Author: Helen Thomas

I read this very good article yesterday and wanted to share it. 

Business Intelligence and Performance Management Rising to the Top of the Supply Chain Executive’s Agenda

A metric fundamental to managing the retail supply chain is weeks of supply (WOS). Weeks of supply tells the inventory manager how long the current on hand will last, based on current sales demand.  By keeping your eye on weeks of supply, you can avoid inventory stock outs and lost sales.  The basic calculation for weeks of supply is pretty simple: on hand inventory/average weekly units sold.  However, our work with vendors demonstrates that calculating an accurate and useful weeks of supply can be anything but simple.  Let me explain.  An EDI 852 document will provide units sold and on hand.   Very few EDI 852 documents provide data for inventory on order, inventory in transit or inventory in the warehouse.  More sophisticated systems like Wal-Mart’s Retail Link will provide the additional inventory data.  So ,the first issue an analyst working only with EDI 852 must overcome is to gain a complete picture of the inventory in the supply chain – all the inventory.  If you are working with a Home Depot 852 or a Lowe’s 852, you must also gather your purchase order and shipping data, so that you have the ability to understand on order and in transit inventory.  You must also decide how to apply inventory in the supply chain.  That is, will you sum on hand + on order + in transit  to use as the numerator in your calculation?  Or, perhaps you would prefer to ignore the on order due to long shipping lead times and use on hand + in transit.

The next consideration is, how to calculate the average weekly units sold which is the denominator in the weeks of supply calculation. This requires some careful consideration.  If the number of weeks used to calculate the average is not selected correctly, you will arrive at a misleading result.  Consider, for example, the sales for two vendors, as seen in this chart.  One vendor has products which are non-seasonal and tend to have very steady and consistent sales.  The other vendor has products which are seasonal and sell much higher in the warm spring and summer months.  When choosing the number of weeks for calculating the weeks of supply, you want to consider the rate at which your demand changes.  If your demand is fairly steady, like the non-seasonal vendor, a larger number of weeks can be used.  If, however, your demand tends to change rapidly, due to seasonality or based on some event like selling licensed apparel during football season, then you should choose a smaller number of weeks.  Our experience shows that a seasonal vendor should consider a four week window of sales demand and a non-seasonal vendor should choose 8 to 10 weeks.

The final point to make about calculating weeks of supply, is to consult with your retail buyer on the period of demand they are using.  If you are using four weeks, and they are using six weeks, you will arrive at different order quantities.  By discussing the calculation, you may find that your method is more accurate or you may find that the retailer has good reasons for their method.  If you still feel your method is more accurate, then calculate weeks of supply using both methods and track the accuracy over time.  This will provide you with the factual data to either change your calculation method to align with the buyer’s, or demonstrate to them why your calculation is more accurate.

I spent about 9 hours yesterday analyzing sales, order and forecast data for Walmart, Home Depot and Lowe’s vendors, and I am somewhat surprised by my observations.  It’s pretty clear that there are some min/max rules in place, as I can see patterns to the order quantities based on the OH inventory and the order case pack quantities.  However, what surprises me is that I also see a large number of what I would guess are “manual overrides.” That is UPC/stores which clearly need inventory and fall under the minimum OH of other stores, but which do not have an open order, and UPC/stores that are clearly overstocked (e.g. high WOS), and yet have an open order.  It makes sense that there would be automated replenishment rules in place and then some lead-way for the buyer/replenishment manager to make judgment calls, so that leads me to my question….

What do you know about your key retail customer’s replenishment rules?

• Simple min/max ordering?
• Based on OTB dollars?
• WOS trigger?
• At what level is demand calculated?  e.g. Category/Region, Category/State, Sub-category/State
• Under what situations will the buyer do a manual override?

According to the WSJ, the snowstorms that blanketed much of the country in the past week caught apparel retailers in short-sleeves.

Most clothing chains have very little winter clothing left on their racks, the result of tightly managed inventories and better-than-expected holiday sales.

But, with nearly 70% of the country covered in snow, store shelves are mismatched to the weather: filled with new spring fashions that frigid customers aren’t in the mood to buy. The lack of appropriate dress could cost retailers some momentum after improved holiday and January sales periods, said analysts.

An employee at a Gap store in downtown Washington, D.C., said the store had been sold out of cold-weather hats, scarves and gloves for over a month.

