Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on one integrated plan and with clear decision milestones. The alignment is cross-functional and connects different operations functions with each other to set an overall delivery ability. There are always challenges connecting different functions in a company which most S&OP practitioners agree with, still, that is one of the things that the S&OP-process should bridge. Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools have contributed to an improved cross functional communication over time. S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion as e.g., make-to-stock (MTS) and make-to-order (MTO) contexts, may experience even further challenges. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant ones is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge. The intent with this paper is to, based on S&OP-challenges, see if ML can be used to improve these challenges.
In a brief literature review together with empiric data from a single industrial case (SIC), S&OP-challenges were defined and structured. Based on the challenges in several S&OP-sub-areas, classified into data quality, horizontal and vertical disconnects, specific tasks were specified and structured into anomaly detection, clustering and classification, and predictions. Which exact ML-method to use require further work and tests. Still, this is a good starting point to take the next step and the specified tasks could also be used for other practitioners that want to start using ML/AI in their daily activities.