Calculating Weeks of Supply Inventory

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. 

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 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.

The BullWhip Effect

The bullwhip effect is a serious problem for vendors. The article below will provide a comprehensive introduction to the problem and impact. After you finish reading the article do this exercise: calculate the total number of stores stocking your products [# of retailers * average total stores], then multiply by 2%, which is an average out of stock rate. This is the total stores that are currently out of stock on your products. Next multiply by an average weekly unit sales for your key product(s). This is your total lost sales opportunity. 

Leading retailers and manufactures are dramatically improving sales and profits by embracing a new supply chain model built upon sharing point-of-sale demand data and closely collaborating on forecasts. Research conclusively demonstrates demand driven collaborative supply chain models drive more accurate forecasts, fewer out-of-stocks, higher sales, and higher operating margins.

There are many variations of demand driven collaborative models in service, the most popular of which are:

• Efficient consumer response (ECR)
• Collaborative planning, forecasting, and replenishment (CPRF® )
• Vendor managed inventory (VMI)
• Demand driven supply network (DDSN) 

The overlap of these models and generally slow adoption is creating a great deal of confusion in the market. While each model is slightly different in its approach, definitions, and processes, they all have a common goal: share actual demand data with all parties in the supply chain and facilitate real-time collaboration to reduce inefficiency and improve sales.

The purpose of this article is to discuss the financial benefits of implementing a demand driven collaborative supply chain model and provide best practices for getting started.

AVOIDING THE OUCH OF THE BULLWHIP EFFECT

The basic premise of the demand driven collaborative supply chain model is that in order to achieve the highest possible in-stock and simultaneously minimize waste, all parties within the supply chain must have timely access to actual sales demand data and all parties must have a means by which they can work together to coordinate promotions and business planning.

A forecast by definition is an estimate made in advance of an event occurring and is therefore an educated guess. Unfortunately, even sophisticated forecasting software can have an error rate of 50% on promoted items. Most troubling of all, forecast accuracy decreases, moving backward into the supply chain.

The reason forecast accuracy decreases, moving backward in the supply chain,  can be illustrated by plotting sales over time for supply chain participants as depicted in CHART 1. Notice the actual sales demand recorded at the retail point-of-sale has moderate variation. This is because the retailer builds their forecast model using actual demand as tracked through their point-of-sale systems. But notice how much more chaotic and unpredictable the demand curve becomes as you move away from the actual point-of-sale. Demand as viewed by the supplier, wholesaler, and manufacturer is based on estimated sales, which combined with latency and manually adjusted “safety stock”, causes increasingly inaccurate and chaotic forecasts. Research indicates a fluctuation in actual customer demand of +/-5% will be interpreted by supply chain participants as a change in demand of up to +/-40%. As depicted in Chart 1, although actual demand has only changed +/-5%, the reaction of supply chain participants is dramatically exaggerated and is known as the bullwhip effect. Much like cracking a whip, the user only needs a small motion in their wrist (point of sale) to cause a huge motion in the end of the whip (manufacturer).

The bullwhip effect causes inaccurate forecasts, inefficiency, and waste within the supply chain. Anytime the forecast line of a supply chain participant (e.g. chart 1; wholesaler, manufacturer, or supplier) is above or below the actual demand line (e.g. chart 1; retail sales) inventory levels are not optimized and out-of-stocks or inventory build-up will occur. The U.S. Department of Commerce estimates $3 trillion in excess inventory is locked in the U.S. and European supply chains. The bullwhip effect is exacerbated by the parties within the supply chain who do not have an accurate understanding of actual demand. In other words, they are forecasting, but the inputs upon which their forecast model is built are inaccurate.

Accurate forecasting and close coordination between supply chain partners can help to eliminate the bullwhip effect and dramatically increase overall profitability. The most effective way to improve forecasting accuracy at each step in the supply chain is to base the forecast on actual sales demand data. In this way, each point in the supply chain can be demand driven and the parties can collaborate on the same forecast inputs. In addition, promotions can be coordinated and managed to maximize sell-thru without causing supply disruptions. As information quality improves, cycle times are compressed through the entire supply chain process.

