Inventory optimization remains a key focus area for SAPinsiders, as highlighted in our most recent research report,
Inventory Optimization and Management. When organizations undertake an inventory optimization initiative, they typically leverage conventional multi-echelon inventory optimization tools that leverages inventory placement in conjunction with integer programming and statistical methods to determine how much inventory to hold and where. A network model helps determine which products need to be placed in an stocking location, based on the customer demand historical data for the region. However, algorithms like association-rule mining can further help fine tune these analytical methods in the logistics world. In this article, we will touch upon how this algorithm can be leveraged in multiple aspects of logistics.
Association-rule mining is an unsupervised analytics approach that aims to identify groups of items that frequently co-occur together. This technique has been used in the world of marketing analytics extensively for a while. An example is what is popularly known as “market-basket” analysis in retail. If you have observed that items that typically get consumed with bread, like peanut butter, are placed close to bread section or are in the same aisle, it is because of association-rule. This is a very simplified example, but the gist is that the association between products is the underlying basis of all cross-selling.
In the case of our inventory example, association- rule mining can help fine tune product placement at both warehouse and aisle level. As far as inventory placement goes, it can provide an additional layer validation to ensure optimal product placement. So if there are two warehouses serving a region, you would want products that get ordered together, placed in the same location (provided other constraints, like capacity allow that).
But another interesting utilization can be in warehouse layout design and slotting. Incorporating results from association-rule mining analysis into warehouse layout design and slotting can significantly enhance the efficiency of picking process. Specifically, in ecommerce exclusive warehouses, where pieces and eaches picking is the norm. The reduction in picking time can help enhance warehouse throughput. Applications extend in transportation management and planning as well. An example is load optimization. You can build a heuristic that leverages historical load optimization algorithm results to fine tune your load building process further, even helping contribute to flow automation strategy.
These are just examples and the opportunities to leverage this algorithm extends beyond logistics. Whether it is association-rule mining algorithm or other data science approaches, supply chain is a rich ground for experimentation.
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