Any good warehouse management system (WMS) must have a robust demand forecasting module without which one’s warehouse performance would be sub optimal. While it’s true that predictions are difficult, the science of forecasting is improving thanks to ‘big data’, AI and machine learning. You may already have a demand/stock forecasting element in your WMS but if it is not updated by taking advantage of forecasting improvements then expect a rough time from your competitors who are ahead of you on that. Relying on outdated statistical analysis of historical patterns means a cleansing operation is now overdue, especially given the new challenges posed by e-commerce.

This article was first published in the March 15th 2020 issue of Warehouse & Logistics News, subscribe to the magazine by clicking here.
One of the great opportunities of IT is the ability to gain almost real-time data. For several decades, for example, leading food retailers have harnessed weather forecasts to predict near future demand. A pending heatwave could send demand for beer soaring four-fold over a few days, leading to pubs with no beer, surely one of the most distressing sights. But even before these latest IT advances, stock forecasting programmes, a mirror of demand forecasting, had the ability to cut stocks by up to one third without harming customer service levels. That is a huge advantage, given that warehouses put money to sleep because inventory holding costs alone can dwarf all other warehouse running costs combined. And some of these stock forecasting modules have been known to deliver a two-week payback.

Is there, however, something still missing or neglected that could leave egg all over faces? Yes, there is, and that is lack of corporate resilience to recover quickly from any major disruption. History can be a good business teacher and one only has to go back to the Japanese tsunami of 2011 which left factories for cars, cameras and mobile phones around the world idled for want of JIT-supplied components, leading to multi-billion pound lost production levels and all because Japan was a choke point for over 90 key products to supply them. Verily, history has repeated itself today with the Corona virus where another all-eggs-in-one-basket (China) situation has caused global angst over JIT-supplied components. The fact is, modern supply chains bear the seeds of vulnerability to high impact/low probability events.

OK, so businesses are not likely to surrender the cost advantages of JIT to get around the problem so that leaves the necessity to be more resilient than your competitors which calls for investment in the ability to recover quickly from any disruption because the number of possible disruptions from a global supply chain is endless. Consider having exclusive arrangements with companies you only marginally deal with so that in the vent of your main supplier being knocked out temporarily you could quickly switch to meet your demands. Try to develop supply chains in which products are not customised until the last possible moment.

Developing part and platform commonality and modular product designs so that the same part can be used in several products could also be useful, along with increasing one’s use of standard rather than special parts. Also consider tying yourself to suppliers in flexible contracts allowing for changing quantities and delivery times.

At a more localised level it is interesting to see how even simulation is also advancing to help logisticians when storage expansion looms. A recent development is ‘digital twins,’ a market researchers say will grow by 38% a year to reach $26 billion by 2025. Logisticians will find this unique development very useful in developing new distribution centres.

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