Probabilistic Digital Twin + AI/ML ensures end-to-end SC Synchronization.

Creating service-driven supply chains hinges on three key elements: Demand Sensing, Probabilistic Inventory Optimization (MEIO), and multi-echelon replenishment planning. These elements, when working in synch and in a tightly integrated manner, ensure accuracies throughout the supply chain and delivers optimal service levels, ongoing basis. These service-driven stochastic models stand apart from conventional deterministic planning approaches where monthly quantity forecasting, ABC inventory norm setting, and single-echelon supply planning are used to drive the supply chain.

For example, Demand Sensing transcends quantity-based forecasting by employing line-order level details. Influencing factors and their impact can be modeled at the granular most levels, and provide shot-term projections. To fully leverage these capabilities for effective execution, seamless integration between demand, inventory, and replenishment modules is essential, ensuring zero latency among the three.

Just like the fragmented technology land scape (which was discussed in the last episode), latency between functional modules is a service level killer and Bullwhip promoter . But many S&OP solutions have evolved through acquisitions. Consequently, forecasting may utilize a different data structure from replenishment, with later additions like inventory planning modules introducing yet another data structure. Such discrepancies can severely hinder solution’s capability to generate an effective Master Plan that's executable in order to achieve the expected level of inventory vs service level optimization.

Ideally, multi-echelon replenishment planning should have production and procurement planning tightly integrated with DRP so that bullwhip can be tackled all the way upstream, to production and procurement. This ensure maintaining probabilistic nature and optimal deployment of inventory buffers along the distribution nodes and also along BOM node, avoiding the excess FG, raw/WIP inventory build ups in the network. Such solutions can tackle Bullwhip all the way up and achieve maximum optimization and end-to-end synchronization.

Whatever be your current technology landscape, having a probabilistic planning backbone, which is continuous from demand through inventory and supply to raw procurement, would greatly facilitate continuous optimization and end-to-end synchronization. These self-adjusting models are capable of addressing uncertainties, both on demand and supply sides. Such probabilistic models in tandem with existing planning landscape; processes, and technologies, can yield remarkable results across manufacturing, distribution and retail types businesses.

These probabilistic (stochastic) backbones can continuously provide feedback loops on how far the current performances and Master Plans deviate from an optimized version. They offer insights into corrective actions to achieve various business objectives related to inventory investments, order fulfillments, and service levels across multiple scenarios, including long-term strategic and short-term operational scenarios within the month. This feedback mechanism acts as a mirror for supply chains to reflect on, enabling them to identify key value drivers, optimization possibilities and implement corrective actions to realign the supply chain to achieve real business objectives.

In summary, the conventional approach to planning is single-echelon based, where each stage in the supply chain is making decisions in isolation. This is grossly inefficient. Upstream stages have very little visibility of their downstream counterparts. As a result, the volatility of the demand as it is passed upstream increases rapidly (the bullwhip effect). Bullwhip makes statistical forecasting much harder, resulting in both higher forecast error and wrong inventory allocations. Additionally, it creates large instability in the supply chain causing expediting and firefighting in the replenishment process. To tackle supply chain uncertainties, embrace probabilistic models having end-to-end integrated version on the same platform.

Though business leaders recognize supply chains as a strategic asset and a primary business enabler, in most organizations, supply chain planning lags behind. Despite process improvements and having a dedicated Data Science Team, many companies still rely on Excel sheets or MRP solutions for their daily decision-making.

Conversely, companies that have undertaken costly technology transformations often find the journey long winding, failing to realize tangible benefits. Even companies with a robust Sales and Operations Planning (S&OP) in place, can still encounter challenges in achieving On-Time In-Full (OTIF) order fill rates or incorrect/excessive inventory problems compounded with unreasonable transportation costs etc.

Process enablement using technology solutions is a crucial first step. Bringing intelligence and granularity into it can provide maximum results. While strategies like "Demand-Driven," "Supply-Driven," or "Market-Driven" are great strides towards supply chain excellence, it's crucial to recognize that supply chains ultimately revolve around the service levels provided to customers. Therefore, adopting Service-Driven technologies can lead to significant tangible business benefits.