Two recent reports say that in this recession many companies are very focused on inventory optimization.

The first report by Capgemini Consulting asked more than 300 respondents, “What are the most influencing factors on the supply chain agenda for 2009?” Not surprisingly, the economic downturn topped the list of drivers for about two-thirds the audience. Meeting changing customer requirements was second with 46% of the responses. (More than one driver was allowed).

Then Cap asked which supply chain projects made the cut in this environment. We were pleased to see Inventory Optimization at the top of the list with a 48% response. Following closely behind were Supply Chain Strategy projects and Improving long term forecasting/planning.

Soon afterwards, a second report came to us, this time from AMR Research’s Lora Cecere. Lora now has a value chain team reporting to her. One of the first publications from her new team was a report she co-wrote with Jane Barrett and Paul Lord, two of her new analysts. The title of the report pretty much sums up the message, “Make Money: Inventory Strategies That Matter”.

The AMR team says, ”Credit is tightening, demand is falling, and all eyes are on working capital.” They say that “AMR Research inquiry calls on inventory are skyrocketing.”

AMR calls on companies to adopt winning inventory strategies to deal with these uncertain times, saying “traditional inventory practices are not enough”, and its time to turn things up a notch. They point to “newer best-of breed inventory optimization tools for tactical and operational planning” which incorporate “probabilistic, multi-tier technologies built using stochastic, nonlinear techniques.”

What can I say? We agree.

The AMR team also goes into a good discussion about the issues of demand variability and reducing the bullwhip effect.

These reports are very much in-line with our March blog post on reducing inventories in a recession (See below “Don't Use a Meat Ax to Cut Inventories”). Every company is, or should be, doing it. The key is to do it intelligently in a way that doesn’t increase your stockouts.

You’ve heard of “fashion felonies”, like wearing a striped shirt and plaid pants. (Who knew they didn’t go together?)

If you are a supply chain manager in the fashion industry, there is another fashion felony, one with larger potential consequences than just a little social embarrassment. This is the business that invented the term “overstock”. When inventory goes wrong in the fashion business, it can really go wrong. Owning millions of dollars of goods that you have no hope of moving can keep even the toughest manager up at night.

As you would expect from a company with an office in Milan, we have worked with quite a few customers in the fashion industry. Here are some of the inventory issues we see in this business.

1. Trading Off Margin for Short Lifecycle Products — Fashion-oriented businesses typically have short lifecycle products whose inventory planning requirements are complicated by seasonality, promotions, competitor actions and a certain “hit or miss” aspect to planning the inventory mix. Achieving high service levels is critical “in season” because out-of-stocks immediately translate into lost sales. At the end of the lifecycle, excess inventories leads to discounting, which severely impacts gross margins and also cannibalizes the next product launch. The trick is to successfully trade off between lost margin from in-season, while exiting gracefully at the end of life.
   
   
2. Managing Continuous Products — In addition to managing products with less than a yearlong lifecycle and seasonal behaviors, an important portion of most fashion businesses is based on continuous products with lifecycles longer than a single season. This means managing two dramatically different inventory types in parallel.
   
   
3. Optimizing Upstream/BOM constraints — Fashion-oriented companies that have vertically integrated or outsourced manufacturing are exposed to significant write-off risks for on-balance sheet raw material/component inventory as well as liability for off-balance sheet inventory at their suppliers. At the same time, a lack of accounting for upstream supply constraints such as lead times, supply unreliability, manufacturing frequencies, minimum order quantities, frozen periods, and flex limits can lead to finished goods shortages and expediting. Fashion-oriented companies face a balancing act of managing upstream component and downstream finished goods inventories in this short product lifecycle dynamic supply chain environment, requiring staging and postponement optimization.
   
   
4. Complexity and Scale — The different styles, colors and sizes create a very high number of SKUs per brand. And the distribution networks are vast, often entailing networks of millions of SKU-Locations. On the supply side, globalization requires dynamic evolution of the supply networks and alternative sourcing.

More consumer-oriented businesses like electronics are exhibiting behaviors like the fashion industry. So maybe some of these issues are familiar to you.

Dan Gilmore over at Supply Chain Digest just wrote a blog piece about how many stock outs he is seeing these days in retail stores. Dan wonders if retailers haven’t gone overboard in their desire to cut inventory and cut costs. Here are my thoughts.

There’s a big difference between intelligently reducing stocks and taking an axe to your inventories. Unfortunately cost-cutting companies will often take the meat cleaver approach, with the kind of impact Dan described, including unhappy customers and lost sales.