Macy’s Inc. said, it’s My Macy’s merchandise localization program, which lets buyers modify merchandise assortments based on local needs, helped it avoid shortages. A spokesman said Thursday, that the department store chain planned for fresh flows of coats, gloves and hats in February and March in cold-weather markets. “Macy’s continues to have ample supplies of cold-weather merchandise,” the spokesman said.

This is an interesting example of how using EDI 852 and analyzing POS data may have been able to help avoid out of stocks. Although the fashion supply chain tends to have long lead times, if retailers and vendors had been more closely watching the weather and local demand signals, they may have been able to either reallocate inventory between warehouses and stores, or perhaps place additional orders.

A recent RIS article  titled, “How Much Are Out-of-Stocks Costing You? Much More Than You Might Think”, By Greg Buzek, provides more evidence that retail out of stocks are costing vendors huge lost sales.  Buzek quantifies the scope of the loss; “A retailer that invested in completely fixing its out-of-stock problem, would gain a solid competitive edge. The average retailer could increase same store sales 3.7%, by converting all perceived out-of-stocks into transactions. Specialty soft goods could have the biggest potential win: solving out-of-stocks would boost their same-store sales 7.1%, while department stores would see a 4.2% jump.”

The good news is we have seen dramatic improvements in in-stock performance by active store and item level analysis.  The methodology is pretty straightforward:

1. Determine the lead time from order to product arriving at a store.  Let’s say this averages 2 weeks.  This is your minimum on hand weeks supply to avoid a stock out.
2. Next calculate the average weekly sales velocity for each item, and each store.  Yes, you must know the average sales velocity for each peg or shelf position.
3. Calculate the weeks supply on hand for each item and store by dividing the current on hand inventory by the average sales velocity.
4. Filter the results to show only those items with less than the 2 weeks supply on hand.  These are the stores you need to make sure you place an order immediately to avoid a stock out.

This type of analysis is not hard to do, but if you don’t have the proper tools it can be very time consuming.  But, it’s well worth the effort.  If you can improve your in stock performance by even 2%, you stand to gain significant sales.

Next Article: Increasing Sales By Managing Out of Stock Inventory

The WSJ reported retail sales Rose 3.3%, showing signs consumers are returning to stores.  This is a great sign for the retail market as it seems a turnaround may be in the works.  Macy’s posted a 3.4% increase, Saks reported 7% and Costco 8%.  As demand begins to increase, vendors need to keep a careful eye on the supply chain.  Retail buyers have been operating on low open to buy for over a year, so inventory levels may be below where they will need to be to satisfy demand.  Vendors using EDI 852 data for forecasting need to make some careful adjustments to their forecasting model to not be caught by surprise.  Here’s why.  Forecast models use historical demand as the foundation for current year predictions, but last January was a terrible month for retail sales, so a simple look at comp year demand will give a misleading result.  To correct for this, vendors should be considering not only last year’s demand, but also the prior year’s demand and the current period trend.  By combining these three numbers, vendors will have a more accurate model and hopefully not get caught by surprise.  But even with a good forecast, we expect sales to be unpredictable for the foreseeable future, so vendors must carefully watch demand and inventory levels by analyzing the EDI 852 data weekly or even daily and making push order recommendations to their buyers.

I went golfing the other day and got paired up with a guy named Henry. During the course of the afternoon, I came to find out Henry is a very successful business man who spent part of his career in insurance and now owns more than a dozen hotels in ‘retirement’. To me, retirement means nothing but golf and sitting on the beach, so I’m not sure Henry is ‘retired’, but anyhow. He shared an interesting story with me.  He was having lunch with another very prominent business man, which if I wrote his name 80% of you would recognize. Henry asked him, “What is the key to your success?” This man considered the question for only a brief moment and said, “Oh it’s very simple”. “If a job is worth doing, then I hire someone to do it, if it’s not worth doing, then I don’t do it at all.” I was struck by how profound and simple that was. He had created a simple method for deciding how to focus his energy where it counts the most and avoid becoming entangled in the day to day details. I was thinking about this in relation to our business and was struck by the fact that many vendors that come to us for EDI 852 and POS reporting, are only doing so because their buyer told them they needed to, or because an executive at their company thought it might be a good idea. They are not committed to analyzing and using the data to improve their business. On the other hand, we have vendors who dig into the details of the data every week and find out of stock issues and sales opportunities. They are making the most of the data and the results show in their growth and inventory GMROI. So, the bottom line is this; if you are considering EDI 852 / POS data analysis, don’t stick your toe into the water, jump in all the way and make the most of the data, and your business will improve.

