Following the Money: From S&OP to S&OE in Supply Chain Investments.

Over the years, companies have made substantial investments in supply chain planning software, encompassing solutions such as MRP, DRP, MRP II, Production Scheduling, APS and, more recently, S&OP. Despite these investments, the realized effectiveness of these solutions often falls short of expectations.

Consulting firms frequently advocate solutions that align with their own interests and established consulting practices. In response to these recommendations, business leaders have allocated significant resources to implementing such solutions, primarily to remain competitive and sometimes as a means to safeguard their positions. This reliance on consulting firms has seemingly reduced decision-making risks, instilling unwavering confidence in their choices. But where has all this money gone? Since the term "supply chain" was coined in the early 80's, millions were poured into it in the form of technology investments. Have companies achieved their RoI projected in their business cases? rarely..

Now a new wave has started. Experts are coining a new terminology: S&OE, or Sales and Operations Execution. In simpler terms this represents an evolved iteration of the old day Master Planning. What implications does this hold for traditional S&OP planning? This comes from a growing recognition that S&OP alone cannot provide the comprehensive plan necessary to effectively support operational planning. The disparity between the S&OP plan and day-to-day operational planning is becoming increasingly evident.

Navigating this disparity poses a challenge: preserving the intended purpose of S&OP without overstating its capabilities. It's essential to acknowledge the strengths of S&OP while recognizing the demand for more robust planning solutions.

Limitations of typical S&OP solutions were highlighted in a previous article.

Bulk of the limitations can be avoided by adopting Multi-echelon planning. This considers the interconnectedness of different levels throughout the entire supply chain. This holistic approach extends from the demand side through intermediate stocking locations to the most upstream supplying locations, and even to Bills of Materials, achieving end-to-end synchronization.

Planning Solutions are of three categories: Heuristic-based, LP-based and Probabilistic. Heuristics may look simpler and logical, but extremely difficult to maintain mainly due to the fact that supply chains are not static. Hence the rules need to be rewritten quite often. Another challenge is it's very difficult to write rules which can emulate multi-echelon planning as the inter-dependencies and tradeoffs are quite large and vigorous.

Multi-echelon planning can be achieved using LP-based solutions, but with limited scalability and flexibility. The best choice seems to be Probabilistic Digital Twins which can process Stochastic Demand-Supply Signals across the Network and to the Bills of Material. This allows for effective Master Planning with the required granularity. These models are robust enough to hold on even at a daily granularity.

Regardless of your current planning technology, these probabilistic backbones can serve as dynamic feedback mechanisms, continuously assessing current performances against optimized benchmarks they model. They provide valuable insights into necessary corrective actions, spanning long-term strategic planning to short-term operational adjustments on a daily basis.

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