Archive for the ‘supply chain’ Category

Retail Sales Increasing

April 7, 2010

Despite uncertain economic reports retailers are expected to report increased sales according to an article in the WSJ.  Of interest to this blog the article noted retailers have been doing a good job of managing inventories in a difficult environment.  As we work with our clients a great deal of emphasis has been placed on forecasting sales demand and identifying out of stocks.  We have blogged a lot about how to use EDI 852 and point of sale data to manage inventory effectively and now is a great time to get started if your organization is not fully using data to improve your business.  With instock rates in the 98% range a vendor still has the opportunity to increase sales significantly with better inventory management.  Don’t miss your opportunity to get that last 2 or 3% of sales.


Supply Chain Analytics: Challenges and Solutions

March 11, 2010

Retailers and vendors in today’s retail market face the unenviable challenge of reducing costs and maintaining margins despite falling overall sales and slow-to-recover consumer demand. One of the areas in which retailers are pushing back onto vendors to is inventory management, which for vendors too often translates into retail partners that reduce overall inventories and require tightened delivery deadlines.  Retailers view the supply chain as one of the key places in which costs can be reduced—or better yet, passed off onto someone else—as a means of keeping shareholders happy despite reduced POS sales.  Wal-Mart continues to set the pace in this area, reducing its overall inventories across the board, reducing its brand assortments[1], adjusting its purchasing methods[2], and imposing tough penalties on those that miss their Must Arrive By Date (MABD).[3]

Thus, the impetus has fallen to vendors to manage their supply chains more efficiently so that the cost-savings being realized by their retailers’ inventory adjustments might trickle down to them as well instead of becoming a proverbial albatross.  And while the “glass pipeline” may remain elusive, industry experts postulate that, “Visibility of supply chain costs have never been better.”[4] Since, then, there remains continued pressure on everyone in the industry to reduce costs, there exists an opportunity now to address supply chain optimization unlike any time before.

As in all such processes, the first step in addressing this optimization is identifying the major challenges, which while not simple by any means, can be boiled down to three major focal points:

  1. Reduce supply chain costs
  2. Improving the responsiveness of the supply chain
  3. Managing demand volatility and Variability[5]

From an IT perspective, there are things that can be done with the data already being generated or received by most companies (even small ones!) to address some significant portion of each of these.

Reducing Supply Chain costs

While the operating costs of a supply chain are often the easiest numbers to point to and the most difficult for IT to address, there are data sources that can be leveraged to reduce costs.  For example, purchase orders, shipping data, and RTV (return to vendor) data is either generated internally or is received from retail partners (sometimes in a very straightforward EDI 812 document).  Unfortunately for many companies, these data sources come from disparate business systems and are stored in multiple locations, so tracking a single PO from the time the order was received thru the supply chain to its delivery at a store or in a DC is an arduous task requiring proficiency in Excel and fraught with the potential for human error.  Further, when compounded by the volume of orders received that many vendors keep up with, the task of tracking becomes futile, since the actionable information it generates rarely is identified in time to take the given action, but rather is often merely a confirmation of what has already been made known by retail partner that fined the vendor the late delivery or shorted pallet.  Thus, the lost efficiency of the analysts and the fees assessed by the retailers become additional costs in too many cases, and analysis of this data is simply not conducted.  However, those vendors that are able to aggressively track this data and address issues that may arise in a timely manner can avoid fees and improve their relationships with their retailers.  Unfortunately, upper management often struggles to see beyond the concrete costs figures and consider these less concrete but no less important opportunity for increased revenues or avoided fees.

Improving Responsiveness and Managing Demand Volatility and Variability

The delayed turnaround inherent in the difficulties discussed above relate directly to improving the responsiveness of the supply chain.  That is, supply chain utilization must address two areas of responsiveness:

  1. Responding to existing issues
  2. Responding to potential issues

Existing issues, as already discussed, are difficult to ID due to the disparate sources of data and the corresponding amount of time it takes to collate the information and determine what issues actually exist, since addressing existing issues is time-sensitive.