The primary purpose of a store analysis is to identify the stores which are making the largest contribution to total sales. When the highest contributing stores are identified, an analyst can study the characteristics of those stores, including SKU assortment, demographics, promotions, min/max on hand, and make recommendations on how other stores can be improved to enhance performance. An important objective of a store analysis is to grade stores by performance into major categories, to save time and focus out of stock and forecasting on the highest contributing stores in future analysis. The data provided in an EDI 852 document provides all the necessary information to conduct a store sales analysis.

Buyers often have a store list with categories defined A,B,C,D based on sales performance. However, the buyer’s categories are typically assigned based on the total sales for a given store or total sales for a product category. This may result in a different performance ranking than the analysis on your specific SKU’s. Because plan-o-gram decisions are made based on the retailer’s store categories, you may find that an A store for your items is considered a B or C store based on total sales. Request a list of store categories from your buyer and compare to the categories from your analysis. If there are variances, we recommend you meet with your buyer and discuss adjustments to the plan-o-gram based on store performance for your SKU’s.

Why should you conduct a store sales analysis using your EDI 852 data?
· Identify variances between the retailers general store grade and the actual store grade for your SKU’s.
· Understanding a store’s performance relative to its peers allows you to focus your attention where it is most needed.
· Identify the best allocation of promotional dollars
· Work with the buyer to put the right plan-o-gram in each store
· Retail replenishment decisions are usually made based on their store grade, if it’s not correct for your SKU’s, there is a problem you can correct
· Compare store grades across retail partners in the same geographies so you can identify new expansion opportunities.

Additional information and detailed instructions are available at https://www.acceleratedanalytics.com/whitepapers/

Vendors are working hard to understand how to best use retail POS and inventory data, which is made available via EDI 852 or a web portal. Here are five very common questions vendors ask as they work with our team to put a data analysis solution in place.

What is the difference between EDI 852 and data available on my retail customer’s web site? The most obvious difference is the format of the data. EDI 852 is a standard document template, but it is encoded using line identifiers and other language necessary for computers to make sense of the data. EDI 852 must be parsed and translated to be of any use to a business user. Data available in a retail portal is typically either presented on screen or saved into a text or spreadsheet format. These files do not require translation and can be opened in a variety of Windows programs. A second difference is the level of detail available. An EDI 852 document always includes units sold by UPC but it may not include on-hand data. And receiving store level EDI 852 data is often an additional selection and cost. Most retail portals will provide detailed store level data files, or presentation of detailed data on the screen. Finally, and most importantly, EDI 852 values for each UPC can be different than the values reported in a file available on the portal. This can be due to different reporting periods, different source and/or additional source system data, or a different method of handling of returns.

If I can choose between EDI 852 and a file from my retailers’ portal, which one should I choose? This decision comes down to a few factors. First, does the retailer charge a fee for sending data via EDI as opposed to accessing the data on the portal. Second, does the EDI 852 data provide less information than the portal. For example, as noted above, some EDI 852 files do not include on-hand or store level data. Finally, research the data accuracy of the two sources and choose the one which will best support your decision making process.

What types of reports should I be using? There are three reports that form the backbone of retail POS data analysis: item sell-thru by store, inventory on-hand by item and store, and top selling items. From these three reports you can create a library of very useful decision support tools segmented by geographic region, product category, and by retail partner.

Why should I consider an outsourced service for POS data analysis? For most vendors, working with POS data falls outside their IT organization’s typical scope of expertise and tools. Simply put, there is a fairly large volume of data which requires translation, scrubbing, and organization into a sophisticated data warehouse. The data does not fit into most organization’s ERP, forecasting, or accounting system so the IT department is faced with building a custom application. Then, end users need a simple and quick tool to access the data for analysis and decision making. An outsourced service can deliver the necessary engineering and software tools in a very short period of time without an expensive investment. And outsourcing provides a cost effective monthly expenditure which aligns with your cash flow instead of a large capital expense.

Why can’t I just use a spreadsheet for analyzing POS data? Spreadsheets have many limitations when it comes to analyzing POS data, not the least of which is simple row and column limitations. But more importantly, there is a significant amount of work required each day or week to accept, transform, format and analyze data in an Excel spreadsheet. Time which your staff can avoid all together by using more sophisticated tools and/or an outsourced service. In addition, spreadsheets are generally not well suited for team based collaboration on data. Each time a spreadsheet is opened, the user has the opportunity to change/edit data, which can rapidly deteriorate the quality of the data and cause significant duplication of effort.

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