Integrating Machine Learning into Manufacturing Execution Systems
Making sense of your environment.
Every manufacturer has the opportunity to embrace machine learning and integrate it into their manufacturing execution systems with the goal of achieving high-quality, low-error/defect processes at the lowest cost possible. Machine learning, as a branch of Artificial Intelligence (AI), has made it possible for companies to realize such goals. Machine Learning allows computerized systems to learn from data, experience, and examples, eliminating the use of fixed or pre-programmed rules. It derives this capability from capturing, storing, and translating data into predefined strategies. There are several benefits that accrue to manufacturers through the implementation of Machine Learning (ML) in their manufacturing execution systems. These include enabling continuous maintenance, higher quality controls, real-time and automated error resolutions, reduced production costs, increased productivity and efficiency of the process chain. Manufacturer’s high interest in ML/AI and its early adoption is driven by several factors, namely by demands from shippers to align the supply chain, recent advancements in technology and continuous investments in data visibility by the market/industry leaders.
In the production line, ML plays a key role in answering some manufacturing questions. As it’s true that machine learning is largely based on data collection and interpretation within the manufacturing process, the questions are laser focused on the data that are most crucial. These include:
- What different kind of system failures can be experienced within this machine, system, and/or component?
- What types of failure events does the manufacturer wish predict in order to reduce or altogether avoid?
- Does the failure happen suddenly, or does it occur by gradual decline before complete malfunction?
- Which components of the system or machine commonly experience such failure?
- To ascertain the state of the machine/component’s health, which parameters must be taken into consideration?
- What are the required levels of frequency and accuracy in measurements needed?
By answering these questions, the manufacturing process can alter its course of production towards high performing systems and staff with top-notch quality. The manufacturing benefits gained by integrating machine learning into the process are discussed below.
The best instances of machine learning can be exampled by those cases in which companies are looking to better derive knowledge from their past production experiences. By analyzing the large volumes of data currently available, machine learning applications are able to identify recurring patterns and use these as reference points to create new data streams.
Detecting anomalies and trends in real-time within the running system enables predictive process automation. The substantial amounts of data (in many cases referred to as big data) can therefore be leveraged by companies for their benefit.
- Continuous Maintenance with Intelligence Monitoring:
Manufacturers that integrate machine learning in their systems implement the use of sensors to gather the data that will then be run through a myriad of algorithms in an effort to identify anomalies and provide insights that have never before been achieved. Transformation of technology makes the sensors become smaller, and relatively cheaper. It therefore makes it economically viable for companies to monitor the operations of their varied machinery at all times. However, such data produced by the machines must be critically analyzed before it can be used.
2. Incrementing the production capacity:
With Machine Learning, it is possible for manufacturers to optimize their production resources, from teams and machines to suppliers and vendors. Manufactures are able to adapt to the ever-increasing complexity of client/market demands. Machine Learning optimizes the production process by making use of the best fit machines, staff and suppliers. This optimization is best seen through the reduced costs of production. This cost reduction is drawn from the minimization of redundancies within the manufacture process.
Manufacturers using ML implement smart manufacturing systems which capitalize on the predictive data gained from the production processes. As such, companies can increase the general yield rates at the plant, production cell and machine levels. Implementing ML will increase the company productive levels by up to 20% (minimum).
3. Perfection of the supply chain:
A greater percentage of the complex manufacturers are constantly making trade-offs when trying to determine which client orders to fulfill first. By collaborating more effectively through ML, manufactures and their suppliers (buyers and sellers) can reduce the major occurrences of stock outs. Companies can make their raw material requirements more forecasted, which would help their suppliers beat their delivery dates. Improvements to the logistics operations will become apparent as companies are better able to mitigate risks, improve supplier relations, and much more.
4. Quality Control through Continuous Testing:
Improved Quality Control represents, possibly, the best aspects of Machine Learning. Integration of Smart Applications make it possible to check on the precise status of products at any stage of the manufacturing process. Once this has been proven, predictive measurements can be generated to react in real-time by adjusting input levels to keep production not only going, but also ensuring product quality throughout your processes.
Most companies have traditionally followed a practice of producing first and counterchecking on the quality later. Machine learning eliminates this hurdle. With ML, such systems of conventional testing will be completely eliminated. Software has the awesome capability of being able to predict product quality (with millimeter precision) right from the first steps of introduction; when raw materials are introduced. This makes it easy to identify any defects on the products early. Self-learning algorithms allow ML applications to report predefined error sources and detect the sources of error that previously lay unknown.
5. Provision of more relevant data to operations, finance and supply chain:
The success of manufacturing companies often lies in the balance between optimal factory production and demand constraints. Many manufacturing companies do not have their information technologies integrated. This presents a challenge to cross-functional teams when they are set to achieve a common goal. With ML it is possible to bring an entirely new level of insight on the production process. It is possible to unify the goals of the production process. Workflows, inventory and Work in Progress (WIP), plus the value chain decisions, can be all in a single unified direction. This is through the availability of shared data throughout all the interlinked organizational departments/segments.
Conclusion:
The primary benefit that companies can derive from machine learning integration into their existing MES is the ability for them to turn their data loads into substantial strategic advantages. Machine Learning increases the amount of insights companies have about their industry, factories, products, customers and market in general. Machine Learning solutions give the manufacturer foresight into the production process, enhanced quality control, almost exponential process monitoring and optimization along with continuous equipment maintenance capacity.This is the closest thing to a “silver bullet” that is available for manufacturing today.
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Michael Stattelman
