Most companies are still trying to use Excel to optimize their inventories. Supply Chain Insights’ 2018 Inventory Optimization Technologies Study suggests the number may be as high as 75%.
These companies struggle to meet service level requirements while respecting financial impacts, and have almost no chance to do it optimally. So inventory optimization software is still “low hanging fruit” for those seeking higher levels of supply chain maturity and improved use of supply chain analytical tools.
Most supply chains are complex and getting more so. Typical challenges that make inventory management and optimization hard to accomplish with spreadsheets include:
- Diverse inventory mix needs that don’t mesh well with ABC inventory classification or simple rules of thumb
- Multiple demand streams, each usually with different service level requirements
- Global supply and demand networks
To get the inventory needed to make supply chains successful, and to get the right performance outcomes, requires accounting for dozens of variables such as standard cost, order quantity, run-out time, lead time, and sustainability. Then there are factors such as seasonality, replenishment constraints, manufacturing constraints, promotional impacts and new product introductions. As each additional variable is accounted for, the spreadsheet approach becomes more difficult to manage and less likely to generate desired results. Or as Cecere says simply, “The supply chain is a complex system that cannot be adequately managed through calculations on a spreadsheet” (Inventory Optimization in a Market-Driven World).
But that is just the beginning. Much of inventory is a hedge against uncertainty. If you could predict your demand exactly you wouldn’t likely need as much inventory. So another critical requirement is adequately modeling uncertainty.
Demand uncertainty and volatility (such as caused by natural fluctuations, sudden market shifts and extreme seasonality) necessitate extra inventory. Two additional demand uncertainty variables are demand order size and order frequency. For instance, demand streams consisting of a few large incoming orders require more inventory than similarly sized demand streams consisting of many smaller orders. The flow of small orders creates a natural probability-based consistency that fewer larger orders does not provide. Demand streams with few large orders, or lumpy intermittent demand, require inventory requirements with yet another degree of complexity.
Then there are supply side uncertainty variables, such as:
- Supply order reliability (orders arriving on time)
- Lead time from order to receipt
- Frequency of placing supply orders
- Inventory needed to mitigate the risk of short shipments
Of course these variables can be ignored, which is what many low maturity (according to Gartner, level 1 or level 2) supply chains – where spreadsheets are common – often do. But it shows up in performance. Gartner studies have shown that low maturity supply chains commonly underperform compared to higher maturity supply chains.
So spreadsheets are not conducive to the more aggressive goals of higher maturity levels. Cecere’s suggestion is, “Blow up your spreadsheet ghettos within your organization and challenge your company to think more holistically about the role of inventory in the market-driven value network.” If you have your sights set on improved supply chain performance, it can be a good place to start.
Click below for analyst recommendations on how to select an appropriate inventory optimization software tool for your business: