demand signal repository Archives

Takeaways from Oracle OpenWorld 2010

Logistics Software Vendors | By Steve Banker • 09/30/2010

I attended Oracle OpenWorld 2010 last week (Oracle is an ARC client). I always enjoy going to this conference; it provides a great opportunity to speak with very smart supply chain professionals and hear case studies of cutting-edge SCM processes.

Here are my high-level key takeaways from the conference.

Oracle has the broadest set of supply chain applications. You expect supply chain vendors to have supply, inventory, and demand planning solutions. You expect ERP supply chain solutions to also include warehouse, production, and transportation management functionality. A few ERP companies also offer global trade management. Oracle has all of these solutions. But it also has real-time sales and operations planning (S&OP), a solution that helps orchestrate sales and operations planning; a demand signal repository (this is the future of supply chain management in the CPG and retail industries, and I’m disappointed that other leading software vendors have not made a commitment to DSR); a very interesting scenario planning application called “Advanced Planning Command Center” (you can’t do justice to this application with a written explanation; you need to see it demonstrated); and Oracle has just introduced a distributed order management solution called “Distributed Order Orchestration.”
The breadth of Oracle’s solution stack is based on complementary solutions and technologies. I’ve long been a believer that you can’t do good planning without clean data. Master data management (MDM) solutions help ensure clean data, and product information management (PIM) is perhaps the key MDM solution that supports supply chain management. Further, the sales and operations planning and strategic and annual business planning processes are starting to merge. Oracle’s Hyperion financial planning solutions are beginning to show up in supply chain implementations. I attended a case study presentation by a spirits company that implemented the Hyperion solution. Now, as part of its S&OP process, the company has much better numbers when it examines the profitability of its different business units and customers. This is because supply chain planning solutions rely on margins in their optimization runs. The Hyperion solution allows the spirits company to roll in more accurate intercompany transfer pricing and promotional dollar spends on various brands to produce scorecards that are much more accurate.
Even though Oracle is an enterprise applications company, many of its applications are among the most functionally rich in the industry. The Demantra applications (demand management and predictive sales trade planning) are particularly noteworthy in this respect.
Inspirage is emerging as key consulting and implementation partner for Oracle in the supply chain planning area. A few years ago, I studied boutique supply chain consulting firms and produced a report titled “The 10 Coolest Boutique Supply Chain Consulting Firms.” If I were to do that study today, Inspirage would certainly make the list.

No vendor is perfect, and not all of Oracle’s applications, particularly at the execution level, have best-of-breed functionality. And some of its customers grumble about maintenance costs. Nevertheless, Oracle is among the most visionary and noteworthy of the supply chain solution providers.

Takeaways from JDA User Conference

Logistics Software Vendors | By Steve Banker • 05/26/2010

I attended the JDA user conference last week, FOCUS 2010 , which took place at Caesars Palace in Las Vegas. JDA completed its acquisition of i2 Technologies at the end of January, and what was most interesting to me was to see how the merger was proceeding.

Historically, i2 and JDA have had very different cultures. i2 tackled some of the most complex supply chain problems from some of the largest companies in the world. At i2 user conferences, you would hear customers talk about high risk, high reward five year transformation plans. i2 was exciting, but also a bit dangerous. Some of these customer transformations did not succeed. Some of the complex solutions i2 developed were not sellable to other customers, or maybe only to a few other customers. And, not surprisingly, some of those one-off solutions were the equivalent of beta solutions, glitch prone and not fully usable.

JDA’s culture is centered on being a “software” company. The company has little to no interest in developing one-off solutions. JDA is interested in repeatability, maintainability, and high customer satisfaction (the company claims 97 percent customer satisfaction).

Overall, I thought the conference showed that the merger is proceeding well. A few points of evidence:

JDA has retained key i2 personnel, people who I greatly respect, and they are in positions of authority.
JDA has developed a roadmap to converge its existing and acquired product sets. Existing products from JDA and i2 will continue to be supported. Eventually, a converged product will emerge that contains the best features and functions of both solution sets. Meanwhile, certain products from JDA and i2 have been designated as the “go to market” products—i.e., the products that will be sold in new sales situation.
JDA has a coherent convergence plan for its multi-echelon inventory optimization and transportation suites, the product suites that we track most closely. The “go to market” products in these suites are the ones that we believe to be superior, and some of them came from i2.
There is evidence of cross pollination. Traditionally, i2 has been strong in high tech, while JDA has been strong in consumer goods and retail. The convergence product teams I talked to had ex-i2 folks as well as legacy JDA folks involved. These teams are looking at the product sets from a cross-industry perspective and some interesting ideas are emerging as a result. Further, JDA has some good merchandising and category management solutions. I believe that in CPG, category management and supply chain management are beginning to merge based on increased availability of downstream data that needs to be leveraged by both groups.
JDA has rebranded itself as “The Supply Chain Company.” I used to view them primarily as “The Retail Company.”

However, some challenges still remain. Here are two concerns, one perhaps self-serving:

JDA’s vision is to have supply chain solutions that span from the factory to the retail shelf. A demand signal repository (DSR) is a key solution component to make this happen. However, DSRs are an emerging, messy solution likely to be bought only by category leaders and the number two company in a category. In other words, this solution is not the repeatable “off-the-shelf” software that fits the JDA development and maintenance philosophy. However, if JDA continues to lack a DSR product, Oracle is likely to emerge as the leader in shelf-level collaboration.
Analysts and the press were barred from more than a third of the sessions. I could not help but wonder: what are they hiding or embarrassed about? This is the only user conference that I’ve attended where analysts were barred from sessions. If competitors can attend these sessions, why not analysts and the press? I was told that keeping analysts out of a session is the carrot used sometimes to get an end user to speak. But that explanation does not fully explain what was going on because many of the sessions we were banned from were led by JDA personnel and had no end users speaking.

In conclusion, May is user conference month in the supply chain software industry and I’ve attended several so far. This conference had more good supply chain end user stories than the others I’ve attended.

Filling a Void in Shelf-level Collaboration

Consumer Packaged Goods (CPG) | By Steve Banker • 12/17/2009

I recently spoke with Mike Spindler, the CEO of ShelfSnap, about the company’s entry into the “shelf-level collaboration” arena.

(Quick aside: As I mentioned in a previous posting, I prefer the term “Shelf-level Collaboration Solutions” over Demand Signal Repository (DSR) products because I believe it’s both more accurate and evocative.)

ShelfSnap is in start-up mode, and although the company has conducted some interesting pilots for large consumer goods companies, it has not yet been selected as a long-term partner by these companies. Nevertheless, I think ShelfSnap offers an interesting solution that fills a void when it comes to shelf-level collaboration.

Right now, DSRs are populated mainly with POS, syndicated data, store-level inventory data, and shipments from the retail DC. One form of shelf-level collaboration occurs when a DSR merchandising application sends out an alert saying that an SKU at a particular store is out of stock at the shelf, or that it is selling at a much slower rate than expected. These alerts are based on inference. The application does not know that a store has not restocked the shelf; it makes that prediction based on math. The math can be very simple: this product is jumping off the shelves at other stores but it’s not moving at this store; we know that inventory was shipped to the store, so this must mean that stockers have not moved the product out of the backroom yet. The math gets a lot more complex when it comes to predicting things like inaccurate perpetual inventory levels.

Mike’s point is that these are inferences. If you tell a Walmart store manager that a shelf needs to be restocked in the hope that he will assign someone to do the job, you better be right. You don’t want to generate false positives and send labor to the shelf to fix a problem that does not exist. Even if only 10 percent of your alerts are false positives, you could become “the boy who cried wolf” too often. So, some consumer goods companies use the merchandising alerts to reroute their third party merchandisers to visit a particular store to make sure the alert is correct.

But Mike points out that this does not fully solve the problem. Mike began his career at Nielsen, where he used to ask a group of his managers to separately go to a store and check to see if the planogram was correct for a particular category. In this store, Product A should have three facings and be in the middle of the top shelf, Product B should have one facing and be on the far right hand side of the bottom shelf, and so on. Mike said the managers never came back with the same data.

