be defined much faster and accurately. If you look closely many of the prevailing standards have the similar generalization but unfortunately have been overengineered past the point where they are practical.

Quickly understanding how a specific manufacturing system operates, from the machine to line and to the plant levels is the basis of digitization, its secret to gaining Visibility, Transparency, Predictive Capacity and Adaptability. Remember that is what we are after, the technology is just a means. If we can make it easier, more democratic, and adopted by the frontline masses then the network effect kicks-in and transformation happens faster, we gain productivity faster and we are well on our way to cross the digital divide.

To Data Model or not to Data Model

The ongoing debate about where and how MES fits in new era of digital technologies is raging. Its not surprising and in fact to be expected in any kind of change, basically the old guard vs the new guard. Of course you have to believe that the 4th industrial revolution is really a paradigm change. Something that I clearly align with and have some background to do so since I have been studying this phenomena since the 1990s.

As in other paradigm shifts there will always be a bit of the old that is part of the new. Steam power has not completely disappeared, it still relevant in specialized application but it is not the main source of energy powering industrial operations. This leads us to ISA-95 that I believe is a relic of the current "industry 3.0" era and not directly relevant in the new digital paradigm. (note I purposefully am trying to minimize the use of "Industry 4.0" since it starting to get a negative connotation with all the hype going on). But, that being said there are elements of ISA-95 and other best practices that may be relevant in the new paradigm, ie the old in the new?

If we let history be our teacher we can probably come up with some prediction and that is where the data model topic is interesting. ISA-95 includes a data model and all the established MOM solution include a data model that based on the available technologies at the time that seemed appropriate. The question is then; is the quest to achieve the nirvana of one standard monolithic data model for all manufacturing achievable and is it still relevant with the new digital technologies? The answer I think is clearly no and no, as far as I know there are very few, if any, examples of an organization achieving a real working standard data repository for all its operation and its not because of lack of trying.

The bottom line here is that striving for a single standard data model in a monolithic repository is a fools errand, regardless of if we try to implement it with modern digital technologies. That being said a common, shared and interpretable view of manufacturing operations is still needed and critical. In fact it's at the core of Industry 4.0, in that its the data and information that gives us the Visibility, Transparency, Predictive Capacity and Adaptability. This holistic view into the manufacturing operations is also at the core of the CIM concept from the 80s that advocated a common "shared knowledge" that all operational activities in plant uses in order to streamline to manufacturing of products. That means that both paradigms are aligned around the same challenge that to improve manufacturing operations we need to all have a common understanding and view into the operation!   

The CIM Enterprise Wheel (c)1993, SME.

The difference is how we achieve this common and shared view (information and knowledge). In the old paradigm it was the notion of a strict and rigidly structured data model, in the new paradigm we have relaxed these requirements to allow for analysis from both structured and unstructured data. I can hear the skeptics already; how can you gain any insights with different solution each having their own data structures? A few things to consider here: We do need context and this context should be defined at the source. We need to simplify data structures and get away from multiple levels of abstractions needed to run monolithic process driven solution.  Adhere to some simple shared guidelines using a consistent data dictionary that allows for flexibility within your organization. (I know this sounds overly simplistic and see part II of this blog post). With these principles we can adopt many of the modern digital tools to curate views into our data, on demand with the flexibility needed for common and personalized views and insights including of course AI.     

Let's take at an example where different solutions all represent some data about a lot of materials and its product code. The material can be referenced as Lot, Batch, Units, Pack, Kit, etc and the product code can be references as SKU, Item ID, Product, Material Number, etc. We of course immediately recognize these different names as similar because we understand how they are used. In the old paradigm we had to enforce strict rules in structure and semantics for software solution in order to visualize and analyze this data. That is however changing with new digital technologies and modern analytics platforms.  

It is also where AI can help, you see simply put AI is good at finding patterns. Its not that AI understands what the meaning of Item and Material Number is. It simply is looking for similarities in the relationship to other data structure and how its used to see that Item and Material Number really are very similar. With enough data volume and variety this can be easily detectable. Notice I said volume and variety this is where Cloud based system are important. Using isolated traditional monolithic system data sources will never get you to this point, even if they are lift and shifted to the cloud. You need a modern cloud native operational platforms that provides easy access to the their data that can be amassed and used to identifying these patterns.

I know there are a number of concepts discussed here and there may be some lack of depth in the discussion. I promised a follow up on this post with some more detail. But assuming this is true, just think about it. It means we can relax the strict data type and structure requirements and allow citizen developers to extend template data structures to create solution to solve operational problems and know that we can still gain valuable insights about operations, and again the more data we have to more insight we have. The conclusion here is: prioritize data volume and variety and not monolithic structures.

My head is in the Cloud

I have spent the last 2 months coming up to speed with SaaS, The Cloud, Big Data and advanced analytics. Wow, I am amazed at how far technology has come. In the last 7 years it seems I have moved further and further away from software technology with my increasing operational responsibilities. I did my best to follow the general trends and progress but really never in enough depth. I knew there was something “big” happening but kept thinking “I know what is going on…”, “I should have a general understanding…”. When I stopped to really understand how far some of these technologies, we group under the “Industry 4.0” umbrella, have come I found it quite astounding!

The power of artificial intelligence (AI) and machine learning (ML) algorithms that are able find patterns we never knew existed in vast amounts and use this information to predict future behavior is amazing. Stories such as the one where Target (the retail store) was able to identify a girl as pregnant before her dad knew, stand as proof. I have also found the ease at which data can send data to the Cloud is just hard to understand. We have been fighting the connectivity and data contextualization problem for such a long time that its hard to believe it can be done any other way. If we take a breather, step out of the ditch and take a look around then we can quickly realize - we don’t need to do this anymore! Data can be collected by software agents running on everything from a server to a sensor and configured through a digital twin. There is no need to format, convert, transform or contextualize the collected data. In fact the AI and ML algorithms work best if we keep it unstructured and the smaller the data sets the better.

This is all very exciting and the more I think about how we can use these technologies the more more encouraged I am that we can achieve true manufacturing intelligence. The ability to have a real time picture of everything that is going on in our manufacturing plant is simple and achievable. On top of that we can discover relationships between processes and artifacts that we never imagined existed, and use these to improve, to better operate, and maybe stop the great firefight that is operating a plant. We can transform the daily operational meetings to something akin of a weather forecast - wouldn’t that be something to watch?