By Roch Gauthier, Aspen Technology’s Senior Product Management Director
Even before the onset of the current Covid-19 pandemic, some manufacturing companies in Malaysia were already exploring the idea of a self-healing supply chain. The term ‘self-healing’ includes two concepts: the first being that data and parameters in supply chain planning and scheduling models should be automatically calibrated to maintain their accuracy; and the second is being able to quickly adapt by re-planning and re-scheduling supply chain and manufacturing operations as needed when unexpected events occur.
A self-healing supply chain is therefore impossible without a robust digital twin.
What is a Digital Twin?
The digital twin is an evolving digital profile of the historical, current and future behaviour of a physical object or process that helps optimise business performance. It provides a valuable model of asset health, forecasting and recommending action to avoid degradation and asset failure events. Most importantly, digital twins incorporate business models representing scenarios for product creation, operations, supply chain, trading, effective asset utilisation, risk, customer satisfaction, and profit.
Enterprise level digital twins are an important emerging area. Such models enable rapid analysis of enterprise profit opportunities and effectively present actionable information to the executive level. For example, a multi-asset supply chain planning model that can optimise the utilisation of a network of manufacturing plants, transportation and storage facilities for maximum profit and customer satisfaction.
According to a recent report “Beyond Covid-19: Supply Chain Holds the Key to Recovery” published by Baker McKenzie and Oxford Economics, the pandemic has resulted in an unprecedented global supply chain crisis.
Although the road to industrial recovery has begun, it is essential that businesses turn to digitalisation of supply chains to achieve resilience and sustainability. Digital twins are therefore extremely valuable tools for process manufacturing industries during these uncertain times. Below are numerous examples of how supply chain digital twins have helped organisations achieve business continuity in various phases of the pandemic.
Phase I: Protecting the People and the Business
Keeping Plant Operations Personnel Safe
The priority at the onset of the pandemic was to keep employees safe. In this phase, many companies rapidly incorporated physical distancing constraints into their production planning and scheduling digital twins in order to keep their manufacturing operations personnel safe.
The digital twin technology ensures that operations personnel practice adequate physical distancing while simultaneously considering complex manufacturing equipment constraints related to using alternating lines/ equipment on various days of the week.
Managing Uncertainty via Powerful “what-if” Optimization Scenario Analyses
The second priority in this phase was to protect the financial health of the business. Many organisations mobilised their teams to evaluate the financial and operational implications of numerous short to mid-term business scenarios.
Companies also used their end-to-end supply chain planning optimisation digital twin to run and analyse a significant number of scenarios every day. The digital twin allows companies to rapidly develop contingency plans on how to best respond to circumstances as the uncertain future unfolds.
Phase II: Adjusting Processes to Achieve Continuity
Keeping Work-from-Home and On-Site Teams Constantly Aligned
Maintaining business continuity and ensuring a safe and reliable supply chain became much more challenging when some of the people who usually work at the manufacturing sites were directed to work from home. But the digital twin technology has helped the supply chain and manufacturing operations team stay aligned.
Different teams are still able to collaborate via live web-based views of the latest published manufacturing schedules, view projected inventory positions, identify problems ahead of time and help everyone maintain situational awareness about what is happening at the manufacturing facility and finally working towards a common goal.
Quick Adaptation to Demand and Supply Changes
Digital twin technology has also allowed companies to adapt to changing demand and supply conditions while still having the capacity to improve cash flow, ensure on-time shipping performance and flex production output by aligning their demands, capacity, supply and operations on a daily basis.
Phase III: Preparation for Recovery
Monitoring for Signs of Recovery and Rebound
Since the beginning of the pandemic, most companies reported that the accuracy of their demand forecasts are considerably less accurate than before. In preparing for a recovery, some companies are using digital twins to closely monitor changes in demand trends week-over-week.
Re-designing the Supply Chain
The pandemic has exposed the vulnerabilities in today’s global supply chains that were designed primarily for efficiency. However, there is an opportunity for manufacturers to design their businesses to make them more resilient.
This will involve analysing and building redundancies for critical product lines and associated production and supply capabilities including reviewing existing suppliers and locations; substitution options; as well as existing manufacturing locations and the current flexibility of manufacturing resources. These are areas where an end-to-end supply chain optimisation digital twin can be leveraged to explore and analyse various supply chain and manufacturing design alternatives to build a more resilient business.
Strengthening the Supply Chain through Self-Healing Capabilities
The self-healing supply chain ensures that the supply chain digital twins remain as accurate as possible, reflecting demonstrated plant, equipment and process performance. This is achieved by automatic detection of data inputs that may no longer be valid amongst the tens to hundreds of thousands of manufacturing data inputs (e.g. processing time, yields, setup times, cleanout times, transition times, etc.) used by supply chain digital twins.
A self-healing supply chain has the capability to identify these proverbial “needles in the haystack” with minimal time and talent input. Self-healing supply chains can also combine both predictive and prescriptive technology. For example, low-touch machine learning (ML) is used to predict with high degrees of certainty a manufacturing equipment / asset failure in advance.
Prescriptive mathematical optimisation (MO) methods in supply chain planning and scheduling digital twins make use of these advance equipment failure warnings to answer the question “When should we take planned downtime (in advance of the predicted failure event) to ensure minimal disruptions, costs and impact to customer order commitments and relationships?”
Digital twins provide insights so organisations can adapt to this new operating environment and keep supply chain and manufacturing operations running as nimbly and efficiently as possible. Building digital capabilities now will help organisations prepare for the ongoing uncertainty that is likely to continue into 2021 and beyond.