FINANCIAL IMPACT OF DEMAND DRIVEN COLLABORATION

Numerous studies provide objective financial results from sharing demand data and collaboration including studies from the National Retail Federation, AMR Research, and VICS. The reason the financial benefits are so compelling is they impact both top line revenues and bottom line operating margins; and, these programs are a win-win for both the retailer and the supplier since the financial impact between the two is directly linked.

Financial Impact #1: Increase sales by 5-10%
Average retail industry out-of-stock rates average 8% and can be as much as 40% on promoted items, which results in a loss of sales between 5% and 10%. Demand driven collaborative supply chain programs drive higher sales by improving in-stock rates between 2% and 8%.

Financial Impact #2: Increase operating margins by 5-7%
Many retailers are managing in-stock performance by increasing their base replenishment levels and carrying additional “safety-stock.” While this approach offers a short-term advantage, the long-term drawbacks are obvious; higher operating costs due to slow inventory turnover and higher logistics costs. By sharing demand data, forecast accuracy is improved and less safety stock is required to keep in-stock levels at target. Less capital is tied up in inventory, improving both return on invested capital (ROIC) and gross margin return on investment (GMROI).

Demand driven collaborative programs also have a positive effect on customer brand loyalty. This is because stock-outs also cause another, even more insidious problem – 32% of consumers who can’t find an item will go to another store. If the stock-out occurs three times or more, the consumer is likely to purchase a different brand. So today, the retailer may loose the sale, but tomorrow, so may the supplier. This dynamic creates a powerful incentive to work together for accurate forecasts. As an example, by working with suppliers and closely monitoring point-of-sale data for its 1,450 fastest-selling items, H-E-B Grocery was able to reduce its out-of-stock rate by 22.5% in just eight weeks. Similarly, Sainsbury tracked its 2,000 top items and increased sales by 2%. These are powerful examples of how leading retailers are using demand data and working in concert with suppliers to dramatically improve operating results.

EDI 852 Improves Sales and Inventory Handling

Models for supply chain excellence, including vendor managed inventory (VMI); collaborative planning, forecasting, and replenishment (CPFR); efficient consumer response (ECR); and demand-driven supply networks (DDSN), are proving difficult for most retailers to implement.  A fundamental challenge inherent in these models is sharing point-of-sale demand data with suppliers. Although EDI 852 is a common solution, retailers are finding EDI alone does not solve the problem and provide the promised benefits. Why? Because sharing data alone is not enough… suppliers need a tool to analyze the data, draw out insight and then take action.

Now, there is a solution - Accelerated Analytics provides the tools necessary in a hosted solution, so neither the retailer or supplier have to make a costly technology investment.

Accelerated Analytics is a hosted power tool for sharing point of sale (POS) data analysis with suppliers. EDI 852 product activity data based on point of sale (POS) transactions and transmitted via EDI 852 is the backbone of a successful vendor managed inventory program. Unfortunately, our experience has shown very few suppliers are able to use the information. The suppliers we've spoken to are frustrated by what they perceive as an overwhelming amount of point of sale data, because they don’t have the tools to analyze and report. Sending data via EDI 852 to suppliers simply does not translate into an optimized supply chain. Retailers are frustrated with suppliers because they cannot actively participate in vendor managed inventory and sales promotion without the data. It’s a no-win for both sides and a huge lost opportunity.

Until now, the best retailers could hope for was to transmit EDI 852 files to their suppliers, who would accept the data and either print a static report or export the data into Excel.  Already busy suppliers found static reports to be of limited use.  Getting the IT department involved to deal with a huge data file was expensive and too much of a hassle.

