replenishment Archives

Frito-Lay Drives Shelf-Level Demand

Consumer Packaged Goods (CPG) | By Steve Banker • 04/20/2010

I believe that one of the emerging trends that will differentiate leaders from followers in the consumer goods industries is that leaders will excel at integrating their demand management and replenishment processes with category management. Consequently, it benefits supply chain executives to better understand leading practices in category management.

Supply Chain Digest recently aired a videocast featuring Dave Boissevain, Director of Retail Strategy and Planning at Frito-Lay, which laid out at least one leading practice: the use of store-level planograms.

A planogram (POG) is a diagram of fixtures (e.g., shelves) and products that illustrates how and where retail products should be placed in a store. Leading retailers have moved to having category captains produce POGs that are communicated and explained to the retailer for all the goods in a category (like salty snacks in the case of Frito-Lay).

It is the category captain’s job to maximize the profitability of the retailer’s shelf. Think of this as the velocity at which store profits are created based on product margin, supply chain costs, and how goods flow off the shelf. This is sometimes called “return-on-shelf.”

In putting together a planogram, the category captain needs to use syndicated data and point of sale (POS) data to understand trends.

They also need to understand product affinities to better allocate shelf real estate to profitable stock keeping units (SKUs). Frito-Lay conducted a study where it went into certain stores and eliminated 10 percent of the salty snack SKUs. The company then eliminated 20%, 30%, and 40% of the SKUs in that category. It did this to better understand the incrementality of the item. If an item was removed, how would it affect the sales of other SKUs in the store? The interesting thing that Frito-Lay discovered is that for large format stores customers actually liked the layout better when the SKUs were reduced by 20 or 30 percent.

Historically, planograms were “one-size-fits-all” that applied to every store in a chain of a similar size. So, all stores with 20-ft aisles used one POG, all stores with 100-ft aisles used another, and so on.

Eight to ten years ago the state of the art improved. Category captains began to do demand-based clustering. So, if a chain had 100 stores, they might have 7 different demand clusters. Planograms would then be created based on the demographics of that cluster and the size of the aisle. Thus, a category captain could end up creating 49 different POGs if the chain had different size stores in all clusters. Because a category captain was more than likely to be a category captain at other (often larger) chains, the creation of these POGs was labor intensive.

Now, the next leap has been made: the ability to dynamically create store-specific POGs based on an optimization engine. This engine produces POGs in 3 seconds versus the 30 minutes it used to take. Consequently, not only can you do store-specific POGs, you can now do them more frequently, maybe every month instead of annually. Frito-Lay is using a new product from JDA Software (an ARC client) for this purpose.

When Frito-Lay moved from POGs developed for store clusters to store-specific POGs, it improved growth by 2 to 4 percentage points! What Frito-Lay found was that the assortment, based on the POG, might be fine Monday through Thursday, but a store-level POG really drove growth in peak selling times, Friday through Sunday. Thus, moving to store-level planograms creates more demand. This forward plan for increased demand needs to be integrated into the supply chain planning and replenishment processes.

To get retailers to agree to this, Dave said that they need to be educated on how the black box optimization engine works. Retailers, after all, ultimately make the final decision on assortment. Moving to monthly POGs also creates more work for retailers. Finally, better planning has limited value unless the plans are fully executed. Frito-Lay is experimenting with a solution from ShelfSnap to help improve execution.

However, Dave said that once retailers try this, they don’t go back!

Dear Valued Walmart Supplier: The Walmart Supply Chain Reliability Program

Consumer Packaged Goods (CPG) | By Steve Banker • 01/19/2010

We are coming up on February 1^st 2010, the day that Walmart starts enforcing the penalties associated with its Supply Chain Reliability Program. If you are not familiar with the program, below are excerpts from the letter that Walmart sent to suppliers on Oct. 15^th, 2009.

Dear Valued Supplier,

Walmart and Sam’s Club are continually seeking to improve our supply chain efficiency…Therefore, in order to better serve our customers and members, Walmart and Sam’s Club recently partnered with U.S suppliers, across all business units, in a collective effort to develop the Supply Chain Reliability Program. We are excited about rolling out this initiative in collaboration with a broader assortment of valued suppliers. The new program will help us operate at the lowest possible cost and provide the best possible service. The key facets of this initiative are as follows:

Walmart and Sam’s Club incur additional costs for inventory carrying cost, warehousing, store/club labor and shrink for shipments arriving early, and lost sales on short shipments and shipments arriving late. In an effort to improve on-time delivery, a reimbursement charge of three percent of the cost of goods sold (COGS) will be applied to each case not delivered and recorded in our system within the MABD [Must Arrive By Date] delivery window.

