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.

Skepticism Stifles More Progress Than Failure Ever Does!

Much of what I have written about lately deal directly or indirectly with the general confusion around the topics of digital transformation in the manufacturing industry. I try to bring clarity to many topics such as Industry 4.0, digital transformation, IIoT, etc. and I am seeing a significant change in the industry especially in the last 6 months. Yet, skepticism about the ongoing paradigm change driven by digital technology is still rampant. 

Digital transformation is the engine that propels businesses forward in today's dynamic world.  Companies that leverage new technologies to automate operational activities, improve operator productivity, improve efficiencies, and manage quality are poised to outpace their competitors.  However, lurking in the shadows of this exciting shift is a potential innovation killer - skepticism.  Unfounded negativity towards digital initiatives can create a culture of resistance, stifle groundbreaking ideas, and ultimately hinder progress. Its understandable that new technologies need to be evaluated, and that without proper change management digital transformation can fail. However by being a skeptic you are robbing your organization of potential productivity gains. 

Writing off emerging technologies too soon is a centuries-old practice. New technologies often seem to inspire equal - and often counteracting - surges of enthusiasm and skepticism. 

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

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.

The Waste of not having Digital Data

My team started using Monday.com around a year ago. We needed a project management tool and Monday.com was our tool of choice. When I looked at it initially it seemed like any other project management tool, albeit cloud based and much more user friendly. Some may even consider it a glorified (or maybe more appropriately specialized) spreadsheet.   

I didn't think about it much, it was a PM tool and its use included the typical interaction as a project team member and for operational oversight. After a few month the team started showing graphs and charts based on the data that it captured. We could now easily see how long tasks take, how many project were over time, hours logged on projects, and more. As time progressed we had more and more data about our project execution and delivery operation. With that we started making better and more informed decisions such as optimizing teams, identifying risky projects, how many projects we are able to effectively run simultaneously, resource balancing, and much more. Then the team started putting in alerts, e.g. projects overdue, risk not being mitigated, and proactive actions. A year into using the tool we have now a services operations that is predictive and adaptable (this is a reference to the Industry 4.0 maturity model below) all on the merit of real time granular data that we is being captured just from us doing our job - without any extra or special effort in data collection.  

Industry 4.0 Maturity Model

Here is a tool that on first glance seems like just another PM tool. However since its a modern digital tool it instruments the project tracking and management process, capturing detailed data about every tasks and making that data intuitively available for all to use. That is the power of digitations, we see in all digital tools but it sometimes is lost on us when we reflect on something that we have done forever. For example Google, Facebook, Amazon all do this inherently - they capture granular data of everything. and with that data they are able to learn, improve and act.  This is what happens when you instrument your operation with a digital tool.

So if we take this as an example and reflect on any manufacturing operation it means that as a start we have to instrument and digitize. However the way this is approached in most manufacturing shop floors that I have experienced is by complicated data capture systems that require not only a immense effort in implementation but also constant battles from the frontline to use. Inputting data by the operator is typically an additional task they have to do, its not serving them any, it does it provide value to them and is essentially a form of waste. This results and data silos, or as how I like to call them "data puddles" that really do not provide much more than some isolated metrics. 

That of course is if there is any data capture at all! Many manufacturing operation are still operating with manual data and information in the form of paper, performance whiteboards, isolated spreadsheets, visual boards, etc. 

A compilation of photos from different shop floors I have visited.

We have to make the connection here, transforming digitally is not just another IT/OT exercise, its not a project you can execute to implement data capture technology (see post about crossing the digital divide). True digital technology is adopted not implemented, it starts by taking some manual task or operations and instrumenting them like Monday.com does for project execution. The first step in any digital transformation journey is to start to instrument your operation by providing digital tools to your operators that help do their job while at the same time collecting data - not the other way around. 

The interesting thing about doing it this way is that its in fact pretty easy, and at the same time seamless for your operations to adopt. Simply because it helps them do their job, and provide immediate feedback thru data visibility about their operation. It is how they become more productive, and productivity is what digital transformation is all about.  