The ShelfSnap solution is based on workers using a digital camera to take a picture of the store shelf, and then the solution’s patent-pending Image Recognition Technology analyzes color, package size, and label design to identify the correct product. These pictures are fed into the ShelfSnap Analytical Engine that then generates quantitative analytical results: What shelf was the product on? How many linear feet did the facing have? Was the product out of stock? This data can then be turned into prioritized merchandising exceptions. They also provide analysis of new product cut-ins, displays, and point-of-purchase display reporting.

ShelfSnap Analytics (Source: ShelfSnap; click to enlarge)

I basically see the ShelfSnap solution as providing a practical way for manufacturers and retailers to use planogram data for shelf-level collaboration, or for retailers themselves to have more effective task management at the store. I also see it as improving the alerts generated by existing DSR solutions. Say a DSR is predicting that the perpetual inventory level at a particular store is incorrect (the store says it has 12 of the item, but the DSR application thinks it only has 9). This math is based on lower sales than expected for that item at that store. But if the product is supposed to have three facings on the prime shelf but it only has one facing on the bottom, the product location could explain the variance in sales rather than an incorrect inventory level.

In a recent posting, Adrian Gonzalez quoted Jim Stengel, the former global marketing officer at Procter & Gamble, on the greatest challenges facing marketers in the consumer goods industry. To paraphrase slightly, Jim asked how consumer goods manufacturers could add “digital” to “their branding model?” How can they “work with retailers to have maximum impact on value creation?” This is an application that contributes in both of those areas.

How to Best Describe Demand Signal Repositories

Consumer Packaged Goods (CPG) | By Steve Banker • 12/14/2009

Angélique Moon, Director of Product Management for Consumer Goods at Oracle, briefed me recently on Oracle’s Demand Signal Repository (DSR) solution. At the end of the presentation, Ms. Moon and I bemoaned the fact that the vocabulary used to describe these types of products is not very good. Shortly after, I had a similar conversation with Razat Guarav, the SVP of Sales at i2 Technologies (both Oracle and i2 are ARC clients). For me, this topic goes beyond semantics. If the products and applications in this area are not properly named, then many supply chain professionals will not understand what they do. And what they do is the next frontier of value chain management in the consumer goods value chain.

Ms. Moon made the point that the term “demand signal” focuses you on the demand data you are using. The term “demand sensing” is similar in that regard. However, for most companies, the most important demand signal they sense (i.e., demand data they consume) is point-of-sale (POS) data. Almost all consumer goods supply chains make use of demand signals; unfortunately, most of them use shipments from their distribution centers (DCs) to retailer DCs as their demand signal. We’ve known for years that using shipment data for your demand forecasts leads to the bullwhip effect.

From a demand data perspective, if we used the term “store consumption” data, most supply chain folks would interpret this as POS data. However, the folks in marketing and retail sales accounts, who also need to use the data in a DSR, would likely confuse this with syndicated data. They would reserve the term “POS” exclusively for data received directly from a retailer. A term like “downstream data” could provide a clearer meaning to all. At this point, however, the term “Demand Signal Repository” is well established, so I don’t see it changing to “Downstream Data Repository” even though I think it’s less confusing.

A DSR holds downstream data, but it is the applications and analytics that reside on top of the DSR that provide better plans or responsiveness. Several DSR vendors have made the point to me that by feeding DSR data into the right kind of supply chain application, your processes become “demand driven.” So the term “demand driven” refers to the nature of a supply chain process, while DSR refers to the data.

But when most supply chain professionals hear the term “demand driven,” they think of a fast and responsive supply chain. DSR supply chain applications will improve your supply chain, but they won’t necessarily make your supply chain fast and responsive. What a company accomplishes with granular store-level demand data depends upon what type of supply chain it has and what it is trying to accomplish.

