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The Intelligent Food Chain Archives |
Source URL: http://www.decisionpath.com/foodindustry3.php
PerformancePath for Food & CPG Companies
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Welcome to The Intelligent Food Chain Archives - a series of news and opinion pieces where we explore how business intelligence (BI) can be used in the food value chain. Please check back often since we post a new article every month. Comments? Please send us a note at FoodIndustrySolutions@decisionpath.com Simply click on the title of interest to read the full entry.
“It’s the economy, stupid” was a political catch phrase used during Bill Clinton’s successful 1992 presidential campaign against then incumbent George H. W. Bush. It captured the notion that Bush had not adequately addressed the economy, which had undergone a recession. While some consider it ungrammatical, “it’s the economy, stupid” memorably focused Clinton and his campaign workers on the economy as an issue, and helped him unseat Bush. As we work with clients to design, develop and implement new business intelligence (BI) applications, it’s common for the client team members to become excited about the capabilities of the technology. Yes, BI software tools are incredibly powerful and have features that will blow your socks off. But . . . the software is merely a means to the end. The end is return on investment through improved business results, and that end requires the information provided by the BI application and the business process change to utilize it. Therefore, an equivalent catch phrase for BI teams might be “it’s the business process change, stupid” or, more genteelly, “it’s the business process change, folks.” Back in February, the business media made much of Proctor & Gamble’s decision to cease placing Electronic Product Code (EPC) tags on promotional displays going to Wal-Mart’s RFID-enabled stores. Use of the EPC tags gave both Wal-Mart and P&G visibility into the location of the displays in the store (back room or on the sales floor) and whether the display needed to be restocked with product. Why did P&G decide to stop? Its own research showed that EPC has the potential to improve merchandising effectiveness, increase sales, and raise consumer satisfaction. The buzz is that P&G stopped because, although it was using the EPC data to direct merchandisers to stores with promotional compliance issues, it did not believe Wal-Mart store associates were acting upon the data to improve product availability and compliance. In other words, the information was available, but there was insufficient business process change at the Wal-Mart stores to leverage it. Another example of insufficient business process change is reward programs. These programs are intended to produce benefits for both the store and the customer. Many supermarkets have “reward card”-based programs, and collection of the data at time of check-out is straightforward. The supermarkets have found it much more difficult to determine exactly how to leverage the extremely detailed customer purchase data in ways that both help them (better product assortments, more effective merchandising, higher “share of wallet,” and ultimately, more sales) and provide a better experience (provide only messages and offers of interest to that specific customer) for the customer. The unresolved challenge is how best to change the stores’ business processes to effectively utilize the reward card purchase information. Too often, the business process change to utilize the information provided by BI is an afterthought left to the very end of the project, when there’s no time, no money, and no enthusiasm remaining to do it properly. A better way is to specify the business process changes at the beginning. We often think of “requirements” as being what information the BI application must provide, but the comprehensive way to think of them is:
During the execution of a BI project, it’s easy to get caught up in the day-to-day challenges and exclusively focus on the technical tasks of building the BI application. We cannot limit our focus that way and ultimately be successful. The information we provide is of no value until business users consume it and apply it in their business processes. So remember, it’s the business process change, folks! Aberdeen Group research shows that unclean data remains a problem, even for best-in-class companies. Our consulting experience at DecisionPath confirms that data quality is a ubiquitous problem for companies working to implement and leverage business intelligence (BI). We generally have two types of clients:
Implementing BI doesn’t create data quality problems, but it exposes the ones that you already have. Every BI initiative must, sooner or later, address the issue of data quality. In order to have high-quality information with which to make decisions, an organization must have a culture in which correct, complete data is recognized as important. And this culture must extend to every employee who creates, inputs, manipulates, or otherwise touches data. Let’s talk about a specific food industry example. I recently went to the supermarket and purchased ten frozen entrees. All of them were the same brand, but they were of four different varieties – three each of my two favorites, and two each of two other varieties that I wanted to try. The cashier scanned one of the ten frozen entrees, and then input a quantity of ten on her keypad. The four varieties all were the same price, so she charged me the correct amount. However, her actions led to the point-of-sale (POS) data being incorrect, the store inventory being incorrect, and all of the downstream uses of that POS and store inventory data – store replenishment, sales mix by variety within the brand, item assortment and category management analyses, and so on – having inaccurate data. And, if this grocery retailer shares its POS data with its trading partners, they got inaccurate data, too. Let’s assume that the cashier is trying to do a good job and isn’t just being lazy. Does she understand that it’s critical to capture both the correct price and the correct item number at check-out? Do both the supervisor of cashiers and the store manager? That’s one of the biggest