With that in mind the simple way to start any digital transformation project is to do a Gemba walk to identify the waste of paper or no data. Find the places where instrumenting a process can provide a quick productivity increase. Instrument the process with a digital tool and see experience the boost yourself. Think of PDCA cycle with this level of granular and real time data. That is what digital transformation is all about.

Remember this can only be done by a digital tool that can be adopted (implemented, and used) with no specialized skills (democratized) and fast - within a few hours. Be warned a technology that does not provide these basic requirement, i.e. democratized and fast time-to-value, is not true digital (Industry 4.0) technology.

Smart Manufacturing / Industry 4.0 - 20 years in the making.

Hello everybody, I am back after a bit of an extended hiatus. I am finding my way back to blogging starting with some reflection on the past and view into the future.

In the late 1990s I was part of a international research effort that lay the groundwork for what we know of today as Industry 4.0 or Smart Manufacturing. I specifically was interested in the architecture of what is now known as IIoT. In 2000 I wrote an article in the Journal of Manufacturing Systems about these new concepts and predicted that it would take 15 years to become mainstream. I guess I was young and optimistic, it has already been close to 18 years and although its still not mainstream it has started to move in that direction.

At that time the Internet was at its infancy and we could only dream of what is now possible with today’s technology. it was called many different thing such as multi-agent systems, Holonic Manufacturing Systems, Adaptive Manufacturing, and a few other such concepts. It was also called Intelligent Manufacturing, there was even an international consortium named that (and it still exists www.ims.org!). It is also one of the reasons behind the name of this blog. The joke was that what we did up to then was not intelligent :-) However the underlying assumption was that it will be a paradigm shift, a new way of thinking and a new way of operating that is brought about with technological advances.

Now, in the last many years I have been deeply immersed in the pharmaceutical industry and I have come to understand how immature this industry is from a manufacturing operations and technology perspective. Yes biologic manufacturing is novel, complex and groundbreaking however looking at the operations of a pharma plant with paper based systems, traditional automaton and maybe some MES it is nowhere close to the likes of Automotive, Aerospace and even CPG. Yes, this may be a broad and general statement but there is truth to it! Risk averseness driven by regulatory requirements is clearly one of the reasons and probably also why there is little innovation in the manufacturing operations space here, however that should not be an excuse.

So where am I going with this? I think that the pharma industry is poised to take a generational leap and may be able to skip the current or traditional manufacturing operations paradigms and go directly to Smart Manufacturing - bold! it is going to take more than putting a smart sensor on some equipment or sending some data to the cloud for analytics, but its a start. Most of all it is going to take leadership and a vision. There are a few people out there that I know will be able to do this - you know who you are. I am also ready for the challenge - It is a paradigm shift, we must think differently and operate differently.

How hard is it to define MI requirements?

Well let's say its not straightforward! For quite some time I have been trying to explain the difficulties in providing clear specification for Manufacturing Intelligence (MI) system. In addition the life science industry is still bound by traditional methods of analysis, requirements specs and functional specs that simply put will not work for MI. MI's whole purpose is to provide information to somebody to support his creative behavior as he explore root-causes and solutions in the dynamic world of manufacturing operations. (This is an extract from a forthcoming article in Pharmaceutical Engineering).

The right approach is based on the critical element of understanding how people use information to solve problems and gauge performance. There is a clear need to provide effective and relevant information necessary to support the information consumed by the different roles in the manufacturing operations. Identifying what needs to be measured is a fundamental principle but it is not sufficient, the information also has to be arranged in a usable manner. Therefore it is important to study and understand information consumption patterns by roles. Take for example the information consumption pattern for a supervisor in a biotech plant that is creatively analyzing a production event.

The production supervisor glances at his dashboard and observes that the Cell Density is not within acceptable limits. He immediately navigates to view the “Cell Density by Time” trend over the last 2 weeks and observes a negative trend beginning around “Mon.” that indicates something is seriously not in order. 

