In a digital world, it's amazing that many companies are still using Excel to manage their inventories. Supply Chain Insights’ 2018 Inventory Optimization Technologies Study suggests this number could be as high as 75%.
The traditional manual way of working is fraught with challenges. It's time-consuming and littered with opportunities for error. Unsurprisingly, companies using Excel struggle to hit their service level targets and, in the worst scenarios, can be a cause of increased cost and lost sales due to inventory not being optimized in line with customer demand patterns.
For retailers using spreadsheets, moving to inventory optimisation software is still a quick win in terms of reaching a maturer way to manage the retail supply chain using analytical tools that can rapidly deliver significant value to the business.
Most retail supply chains are complex and getting more so. The typical challenges that make inventory management and optimisation 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 inventory in the right place at the right time and ensure the supply chain is able to successfully meet customer demand 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 states “The supply chain is a complex system that cannot be adequately managed through calculations on a spreadsheet” (Inventory Optimisation in a Market-Driven World).
This is just the beginning. Much inventory management is a hedge against uncertainty. If you could predict demand exactly you wouldn’t need as much inventory, so another critical requirement is adequately modelling uncertainty.
Demand uncertainty and volatility - such as 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 large orders doesn’t 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 this shows up in performance - Gartner studies have shown that low maturity supply chains commonly under-perform compared to higher maturity supply chains, i.e. those that use specialist software and analytics.
Spreadsheets are no longer conducive to the more aggressive goals of companies wanting to ensure they are managing their supply chain so that cash-flow is maximised and customer demands met. 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.
Contact us to discuss how you can optimise your inventory.