Right products are needed at the right time to ensure that demand is serviced to the best possible extent. However, getting it right particularly when there are a large variety of items is complex. Maintaining Right Levels of Inventory and maximizing the Returns on Working Capital is the key to the growth of any business.
1. Imagine, you started your e-commerce business with limited Investment, and you end up stocking products which are not selling.
2. You are a large manufacturer and you are continuously introducing new Products but are not able to scale since the products are not stocked at the right locations. As a result, your Investments into new products are not giving you the right returns.
3. You are in the Lifestyle or Fashion business and you incur huge costs of Mark-downs due to obsolescence.
4. You are in a Perishable business and you incur heavy losses due to wastages.
5. You are a spares business and there are over 50,000 items to manage at 1000s of Dealer locations. Most of these spares don't have a regular demand, with some products selling only a few times a year and some in bunches. You end up carrying disproportionate amounts of inventory to meet the demand.
To add to the above there is a cost of carrying Inventory (Manpower, Warehousing, Insurance etc.). And most importantly, we are still not fulfilling the demand and losing Sales. We all know that having the Right (or Optimal) level of Inventory is the key to the businesses, manufacturers and traders alike. So the question is, why don't we just get this right? And the plain answer is: it is not easy. What makes it complex is the following:
There are just too many combinations of Products and Locations. Consider the spares example above. We are talking of 50 million Product-Market combinations. This of course is an extreme case but a few million combinations of Products and Markets is common. Getting the Inventory Strategy right for this large combination is not easy.
There are a lot of variables which can impact inventory and Each of these can play a significant role. Things like Demand Variability, Lead-times, Lead-time and Supply Variability, Production and Distribution Lot sizes and many more. And these variables can have different meanings at different echelons of Supply Chains. For example, lead-time variability could for a downstream depot could mean dispatch lead-time variability (Receipt Date - Dispatch Date). But for a Factory this could mean the variance between Production Plan Date vs. Actual, and for Purchasing this could mean variability of Supplier Lead-time (PO date - GRN date). But this is good news as well. It provides us with many levers to play with and improve the results.
Right measurement of Service Levels is the key to having the right Inventory. If you are not measuring it right you could be losing significant business or losing on new Products without being aware of it. Some companies measure product availability against the Norms at each location because they don't capture Back Orders. This can be really misleading as there is no way to measure the quantum of lost sales, and large Orders that are lost are nowhere in the system. There are others who measure Case Fill which reflects well on the overall business health but leaves out the tails items which could be critical for New Initiatives.
Here are our tips to getting your Inventories sorted. To start with there are some quick wins, and then some higher hanging fruits to aim for going forward.
A lot of Manufacturing and Trading companies have some sort of Formula to map the relationship between Inventory and Service Levels (or Stock to Service Curve). It could be as rudimentary as is available in Supply Chain Textbooks (like Safety Stock = Z-score for the Desired Service Level X Average Daily Demand X sqrt (Lead-time + Review Period)). Sounds familiar ?. What we recommend is to ensure that the Formula used should represent the fill rate that you are trying to measure (Order, Case or Line Fill) and address all uncertainties like Demand Lead-time and Supply. We need to ensure that we cover against all these uncertainties.
Based on the above parameters, a unique relationship will be formed between Inventory and Service Level (Stock to Service Curve) for each product at each location as shown below. In the example below we can see that this relationship changes with locations as well as with Products. Here the variabilities are much higher for Location 2.
If the input is Junk output can be no better. We need to carefully understand each of the above parameters to get the Inputs right. Following are some key points that need to be kept in mind.
Location combinations - remember, this is not Product A, B, C classification which most people talk about. The same product can behave very differently at different locations. It's important to get an A, B or C class for each Product and Location combination based on the above characteristics (Demand Variability, Lead-times, Review Periods, Supply Variability etc.). Following is an example of Product-Location classification for a company with five Products that is operating at five locations. Once the segmentation is done then treat these segments differently to achieve the overall target. For Example, your A class could be 98% service level, B could be 90% and C 85% to get an overall weighted average of 95% service level.
Getting your distribution model right (like direct distribution vs. Hub-Spoke model) for each product-location combination is another key factor to in dealing with Demand and Supply Uncertainty. For highly volatile items (product-locations) create a Hub-spoke model. For low variability and high-volume items follow a direct distribution model. For Supply variability items create an upstream hub to decouple the rest of the Supply Chain. It’s A fit-to-suit model for each product and location combination needs to be carefully designed.
There are two aspects to this: (a) Where; and (b) In what form.
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