To begin the analysis, he examines the “Media Batch Feed Schedule” to see if there is any correlation between the trend and the Media that is being fed to the bio-reactor. This action is obviously based on intuition possibly because he has seen that before. Seeing that there is a correlation between the change to a new Media batch feed when the trend start he decides to take a look at the “Exception By Batch” information and notices that this specific batch had an unusual number of exceptions. He then dives deeper into the data by analyzing an exception Pareto for the suspect batch. He finds a high number of operator errors, which clearly highlights the root cause of the trend. Finally since he is accountable for operational profits he decides to take a look at the cost impact of this event in order to understand what the impact is to the plants financial performance (see graphic below). Unfortunately the cost impact is substantial and thus he as to take action to mitigate this increased cost. 

The scenario shows the power of “Actionable Intelligence”. The supervisor has all the information he needs in order to quickly and effectively analyze the situation to determine root-cause and he can take action based on the results. The path that the supervisor decides to take in the example above is one of several that could have been used to detect and diagnose the Cell Density performance issue. It is this type of self-guided or self-serve analysis that really shows how information is consumed to meet a specific goal and should be the common pattern for the information required by a specific person or role. These requirements have direct bearing on the information context and data structures that must be provided, and the dimensions by which the metric is analyzed or “sliced and diced”. Although this seems trivial at first the requirements that this analysis patterns has on the underlying information and data structures is significant and is a critical component of the system design. It is not enough just to collect the data; it has to be arranged in a manner that enables this unique type of analytic information consumption.

Why Do We Still Use Spreadsheets?

Sometimes I find some unfinished article while cleaning up – something I should obviously do more regularly! So here is one of these excerpts that I found particularly relevant as I am discussing the topic of “Manufacturing Intelligence” with a number of companies.

Manufacturing systems software vendors continuously tell us that you cannot have visibility into your operations without a software application, which I have to agree is generally true. This forces us to sift through the onslaught of offerings full of buzz words such as “metrics”, “digital dashboards”, and “business intelligence platforms”. Yet, it is remarkable that one of the most commonly used tools to capture and manage information from the shop floor is The Spreadsheet - typically Microsoft’s Excel. In some cases, even with a major ERP system investment, the Spreadsheet is still the primary source of timely data collection about the manufacturing operations. In other cases, expensive solutions are put in place to capture and collect data from automation equipment but fail to provide the information in a useable context and once again users resort to the spreadsheet.

Why is it then that manufacturing organizations resort to solutions that are based on a spreadsheet? It is typically not because of lack of understanding about information systems or the skills required to use them. It is because a spreadsheet provides the flexibility and ability to manage and present shop floor information in the most useable and advantageous manner. (By the way the common term for “manage and use” is “information consumption”.) Remember that a manufacturing manager’s main focus is productivity and quality. They use this information to obtain metrics about the value stream that they are trying to manage because they need to know how they are performing in real time. This need is similar to that of a sport’s team, where you know where you stand at every second of the game. You don’t have to wait until tomorrow morning’s newspaper to know who won the game. Running a manufacturing operation without real time metrics is like bowling without being able to see the pins. You can see some of the action, you know that something happened, but you don’t know what the result was.

Of course in recent years, manufacturers have gained some visibility with the increased application of technology, but they are still far from what is possible. I also believe that most of the vendors are clearly aware of the needs and I hope that they we will soon start to see Manufacturing Intelligence applications with the flexibility and convenience that we really need.

A Foundation For Quality (and performance...)

Next week is ISPE’s annual meeting in Texas and once again I am participating in an interesting session titled “Operational Excellence - A Foundation for Quality”. We held a similar session last year and it was a great success with more than 90 people participating. The topic that I am covering this year is about the current capabilities of automation technology andinformation systems that provide a vital ingredient in enabling operational excellence. I will be joined by speakers from Pfizer, Celgene, Amway and of course NNE Pharmaplan.

My co-presenters will share experiences about OperationalExcellence, Quality by Design (QbD), and Process Understanding. All-in-all an interesting combination of topics that may initially seem loosely related but they are in fact deeply related. They stem from the some old notion that manufacturing performance is a holistic concept. I have mentioned in a previous post that we are “re-inventing the CIM wheel” but at the same time we are also looking at it in a new perspective with much more modern and usable technologies.  I strongly believe that before we venture with new ideas we have to look and learn from the past, it is very likely that somebody has had a similar problem and may even have a solution. So CIM, PAT, QbD, DFM it really is all related, related to trying to excel at we do and work more synergistically to accomplish our manufacturing goals. 

