Gradual and deliberate ‘change for the better’ eventually improves productivity and facilitates efficient processes.
Kaizen, the Sino-Japanese business philosophy, urges you to strive for continuous improvement. Applying the principles of Kaizen to manufacturing focuses on improving the four main elements of manufacturing — product, process, people, and environment. Kiichiro Toyoda, the founder of Toyota Motor Corporation, championed the philosophy by saying, “The ideal condition for making things gets created when machines, facilities, and people work together to add value without generating any waste.” 
Harnessing the power of data analytics thus becomes crucial to a company’s continual process improvement strategy. Manufacturing process control optimization drives you toward achieving several business goals, from improved production efficiency to reduced production costs and increased customer satisfaction. However, integrated data analytics by solely relying on limited human capabilities is not only a terrible challenge but can also be misleading and flawed. Measurement and monitoring of Key Performance Indicators (KPIs) are essential for process control, but with limited analysis capabilities, it gets difficult to control wastage completely.
The manufacturing sector has thus undergone a radical transformation over the last few years with the advent of Artificial Intelligence (AI). Machine learning is a subfield of AI that enables machines to become intelligent with precise data analytics. It makes manufacturing process optimization possible by utilizing a simulation-based dynamic programming method called Reinforcement Learning (RL).
Reinforcement learning is an unsupervised machine learning algorithm that has revolutionized the continuous manufacturing scenario. It enables an autonomous system that learns from interactions with the industrial environment in real-time, trial and error, and does not rely on preprogrammed training. RL catalyzes SMART manufacturing by learning from accomplished tasks, incorporating changes in behavior based on feedback about the results of previous actions.
The biggest challenge for manufacturing optimization lies in collecting and cleaning vast amounts of data from different sensors every day while maintaining consistent uptime of production facilities. In many manufacturing systems, continuous state variables and discrete actions get detected. Therefore, process optimization becomes a highly complex task. Breaking down siloed data is essential and controllable parameters need monitoring and adjustment as they directly affect the production throughput.
Though these nonlinear dynamics can make process optimization challenging, it is not that difficult for RL algorithms to converge toward optimal control policies. Since experimentation on the shop floor could get expensive, the best action path gets derived through simulation. A digital twin of the physical production system gets created by domain experts who understand how the system works. The algorithm then learns the complex relations between the various parameters besides their effect on production and output. The feedback loop helps the goal-oriented algorithm learn and make sequential decisions while building a framework for the ideal process flow to generate desired results. In operations, RL-based approaches have delivered consistently impressive results.
Manufacturing is a complex system, where failure at balancing the customer’s demands with production costs, inventory, batch-cycle time, and quality can result in catastrophic outcomes. Manufacturers need to take a data-driven approach while developing robust, adaptive, and scalable solutions. Reinforcement Learning is an effective tool for predicting and solving specific problems in manufacturing processes. In fact, today 40% of all potential value created by data analytics comes from AI and ML techniques. In totality, machine learning can account for about $3.5 trillion to $5.8 trillion in the annual value — as per a McKinsey report. 
To summarize, RL enables data-driven and flexible processes in production domains. It has, therefore, already been deployed in production systems, proving to be an effective solution for process control optimization.