Walmart and Sam’s Club require domestic replenishment, promotional and new modular purchase orders (PO) to be delivered, in full, within a four day window, comprised of the must arrive by date (MABD) on the PO and the three preceding days. To determine if there is a compliant delivery, replenishment orders will be measured against the original MABD on the PO, and promotional and new modular orders will be measured against the current (aka “adjusted”) MABD on the PO.

Supply Chain Reliability Program overview:

Program compliance is evaluated on each nine digit (department and sequence) supplier number. We measure compliance both by the number of cases delivered outside the MABD delivery window and comparing number of cases actually delivered to the cases ordered. If less than 90% of cases in any month for any supplier sequence number are not received within the MABD delivery window, including cases ordered but not delivered, then the supplier is assessed a reimbursement charge of 3% of the cost of goods. This 3% reimbursement charge is not assessed on those cases that were delivered within the MABD delivery window. Your compliance will be evaluated on the basis of each Walmart business month and you will be provided with an explanation of any reimbursement due which Walmart and Sam’s Club will deduct off invoice.

For most categories, the current delivery window is four days including the must arrive by date (MABD) minus three days. The MABD delivery windows for certain business segments are more stringent (e.g. perishable). Please work with the replenishment managers responsible for dealing with your company to ensure you have a clear understanding of the MABD delivery window that applies to your category.

Visibility will be provided to suppliers via a scorecard on Retail Link beginning on November 1, 2009. A weekly scorecard and monthly report will be available on Retail Link to report supplier performance, PO details, and the potential or assessment of reimbursement charges. The weekly scorecard, monthly report and additional information are located on Retail Link at Retail Link/ Decision Support/ Merchandising Applications/ Delivery Window.

This new program does not currently apply to direct imports orders or direct from supplier to store deliveries.

In total, this program will drive greater reliability in the delivery of products to the store shelves. These criteria are subject to change. We appreciate your partnership in this process.

Sincerely,

xxx

This program raises many questions for consumer goods manufacturers. For example, if an order to a DC fills 2.7 trucks, what are the costs of sending extra inventory along to cube out the truck and reduce transportation costs versus incurring the Walmart penalties versus sending the last load less-than-truckload? Will the answer depend on whether the SKUs in question are part of Walmart’s ‘Win, Play, or Show’ assortment strategy? What sort of analytics can be developed that leverage Retail Link to show revenues at risk? Will supply chain software companies develop optimization solutions to help with this?

If a supplier gets penalized because of poor carrier performance, will it pass the penalties along to the carrier? How would this influence carrier procurement?

Will other retailers copy this program?

One thing is for sure: many consumer goods supply chain executives will be challenged to hit their cost targets in the coming year.

Demand Management vs. Demand Sensing

Consumer Packaged Goods (CPG) | By Steve Banker • 05/04/2009

On April 22^nd RedPrairie issued a press release announcing that it was adding “flowcasting” to its product suite. Despite reading the press release carefully, I couldn’t fully grasp what “flowcasting” was. Fortunately, RedPrairie’s management team visited us the next day and we discussed this new solution.

It turns out that “flowcasting” is synonymous with “demand sensing” and “sense and respond”. The industry has not settled yet on a single term to describe this emerging class of solutions, but I will use “demand sensing” because it is used more frequently.

What is the difference between demand sensing and traditional demand management? In traditional demand management, you start by summing historical sales and shipment data across all customers over some time period. This historical demand allows you to make baseline revenue and unit forecasts. The forecast is done at different levels of aggregation. Typically, the most granular forecast is by SKU by the manufacturer’s distribution centers (DCs). Usually, several DCs are supplied by a particular factory, and there is also a factory-level forecast. If several factories can manufacture a SKU, there would probably also be a regional or worldwide forecast. So while we talk about “forecast” in the singular, this is only true at the highest level where we have summed the forecast across all products and factories for some period of time. That global forecast is disaggregated into multiple “forecasts” for particular factories, DCs, SKUs.

