What is S&OP?
Sales and Operations Planning (S&OP) is, at its simplest, the integrated business planning (IBP) process of determining demand and supply. The objective of S&OP is to create the balance between demand planning and supply planning—taking into account the limited capacity and restrained resources that companies have—with the goals being higher efficiencies, increased profits, and better customer service.
While S&OP is a process that has historically been performed via spreadsheets on a monthly basis, this approach is increasingly dated and incapable of keeping up with an ever-changing complex supply chain. An effective S&OP process considers all elements of the supply chain continuously, running updates for customer orders, and available inventories and capacities, all without the need for manually balancing spreadsheets or only performing updates periodically. Today’s S&OP software, like that offered from Adexa, provides real-time integrated business planning, taking into account all capacity, material, and business constraints. Using machine learning (ML), S&OP software keeps improving its performance and providing the stakeholders visibility into their available capacity and material from the suppliers while recommending actions and correcting the policies.
Once an S&OP forecast is available and examined by stakeholders, a feasible plan is generated by the company to be able to produce and supply their products at an optimal rate and a timeline that is possible and acceptable to their customers.
How Does S&OP Work?
Traditional S&OP process is performed at the rough-cut-capacity planning level as opposed to analyzing the day-to-day specifics. This leads to inaccurate plans which are not executable. With real-time S&OP analysis, we are now in a position to produce accurate plans which deliver reliable commit dates and true financial results using a combination of S&OP and S&OE software in a unified platform. Thus creating a true supply chain digital twin that has intelligence using AI and ML techniques.
Many of the current solutions for S&OP process are manual. They use the same logic as spreadsheet planning does. They also make assumptions that can often yield incorrect financial projections and customer commit dates. For example, they assume fixed lead-times from suppliers. More importantly, they assume fixed lead times for manufacturing regardless of the mix of products. Furthermore, bottleneck resources are pre-determined and resource capacities are represented by buckets: days, weeks or months. We all know that, depending on the product mix, the number of units per bucket can vary greatly and in many cases set-up times may distort the represented capacity significantly. As a result, planners need to spend a lot of time making changes to the plan to make it work.
Why use an S&OP Demand Planning System?
While S&OP is intended to give better visibility to the organization and enable better customer service at higher levels of profit, using spreadsheets to perform an analysis of the different variables and constraints within the supply chain only provides an extremely limited understanding of the realities. With today’s S&OP planning systems, you’re able to harness AI and ML technology to get better visibility and respond to changes faster. It comes down to the question, why spend more man hours using an outdated method of projection to gain uncertain results on a weekly or monthly basis when software exists that gives you a clearer, more updated and productive analysis in real-time? Especially as supply chains are experiencing increased disruptions that can slow down or halt production. Companies that are more aware of how their supply chain is performing have a better opportunity to predict and respond to problems before they arise. To this end, an S&OP demand planning system uses ML techniques to make accurate and reliable predictions of what is needed and when, so that the supply side can produce optimal production plans and inventory levels.
What are the Main Outcomes of an S&OP Process?
The first stage of an S&OP process is understanding the forecasted demand. Since there are many different inputs in this process that affect S&OP, a consensus forecast is generated that is agreed on by all the stakeholders. Once a forecast is generated, the next step is to examine the impact of the forecast on the supply side and inventory requirements, if any. This is done using unconstrained analysis of the supply. The next step is the use of prescriptive techniques such as Multi-Echelon Inventory Optimization (MEIO) or the use of machine learning techniques to decide on safety stocks, as well as the mix of products to be produced and materials and capacity needed from suppliers and subcontractors. The latter is performed using the integrated business planning engine. The above S&OP process requires almost no manual intervention and the S&OP plan generated is immediately executable. This is due to the fact that all the necessary constraints are taken into account by more accurate representation of the supply chain. Adexa’s combined S&OP and S&OE plan enables such a process to run the S&OP process almost autonomously. The end result is optimal use of resources, reliable commit dates and accurate financial projections and of course better use of planning professionals
The Road Ahead in the S&OP Process
Nowadays many companies have been engaged in implementing S&OP system solutions in order to get better visibility and improve the resiliency of their supply chain. According to Gartner, S&OP is really one step above spreadsheet planning and can only take you to what Gartner refers to as “Stage 3” of supply chain maturity.
However, to reach the two higher stages of maturity—Stage 4 and Stage 5—organizations need to have the capability to build a digital twin and perform automated S&OE and execution functions. With this in place, companies can now respond to events in real-time to put the plan back on track. Events could be sudden changes in demand, disruptions that cause late supply arrivals or equipment breakdowns as well as outside forces such as weather-related disruptions. At the same time systems can be made intelligent enough to predict as well as respond to events autonomously.
The perfect quest is for companies to be able to perform autonomous S&OP planning generating plans that are executable without manual user intervention. The main ingredients for the latter are a digital twin that is an accurate and detailed representation of the supply chain, both structurally and behaviorally, and intelligence to produce accurate executable plans and be able to respond to the events, as described above, automatically and in real time.
Adexa’s S&OP platform allows for long-term planning and short term execution of plans. Having both S&OP and S&OE capability in one system means no additional integration between the S&OP process and S&OE process. It also means that companies can go from Stage 2 and 3 maturity to stage 5 by simply adding data, not additional disjointed systems. Adexa provides the stage by stage roadmap to your digitized supply chain and autonomous planning as your data granularity and frequency improves.