In general, S&OP as an integrated end-to-end process aims to coordinate the individual planning processes of the various departments along the supply chain (Sales, SCM, Production, Procurement) in order to increase supply chain flexibility, improve cross-functional communication, and generally create a proactive balance between market requirements and internal and external capacity availability.
What drivers and challenges make a high degree of planning accuracy in demand planning so difficult and how can these be solved or minimized?
- One size fits all vs. segmented planning – It is crucial to consider the various business fields, regions and products as the basis for demand planning. Is it possible to pursue a uniform planning approach here or is there a fundamental difference in customer structures (major customers, project business, trading business, etc.) and the data information available? In this case, it may make sense to pursue different approaches in order to both minimize the effort and improve the overall planning result.
- Finding the right level of granularity for planning – The product ranges of many companies are made up of product families, modular systems, variants, and part types. This complexity in the product portfolio can pose a challenge when it comes to determining the right planning level for demand planning. Is planning per product family or variant sufficient or does a detailed breakdown at parts list level need to be carried out? It is important to strike a balance here between the benefits of a precise level of planning detail and the associated planning effort. The result of demand planning must, of course, provide a sufficient flight level. This is necessary for the supply and capacity planning that follows in S&OP, but it should not create unnecessary complexity.
- Selection and availability of the right demand signals – Different demand signals can be used for demand planning not only on the customer side but also within the supply chain. There are often several sources available, which differ in terms of quality and informative value, and are available in a wide variety of data structures. The selection of suitable signals, which determine the defined segmentation and granularity level of the planning, is a crucial pillar for the reliability and automation of the subsequent demand plan – especially if modern forecasting algorithms are used for system-based proposal generation. External demand signals (e.g., weather data, macroeconomics, etc.) are often considered. Before implementation, the added value must first be validated with actual data and use cases. The continuous connection and validation of external demand sources, on the other hand, would be very time-consuming.
- Organizational preparation and control concept – An additional success factor for good demand planning is ultimately the organizational anchoring. It is advisable to appoint dedicated planning experts (demand planners, sales planners, etc.) who validate bottom-up planning and coordinate it centrally. If a company wants to use state-of-the-art forecasting algorithms, it must also consider that this also requires a different competence profile and corresponding skills – dedicated data analysts are required for this. With regard to control logics, it should also be ensured that demand planning is linked to proper sales performance management. This requires a continuous flow of information back from the S&OP process in order to communicate and enforce allocation decisions, for example, to affected customer groups.