The forecast is then shared with sales and marketing, and possibly customers, and is adjusted based on upcoming promotions, new product introductions, and so forth. While the forecast is used by various groups in the company, the primary driver for the forecast is to create manufacturing plans, often on a monthly schedule (e.g., SKU by factory by month). At many manufacturing companies, the production plan, ideally in combination with DC-level forecasts, then drives replenishment planning for deliveries from a factory to a DC.

In contrast, demand sensing is based on more granular demand data. In the case of RedPrairie’s solution, demand data is based on a customer’s historical POS and in-store inventory data. Then, as is the case for traditional demand management, users collaborate to adjust the forecast for promotions and new product introductions. And the primary purpose of this forecast is not manufacturing planning, but to drive better replenishment planning between the factory and the DCs.

While there are some differences in how individual manufacturers go about their forecasting process, and in particular Demand Management and Sensing solutions, in general the table below highlights the differences as I see them.

Demand Management vs. Demand Sensing (click to enlarge image)

RedPrairie showed us the results achieved by a very large Food manufacturer in supplying one of its largest retail customers. This food company was having problems creating accurate replenishment plans, particularly around the holidays. In 2008, as compared to 2007, their holiday period store sales increased by 20 percent, DC out-of-stocks decreased by 86 percent, and DC Inventory turns increased by 10 percent.

While it’s nice to hear this success story from RedPrairie, I’d prefer to hear it directly from the food manufacturer. RedPrairie will be presenting its new flowcasting product next week at its upcoming user conference and they are hopeful a supply chain executive from the food company will co-present with them. My colleague Adrian Gonzalez is attending the conference (he’s speaking on “Is Green Good for Business During a Recession?”), so if this food manufacturer presents more details about their success with flowcasting, you’ll hear about it here first.

The First Moment of Truth: Part II

Consumer Packaged Goods (CPG) | By Steve Banker • 04/29/2009

Yesterday, I talked about how I’m a fan of the “moment of truth” concept, and I highlighted how we have gone through three phases in trying to solve the “out of stock” problem, but that a new phase is emerging. This new phase, which has begun only recently at some leading manufacturers, includes making a better link between merchandising and supply chain operations.

Merchandising involves maximizing sales using product design, packaging, pricing, and displays in a way that stimulates consumers to spend more. Merchandizing services can be performed by a third party that visits retail stores to determine whether a manufacturer’s products are in stock and on the shelves; whether they have the agreed upon facings; and whether promotional displays have been built and stocked correctly.

In yesterday’s posting, I also mentioned that many manufacturers have only recently begun to realize how their ability to achieve good in-stock and on-shelf performance is being hampered by inaccurate store-level inventory data and poor in-store execution of promotions. These problems are starting to get addressed with fancy new math and better analytics. Companies like Retail Solutions (RSi) and TrueDemand use advanced math to calculate what should be selling at the SKU/store level and compares it to what is actually selling. If you are running a promotion and a particular store is not selling anything, while the other stores are selling great quantities of the promoted item, it suggests that either the data coming from the store is wrong or store employees are not pulling the promoted items from the back room to the front of the store.

These solutions also provide analytics to determine why a SKU is not selling – for example, incorrect store-level inventory, failure to move inventory from the backroom to the shelf, or replenishment failure from the retailer’s DC. TrueDemand can then trigger execution tasks for the retail account team or in-store merchandising team. Despite several attempts, I have not been able to talk to a manufacturer that is using this new math, so I can’t verify how effective it is at detecting phantom inventory.

However, I did talk to one of Retail Solutions’ manufacturing clients that is using the analytics from the RSi Demand Signal Repository (DSR). In this particular case, a key retailer has installed a DSR from RSi and the manufacturer pays RSi for POS and inventory data by store/by product/by week, and for daily promotional item data.

This manufacturer also uses over 250 people from third party merchandiser Acosta to visit this retailer’s stores. The merchandisers check for out of stocks, make sure the end caps are up, and perform other merchandising activities. This manufacturer can now query things like: Which stores have not sold any of product X in the last 12 weeks? Which stores have more than 20 pieces of any item? They then feed this information to Acosta which uses it to determine which stores to visit. In short, if you have the merchandising analytics, and run the right queries, the advanced math may be unnecessary.