challenges BI programs face: insufficient understanding of the importance of accurate data by the people who touch it. Our cashier, her supervisor, and the store manager all understand the importance of balancing the cash drawer at the end of her shift, but they might not understand how critical the other POS data elements are. Here’s another example. A food company at which I once worked noticed an undesirable trend in the freight cost of shipments from one of its plants to its distribution centers. Analysis eventually revealed that the average weight per truckload was much lower than it was thought to be, and therefore freight cost per pound (and per dollar of sales) was higher than it should have been. It turned out that the new-to-that-position person who set up new items in the item master had been rounding the shipping weight of items up to the next integer pound. (For example, if the shipping weight was 8.23 pounds per case, he would enter it as 9 pounds.) The truckbuilding program used the incorrect (too heavy) shipping weight in its calculations and thought the truckloads it built were heavier than they actually were. The item master maintenance clerk, who had received very little training, was sincerely trying to do a good job. He knew that rounding the shipping weight per case down to an integer might result in overweight trucks, so he rounded it up. (It is unclear why he thought that the shipping weight had to be entered as an integer, which was untrue.) Appropriate training for him into how the ERP system worked and the importance of precision in the shipping weight could have prevented the company from spending needless freight dollars. Data cleansing is a normal part of the ETL process feeding data into a data warehouse, and data cleansing tools are becoming “smarter” at detecting data anomalies. But detection and correction in the DW/BI environment is merely a small part of a data quality program. Data quality must begin at the source and continue through every point in the path that data takes to and through the DW/BI environment to its ultimate consumer. If the phrase “quality must begin at the source” sounds like a mantra from lean Six Sigma, it is. But whether you use lean Six Sigma techniques or just good operations management practices to obtain it, data quality is a prerequisite for effective business intelligence. Last week we conducted a survey of business intelligence (BI) best practices in the food industry. Surveys like this always have some interesting data, and this one was no exception. We had multiple responses from several companies, and what was surprising (although perhaps it shouldn’t have been) was the spread of responses from the same company. Here’s an example. Question Which best describes the process for determining BI/DW projects and enhancements? Answers (multiple choice) A. The business makes periodic requests to IT for BI/reporting; then IT prepares specs, integrates data, and prepares B. IT integrates data into a DW in anticipation of business’ needs; then IT responds to requests, ideally using the DW to C. The business led (or is currently leading) a BI strategy to define needs and priorities; then IT will deliver BI/reporting. D. IT facilitated (or is currently facilitating) a BI strategy with the business; then IT will deliver BI/reporting. E. The business defines its own BI/DW needs; then delivers its own solutions without IT involvement. F. Don’t know or no perspective. One company had three responders. One of them selected answer A, one answer C, and the third answer D! What does that mean?! It could mean several things: that the BI strategy was formulated before they were involved, that some of the responders should have selected answer F (don’t know) but didn’t want to admit that, and so on. What these answers also point out is an organizational reality that applies to BI as much as it applies to all initiatives and programs: we might think that we have communicated our strategy well enough that everyone who needs to know and understand it does, but it’s likely that we’re wrong. The quantity and quality of communication required for everyone to hear and understand is much more than we imagine, for several reasons:
The best boss I’ve ever had was CIO of a $1 billion manufacturing company; I was an IT director who reported directly to him. In virtually every internal meeting within IT, Dan started by reiterating our IT strategy. The strategy statement was short and simple, and we tired of hearing it (again). But I can tell you that everyone in IT knew what the IT strategy was! Dan also worked the IT strategy into virtually every briefing he gave to other executives of the company, so that they knew it, too. Dan understood that, if you want people to know and understand the company’s strategy – whether it is IT strategy, BI strategy, HR strategy, sales strategy, or what have you – you must have a short, simple, easy-to-understand statement of that strategy, and you must over-communicate it. (What feels like over-communicating actually is communicating enough.) So, what’s your BI strategy? Can you articulate it in two or three short declarative sentences, using no technical jargon? If you asked your boss, business sponsor(s), users, and BI team members, could they tell you what it is? If the answer is “no,” changing the answer to “yes” is one of the most effective things you can do today to advance your BI program. Product freshness is critical for food products, and the food supply chain uses numerous standard practices – code dating, ship-the-oldest-product-first picking algorithms, physical rotation on the retail shelf, and so on – to manage product age and freshness. Most of these practices focus on the point at which the product is transferred to the next participant in the supply chain. But, from the perspective of an individual participant, perhaps more important than the product age at time of shipment downstream is product age at the time of receipt from upstream. After all, if a substantial portion of the shelf life already is gone before the product gets to you, you might already be behind the eight ball. Let’s use a large national food products distributor as an example. The numbers in the table below are typical for such a distributor.