Metrics & Performance Management - Again

Not that again! Well it still is a very interesting topic an one that I encounter every time I talk to companies in the life science industries. It seems that there is an increased understanding across all walks of life in the manufacturing organizations that one of the main advantages of system is the ability to gain a better understanding of both process and operations. I hear people asking for metrics, and in the context of gauging their performance. I spend a lot of time trying to understand this trend, and I have no conclusion yet. It may be the economic climate, maturaity of the technology, or maybe the momuntum that operational excellence intiatives have gained?

The membership of the ogranziations that I work with such as MESA and ISPE have also shown great interest in this topic and I am participating in a few events that highlight them. On October 27th we have a MESA webcast about "Harnessing the Power of Metrics" and on November 7th we are organizing a special session at the ISPE annual meeting about "Operational Excellence - A Foundation for Quality". The session will be hosted by NNE Pharmaplan and will include speakers from Amway, Celgene and Pfizer. I hope to have some great discussions, feel free to track me down if you are in attendance.

"Lean technology" a Manufacturing Systems perspective

My colleagues and I are in the process of preparing an educational session that will be held at the ISPE annual event in November. So as usual I spend some time looking through my archives for relevant material that I already have – I call it 're-cycling' :-). This time I came across something that I never published or used and so I thought I would share it here. It is an attempt to explore the synergies of the Lean and Manufacturing Systems concepts. Read and tell me what you think…

Manufacturing Technology; A familiar phrase considered part of the everyday vocabulary. What about Lean Technology? Not a common phrase, but the idea of lean manufacturing supported through technology should be as much a part of the vocabulary as Manufacturing Technology. Lean thinking advocates simplification of manufacturing units so they can be more easily shifted to enable the flow of value. So in essence the “lean technology” concept supplements lean thinking by combining state-of-the-art manufacturing with advanced software systems in an integrated environment.

Using information systems in lean manufacturing is not a new concept, nor is it new to the lean movement. Many examples exist that prove that manufacturing (software) system can support a lean organization. Unfortunately most commonly information technology systems for manufacturing tend to become large monolithic systems of great complexity. They are designed to provide generic functionality to fit major industry verticals that can be configured specifically for each implementation. At the same time the uniqueness and the complexities of the specific manufacturing operations make the ability to only configure these solutions more a myth than reality. Most of the system vendors will of course argue against this - however the reality is that in order to meet the requirements of the manufacturing businesses they are forced to implement complex customizations. That is what I wrote on my post about Customize or Compromise.

Although lean thinking advocates the application of a set of specific common concepts, the actual implementation of these concepts in real life tends to be unique to each production line and plant. Information systems that are used to support these lean lines and plants have to be able to provide common functionality to support these concepts. Yet, they also have to be able to support the uniqueness of each implementation. Furthermore they need to be able to support the changes that are inherent in a lean system due to the continuous improvements as they are accomplished.

In summary here is my suggestion for the general functionality of a Manufacturing System that can support a Lean manufacturing environment. (BTW I know that these go against the grain by not using a problem-oriented approach – I will have to redefine this ASAP)

• Value stream: We all know that value is identified by the specific needs of the customer hence the Manufacturing System should be usable and implementable to support only these specific processes that add value. In other words the system once implemented should not require or be constrained to use extra processes or involves extra steps if they are not directly part of the value flow.
• Implement flow: The Manufacturing System should employ a value centric process model that is easily managed and accessible to all the relevant people. This will allow a transparent view of the value flow and thus allow engineers and operators to ensure production flow, be it a one-piece flow, supermarkets, or other relevant Lean solutions.
• Execute Pull: This is kind of the obvious requirement that involves the enablement of pull execution and dispatching of WIP. This may include features and functionality to enable or enforce flow and managing kanbans, supermarkets, balancing, etc.
• Enable Perfection: Enable perfection by providing the production and process visibility needed for the continuous improvement efforts. This is the part of the system that provides Intelligence (a topic that I have written a few relevant posts about). In addition the Manufacturing System should provide adequate configurability, extendibility and customizability that support continuous improvement.

