The optimization of NWC (Net Working Capital), which is comprised of Inventory, Accounts Receivable and Accounts Payable, is difficult to perform manually as there are large amounts of data with multiple influencing factors and levers that determine their level. Factors that contribute to the complexity include, inter alia: multiple production sites, legal entities, countries, currencies, high number of SKUs (Stock Keeping Units), minimum production quantities, production sequencing where certain processes must take place before others.
The objective of the challenge is to leverage an (existing) application that can visualize, forecast, and simulate (when adjusting levers) the NWC positions by using existing process data in SAP (and sometimes other tools), and then using ML (Machine Learning) make proposals for optimization actions. The application requires a hybrid approach (domain know-how/process rules, ML).
First initiatives within company
Activities already undertaken include:
– Investigation into off the shelf tools. Until now, no complete application that consolidates all three elements of NWC was identified. Simulation is only possible where all three elements are present in one interface and this was not possible.
– Individual analytics per process are performed manually, with limited or no ML capabilities.
Need for support
The expectation for the solution is to leverage an (existing) application that can:
– Access the end-to-end data of the three elements of NWC
– Provide an interface for viewing the data, and drilling down into the data
– Provide an interface for viewing and modifying individual parameters
– By leveraging ML and AI functionalities:
– Identify and propose optimization possibilities
– Learn from past decisions and contributed expert knowledge (e.g. defer for a defined selected time period, action/execute a decision for a defined time period, do not execute/action at all)
– Learn from business basics (e.g. negative inventory levels are not possible)
– Feedback Loop: Checking the success of the implementation of optimization proposals
Diese Challenge ist aus dem Jahr 2022 und ist beendet.
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