Accelerated Analytics changes this entire picture by accepting point of sale data via electronic data interchange EDI 852, or nearly any standard format from the retailer, and providing it to the supplier via our web based point of sale analysis power tool. Accelerated Analytics hosts 100% of the hardware and software, so neither the retailer or supplier have to invest a single dollar.

What if my organization is not using vendor managed inventory (VMI)?
Even if your organization is not currently using vendor managed inventory (VMI), Accelerated Analytics can provide a significant supply chain benefit by reducing costs, reducing inventory levels and increasing profits. Efficient supply chain management requires the rapid and accurate transfer of information throughout a supply system.  Accelerated Analytics accomplishes this goal quickly and with no expensive new infrastructure.  Accelerated Analytics can be used as a tool for VMI or as a stand-along solution separate from VMI.

What if my organization is not using EDI or only using EDI with a small percentage of vendors?
No problem, Accelerated Analytics can accept POS data via nearly any custom data format. In fact, in many situations, there are benefits to not using EDI because we can accept a richer and more granular data set, which improves the analysis results.

Our suppliers are not asking for POS data.
No surprise, they probably lack the tools to manage and analyze that volume of data and they know with greater knowledge comes greater accountability. Successful business transformation does not begin as a reaction, but rather because business leaders have the vision to proactively invest in tools which drive their business forward faster than their competition. If your organization was the market leader in supply chain collaboration which improved your in-stock and inventory turns by 25 or 30%, what would happen to your market share position?

What is CPFR?
(CPFR) Collaborative Planning, Forecasting and Replenishment is a business practice that combines the intelligence of multiple trading partners in the planning and fulfillment of customer demand. Accelerated Analytics provides the infrastructure to support CPFR by connecting retailers and suppliers together with a POS data analysis tool, shared business best practices and exception management.

Scan Based Trade Growing?

I'm making a prediction - Scan Based Trading (SBT) will gain popularity in 2011 and will emerge in categories not typically engaged in SBT.  In an article today in the WSJ titled "Retailers Are Sold on Frugality", Walmart, Home Depot and Lowe's are predicting the upcoming holiday season may be poor.  They believe consumers remain very cautious about spending.  The good news, if there is any, is that retailers have successfully cut expenses during the downturn [see figure 1], so most are making a profit despite lower sales.  The bad news is that they are doing that by tough price negotiations with vendors, more reliance on part time workers and generally lower spending across the board. 

 

"How in the heck can you increase earnings with tighter revenue?  The answer is that we [Home Depot] expect some expense relief."  Chief Financial Officer Carol Tome.  If you are a vendor to a major retailer - take note of that quote.

Back to SBT.  Scan Based Trade relationships with vendors have been around for a long time, but they have traditionally been reserved for fast moving food and consumer goods.  Things like bread and milk, etc. However, savvy financial executives at retailers have realized moving from a traditional purchasing model to an SBT model removes inventory from their balance sheet, reduces their labor and shipping and therefore dramatically increases their bottom line.  Let me give a real world example.  Accelerated Analytics processes data for an SBT program at one of the largest drug store chains in the US.  This retailer is in the process of moving their entire book department from a traditional model to SBT.  In the past, the retailer had millions of dollars of book inventory on their balance sheet, but today they have no books on their balance sheet and their vendor handles all DSD shipping, in store merchandising and carries all the inventory on their balance sheet until sold.  When the book goes through the cash register, the sale is split between the vendor and the retailer.  The retailer essentially provides shelf space and the vendor handles the category from start to finish.  The vendor benefits because they have more control on merchandise assortment and planning and they get paid faster.  With daily sales and payments based on the actual sales, the vendor no longer has to wait for 90 days to be paid on a traditional invoice.  The retailer wins because their balance sheet is dramatically improved, their labor is reduced, they avoid mark downs, etc, etc.