Types of DSR Supply Chain Apps and Planning Horizons (Source: ARC Advisory Group; click to enlarge)

A food and beverage manufacturer, for example, can use DSR data (POS, store inventories, shipments from retail DCs) to become more demand driven by making its replenishment process more responsive to what is actually selling in a grocery chain’s stores.

But if you are an apparel manufacturer with a supply chain that starts in China supplying retail outlets in North America or Europe, DSR data will not make your supply chain “demand driven” in the sense that it becomes more highly responsive. Lead times of three to six months will make that impossible. But DSR data (POS, store loyalty, syndicated data, etc.) will improve an apparel company’s demand forecasts. Thus, instead of speaking of “demand driven” applications, we should really speak of supply chain applications that are capable of leveraging downstream data.

Some supply chain professionals speak of “sell into” versus “sell through.” The former applies to shipments to a retailer’s DC, while “sell through” refers to when consumers take product off the shelf and but it. DSR applications, therefore, can be considered “sell through” applications. The downside of this term is that it sounds too much like a Customer Relationship Management (CRM) application.

Sell Into vs Sell Through (Source: ARC Advisory Group; click to enlarge)

Mr. Guarav refers to i2’s solutions in this area as being focused on “shelf-centered collaboration.” This is a term I like because a core part of the emerging DSR-based predictive analytics are focused on reducing out-of-stocks, detecting phantom store inventory, and discovering the root causes of both. To the extent that these analytics have workflows attached to them, for example, having merchandisers check a particular store for phantom inventory, these could also be considered “shelf centered applications,” which is the best terminology I’ve heard in this area.

Phantom Inventory Creates Havoc in Retail Supply Chains

Inventory Management | By Steve Banker • 11/18/2009

In my “What Causes Shelf Out-of-Stocks?” posting last week, I reviewed some research conducted by Walmart on the major causes of shelf-level out-of-stocks. One reason, occurring about 20 percent of the time, is phantom inventory. Phantom inventory occurs when the store system shows inventory available but none really exists. It also occurs when the system shows more inventory than what is actually available in the store; this can be caused by theft, broken products, mis-picks or checkout issues, for example.

When a store system shows inventory that doesn’t really exist, a store manager could easily assume the product is not selling well and thus decide not to reorder it. When the store system shows more inventory than what really exists in the store, this can also create havoc. I did not fully understand why this was so until Cedric Guyot of Retail Solutions walked me through some slides and explained it to me.

Stores have min/max replenishment logic just like other nodes in the supply chain. Imagine that for a particular SKU, the min is seven and the max is fifteen, and that the store reorders every time the inventory level falls to seven or lower. When the store reorders the product it makes sure the delivery will bring the number of items in the store up to 15. These min/max targets are based on a service level analysis – e.g., the minimum target level that will lead to a 99 percent in-stock rate for that product. In other words, with this minimum target level, if the store operations folks do a good job of keeping the shelf replenished and the inventory accurate, there is only a one percent chance, based on the demand history of that product, that a customer will not find this item in stock when they want to buy it.

Phantom Inventory Leads to Lower than Targeted Service Levels (Source: Retail Solutions; click to enlarge) — Reorder Minimum Target Level of 7 Leads to Only a 1% Chance Customer Won’t Find the Product In-Stock (Source: Retail Solutions; click to enlarge)

Now assume that there is some phantom inventory, that there are two less units of inventory in the store than what the system shows. As the chart below shows, the target service level for a min of 5 is only 95 percent. So, if this min reorder point had been chosen, customers coming into the store would not have found that product in stock five percent of the time. Further, if you look at the shape of the service level curve, the more units of phantom inventory, the greater the drop-off in service level. If the store inventory system says there are seven units of the product available, but only two really exist, the service level would be only 69 percent. The shape of this curve is pretty typical. The more phantom inventory you have, the easier it is to have truly unacceptable, yet hard to detect, shelf service levels.

Finally, when the level of phantom inventory becomes greater than the reorder point quantity (eight in the above example), the store stops ordering altogether, creating long out-of-stocks that can only be fixed by an inventory adjustment.