For a product with a one-year shelf life, many of the distributor’s customers specify that the inventory they receive from it must have at least 90 days of shelf life remaining. So, a relevant question for the distributor is, what is the age (how much shelf life remains) when it receives the product from the manufacturer? It is common for distributors to grow though acquisition, which means that a single enterprise might be the combination of multiple acquired entities, each of which has its own ERP and warehouse management system (WMS). Obviously, monitoring the age profile at time of receipt for such a complex situation (many products, many suppliers, a wide variety of shelf lives, multiple DCs, multiple systems, and so on) requires an automated solution. Business intelligence (BI) to the rescue! The relevant data – primarily, the shelf life of each product and the code date when received – reside in the distributor’s ERP and WMS systems. But it takes both data warehousing and BI technologies and methods to:
Spoilage (inventory obsolescence due to product age) is a significant issue for food distributors. Profit margins are thin, and spoilage losses fall straight to the bottom line. One way to reduce spoilage is to work with suppliers to obtain product from them that has more shelf life remaining. In order to focus that effort, the distributor must have information regarding which products from which suppliers to which DCs have short shelf life remaining at time of receipt. A BI application can provide this critical information. The “Big 3” American automobile manufacturers are a popular target for scorn these days, especially since they have requested, and received, bailout money from the federal government. Their challenges are many: a steep decline in consumer spending, restrictive UAW contracts, state laws that make it difficult to weed out weak dealers, healthcare costs for retirees, and so on. Perhaps their biggest challenge is to make cars that people want to buy. In a recent post on his Wall Street Journal blog, Gary Hamel traced the problem of uninspiring car design (making cars that people do not want to buy) to a lack of understanding of perceived customer value. In his words, “the senior executives {in this company} had a hundred ways to parse costs, but were mostly clueless when it came to dissecting perceived customer value.” What can we learn from the troubles of the domestic automobile industry and apply to the food industry, and, more specifically, to business intelligence (BI) for the food industry? The obvious parallel is that food companies also need to understand the attributes of their products, its packaging, brand positioning, and the shopping experience its customers and consumers value. The budding consumer insight movement in consumer packaged goods, including food products, is an attempt by manufacturers to understand how consumers perceive value. But let’s think more broadly. We who work with business intelligence love data, like to measure things, and endlessly pontificate about a “single version of the truth.” We are comfortable working with costs, because they are discrete, “hard,” primarily internal to the organization, and easy to either obtain or calculate. Customer value, on the other hand, is “soft,” external, and often difficult to calculate or even estimate. Often, we prioritize our BI efforts based primarily on what data exists and is easy to obtain. Such prioritization is a mistake. We also must consider what information, insight, and understanding will contribute to the value of the business and work to increase that value, even if doing so will be quite difficult. If we limit ourselves to BI initiatives that use easy, “hard” data, we risk both our efforts and our enterprises becoming like the Big 3 automakers: irrelevant and uncompetitive. No enterprise, be it commercial, governmental, or not-for-profit, can cost-cut its way to success. Ultimately, it must satisfy the needs of and bring value to its customers. Our highest purpose in BI is to help our enterprise do just that. The Wall Street Journal recently carried an article describing how various food manufacturers, including Kellogg, Campbell Soup, Kraft Foods, and ConAgra, are emphasizing low-priced, but high-margin, brands and products in their advertising messages. Achieving low price but high margin requires creative approaches; some of the ones mentioned in the article include reformulating recipes to use cheaper ingredients and reducing portion/package sizes. But more fundamental is an understanding of the profitability of products and individual items: without that as a base capability, either reducing the price or adjusting various cost components entails high risk of unintended and potentially disastrous financial consequences. The last thing a food manufacturer wants to do is stimulate through advertising demand for a brand, product, or item upon which it doesn’t make any money. Both the revenue side and the cost side of product profitability have their challenges. On the revenue side, the challenges are to understand the price elasticity of demand, price points, and all relevant aspects of purchaser behavior. Such understanding requires of analysis of the buying behavior of all channel intermediaries (distributors, wholesalers, and retailers) and point-of-sale data for all items at all selling points in all sales channels. Business intelligence (BI) often is used for multi-dimensional analysis of purchase behavior. The cost side of product profitability requires us to know the costs associated with each individual item, not just costs in total. Activity-based costing or similar methods can be used to do this; all of them require the collection, transformation, management, analysis, and understanding of large volumes of cost data. Usually, the ERP system handles the collection and a