Digital Twins are rapidly transforming S&OP

Digital Twins are rapidly transforming S&OP.


We live in a world of ‘never normal’ with frequent disruptions like the COVID-19 pandemic, geo-political tensions, and Shanghai port Lock downs that inevitably increase supply chain costs and impede product flow, causing frequent stock-outs. 

Planning processes, along with associated tools and applications, have not evolved fast enough to model these disruptive events to simulate the impact on product flow and revenue, and costs across the enterprise value chain. Traditional S&OP planning has lagged due to high dependence on historical time series data and rigid time-bucketed planning fences. They have not been agile enough for re-planning or continuous planning at speed. 

With the changing technology landscape of in-memory computing, planning platforms started to drive hybrid solutions that considered historical time series with periodic repeated events like holidays and seasonal events. Machine learning algorithms were used to superimpose the effects of these events with time series forecasts as a baseline.  The results were still sub-optimal, with planning accuracies and forecast and schedule attainment still not meeting business expectations. The gap that was yet to be filled was closing the planning loop with real-time operational signals that were fully digitized. 

Modern cloud-enabled platforms have high computing and processing speeds. They can run multiple outcome-driven scenarios ingesting real-time signals in the near-term tactical 6-12 weeks, building a robust short-term tactical planning process. This modern API-enabled cloud architecture can ingest real-time data that are context-rich to identify risks and opportunities at all levels of detail.  

For example, real-time signals from transportation carriers and sensors embedded in products, packages, pallets, or containers with real-time location tracking along with conditional monitoring information can transmit route delays and the deterioration in product quality. The digital twin can process this information to figure out alternative solutions to avoid product delays for the customer. 

Digital twins can also orchestrate demand and supply imbalances and help right-size or optimize finished goods inventory across all the internal nodes of the supply chain. It takes into consideration manufacturing variabilities like machine hours, labor, machine run times, variabilities in lead times, lot sizes, set-up time, and changeovers, along with safety stocks and other parameters. These can be modeled to drive the best outcome, like higher shelf life for finished packaged goods and minimizing waste while ensuring higher profit margins. Digital twins can identify risks early and recommend appropriate mitigation strategies while closing the loop between tactical planning and logistics and manufacturing operations execution in the near short-term. 

With digital twins, trade-off decisions on transportation mode, flexible buffer manufacturing capacity, etc., typically earmarked for the longer-term strategic S&OP or IBP layer, can now be made in a near-term tactical three-month horizon. These decisions can continuously inform or feed the strategic layer of S&OP or IBP, driving the convergence of strategic and tactical layers of planning.

With the advent of cloud-enabled next generation of digital supply chain twins or Supply Chain Command Centers, rigid time fences and boundaries between planning and execution are blurring and what is emerging is continuous planning at speed with closed-loop integration of planning and execution.  

ParkourSC’s digital twin platform models complex supply chains and interdependencies. It helps businesses with continuous planning by ingesting real-time operational signals based on ground truth, thereby reducing planning variability. The platform uses a low-code solution to embed decision intelligence in the digital twin triggered by operational signals. These supply chain recipes are used to notify, automate, and collaborate and can be extended to include optimization and/or predictive capabilities to model risks and appropriate mitigation strategies.