I should point out that the service level curves shown above are hypothetical. Store-level SKU service levels would differ for every SKU in every store based on that store’s/SKU’s demand history.

In conclusion, Mr. Guyot tells me that most studies show 40 to 50 percent of the items in a store have some amount of phantom inventory. This is clearly a serious problem, but a problem that current Demand Signal Repositories are helping to solve.

Retail Theft at Record Levels, Supply Chains Adversely Affected

Consumer Packaged Goods (CPG) | By Steve Banker • 11/12/2009

Shoplifting is at record levels ($115 billion from July 2008-June 2009) and this is causing supply chain problems, not only for the retailers, but also for consumer goods manufacturers.

The Centre for Retail Research (CRR) published its annual Global Retail Theft Barometer report on November 10^th. CRR interviewed 1,069 large retailers with total revenue of $822 billion. A summary of the report is available at here.

Here are some of the key points:

The study monitored the costs of shrinkage and crime in the global retail industry between July 2008 and June 2009, and found that the rise in shrink occurred in all regions surveyed, with the greatest increase in North America (+8.1%), Middle East-Africa (+7.5%) and Europe (+4.7%).
Employee theft is perceived as the biggest contributor to shrink in North America, responsible for 44% of all losses. The average employee in North America stole nearly $1,900 per incident.
In 2009, some of the highest average shrinkage rates were found in apparel/clothing and fashion/accessories (1.84% of revenues) and in cosmetics/perfume/beauty supply/pharmacy (1.77% of revenues). In apparel/clothing and fashion, the highest shrinkage losses were seen in accessories (3.85% of revenues) and in fashion/tailored clothing (3.64% of revenues). For food items/groceries, the highest shrinkage was reported in fresh meat with 3.38% of revenues.

So, why does this matter to supply chain groups?

As I’ve written about in the past (see “Should Logistics Personnel Work in Retail Stores?”), there is a growing trend in the retail industry towards having supply chain personnel work in stores, responsible for front-of-store replenishment. This means that supply chain organizations are increasingly becoming responsible for policing employees not only in warehouses, where that job is easier, but also in stores, where the task becomes more difficult.

But theft also creates problems for consumer goods manufacturers. Long term forecasting, replenishment, and the sales and operations planning process (particularly surrounding promotion planning and new product introductions) are all more accurate if consumer goods manufacturers use the most granular downstream data available. This means having access to store inventory, POS, and even syndicated and store loyalty data. In short, when it comes to running an effective supply chain, using “sell-through” data (what customers are actually buying from stores) is far better than using “sell-into” data (what manufacturers are shipping to retail distribution centers).

Take the forecasting problem. Apparel companies, which experience the highest shrinkage rates according to the CRR survey, benefit from POS data for sales forecast purposes, even if their supply chains are too long to benefit from more effective replenishment (most apparel supply chains begin in China/Asia and have lead times of up to six months). At the end of a selling season an apparel company might see a sales balance of zero for a particular SKU sold through a particular retail chain. But this does not tell the company whether that SKU sold out early at full price or whether it had been marked down. Similarly, POS data can help a clothing company improve its forecasts by geography and type of customer.

But that data needs to be both clean and accurate. Theft that occurs at retail outlets contributes to inaccurate perpetual inventories at the stores.

But even if theft levels were reduced substantially, it would not solve the other big contributor to inaccurate sell-through data: poor scanning discipline at the cash register. The other day, for example, canned soup was on sale, so I bought ten cans, but three different varieties. All the cans were priced the same. At checkout, the clerk took one can and scanned it ten times (I hope she scanned it ten times and not eleven or twelve). This saved time, but the resulting sell-through data was inaccurate. Very few retail food chains have POS discipline (7-Eleven Japan is among the few exceptions I’ve come across). This discipline is much more common among apparel retailers because they are not dealing with volume checkout issues.

So, what can an upstream manufacturer do about this? From a technology standpoint, there are Demand Signal Repository applications from vendors such as Retail Solutions and TrueDemand that use advanced predictive analytics to predict when in-store perpetual inventory and sell-through data is wrong.

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