This is a huge paradigm shift, SBT in the book category is revolutionary.  Think SBT will never apply to your category - think again.  I've had conversations with retailers and vendors about moving cosmetics, electronics, clothing and hardware to SBT.  It's not a question of if, it's simply a question of when.  Start thinking today about when your organization can support and even recommend SBT to your retailer and you will be way ahead of your competitors.  Trust me, your CFO would like to be paid daily instead of net 90.

Have you heard of "Blue Ocean" strategy?  If not, I highly recommend looking up the Harvard Business Review article and you will see how SBT is a blue ocean strategy for your business. 

I'd like to hear your thoughts on the challenges/opportunities for SBT in your category.

Lessons Learned From Early Adopters of Demand Driven Supply Chain Technology

Very few retail organizations are structured to handle the business disruption or cost of being the early adopters of new technology. This is especially true when the technology has a direct impact on the communications with all of their largest suppliers. However, over the past twenty-four months, we have begun to see the adoption of demand driven supply chain strategies beyond the early adopters, and into a much larger and more wide-spread early majority. This is causing many retail executives to take notice and begin to seriously plan out their organization's approach. This leads to two key questions we consistently hear from these executives; first, what are the world-class leaders doing, and second, how can we quickly and cost effectively realize the process improvement without a huge technology investment?

One of the key lessons learned from watching the early adopters implement the demand driven supply chain is, that technology is only a small part of success. The technology is simply a foundation which allows buyers and suppliers to establish collaborative joint business processes. In fact, if approached as merely a technology project, one might as well just use EDI or Excel and call it a day.

However, world-class leaders are sharing with suppliers a rich data set, including SKU level data, and they are providing the information analysis tools to make the data immediately actionable. Sharing a rich data set provides a supplier and buyer the framework for a valuable and detailed conversation around sales and inventory. End users can begin their evaluation at a category or product family level, and then drill down the hierarchy all the way to an individual SKU.

Rather than a data dump using EDI, Excel or downloadable files in a vendor portal, world class leaders have implemented information sharing tools. Information sharing tools transform rows and columns of data into visual performance dashboards and provide exception condition monitoring, so an end user can quickly interpret the information and take necessary actions. They bridge the gap between bytes of data and the information necessary for managing in-stock levels. With the addition of an exception dashboard, the end user can be quickly alerted to problems and opportunities without having to scrutinize each SKU individually in some massive spreadsheet. The dashboard also establishes one common version of the truth, so time previously spent explaining how reports were calculated, can be spent finding the next big cost savings. Our clients tell us that just having everyone on the same page can save dozens of hours each week, then multiply that times hundreds of suppliers.

The second question we often hear, involves how to most cost effectively implement a demand driven supply chain. Here, the lesson to be learned from the early adopters is the cost they paid to build the infrastructure. For example, Wal-Mart estimates a $4 billion dollar investment into Retail Link, which is a shocking number, until you consider the scope and complexity of the project for over 10,000 suppliers. For example, a mid-sized specialty or hard goods retailer with 600 stores, is likely to have about 2,000 suppliers, 400 of which are on active replenishment and will be part of a data sharing program. On average, each of the 400 suppliers will have two users (one sales and one operations), and the retailer will have approximately 50 users who need to directly collaborate with the supplier community, so there is a universe of approximately 850 users to provide with data, software, training and help desk support. The end user reporting tools sit on top of a database, which can easily grow to multiple terabytes because world-class leaders are using a very rich data set, including SKU level sales and inventory across a historical timeline of 18 months. Anytime there is a large technology infrastructure cost to support a business process for several hundred globally dispersed end users, you have a good candidate for outsourcing. And as previously stated, the value is derived from the collaborative exchange between the buyer and supplier, not from the transfer and management of bytes of data, so why not turn that variable cost into a fixed monthly purchase and let the provider manage the risk. Outsourcing also provides the additional benefit of a service level agreement, so all parties can be confident the system will be available and ready when they want to use it.