I also talked to a second RSi manufacturing customer that recently began paying for DSR data from a large retail customer. They anticipate that the data will allow them to better phase in and phase out products. With this particular retailer, they pay 50 percent of the base product cost to the retailer once the phase out date has passed. If the product costs $2, they pay the retailer $1 for the effort involved in pulling the product off the shelf and disposing of it or returning it to the manufacturer. This manufacturer can now start monitoring the inventory positions at the stores six months prior to the phase out, and take steps to reduce costs for both the merchandising and the supply chain organizations.

In the supply chain, we have typically viewed marketing, and their ongoing promotions that disrupt the supply chain, as the enemy. But they don’t have to be. Rumor has it that for a very large retailer, better merchandising analytsics was a key impetus for their “green” initiative. This retailer tracks its shelf space data using a metric that is dollars/square inch/time period. In order to improve this metric, the merchandising teams at some manufacturers are developing concentrated products, like detergents, that have the same potency, but contains much less water, thus enabling more units to fit in the same space. This also results in less packaging and more efficient transportation. In short, it was better merchandising decisions that led to a “greener” and more efficient supply chain.

Visualizing Replenishment

Inventory Management | By Steve Banker • 03/30/2009

I recently read an academic paper from the WHU-Otto Beisheim School of Management called “3^rd Party Logistics Providers’ Role in Replenishment” that I liked. The paper contained good information, but it was also a bit long and kind of dense (although not nearly as inaccessible as most academic articles). I realized after reading it that the vocabulary used by most supply chain managers to talk about replenishment is extremely limited. This means that the models we use to think about replenishment are also imperfect. Therefore, in order to make intelligent decisions about which style of replenishment is best suited for a particular supply chain, we first need to visualize the different models available.

So, my goal today is to show you (in a concise way) the core model from the article, along with my own spin on it.

Replenishment spans across planning, management, and the actual ownership of inventory. Buyers, vendors, and various third parties typically play a role in replenishment, and the inventory managed spans across raw materials, work-in-process (WIP), and finished goods.

The Replenishment Hierarchy (Source: WHU paper, with ARC edits)

Based on this hierarchy, you can model different styles of replenishment by asking which party is responsible for inventory forecasting, inventory management, and inventory ownership.

Traditional Replenishment Models (Source: WHU paper, with ARC edits)

However, these traditional models don’t portray the full array of replenishment models available when you consider the full set of players that can participate in this process. First of all, many manufacturers have outsourced their logistics and manufacturing processes to third parties. These third parties often take responsibility for inventory management and perhaps also take ownership of certain types of inventory. However, outsourcing inventory planning is far less common. In short, working with third parties is very common for supply chain execution, but very uncommon for supply chain planning.

Actually, there are other third parties besides contract manaufacturers and logistics service providers that could be involved in inventory planning. These third parties have distinct value propositions depending on the type of inventory involved and whether you’re talking about inventory planning or management.

Contract Manufacturers: Management and ownership of raw materials and WIP are a natural extension of current activities.
Third Party Logistics: Inventory management of raw materials for supplier hubs feeding factories; inventory management of finished goods for their customer’s customer; inventory ownership for 3PLs with performance-based logistics (PBL) contracts .
Consultants: Ability to do advanced forecasting and inventory planning, potentially using very capable but low cost planners from offshore locations. Support for their customer’s S&OP process.
Software vendors: Ability to use demand planning and multiechelon inventory optimization tools to their fullest extent. Support for their customer’s S&OP process.

When one considers players in the replenishment process other than buyers and vendors, it becomes readily apparent that new replenishment models are possible-at least nine new scenarios, and many more if you consider collaborative planning scenarios.

Sample of Possible New Replenishment Models (Source: WHU paper, with ARC edits)

Further, there is a natural tendency to think about replenishment in terms of finished goods. A broader perspective is necessary. The diagram below shows just a few of the many possible options.

visualizing-replenishment-4 — (Source: WHU paper, with ARC edits)

In conclusion, when thinking about different types of replenishment models, it is also important for companies to think about creating joint benefits. In the Buyer Managed, Vendor Owned scenario (also known as consignment) the buyer wins while the vendor incurs extra costs. Those costs are eventually passed along to the buyer in the form of higher prices. However, in classic VMI while the vendor does hold more inventory, it also gains some flexibility in terms of when to schedule production runs. This can lead to lower production costs and a win/win relationship that saves both companies money.

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