Potential issues are no less difficult, since these are often identified by considering all the aforementioned data sources and then including additional data sources such as POS data (from which forecasts are derived).  Mike Griswold, VP Retail for AMR Research, says, supply chain optimization “involves better forecasting methods and moving away from looking at warehouse shipments and toward POS and online sales data.” He goes on: many vendors fail to utilize POS data effectively for addressing supply chain issues because “it’s easier to get your arms around warehouse shipments because you’re dealing with weekly or twice-weekly sources of data.  When you get to POS, you’re getting down to day-level granularity for items and stores, and creating a forecast for three or four weeks out requires a fair amount of processing power.”[6] Of course, Griswold qualifies his position—forecasting based on POS and other data sources isn’t the final step.  “Retail is not designed to be an inventory holding area,” he says. “You may [get] an order for 1,000 televisions to be deployed across 100 stores, but not every store can handle 10 of each item.”[7]

Thus, forecasts must be based on actual POS historical sales, current trends, other considered supply chain factors, and tempered by the limitations of the stores for which the forecasts are generated.  Retailers provide a shelf-space and assortment designation (called plan-o-grams, modulars, sets, etc.) for most vendors which allows vendors to consider these factors when filling orders, and combined with their own warehouse quantities and capacity, now a very comprehensive and useful picture emerges, from which one may then deduce those potential issues and act to address them, instead of reacting after they become a time-sensitive emergency.

How Accelerated Analytics® Can Help You Optimize Your Supply Chain

Unfortunately, University of Pennsylvania professor of Operations and Information Management Marshall Fisher says the industry trend for vendors faced with the decision to have too little inventory and lose sales or have too much and be forced to liquidate leans toward the former. “Most companies are just moving along with less inventory. They are downsizing to meet less demand and accepting higher stockouts. The risk of a lost sale is smaller than having lots of unsold inventory.”[8]

But what if you had an integrated database solution that tied all of the disparate sources of data together into a single source of truth, from which actionable decisions could be made on timely, comprehensive data? The Rainmaker Group™, creators of Accelerated Analytics®, was first a business intelligence (BI) company and its expertise in BI solutions can be leveraged to create such an integrated database behind the Accelerated Analytics® interface, creating a powerful yet user-friendly tool that business users need and which management can understand.

Advantages offered by Accelerated Analytics®:

  • Integrated database to tie together all your data sources (P.O. files, Shipping documents, POS data, Plan-o-gram files, and more!) in a single location from which may be derived a single source of truth.
  • User-friendly reporting solution which provides rapid access to any of the data in the system and reduces the overhead normally associated with the collation and calculation of data
  • Exceptions reporting to identify shipping delays, stockouts, etc. automatically as often as required.
  • Proven forecasting methodology to generate proactive forecasts based on actual sales and inventory information


[1] Reda, Susan. “With SKU Reductions Under Way, Which Will Survive?” Stores March 4, 2010.

[2] Birchall, Jonathan. “Walmart Aims to Cut Supply Chain Cost,” Financial Times. 3 Jan 2010.

[3] Cassidy, William. “Wal-Mart Tightens Delivery Deadlines.”  The Journal of Commerce. 8 Feb 2009. 

[4] Lewis, Len.  “Delivering the World: Navigating obstacles in pursuit of global supply chain optimization.” STORES Magazine. February 2010.

[5] Based on the results of a Supply Chain Leaders’ survey conducted by IGD, a London-based consultancy.  Lewis, Len.

[6] Lewis, Len

[7] Lewis, Len

[8] Lewis, Len

Calculating Weeks of Supply

February 25, 2010

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 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 sum mer 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 your method is more accurate or you may find 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.

Retail Replenishment – How tuned in are you?

February 23, 2010

I spent about 9 hours yesterday analyzing sales, order, and forecast data for Wal-Mart, Home Depot, and Lowe’s vendors and I am somewhat surprised by my observations.  It’s pretty clear there are some min/max rules in place as I can see patters 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 open order, and UPC/stores that are clearly overstocked (e.g. high WOS) and yet have an open order.  It makes sense 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 customers 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?