Our article titled “The Case for Supply Chain Collaboration”, and published in vol 1 of the Journal of Trading Partner Practices, identified the financial opportunities associated with the demand driven supply chain. These benefits include increasing sales by 5 to 10% and operating margin improvement of 5 to 7%. Now that the early adopters have paved the way and learned the tough lessons, we are seeing the early majority start to move. Now is the time to talk with your team and determine how best to implement a demand driven supplier collaboration program at your organization.

Supply Chain Analytics: Challenges and Solutions

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 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 through 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 the 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 opportunities 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 and 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? 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.  http://www.stores.org/Merchandising/2010/03/cover.asp. 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. http://www.joc.com/node/416490. 8 Feb 2009. 
[4] Lewis, Len.  “Delivering the World: Navigating obstacles in pursuit of global supply chain optimization.” STORES Magazine. http://www.stores.org/SupplyChain/2010/02/cover2.asp. 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

BI in the Supply Chain

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

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By Viktoriya Sadlovska and Nari Viswanathan
 
In the context of today's complex demand-supply networks, in which visibility into key performance indicators across the entire network is key to business success, companies have begun focusing more strongly on their supply chain Business Intelligence (BI) capability, as a key enabler of strengthening or regaining control over their supply chain networks. Focus on supply chain BI will remain strong in 2010, contributing to operational and strategic supply chain improvements at the top-performing companies. 
 
The only way to ensure that a business is able to adapt to changes fast enough is to establish an adequate level of supply chain intelligence, i.e. put in place processes and tools to effectively monitor supply chain performance and notify specific process owners and managers before problems turn into disruptions. These capabilities should not only serve as each supply chain's operational "command and control" center, but also help uncover new revenue and savings opportunities with the help of advanced analytics.
 
In order to successfully monitor, capture and analyze performance data in a complex supply chain, top-performing companies across industries have implemented a series of capabilities and software enablers to help them in managing this mass of information. Having a supply chain business intelligence technology that is designed to integrate data and event flows across the broad array of departments, functions and roles within the global enterprise is an advantage versus an infrastructure that is not designed with such robust connectivity and functionality. A company needs to be able to integrate information across internal and external groups and trading partners and enhance collaboration and agility during tracking and responding to the myriad of supply chain events.

Dashboards and Scorecards
Multiple Aberdeen research studies have shown that Best-in-Class companies are more likely to use internal dashboards to measure supply chain performance, and external scorecards to measure their supply chain partners' performance. Scorecards help companies formalize the evaluation of supply chain partners’ performance in order to improve the supplier and services provider selection process, potentially adopt performance-based incentive programs, and improve overall supply chain partner relationships.
 
It is important to ensure the adequate quality of the data feeding the above-described systems. Even if information is timely, it is worth nothing if it is inaccurate. In Aberdeen Group’s recent study - Supply Chain Intelligence: Adopt Role-Based Operational Business Intelligence and Improve Visibility - Best-in-Class performers dedicate a lot of effort to making sure that the data exchanged is accurate and complete, which enables them to make the right decisions for their supply chain. Best-in-Class performers in this study are 85% more likely than all others to report that data obtained during supply chain monitoring is accurate over 90% of the time (48% versus 26%). Some solution providers offer their customers help in cleansing the data, or even embed the data cleansing capability into the systems.
 
In the same study, when asked how companies planned to improve supply chain visibility software capabilities, responses included:

• Improve data quality and timeliness of status messages - 66%
• Enhance analytics capabilities - 56%
• Add warning alerts if actual events deviate from plan - 46%
• Incorporate additional status events - 40%
• Increase the number of trading partners providing status information - 40%
• Add escalation policies to help manage alerts - 30%
  

Best-in-Class respondents were 21% more likely than all others to focus on improving the analytics capabilities. Supply chain analytics (e.g. dashboards showing on-time versus late shipments along with detailed shipment information, charts and graphs with information on current shipment location and accumulated landed costs) are contributing to more effective decisions, improving both the quality of supply chain decision-making and time-to-response.
 