EMI or BI - what are those anyways?

Lately I have had inquiries from a couple of customers about providing “intelligence”, as in manufacturing intelligence, well I believe that is a good topic I said… (Hint, see my blog’s title).

In most cases their curiosity came from either seeing or working with an EMI or Enterprise Manufacturing Intelligence system. However when I asked about how they would like to use the intelligence I found out that they where asking about BI – Business Intelligence behavior, yet they wanted manufacturing metrics like the EMI system’s demo. That is an interesting discussion that I have mentioned a few times in this blog. I believe that what they really want are timely and sometimes real-time intelligence about their manufacturing operations provided in KPI lists, metrics and charts but also analytics capabilities such as “slicing and dicing” and “drill downs”.

Unfortunately I have found that such solutions are rare if they exist at all. It seems that EMI is a separate animal than BI and the problems that they are trying to address are more complicated than they generally convey in their sales and marketing information. Most EMIs do not provide an effective way to aggregate and correlate information to support BI style analytics, while the BI vendors’ understanding of 'real-time' and timeliness leave much to be desired for application in the manufacturing operations world.

What is Intelligence, and why do we need it?

I believe that it is time to get back on topic in this blog, which is Intelligence in manufacturing and manufacturing systems in general. With that in mind I was looking thru my archives and came across something I once wrote as a positioning statement for an intelligence product – I guess it is not hard to figure out what company that was for? So here goes…

In one of my previous posts I tried to bring up the point that we need to consider metrics in the context of what they are needed for and how they are going to be used. I believe that is the best way to understand how to provide the right intelligence in a given scenario. But what is Intelligence? Well that is a very serious subject, but let’s take in the context of manufacturing and process improvement.

Intelligence implies the ability to comprehend; to understand and profit from experience. As such Intelligence is information valued for its timeliness and relevance rather than its detail or accuracy in contrast with "data" which typically refers to precise or particular information, or "fact".

In the context of manufacturing, Intelligence is a fundamental ingredient influencing the system’s level of performance in reaching its objectives. A manufacturing business system (humans included) is a system that learns during its existence. In other words, it learns, for each situation, which response permits it to reach its objectives. It continually acts and by acting reaches its objectives more often than pure chance would indicate. We can observe the following about Intelligence in manufacturing:

• It is an enabler for performance – Performance Management
• We need to act on it – Actionable Intelligence
• We need to learn from it – Continuous Improvement

Intelligent manufacturing is not a smarter way of producing things; it is a human centric approach where humans interact with the process be it automatic or manual, gathering the right information to take intelligence decisions based on actionable information. It is much more than visibility. Just having the information is of course helpful, but it needs to be taken one step farther. It needs to be provided in a way that people can intuitively capitalize on it using their knowledge and understanding to make effective decisions.

Henri Poincare once noted in a related topic that “Science is facts; just as houses are made of stones, so is science made of facts; but a pile of stones is not a house and a collection of facts is not necessarily science.”

What is an effective decision then? It is a decision that has an outcome that drives increased performance and continuous improvement. Intelligence is therefore not solely about metrics, KPIs or the ability to drill down into the data. In order to increase performance we need to quantify what is important. Hence intelligence is about quantifying what is important, or quantifying the unquantifiable.

Using metrics and what it tells us about Manufacturing Intelligence

In continuation to some of the other posts in the topic of metrics and KPIs, this time I would like to discuss the use of metrics. So, not so much what the metrics are or which metrics are most important, but how do we use them. If we look at how analysis is performed we may be able to gain some insight into how the data should be collected and structured. This may be for example to support lean or process improvement initiatives. The idea regardless of methodology requires that the team or person gain an understanding of the process and its behaviour based on the data at hand.

I think the best way to frame this up is to tell the story of a persona in a simple scenario. Let’s consider Patrick Process Engineer who is trying to understand yield fluctuations specifically and maybe the yield’s behaviour in general. He is perplexed about why he cannot accurately predict yield given that most of the processes are “in control” (yes, another one of these myths about processes). What he really is striving for is an understanding the process and the best way to investigate it – in other words analyze the process.