portion of the transformation and management of cost data; however, the remaining transformation and management of cost data, and all of the analysis and understanding of it – for product profitability, customer profitability, and other purposes – requires BI. Both the revenue component and the cost component of product profitability illustrate what BI brings to the party for food manufacturers: sophisticated analysis of large volumes of data to inform business decisions about key determinants of profitability and business success. A recent article by Mark Penn in The Wall Street Journal attributed the success of the Bernard Madoff and Marc Dreier frauds to the customers of these two con artists not questioning their results, not reading their monthly account statements, taking everything about the offered investments at face value, and making decisions based on brand and personality rather than an analysis of the numbers. What, you might ask, do these financial services scandals have to do with the food industry and business intelligence (BI)? Simply this: the essence of BI is to provide useful information to decision makers so that they can make more informed (and therefore hopefully better) decisions. BI enables management by fact. But BI’s value remains latent unless its intended users actually read and pay attention to the information it provides. One of the questions we typically ask our consulting clients at the beginning of an engagement is: What information do you not have that, if you did have it, would enable you to make better decisions and achieve better business results? The discussion this question stimulates surfaces requirements for new BI applications that are tightly linked to what matters to the business. Perhaps a question that we should be asking is: What useful information that already is available to you do you not use, and why not? In his article, Mr. Penn related that, at a meeting of his condo association, he asked how many people had read the closing documents when they purchased their condominium, and only one person had. That’s a very common phenomenon: people don’t use the information available to them. Before investing in BI applications to provide additional information, most organizations would be well served by making sure that they fully leverage the information they already have. Historically, business intelligence (BI) was a tool whose use was concentrated in a small set of highly computer literate, analytically skilled power users. Typically, such users were staff analysts at the corporate headquarters, and their use of BI was by self-selection: they chose to learn it and use it because it helped them do their jobs. As business intelligence has become more mainstream, its potential application beyond a small headquarters analyst community has become apparent. Potential applications of BI include scorecards and dashboards for executives, BI embedded into operational systems for front-line workers (whether that front-line worker is on the manufacturing line, a call center agent, or the manager of a retail location), and analysis for/by trading partners both upstream and downstream. For the proponents of BI, this recognition of BI’s widespread potential is both a blessing and a challenge. The blessing is that our evangelism and education efforts are succeeding in causing the enterprise to recognize that business intelligence can be a key enabler of process improvement and better business results. The challenge is that the increased demand for BI – more applications, more users, wider adoption, and so on – exceeds the capacity. This capacity shortfall exists in both the IT department and the functional departments that comprise the users and potential users of BI. It exists in the IT department because the supply of skilled BI project managers, architects, designers, and developers is limited relative to the demand for people with these skills. Yes, contractors can be used for some of the tool-specific work, such as developing reports using Business Objects, and some of the work potentially can be sent off-shore. But finding people to do the high-skill strategy, business alignment, and architecture work can be quite challenging. To use a construction analogy, there are plenty of people who can drive nails and hang drywall, but few architects and engineers. The capacity shortfall faced by the potential consumers of BI – “the business” – is more subtle. At a basic level, it might mean needing to train users on how to use BI software to run reports and analyze data. But the capacity required to effectively utilize BI is more than that: it is the analytical capability to understand the information provided by BI and leverage it to make better decisions and achieve better business results. The value of a BI application remains latent unless the business has the talent to leverage the information it provides. It is this analytical talent that is in short supply and lacking in many organizations that aspire to use BI. BI capacity has a strategic dimension as well. The fundamental requirement is that executive management is committed to “management by fact,” whether the facts are what they desire them to be or not. If top management isn’t willing to look at the cold hard facts without blinking, there’s no ROI to a BI application that calculates those facts, surrounds them with useful context, and presents them in a visually appealing and easy-to-understand way. These components necessary for BI success – BI strategy, program management, and architecture skill in the IT department, analytical talent in the business functions that will use BI, and executive commitment to management by fact – collectively are called “BI readiness.” Without sufficient BI readiness, no business intelligence initiative will be truly successful. While external assistance from consultants can