Have you noticed that the out-of-stocks are often in the popular items, not the “cats and dogs”? Even in good times, to keep inventory costs in line, companies (manufacturers, retailers and wholesalers) often run too “lean” on many of their better selling items, adversely impacting revenue and gross margin.

Many cost cutting initiatives then exacerbate the problem by reducing inventories across the board or even focusing on the items with the largest inventory value, which may include those items most needed to sustain the business.

The better approach is to “fix the mix” by selectively reducing those items that contribute the least to service levels and incremental profitability. This requires a more intelligent approach to inventory management, one that requires a highly granular understanding of changing demand patterns and demand and supply variability.

This problem is most acute in “the long tail” where much of the fat is often concentrated. Traditional inventory management methods based on simplified models don’t understand the demand requirements in this area very well and can’t properly size the stocking requirements, so much of the excess inventory can be found there.

Traditionally, when it came to supply chain planning, demand and supply streams have been projected as a specific deterministic value. For instance, 22 pallets of a specific item will flow through a given location on Monday.

But that single number is just a guess. It could be more or less, depending on how a host of assumptions and projections actually play out.

Yet despite the fact that this number is so uncertain and it’s not real – it’s only a guess –the whole supply chain is driven off this one number. And if it’s wrong, or should we say when it’s wrong, we’ve got all kind of problems. Review processes are set in motion. Costly expediting and reaction (or worse, overreaction) may begin. Up and down the supply chain. All because the number wasn’t the exactly the one we planned for – which was really just a guess to begin with.

A probabilistic supply chain model, like we use at ToolsGroup, uses a different approach. It assumes that this value is not a single number, but a probability function. So instead of saying there will be 22 pallets, the system says that there is a 50% chance that there will be 22 pallets, a 25% chance that there will be 21 pallets, a 15% chance that there will be 20 pallets, and so on. Rather than a single number, you get a range of values with different probabilities, also known as a probability distribution.

With this approach, you model the entire supply chain through this probabilistic distribution. And because it so much more mirrors the way the world really works, it has an inherently better chance of correctly describing the expected outcome and getting the planning process correct.

A simple example would be filling a truckload. If you have fixed numbers, you don’t know how best to fill the truck. When you have probabilistic requirements, you can use the natural uncertainty of your supply chain and the flows of materials to load the truck, and you will fill it in a way that optimizes your margin (or whatever goal you have set for yourself). And because you understand which outcomes are likely and which are not and can cause a problem, you can focus your review and expediting on those situations that truly deserve a reaction. Because you weren’t expecting an exact number, but a range of numbers with probabilities.

Labels: probabilistic modeling, probability distribution, supply chain planning

Since we at ToolsGroup began making a big deal about lumpy demand, a question we hear from time to time is “Just what is lumpy demand?”

Lumpy demand is demand that is intermittent and hard to predict. A commonly accepted threshold for intermittent demand is the point where there is at least a 50% probability of having a time bucket with zero demand. If more than half the time you have no demand for an individual SKU-Location, you have intermittent or lumpy demand.

Some products, like slow moving spare parts, have naturally infrequent and lumpy demand. But many other products have demand that becomes lumpy due to changes in the business and supply chain environment, such as when the demand stream is split into smaller and smaller time buckets. As the demand stream gets split more ways, it usually changes from a relatively smooth flow to variable and erratic. This happens more often than most people think, because of three business trends that are driving the growth of lumpy demand.

The first is product proliferation. Customers have far more choices than ever before. This divides demand into smaller buckets.

The second issue for most companies is that they are replenishing more frequently. Shorter time buckets mean more demand variability. The same SKU may look like a “fast mover” (relatively stable demand) if observed in monthly buckets, but looks like a “slow mover” if observed in weekly buckets, and appears intermittent or lumpy at the daily level.

Third, many manufacturers who used to focus on stocking big regional distribution warehouses are now minimizing out-of-stocks further downstream, often at the end node of demand. As the replenishment planning focus shifts from the primary distribution centers to secondary distribution centers and retail shelves, demand is increasingly disaggregated into smaller demand streams.

Lumpy demand is much more challenging than high volume mainstream business. Demand variability is high and typically skewed. Supply chain noise increases. Demand signals are harder to read. As you would expect, forecasting and inventory management in this environment is more challenging. Supply chain planning gets tougher, and you’ve got a lumpy demand problem on your hands.

Labels: demand stream, intermittent demand, lumpy demand, product proliferation