The coronavirus pandemic has created chaos and confusion across our industry. At Taiga, our mission is to bring order to chaos by identifying new trends rapidly through data analysis. We created the New Normal Newsletter to share some of the most common questions that we’ve received and the resulting analysis.
In this week’s edition, we’ve focused on the challenges covid has caused with inventory management. C-store operators have frequently mentioned two new problems that illustrate the difficulties:
- “Product sell-through rates have changed dramatically on dozens of common and uncommon items. This has made keeping up with out-of-stocks practically impossible.”
- “On top of that, my suppliers are reducing the frequency of their deliveries – many of them will only come once a week. So now my purchase decisions are larger and riskier.”
Observation #1 The“Just-In-Time” inventory model no longer works.
Prior to Covid, you could look up the quantity of an item sold last month, call it the “Sell Through Rate” and be pretty accurate. Since Sell Through Rates were steady, and your suppliers were willing to deliver on demand, you could shave the amount of inventory on hand, ordering just what you needed while still avoiding “stock outs”. Leveraging your intuition and experience, you would add additional inventory in consideration of holidays and sporting events. The just-in-time inventory model was working.
In the New Normal, Sell Through Rates seem to be wrong, wrong for a few of the top ten items that you normally pay attention to, and when you dig deeper, you will realize that it is wrong for 40% of the rest of your inventory. To successfully adapt to the New Normal, you will need better analysis and be willing to spend a little more time managing your inventory.
Observation #2 This is not a case of “random volatility” of sales. Sell Through Rates can be measured and are driven by predictable trends.
Sell Through Rates are changing because your customers are making consistent changes to what they buy (their “Market Basket”) and you are attracting some new types of buyers while losing some others. These are understandable trends, not random volatility, as long as you can sort out the trends. If your inventory manager is throwing up his hands, placing the blame for widespread stock-outs on “Covid chaos,” he needs better analysis to recognize the trends. For example, after looking at the data, we could see that the new “Grocery Buyer” was coming to the C-Store to avoid crowds at the supermarket. As we examined what the Grocery Buyer purchased, we could see right away that their Market Basket does not match more familiar buyers, and as a result the Sell Through Rates were starting to migrate. If your inventory manager can no longer rely on the old Sell Through Rates. Instead, they need to be aware of the new trends, and predict the Sell Through Rates into the future like a hunter leading a duck.
Would you like to see what StoreKeep can tell you about your stores and customers?
Observation #3 C-Store operators cannot afford to wait for trends to show up in months of old data because in the New Normal, new trends are developing weekly.
You remember when you had the foresight to order all that firewood before the big snowstorm arrived? That was pretty easy. But how would you have guessed that one of the hottest items in your store next week would be Aleve? Or mid-priced wine? Chances are you wouldn’t guess, since these items aren’t on your top 50 list, but suppose you could detect these trends in your data so you could quickly order to meet the new demand? Welcome to “Stock Warnings.”
Once the computer knows the on-hand inventory (roughly), the recent Sell Through Rate, and the trends in that rate, it can predict coming stock-outs, issuing Stock Warnings not just on the top 10 but on every category, sub-category and item in the store. We’ve done this for our customers in the last few weeks and were not surprised to see dozens of Stock Warnings across every category. We’re not suggesting that a computer can take over as Inventory Manager, but the computer can point out, deep in the maze of sub-categories, those surprising trends that represent profit opportunities.
Observation #4 The optimal order size for various items will not be found in the plan-o-grams from your preferred vendors.
The vendors don’t know the trends of what is selling in each of your stores. They don’t know your customer mix has changed nor their new market basket mix. The vendors are waiting to see what you manage to sell and what you get stuck with before they can change their plan-o-grams. Plan-o-grams are, at best, slow trailing indicators of market trends, and at worst they are not only trailing, but distorted to meet the interests of the vendors.
You need to find the trends in your local store-level data to accurately forecast demand so you can optimize inventory purchases.
Observation #5 Focus on Tobacco first. Tobacco sales, often the largest retail category, consistently have shown the most Stock Warnings and unexpected changes in product demand.
We’ve noticed dozens of changes in customer demand for cigarette brands and other tobacco products. This is the best place to start getting your house in order because it represents the largest revenue opportunity.
This summer has the potential to be a strong recovery for c-store operators. Fuel demand is gradually improving and we expect that to continue as more Americans choose to drive rather than fly and commuter customers return to work. In addition, some c-store operators will be successful in retaining the new grocery buyer demographic. All of these factors are great opportunities for c-store operators in the coming months if they can be diligent about identifying new trends and serving the needs of their new customers by having the right product mix on hand.
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If you think your organization might benefit from an analysis of your c-stores’ Sell Through Rates and trends as well as relevant Stock Warnings, we are offering a free analysis to four qualified organizations. Click here if you are interested.
Sincerely,
John Oakley
