Research suggests that “Resilience” refers to the capability of manufacturing systems to adapt and cope with disturbances in their functions and recover from the partial setbacks. Manufacturing resiliency demands the generation of models that will guide production toward improved operational efficiency.
“Resilient Manufacturing” is a sought-after concept that has been around for decades. However, IR4 technologies have increased the ability to efficiently collect and analyze data accurately, leading to its resurgence after many years. AI/ML technologies with predictive and prescriptive analytics have enabled organizations of all sizes to implement data-driven decision making.
Manufacturing Analytics in the Past
Descriptive analytics has been the norm in manufacturing forever. Analyzing production summaries and highlighting patterns in current and historical data, generating reports from KPIs and other parameter metrics to track production performance and trends. Descriptive analytics was the traditional tool for a better understanding of the manufacturing environment.
However, the modern manufacturing arena is much more demanding. Adopting flexible processes, real-time insights for process improvement, and timely forecasting to avoid unexpected downtime—a lot is expected of a manufacturing company to grow between competition and become economically resilient.
Most manufacturers have made unprecedented efforts to streamline operations and eke out maximum productivity from the business insights derived from traditional descriptive data analytics. However, the buck can’t possibly stop there anymore!
Decision Making with AI apps and machine learning algorithms has helped a larger number of manufacturers to enjoy the benefits of predictive and prescriptive analytics for enhanced operational agility.
Using AI-based Predictive and Prescriptive Analytics for Resilient Manufacturing
Predictive and prescriptive analytics go further with the descriptive data and provide actionable insights that help you make informed decisions. Predictive analytics will forecast how a process will behave in the future and what the outcomes might be. And prescriptive analytics recommend what you must do to improve the process for desirable outcomes.
The sure-shot way of delivering a desirable quality product is by looking at data from past manufacturing cycles. While that is true, AI-powered predictive and prescriptive analytics give you more. Utilizing both historical and current data, you get timely forecasts of downstream events and accurate recommendations of actions needed for optimal results.
Going from reactive decision-making to predictive and preventive will enable you to unearth hidden bottlenecks, prevent unexpected downtime, and plan the utilization of available resources—to name a few. AI-based data-driven production planning can adjust parameters and tweak operations to increase productivity and reduce operational costs.
“The oak fought the wind and was broken. The willow bent when it must and survived.”
― Robert Jordan.
Flexibility is key to resilience, and data-driven flexible & swift decision making will lead to resilient manufacturing.
We envisage building resiliency into the DNA of every manufacturing company.
Talk to us. Let’s Optimize!
 Thomas, A., Pham, D. T., Francis, M., & Fisher, R. (2014). Creating resilient and sustainable manufacturing businesses – a conceptual fitness model. International Journal of Production Research, 53(13), 3934–3946. https://doi.org/10.1080/00207543.2014.975850
 Erieau, C. (2019, February 20). The 50 Best Resilience Quotes – Driven. Driven App. https://home.hellodriven.com/articles/the-50-best-resilience-quotes/