Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context

Nils-Erik Ohlson, Jenny Bäckstrand, Maria Riveiro (2021). Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context. PLANs forsknings- och tillämpningskonferens 2021, Högskolan i Borås, 20-21 oktober 2021.

Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on the "One Plan" and with clear decision milestones, where “One Plan” relates to the ultimate outcome of S&OP by integrating multiple plans. This alignment is cross functional and connects, not only sales and operations, but also different operations functions with each other, to set an overall delivery ability. There are always challenges when connecting different functions in a company, something most S&OP practitioners agree with, still, cross functional integration is one of the things that the S&OP-process addresses. For S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion of the product as e.g., make-to-stock (MTS) or make-to-order (MTO) contexts, further complexity is added. If these businesses also have long lead times and low volumes, another perspective to the S&OP-process is given when it comes to the balance between demand and supply (DS). Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools are a pre-requisite for the S&OP-process and improves cross functional integration. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant one is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge.

Therefore, in this paper, the purpose is to investigate and define the main sub-areas of the S&OP-process in an ETO-context and discuss how AI, in particular ML, currently supports the sub-areas. To be able to fulfil the purpose, a literature study of the two main fields, S&OP and AI, has been carried out.

The results are pointing at an underuse of ML-techniques for S&OP. Forecasting in MTS- context is where ML is mostly used, and the most common ML-technique is Artificial Neutral Networks (ANN) which is considered as Supervised Learning. The results of this paper will serve as a starting point for further research on the efforts and effects required for improving the S&OP-process in an ETO-context and with what ML-techniques.