Store Level Merchandising Analysis Using EDI 852

February 18, 2010

The following is a step by step process to aid replenishment vendors in identifying stores on an item level basis that are losing sales due to inventory stock outs or inventory that is present but unavailable for sale.  Such unavailable inventory may include lost or damaged items or items on the shelf but not available to the customer for any of a variety of reasons.  This process assumes that the vendor is receiving accurate and detailed EDI 852 Product Activity Data (or POS data via Retail Link or Partners Online, etc) on no less than a weekly basis from their retailing partners.  This article will focus on identifying and addressing underachieving stores. 

Step 1

The vendor will calculate average weekly sales velocity (Avg WS) at an item level across all stores.  This is best calculated using the most recent twenty-six weeks of sales.  Thus, for a given item, the calculation would be:

            Sum(last 26 wks. unit sales) = Avg WS

Step 2

Calculate the average item sales velocity (Avg WS) for each item for all stores for the last ten weeks of sales.  See a sample item sales velocity table  For each item, look at the last ten weeks of unit sales at the store level and separate the items by store into five categories.  For ease of identification, label these categories A-E.  The categories are as follows:

  1. Most recent two weeks of sales.
  • Stores with sales in the last two weeks for any given item will fall into this category
  1. Most recent four weeks of sales.
  • Stores with no sales in the last four weeks for any given item will fall into this category
  1. Most recent six weeks of sales.
  • Stores with no sales in the last six weeks for any given item will fall into this category
  1. Most recent eight weeks of sales.
  • Stores with no sales in the last eight  weeks for any given item will fall into this category
  1. Most recent ten weeks of sales.
  • Stores with no sales in the last ten weeks for any given item will fall into this category

The total percentage of sales of any given item for a given category can be accurately calculated by dividing the number of stores per item in any category by total stores (TS). 

            Total Stores in a Category  = % each category is of the total

This percentage calculation is a better, more accurate way to judge relative performance of each category than by comparing unit sales.

Identifying & Addressing Underperforming Stores

The remaining article focuses on underperforming stores, that is, stores that fall into categories D or E.  Now that you know how many stores are in categories D or E, go back to the list of items and the last 10 weeks of sales and identify what store numbers are present in the bottom two categories and not in any of

the other categories. These stores are stores with no sales in the past 8-10 weeks.  Pull the current inventory on hand for each store.

Out of Stock Stores

Stores with no sales and zero inventory on hand are most likely out of stock stores.  Vendors will want to identify the last week that a given store recorded a sale for a given item in categories D-E.  The vendor can then estimate lost sales by unit for that item/store combination by multiplying the number of weeks since the last sale by the average weekly sales (Avg WS) calculated in Step 1.

            (Avg WS) *[Sum(weeks w/o sales)] = Lost sales by unit due to stock-out (LU)

Lost sales (LU) by unit can also be multiplied by the price of the item to determine lost sales in terms of revenue (LR).

            (LU) * (price of given item) = LR

Inventory stock-out problems are typically due to one of two things: Inaccurate inventory replenishment reorder points or inventory availability issues on part of vendor.  If that item was out of stock due to high reorder quantity, then a vendor can contact the replenishment manager at the retailer responsible for the underperforming store(s) and suggest changing the inventory replenishment set point, using lost revenue (LR) as the rationale for the recommendation.  This exercise can be performed for all item/store combinations that had few or no unit sales for an 8-10 week period (categories D-E) and showed no inventory on hand.

Stores with Inventory on Hand, But No Sales

Some of the stores are going to reflect no unit sales in the past 8-10 weeks, but still have on hand inventory. This typically indicates inventory which is misplaced, lost, stolen, or stock on the shelf but out of view of the customer for whatever reason.  It may also include damaged inventory and inventory otherwise unavailable for sale.  In this case, the vendor would contact the retailer and investigate the problem.  The inventory replenishment system from the retailer will not release an order for new merchandise until the vendor visits the store directly or contacts the store manager to investigate the problem and demonstrate the product is not available for sale.  It is useful when contacting the store manager to know the date of the last unit sold.  This date and the average weekly unit sales (Avg WS) calculated in Step 1 will indicate to the store manager when a sale should have occurred.  That is, if on average a given item is sold every other week and 8-10 weeks have passed at a given store without a sale despite recorded inventory on hand, this is indicative of a problem since 4-5 units should have been sold during that timeframe. 