As a result of superior process and technology capabilities, coupled with a stronger focus on data quality and timeliness, Best-in-Class companies are between 19% and 42% more likely to respond to non-catastrophic supply chain disruptions within hours. The biggest differentiation is on the international inbound side: 51% of the Best-in-Class report this ability, versus 36% of all others. This means that if, for example, a shipment gets held up at a foreign port, they will be notified of this delay within hours and will not miss the opportunity to re-plan the route or resolve the issue fast enough to have the cargo shipped within the acceptable time window.
 
Companies need to obtain appropriate tools for tracking and managing network-wide supply chain performance and collaborative workflows. Network-wide supply chain intelligence paves the way for companies to have the most complete view of their business, including the potential impacts of their customers, suppliers, and other partners' performance on the company's bottom line. With such a 360-degree view of the business, executives can adopt the best supply chain strategies to meet the changing business needs.
 
The benchmark report Supply Chain Intelligence: Adopt Role-Based Operational Business Intelligence and Improve Visibility is available for free download for a limited time. Click here to download before April 23, 2010
 
Viktoriya Sadlovska is Researcher, Product Value Chain Benchmarking & Analysis at Aberdeen Group. Nari Viswanathan is VP/ Principal Analyst, Supply Chain Management at Aberdeen Group.

Calculating Weeks of Supply

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.

Retail Replenishment - How tuned in are you?

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?

Store Level Merchandising Analysis Using EDI 852

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
                               26

Step 2
Calculate the average item sales velocity (Avg WS) for each item for all stores for the last ten weeks of sales.  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:

A.  Most recent two weeks of sales.

• Stores with sales in the last two weeks for any given item will fall into this category

B.  Most recent four weeks of sales.

• Stores with no sales in the last four weeks for any given item will fall into this category

C.  Most recent six weeks of sales.

• Stores with no sales in the last six weeks for any given item will fall into this category

D.  Most recent eight weeks of sales.

• Stores with no sales in the last eight  weeks for any given item will fall into this category

E.  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
                         (TS)

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 by unit (LU) 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 that 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:   Whitepapers on SKU Sales Analysis, Store Analysis, Out of Stock Analysis and SKU Forecasting are available.  http://www.acceleratedanalytics.com/download-whitepapers/

Walmart Tightens Delivery Deadlines

Walmart’s new “must arrive by date” ratchets up supply chain pressure on vendors, shippers and carriers.

Like most shippers, Walmart Stores is looking for a delivery guarantee from its suppliers. Unlike most others, the world’s largest retailer now is demanding one. While many retailers were scrambling last week for any space they could find out of Asia, Walmart implemented its strongest delivery requirements yet on suppliers in the United States, imposing new deadlines for getting goods to distribution centers as well as tough penalties on those that miss the mark. As of last week, U.S. companies shipping goods to Wal-Mart distribution centers must begin to deliver within a four-day window leading up to a “must arrive by date,” or what the company calls its MABD. The requirement will initially apply to suppliers shipping prepaid and truckload freight to Walmart DCs.

What action’s can you take if you are a Walmart vendor? We have started conversations with vendors about how to integrate together various Retail Link data with the vendors purchase order and shipping data, to create exception based reports to show them when they are in danger. The key to not getting hammered by fines is going to be careful management, and with the high volume of orders and shipments many vendors have with Walmart, careful exception based reporting is key.

Retail sales improvement requires careful forecasting

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.

Supply Chain Metrics

We recently found this website which includes a lot of very good information on calculating various supply chain metrics including:

• Back-order reporting
• Cycle-time
• DPMO
• Fill rate
• Inventory turns
• On time shipping
• Perfect order
• Performance to promise

Trade Promotion at Fresh Express

This is an expert from a very good article written by John Walsh in CPGmatters.  

