The burgeoning need to pursue adoption of Industry 4.0 has only been amplified by the large-scale disruptions which have happened in the wake of the global pandemic, which still unfortunately rages on. The pursuit of Industry 4.0 is fueled by drivers like the Industrial Internet of Things, Big Data and AI-based process autonomy at manufacturing’s edge as major drivers, coupled with other critical changes in manufacturing like additive manufacturing, augmented reality, cloud computing and advanced robotics.

McKinsey states that the leading organizations which have made headway in being the most digitized are farther ahead in their digital journey in comparison with their competitors. These organizations have succeeded in most business cases pursued and have actively aimed at being pioneers in adopting Industry 4.0 and enabling technologies in their manufacturing operations and value chains alike, with the goals of being more future ready, more resilient, more agile and faster and more efficient than ever before. They have made it evident that it’s the combination of strategic belief, people involvement from top-to-bottom and across, along with the simultaneous implementation of enabling technologies across the value chain and uses cases which allows for a true and a most beneficial manifestation of Industry 4.0.

It is a given that Industry 4.0 demands end-to-end connectivity of the entire value chain, with manufacturing at the center of the digitization process, where data flows freely from the shop floor to the top floor and beyond. As the data moves forward or upward, it gets refined in such a manner that decisions which may be taken instantly are done so automatically, without process disruption, and operational intelligence, which needs careful review before taking a strategic call, is also made available through the same network of multiple IT applications working cohesively along with the requisite modern technologies, thereby improving the entire value chain’s efficiency, reducing losses, retaining customers and increasing the bottom line results. 

The Industrial Internet of Things (IIoT) has the potential to transform existing manufacturing plants and their value chains. It does this by unleashing the power of true process knowledge and autonomous operation, which it achieves through collection of sensor data from enabled devices and process equipment, while allowing machine to machine (M2M) communications to enable smart actions, based on ML-created triggers and generated by processing data right at the edge of manufacturing.

The real beauty of IIoT and ML creating autonomy lies in the fact that it is fully deployable in the existing processes of most manufacturers. There’s most certainly a need to add additional sensors on existing equipment for new process metrics, or have a proxy application for equipment which is rather unsophisticated but none the less essential. Using ML, you may need to ensure that your raw data is ‘refined’ enough to eliminate errors, reduce redundancies and bring in enough context from outlying processes for ML to be effective.

IIoT is an absolute cornerstone to achieve the kind of efficiency and automation related- results associated with Industry 4.0 deployment. Without IIoT (sensing at the edge), the ability to gather and add value to large amounts of data from the shop floor, would stop short of maximum effectivity. IIoT adds a level of data enrichment required for the digitization process to succeed.

The Big Debate: IIoT or MES?

However, there is a lot of debate around one particular area pertaining to IIoT and its role in Industry 4.0: does an IIoT platform, coupled with single function based applications, have what it takes to replace an MES? In other words, does IIoT along with ML/AI and other specialty applications displace an MES? 

We believe that this point of view does not really consider the Industry 4.0 pre-requisites and subsequent manifestation in its entirety. According to our white paper, IIoT has a thing for MES, it was never the case of IIoT vs. MES; rather the only plausible scenario which exists if Industry 4.0 has to be truly pursued in a value chain is MES with IIoT.


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Let’s understand why. IIoT at its very core is acts as a sort of middleware between the equipment, connected devices and higher level applications. An IIoT platform may possess the ability to harness data and apply machine learning at the very edge, to generate some kind of automatic, learned response or action, while communicating horizontally machine to machine or even with higher level (business) applications to submit data with an alert or input. Beyond this, the IIoT platform needs applications which help execute a particular function, to further use the data collected and turn it into information which may lead to real long-term benefits which go beyond the shop floor—for example, making decisions on the type of materials to process based on the weather, or on the current workforce available, or on the mix of customer orders in the queue.

This is where MES excels; traditionally, MES has been the application which both orchestrates the manufacturing process and provides context based on operational data collected to aid decision making both in real-time and in the continuous improvement context. From a process perspective, the MES is all pervasive, i.e. it covers every single manufacturing function directly and indirectly, affecting the production process and the end product itself. A MES includes the data definitions of the process, the process steps, the process model, and the rules governing manufacturing. Any IIoT platform, coupled with single dimension software, can’t even begin to deliver the kind of results a fully functional, modern MES can deliver when combined with IIoT and a data platform.

The role played by MES in Industry 4.0 is absolutely vital. The MES resides at the very center of the entire digitization process. Why? The MES not only collects the data from the manufacturing process but it also provides a much-needed context to the data, even at the edge of manufacturing. MES-provided context can be the difference between a decision taken which either disrupts or further improves the flow or production. It provides the essential context for successful use of ML through IIoT.

Beyond providing process and business context, the MES is also responsible for orchestrating the entire manufacturing process, which means the MES is an enforcer of rules and enabler of business stimuli which is gained from beyond the process, such as you’d find in supply chain management.

This all-inclusive approach is impossible for a standalone application to deliver, as orchestration of the process requires a holistic coverage of the entire plant infrastructure. The MES basically forms a digital twin of the process and allows IIoT to use it as a framework for enabling all dynamic actions for enabled devices and equipment. The application also by its very nature integrates and collaborates with other IT applications of the value chain, which make it the ideal vehicle for ushering in Industry 4.0 and IIoT.

The most important deliverables of any Industry 4.0 deployment are considered to be speed and resilience. Besides the obvious efficiency improvements, these are a direct derivative of MES responsiveness, visibility and standardization.

Smart Manufacturing Marketplace

At the heart of the Industry 4.0 model is the concept of a smart manufacturing marketplace that promises more productivity, efficiency and lower cost manufacturing operations. By creating a smart marketplace, manufacturing moves away from linear production ‘lines’ to dynamic production models, utilizing a network of equipment across the shop floor. A smart marketplace extends to smart scheduling, smart dispatching and smart services. The smart marketplace, using IoT and intelligence, need not be limited to the factory shop floor. It can encompass a global factory network including external suppliers that can bid to work on product orders. This has the potential to further lower production costs, increase productivity, equipment utilization and throughput time and make the business much more agile in response to customer demands.

Only a modern MES application with its ability to collaborate with IIoT-enabled devices, data platforms and IT/business level applications like ERP, CRM and SCM can effectively participate in this Industry 4.0 marketplace. The requisite data collection, used for process visibility, validation and corrective actions also contributes to the marketplace’s viability of using real-time, process-derived data for decision making.

The MES can alter routing, recipes, work instructions and send alerts across the supply chain to create the speed which is needed, both through integration with the automation layer and generating deeper operational intelligence.

The much-desired value chain resilience also results from standard processes where MES orchestrates workflow in multiple plants; programmatically ensuring that any change needed or any best practice activity or procedure to be adopted will be effectively driven, implemented and documented within the MES.

MES and IIoT are made for each other; however, not all MES offerings are ready for IIoT. The MES’s infrastructure needs to be ready: can it truly accommodate high volume data generated through IIoT; can it provide both ML-enabled edge processing and batch processing of collected data? IIoT is supposed to automate processes with the help of ML; however, any automation achieved, without the context, responsiveness and orchestration that the MES provides would only give sub-optimal results.

So, before deciding to skip MES in the pursuit of automation and digitization, bear in mind that the MES is the most critical part of the desired automation and digitization of your factory. It connects and houses the IIoT-generated data and places it in context with the rest of the value chain. It not only is at the heart of the digital manufacturing marketplace, but also enables value chain-wide improvements, which no IIoT platform alone can ever achieve.