Business Rationale for Store Level Merchandising Analysis

Conducting a store level merchandising analysis can be a time consuming effort for a vendor.  Many vendors have trouble rationalizing the expense, especially vendors with very good in-stock rates.  But even a vendor with an in-stock rate of 98.5% still has 1.5% of stores out of stock.  In a typical 3,000 store chain, this could represent as many as 45 stores out of stock.  If those stores averaged just one unit sold per week, that translates to as many as 2,340 units of lost sales per year.  Since this represents only a single item, and out of stock stores typically are out of multiple items and average significantly more than one unit sold per week per item, this vendor is looking at hundreds of thousands or potentially millions of dollars of lost revenue (LR) per year despite a very high in-stock rate of 98.5%.

Resources: training on SKU Sales Analysis, Out of Stock Analysis, and SKU Forecasting are available.

Retail sales improvement requires careful forecasting

February 5, 2010

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 using 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.

Collaboration Key To Survival

December 29, 2006

Press enquiriesFor Economist Intelligence Unit: Joanne McKenna, Press Liaison: +44 (0)20 7576 8188 For immediate release:  Wednesday, November 15th 2006 

Collaborative partnerships will be key to corporate survival, reveals a new survey from the Rconomist Intelligence Unit

Companies believe collaboration with other companies has become critical to their long-term survival, according to new research from the Economist Intelligence Unit. More than half of the 187 executives polled for this report say collaboration will either form an important part of their firm’s competitive advantage or will actually be central to its survival over the next three years. In addition, 51% say that they have changed their business model over the past three years to take greater advantage of collaborative partnerships. Key areas of collaboration include sharing supply chain data, establishing sales partnerships to tap fast-growing markets, and collaborating on research and development. In each of these areas, collaborative partnerships are delivering significant rewards: 28% of respondents say their biggest collaborative ventures have delivered either more or much more value than expected, compared with 11% who say they have not. 

“What this research reveals is that companies of all sizes are engaging in, and benefiting from, collaboration with other organisations, either locally or in countries where they may have no direct presence whatsoever,” said James Watson, the editor of the report. “What is striking is that a significant minority of these companies acknowledge that they couldn’t even exist if it wasn’t for the collaborative relationships they have engaged in.”   These findings are published today in Companies without borders: collaborating to compete, a report from the Economist Intelligence Unit, sponsored by BT.  Other key findings of the report include:

Collaborating with other firms is now the norm for nearly all businesses. The majority of companies (64%) engage with up to 10 partners, although some have established agreements with more than 100. And nearly all firms expect the average number of partnerships they have to rise over the next three years.
Most collaboration centres on sales and marketing. Firms collaborate for a number of reasons: to provide products they can’t deliver alone, to keep up with competitors or to expand their global reach, to mention just a few. These partnerships are typically being driven by the sales and marketing departments.
 The biggest challenge involves finding suitable partners. About one-third of executives polled for this report say the biggest impediment to collaboration is simply being able to find an appropriate partner. And when they do, overcoming any cultural clashes between the two organisations is a major concern, along with more practical issues, such as getting system integration right or dealing with data security concerns.
Successful collaboration hinges primarily on people skills. Making partnerships work relies more on people than anything else. Survey respondents identified the skills of the personnel assigned to a relationship as the single most critical factor for successfully managing the partnership.

Andy Green, CEO BT Global Services, said: “This research shows the increasing importance of collaborating within and between organisations. The digital networked economy is enabling companies to create new business models, utilise global resources and work in real time with people anywhere in the world as if they were in the office next door. This erosion of the traditional barriers of time and distance means that the ability to partner and work effectively is more important than ever to achieve success in the global competitive environment.” Companies without borders: collaborating to compete
available, free of charge, at: