Predict the Future by Learning from the Past

Predict the Future by Learning from the Past
By: Jordan Reynolds, Principal & Global Practice Leader, Data Science at Kalypso, a Rockwell Automation Business

The consumer-packaged goods (CPG) industry is challenged by raw material shortages, supply chain disruptions, and shifts in consumer spending and requirements. According to a NielsenIQ report, consumers have their own priorities for CPG companies. The majority of consumers want them to improve efficiency and reduce material waste.

It is essential for manufacturers to have access to cutting-edge technologies that simplify, automate, and optimize manufacturing procedures in order to meet these challenges. According to the most recent information from the State of Smart Manufacturing Report, the most significant areas of manufacturing that will be affected by AI are quality (37 percent), automation (36 percent), and predictive operations and production capacity approaches. CPG manufacturers can use these same technologies to increase production and meet customer expectations.

Setting the Stage for Success with Smart Manufacturing Smart manufacturing, like earlier manufacturing trends like lean six-sigma, is a journey rather than a destination. The journey’s theme is getting the right information to the right people at the right time to achieve important goals like sustainability, risk management, productivity, and quality. CPG manufacturers are able to quickly and easily identify areas for improvement throughout the manufacturing process by simplifying and automating data analysis.

Identifying the challenge with the highest priority is the first step in smarter manufacturing. Whenever not set in stone, exploring and putting resources into the right logical devices that drive productivity, expanded control and reserve funds will bring about an effective change process.

Include these items on a smart manufacturing checklist:

  1. A digital, software-defined Operational Technology (OT) stack with modern capabilities for operations management, control, and sensing.
  2. A system for acquiring, integrating, and contextualizing large volumes of operational data that is simple to maintain.
  3. The capacity to rapidly introduce advanced analytical applications such as machine learning, modeling and simulation, and real-time optimization.
  4. As businesses acquire a greater comprehension of the data, they are able to adapt in order to make the data more actionable and work toward maximizing productivity and profitability.

Although AI cannot predict the future, technological advancements and digital transformation have made it possible for businesses to collect data from across the board. How can businesses determine which information will actually be beneficial to their organization and how can they realize these benefits once they have access to unlimited data?

Information planning is a significant test as not all information has a similar worth, and wrong expectations due to low quality information can have serious repercussions. When the appropriate OT data is fed into IT systems, accurate predictive AI is fueled, assisting businesses in avoiding the potentially negative effects of poor predictions. Through mechanized IT/OT assembly, ventures wipe out the requirement for significant information designing endeavors when examination is required.

Before it can be sold to customers, a food and beverage packaging plant, for instance, must go through several stages of production, such as filling, bottling, packaging, and so on. Manufacturers can coordinate various production parameters with a specific batch number and document the origin of a production batch with specific temperature, pressure, packaging thickness, etc. by automatically capturing OT data context at each of these stages. Manufacturers can carry out real-time root cause analyses with this contextualized OT data without requiring significant data engineering efforts. This enables manufacturers to quickly identify the location of the issue, find a solution to it, and use it as a reference point for their efforts to continuously improve in high-stress situations involving urgent safety recalls.

Visualizing the Future OT professionals are looking for machine learning (ML) solutions that can speed up time to value and require little effort from data scientists or training. Big data analytics and machine learning platform solutions that allow IT analysts and data scientists to visually build, train, deploy, score, and continuously monitor ML models are in high demand.

It is difficult to constantly update ML and AI models with new OT production data because consumer demands and expectations are constantly changing. The capacity to comprehend and change information at each step of the information pipeline limits the gamble of mistake during execution, further develops execution following and empowers IT groups to precisely change models depending on the situation.

Predicting the future of your manufacturing business does not necessitate a crystal ball. Only a thoughtful approach can bring about positive and lasting change. CPG leaders are missing out on the most valuable insights across their enterprise and, as a result, areas for growth if they do not have the appropriate technologies or mindset in place. The modern utilization of computer-based intelligence has definitely moved the manner in which producers approach, contemplate and execute business processes. With predictive AI, manufacturers can specifically analyze data from every stage of the manufacturing process more quickly and potentially enhance enterprise processes.