Inventory is a necessary evil.
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.
Let’s look at some scenarios below:
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:
1. Large Product Market Combinations :
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.
2. Variety of Input Data :
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.
3. Right Measurement :
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.
1. Get your Inventory Formula right* :
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.
- Demand Uncertainty – Let’s take an example of a depot which can be replenished once in 5 days (due to truck-load constraints) and shipment lead-time to this depot is 7 days. This means that if you Order at the Depot today based on the Inventory levels, you will get an opportunity to re-order 5 days later and the stock against the next order will arrive only after another 7 days, leading to an exposure of 12 days against the demand variability. You need to cover against the demand variability (variability of demand Plan vs. Actual) for a duration of Lead-time + Review Period. There will be a risk pooling over this period and that’s the reason we see square root in the standard formula defined above. There is an underlying assumption here that the demand variability follows Normal or Gaussian Distribution.
- Lead-time Uncertainty – Again, this is a critical aspect. In the above case, if the lead-time variability was 30% (or 2 days), you will need to have Inventory corresponding to 2 days of demand to provide for this uncertainty.
- Supply Uncertainty – If your suppliers are short of the requisitions due to Stock rationing, Supplier Capacities or Loading constraints, you will need to keep a cover against this.
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.
2. Getting the Data Right :
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.
- Demand Variability –It’s the variance of Demand Plan vs. Actual Orders that needs to be considered. In the absence of back Orders Demand Plan vs. Sales can also be considered.
- Review Periods –For dispatches this is the time taken to create truck loads on a given lane. In case of PTL this can be one day. For production this is the average frequency of production and for procurement this represents the Ordering MOQ.
- Lead-time and its Variability – For Dispatches this can be derived from Goods dispatch and receipts. For production this can be derived from Production Plan date (or requisition date) vs. actual. For procurement this can be derived from PO date vs. Receipt.
- Supply Variability –For Dispatches this can be derived from Goods dispatch and receipt quantities. For production this can be derived from Production Plan quantity (or requisition quantity) vs. actual. For procurement this can be derived from PO quantity vs. Receipt.
3. Segmentation or Classification of Product** :
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.
4. Right Postponement Strategy+ :
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.
5. Right Multi-Echelon Strategy++ :
There are two aspects to this: (a) Where; and (b) In what form.
- Where –should the business remain thin and offer low Service Levels to Dealers, Distributors, Customers leading to a blown up Inventory of the Customer, or should it have Customer facing depots with high service levels, or should the bulk of Inventory be held at Hubs or CDCs ?. Too much inventory with Dealers can block their working capital and can slow the sales down, particularly because they may not have the wherewithal to optimize Inventories. Where to stock Inventories and what service levels to offer at each echelon is an important question. Higher Service Level at a given Echelon will lead to a higher Supply compliance (or lower Supply Variability) downstream and will make the downstream nodes leaner. This however will add to the upstream inventory. Getting this right can bring wonders in terms of Inventories and the final Service Levels to the end Customers.
- In What Form –This becomes relevant when your factory lead-times are large and there are multiple stages. Where to decouple the Supply Chains within the factory becomes a prominent question. Stocking key Intermediates can dramatically improve the performance in terms of Service Level. Another key aspect here is getting the Raw Material Inventory Norms right based on the Demand Variability of Raw Materials, Supply Lead-times, MOQs etc.
Please do get in touch with us for :
- (a) *A detailed formula and to understand this subject better, OR
- (b) **Product Segment Optimization, OR
- (c) +Optimized Distribution Network along with Inventory, OR
- (d) ++Multi-Echelon Inventory Optimization