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Focus: Supply Chain Trends/Issues

Feature Article from Our Supply Chain Trends and Issues Subject Area - See All

From SCDigest's On-Target E-Magazine


May 25, 2011

Supply Chain News: Can Smarter Analytics and Optimization Finally Reduce the Out-of-Stock Challenge in the Consumer Goods to Retail Supply Chain?


Past Industry Initiatives only Achieved Portion of Expected Benefits; Why Managing Inventory Levels across the Network is So Hard


SCDigest Editorial Staff


For at least two decades, consumer goods manufacturers and retailers alike have been focused on reducing inventories and out-of-stocks on store shelves through such initiatives as Efficient Consumer Response (ECR), Collaborative Planning, Forecasting & Replenishment (CPFR), RFID and other programs - with somewhat mixed results. While each of the programs delivered some of the expected results, that was usually accompanied by the sense that some of the potential benefits from these initiatives were left on the table.

In fact, inventory levels in consumer goods manufacturers stayed flat throughout most of the 2000s, and out-of-stock levels at the shelf have also been resistant to improvement. A well-publicized 2007 study by Dr. Thomas Gruen of the University of Colorado and Dr. Daniel Corsten of the IE Business School Madrid, based on funding from Procter & Gamble, estimated that manufacturers lose something close to $100 billion in sales annually due to out-of-stocks at the shelf.

SCDigest Says:


Watson also noted that the SKUs that have too little inventory usually result in costly mitigation strategies, such as last minute production schedule changes or expedited transportation.

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Solving the out-of-stock challenge can therefore pay big financial dividends - especially for the companies that can reach new levels of in-stock performance first, before competitors do.

In fact, according to Remzi Ural, a supply chain lead in IBM's global consumer packaged goods practice, one beverage company IBM recently worked with was able to increase its sales by 12.3 million cases by significantly reducing its out of stocks, in this case throughout its distribution network that served local stores.

Multi-echelon inventory optimization technology , Ural said during a recent Videocast on our Supply Chain Television Channel, is a key tool to reduce out of stocks both in the network and at the store shelf. Inventory optimization has been around for a decade or so, but has only really started to gain critical mass in the past few years. (To see the excellent full broadcast, go here: Videocast: How Smarter Inventory Analytics Solve the “Out-of-Stock” Scenario for CPG Supply Chains.)

What is inventory optimization software? In short, it is a tool that looks at how inventory should best be positioned at different nodes and levels of the supply chain holistically, rather than just optimizing each node/level individually, as is the case in most traditional supply chain planning environments. The result can be significant reductions in inventory with constant or even improved customer service levels.

"When inventory optimization is done right, consumer goods manufacturers can both reduce out of stocks and reduce inventory and working capital requirements," IBM's Dr. Michael Watson added during the Videocast, noting some articles published by SCDigest that explained how the improvement in cash flow that results from lower inventories can actually increase stock market values versus competitors with similar earnings and growth rates.

One place to start with inventory optimization is an analysis that shows not only what SKUs have too much inventory where versus demand, but also what SKUs have too little, as shown in the chart below.



Source: IBM


In the chart, the green bars indicate SKUs where inventory needs to be added, and thus lead to fewer out of stocks. The red bars indicate opportunities to take inventories down without harming service levels. This is accomplished by changing inventory policies and safety stock rules for these SKUs at different levels of the supply chain.

Watson also noted that the SKUs that have too little inventory usually result in costly mitigation strategies, such as last minute production schedule changes or expedited transportation.

(Supply Chain Trends Story Continued Below)



Of course, no company plans to have too much inventory of some SKUs and too little for others. Then why is it so hard to get network inventory levels right?

The challenge is the number of variables that must be considered, Watson said, noting such factors as:

• Forecast error and demand variability
• Lead time and lead time variability
• Order and production cycles
• Committed times from manufacturing or vendors
• Transit times and transit variability
• Order minimums and increments

And those factors must further be considered across increasingly complex supply chains that have multiple levels or "echelons" and often involve multiple production steps (e.g., intermediate good production, then co-packing operations).

It is this total complexity that almost requires strong technology support to manage, as optimizing across all these variables for hundreds or thousands of SKUs is beyond what humans and spreadsheets can accomplish.

Watson also noted that Lean manufacturing strategies can also sometimes conflict with overall inventory optimization. For example, efforts to decrease raw material levels at the plant along Lean concepts can actually result in more finished goods inventory in the network, which is more costly than raw materials or WIP inventories further back up stream.

Inventory optimization will help identify that optimal balance and where inventory buffers are most effectively maintained.

Watson also noted that the right approach to inventory optimization means companies will check plans and inventory policies at different "cadences" throughout the year. For example, more strategic looks at the role of inventory in a business unit might be performed annually, while more parameter "tuning" activities might be performed quarterly, monthly or even weekly depending on the type of task.

Do you agree that better inventory optimization can reduce out of stocks at retail? Is technology essential today to get to this next level? Let us know your thoughts at the Feedback button below.

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While I am tempted to try to remember exactly that famous Donald Rumsfeld quote about “Things that we know, things that we know we don’t know, and things that we don’t know that we don’t know” or something like that, but this question you pose is simply a matter of eliminating the variance (σ²) everywhere you have sufficient data.  For example, the invention of the POS terminal brought the ability to know sales by SKU by location to a level of precision not previously known on a widespread basis; this data, combined with available data about quantities received by location, and the application of basic arithmetic, led to more precise knowledge of actual on-hands—which led to all sorts of statistical modeling for replenishment purposes and so forth.  (And to some real accuracy about factual out-of-stocks.)

With Demand it is rather difficult to eliminate all of the variance, but various pricing schema—from everyday low pricing to the very large promotional discount (75% off) strategy of The Teaching Company—these are all methods of managing demand, but there are so many exogenous factors that this can only be managed, not eliminated.

On the Supply side of the calculations there is new technology which is attacking the heretofore huge gap in real-time knowledge of granular Supply Chain Operations, the newly exploding field of In-Transit Supply Chain Visibility.  The technology, whether it is known as Container Security Devices or Tracking & Sensing Technology or Asset Tracking or something else, is now delivering a level of detailed data in Supply Chain Operations which is closing this remaining gap in our current level of knowledge.  Fully 85% of all losses—be they theft or damages or something else—occur while product is actually enroute, and therefore actually in the SC—which is the major remaining gap in achieving 100% Supply Chain information. Add in the variances of delivery times, travel times, fuel prices—and the sources of variance which can be managed with real-time information grows quite large.

Aberdeen has finally started to track this technology; perhaps Gartner and the others will also discover this new field sometime soon; but the best SC Operators are all now testing or implementing the granular real-time visibility, and we know what they were able to do with the POS data—so technology laggards—time to get moving.

Joe McKinney