Obviously Patrick will be looking at the Yield metric(s) and once he sees some fluctuation he will embark on an analysis. Typically he will perform what I call a high-level analysis, which is kind of a “look-around” in the data and maybe other related metrics to determine patterns. But wait, why patterns. Well whether we like it or not when we analyze processes we naturally look for patterns since that is what complex processes really exhibit. I guess I need to write a bit more about the complex adaptive behavior of manufacturing systems, but let me leave that for a future post. For now let’s just say that the patterns really tell us about the dependencies between the different metrics and parameters in the system.

OK, once Patrick has performed is “high-level” analysis he may then start digging a bit deeper in to some of the areas that may lead him to the root-cause of the fluctuation. He may perform some more detailed analysis, maybe choose to monitor some specific metrics, define some additional more detail or focused metrics. If he really is trying some advanced analysis he may try and observe dependencies between metrics for a period of time. That means monitor maybe a few metrics and how they change in relation to each other.

What is described here is really a simple scenario of human behavior exhibited when we perform troubleshooting. Although this as simple behavior for us, when we think about the data and data structures that we need to support what Patrick is trying to do, we may quickly realize that the complexity abounds. Modern business intelligence concepts such as multidimensional analysis, OLAP, and practices for data aggregation provide the tools. However it takes a lot of work and process knowledge to transform the base process data to such information structures.

This I believe is the main challenge in modern manufacturing systems. Compounding this problem is that effective analysis needs to use information from all the host of system that may reside in a manufacturing business, i.e. ERP, MES, Automation, Historians, LIMS and others. Most manufacturing intelligence tools that are in the market today simply do not address this simple scenario. If we do our inbound marketing work correctly and draw up the scenarios above, with Patrick our main persona, the problem definition is straightforward; however the engineering task required to solve it is not.

When is enough, enough? The art of counting jelly beans.

I was reading an article in National Geographic Blog about counting jelly beans that in a way boiled down what my general belief about metrics (see also another relevant post about metrics). Also, I have lately been involved in MESA’s metrics working group and all of this had me thinking…

The current practice in regards to metrics in operational environments especially in manufacturing is the typical engineering tactic - we need to break down the complexity of the system and figure out what metrics we need. Well one approach is to take a look at what other companies are doing – as in MESA paper we try to figure out if there are any best practices. But is this the right approach for any company or for you? I say no! Metrics are a way to represent patterns in the complex systems that enhance our understanding of its dynamics. We can look at one metric and say we are doing good or bad, which works well if you are a computer since we can take a decision based on this metric. Yet unlike computers, we humans are much better at using our knowledge and understanding to detect patterns and react to them. So if you give us a set of metrics that help us interpret what the system is doing then we do much better. That is exactly what the term "Actionable Intelligence" means.

There are a few important factors that I believe are important:

• A few metrics used independently are never going to give you the true picture of what is going on.
• Simply copying what other companies are doing will not work either, since metrics should be driven by what you are trying to achieve – and that changes with time.
• The Accuracy of the metrics are not necessarily important. What! Yes, I just said it; accuracy is not that important. Let’s think about a jelly bean counting competition, you really don’t needs to know the exact number but be the one that is closest to the precise number, in order to win. Michael Schumacher (if you do not know, he was a 7 time Formula 1 GP winner) once said “… you don’t have to drive your fastest to win, but faster than the guy behind you”.

So why do we obsess about having so much data, detail and precision? Why are we always trying to quantify the unquantifiable, as in 6-Sigma. Maybe it is our upbringing as engineers, that constant quest for details. Really all we need is an indication when things will go wrong and when they are going well? This is of course not to say that sometimes we do have to be very precise as well as accurate, such as in engineering tasks, designs, etc. The art of it all is to know when we are precise enough.

In a blog post Stephen Few states “most poor decisions are caused by lack of understanding, not lack of data”. This is exactly my point, we spend so much time working on detail. For example, how much the ocean water level will rise over the next period of time. Some say a few meters some say 30 meters in the next 100 years. But the point here is that even in a moderate scenario calculation; Venice, New Orleans and the SF Bay Area delta, among other places, will be under water. So if you live there – guess what? You are in trouble, regardless of accuracy.