Emphasizing that trade promotion management (TPM) is a crucial component of any consumer goods company's strategy, a significant amount of time and due diligence must go into the process of selecting a TPM vendor and system, said Kiera Benidettino, Director of Trade Promotions for Fresh Express, the nation's largest provider of packaged salads.
Speaking at the recent Trade Promotion Management Associates (TPMA) Conference in Chicago, she discussed why an effective TPM system is vital and explained the process of choosing the right system.

Concerning Fresh Express, she stated promotional lifts put "significant strain on our supply chain operations," since lettuce is highly perishable and inventory cannot be stockpiled.  The shelf life of Fresh Express' products is typically 15-20 days.  "This means our supply chain is measured in hours," she explained.  So, for her company, a good TPM system must support a quick and eficient supply chain, which requires a "tremendous amount of coordination."

Benidettino noted an effective TPM system is vital to consumer packaged goods companies:

• Spending Magnitude -- TPM is the Number 1 or 2 SG&A expense on a CPG company's P&L.  In addition, expenses  have increased more than 12% annually over the past six years to the point where spending is, on average, 14% - 17% of   gross sales.

• Questionable Results -- Less than 50% of spending reaches the consumer and manufacturer, and retailer margins continue to decline.
  

• Government Regulations -- Sarbanes-Oxley mandates tremendous responsibility for internal controls.  In addition,  SEC and FASB regulations significantly impact forward buying practices, financial reporting and slotting fees.  FASB regulations require all expenditures be identifiable, so a TPM system must capture specifics on trade investment in real time.
  

For Fresh Express, effective TPM matters because promotional lifts impact its supply chain.  "The decision of how much to harvest each day is dependent upon results at the stores, so our biggest challenge in TPM is connecting promotions with our supply chain," Benidettino said.  Its TPM system must support forecast accuracy to ensure service and product quality.  "Therefore," she explained, "when taking steps to find the right TPM solution and provider 'you must know our company' including trade fund management capacity, IT resources and budget capabilities." 

Applying Sun Tzu to Supply Chain Strategy

I have been reading Sun Tzu over the holiday weekend.  Very interesting reading.  It's the type of reading where you cover a few lines and then take an hour to reflect on what it means to your business.

One passage stuck me:
"Therefore, determine the enemy's plans and you will know which strategy will be successful and which will not."

I often find myself in conversations with senior executives debating the merits of expanding their vendor collaboration program.  Typically they already have a program in place, but I am advocating an expansion of that program and the application of new technology.  In these conversations, there is tremendous inertia to maintain the status quo.  After all, why fix a program that's not broken.  At their level in the organization they don't hear the day to day challenges of the EDI manager who is fielding vendor support calls. In fact, most of the time they hear just the opposite from the middle level manager, "Oh, everything is fine Mr. Executive, no need to come and visit me, just keep on moving."

But, if one critically evaluates where the most successful retailers (e.g. enemies) are making investments, one cannot espace the conclusion they are moving to more and more sophisticated vendor collaboration programs.  They are making investments before a problem occurs because they want to enjoy the corresponding lift of competitive advantage.

So, here is what I encourage all executives to do:  create two columns on a piece of paper and write your top five competitors down the left column.  Then on the other side for each competitor write down everything you know about their supply chain initiatives at this moment.  If you are coming up blank, that is your first clue there is a problem.  Now consider what your organization is doing.  What threats or opportunities are evident?  Every time I have gone through this exercise with an executive, we have both been surprised at the results.

Benefits of Collaboration

The benefits of optimizing the retail supply chain using better demand planning and collaboration with suppliers are well documented. Studies of retailers by the Harvard Business, Grocery Manufacturers Association, National Retail Federation (NRF), and AMR Research show results of 15% less inventory, 17% better perfect order performance and 35% shorter cash-to-cash cycles.  

Documented benefits include: 

•  Relationships with trading partners: 57% improved 
•  Stock outages: 38% reduced 
•  Sales: 38% increased 
•  Inventory: 29% decreased 
•  Forecast accuracy: 38% Improved 
•  Internal communications: 24% improved 
•  Asset utilization: 14% better