With rising costs, supply chain uncertainty, an aging workforce, and increased volatility in demand among other challenges, manufacturers are turning to intelligent cloud and edge solutions to increase the agility of their factory operations. Factory agility is achieved through asset productivity, operational visibility, and intelligent production operations. Further, it requires manufacturers to take charge of the factors that are within their control, prioritize their investments and resources, and understand the impact of changes they make.
Traditionally, manufacturers have measured productivity of their operations and assets with KPIs such as Overall Equipment Effectiveness (OEE), which is reported as a percentage using measures of equipment availability, performance, and production quality. OEE is an important KPI, but it can be difficult to compare opportunities for improvement based on a percentage. A large improvement of a less critical issue may be less valuable than a small improvement in a key production bottleneck. For example, an OEE percentage fails to isolate complexities like overtime shifts and changeovers. Further, OEE calculations may not be available in real time, meaning they don’t immediately show the changes expected from improvement efforts or easily roll up across units, lines, plants, or systems.
PTC has partnered with Azure, a cloud computing software operated by Microsoft, to provide strong security measures, increased flexibility, and scale for data. This integration allows for optimized total cost of ownership (TCO) and focus on sustainability goals, quicker time-to-value, and reduced security risks in manufacturing environments.
Thingworx Digital Performance Management (DPM) enables manufacturers to connect to existing systems and unify real-time data across Information Technology (IT) and Operational Technology (OT) that are usually siloed. By pulling important IT and OT data from their systems of record, and standardizing on a single unit of time, DPM creates a closed-loop approach to identifying bottlenecks and measuring impact in the production process. DPM’s unique and systematic approach can help manufacturers identify, prioritize, analyze, and validate the top opportunities for financial improvement.
DPM enables manufacturers to see how much lost time a specific problem is causing, so plant managers can start with the biggest time loss cause, implement a change, and move on to the next most impactful bottleneck—creating a new approach to continuous improvement and enabling factories to measure problems and priorities in a dynamic, agile way.
Let’s step into the shoes of a plant manager, who we’ll call Jean. Jean might need to meet increased demand while also slashing the cost of overtime. How can Jean produce more in fewer hours? By figuring out where time is being lost, and measuring everything on that single scale, Jean can easily see where the biggest issues are and focus on those with the greatest impact. By focusing on and investing in areas with the most impact, Jean will optimize the investment of time and resources and address issues in order of priority.
For example, Jean uses DPM to see a time loss waterfall of bottleneck analysis and performance analysis for a week of production. Jean sees that resetting raw materials is the largest source of time lost that week. Jean discusses the materials reset process with the frontline team and discovers that each person is resetting the machine and raw materials differently. The night shift workers have figured out a way to do the task in half the time as the average day shift worker, and they all use the same process. Jean trains the entire team on the faster process, and the change is implemented across shifts. In the next week, time used for resetting materials is reduced, and this is no longer the biggest category of non-productive time. Jean then proceeds to address the next largest opportunity for recapturing lost time, and so on.
By reducing the time for materials reset, Jean has started the process to achieve optimal efficiency in the plant. With less time required for reset, Jean’s team could produce more in the same amount of time (increasing revenue), produce the same amount in less time (reducing operating costs), and increase overall agility to make changes as the market demands.
Jean used DPM to prioritize where to invest time, understand the cause behind the biggest loss of time, measure the impact of an implemented change, and begin the continuous improvement process. Jean’s materials reset process could then be rolled out to other lines or facilities that may benefit from the improved workflow.
Solutions like DPM enable fast time to value by connecting to data systems that are already in place and enabling frontline workers to account for events throughout their shifts. Furthermore, standardizing to the metric of time, comparisons can be made more easily across shifts, lines, and facilities. DPM creates a close connection between improvements and recovered time that links directly to financial impact.
Unlocking true, transformational value requires a systematic approach to continuous improvement processes and can only be achieved when paired with enterprise scale. Standardization is the key to scale. Azure provides unparalleled data security, which is critical when assessing systems that will scale across regions, business units, and levels of management. It is integral this system is operating efficiently and safely—when paired with DPM, the solution provides unrivaled insights for continuous improvement and optimization.
Tools that create operational visibility, like DPM, are the first step to creating agile factories. Visibility creates insights, and insights improve intelligence and optimization. DPM enables production efficiency, which creates value for manufacturers. The value created from solving one time-loss issue can be used to fund the next digital transformation project, and so on.
DPM on Azure lays the foundation for full digital transformation. More than standardizing data in Azure, it unlocks more advanced factory use cases that will benefit manufacturers. DPM employs out-of-the-box analytics capabilities to identify other scenarios and variables that occur during, before, and after time loss events. With all the IT and OT data gathered using DPM, manufacturers can apply artificial intelligence and machine learning to begin predictive and cognitive analytics on their operations. These digital tools are pushing manufacturers to the factory of the future.
Read this joint paper from Microsoft and PTC