help advance some elements of BI readiness, ultimately it must become resident within the enterprise and be embodied in company employees. Food manufacturers and retailers have numerous high-potential opportunities for business intelligence to significantly improve business results: better category management, fewer out-of-stocks on the retail shelf, better inventory management, a streamlined supply chain, more effective promotion management, optimized pricing, and many more. But these opportunities require BI readiness to be realized. So before buying software or embarking on projects, food companies would be well served to assess their BI readiness and address any shortcomings. A Fortune 1000 food manufacturer in our area started its implementation of enterprise resource planning (ERP) software in 2002. It was a massive undertaking, with an internal project team of almost fifty supplemented by external consultants from one of the Big Four consulting firms. The initial implementation project cost more than $100 million over several years. Recently I had the opportunity to attend a presentation about demand planning made by a representative of that company. He related that, in the demand planning function, they:
From an enterprise-wide view, five years after initial implementation this company:
As I drove home from the presentation, the thought that kept resonating in my mind was, “They spent more than $100 million to implement ERP, and five years later they’re still doing their demand planning using spreadsheets and Microsoft Access.” Yet this company is not a “poor performer” and its situation is not a reflection on the ERP software it selected. Its experience is fairly typical of Fortune 1000 global food manufacturers. Many spend comparable sums of money on ERP and achieve similar results. These results aren’t what executives were promised when they approved the investment of tens of millions of dollars for an integrated enterprise-wide system. For the vast majority of food companies (and, indeed, all companies), the process-streamlining, revenue-enhancing, cost-reducing dream of ERP systems remains unrealized. Why is success with enterprise-wide systems like ERP so hard to achieve? There are several reasons:
What does this discussion of ERP and its failure to live up to expectations have to do with business intelligence (BI)? For food manufacturers, the ERP system is one of the most important data sources for BI. And BI to leverage the ERP data to improve decision making is one of the next steps that often isn’t done due to funding exhaustion or diminished enthusiasm. What should food companies in this situation do? Here are some approaches to consider:
The challenges of effectively using ERP systems are real and will not be completely resolved any time soon. As BI champions, we need to recognize these challenges and factor them into our thinking and planning, but they need not prevent us from accomplishing big things with BI. American families spend 9.8% of their income on food, one of the lowest percentages around the world. For years, growth in personal income and low inflation in food prices have made food a bargain for U.S. consumers, enabling them to grow consumption of new categories and premium-quality products while building affinity with their favorite brands. Consumer product companies have responded with greater variety, more frequent promotions and marketing programs aimed at reinforcing brand loyalty. But things have changed quickly in the last few months, and brand loyalty now stands on shaky ground. After seeing typical changes between 2 and 3 percent annually in food prices, consumers will see an increase of 5 to 6 percent this year. In addition, personal incomes likely will not increase any time soon. As a result, buying patterns are shifting toward lower-priced brands. And lower-income shoppers are not the only ones trading down from their favorite brands: a recent survey by IRI found nearly one-third of high-income shoppers said they bought more private-label products during the second quarter, up from about 20% in the first quarter of this year. As consumers pay more attention to their grocery receipts than to their favorite brands, consumer product companies are revisiting their marketing mix plans. For instance, Procter & Gamble has changed its marketing approach to focus more on in-store promotions. Other companies are focusing more on category management. For companies to be successful in their brand strategies and cope with shifting buying patterns, they need to become more aggressive in measuring the effectiveness of their marketing programs. By incorporating measures such as return on program investment, consumer product companies will be more capable of allocating dollars to the right accounts, channels and products that minimize erosion of consumers’ loyalty to their brands. In addition, companies seeking to retool their category management and shopper marketing programs need to be more proficient at exploiting point-of-sale data. Only by looking at store level and customer segment detailed transactional data will they understand how to tailor promotions to each cluster, market or segment. Business intelligence (BI) solutions provide the blueprint and technology to measure effectiveness and integrate data from multiple sources, both internal and external. Today’s rapid changes in shoppers’ buying patterns, call for marketing mix plans that can change at the same pace. BI provides the means to make those changes. Through 2007 and the first half of 2008, food manufacturers faced steep price increases for commodities like corn, wheat, and soybeans. Food prices in general increased, as manufacturers attempted to recover the commodity cost increases in the pricing of their packaged products. Now commodity prices have declined significantly from their summer highs. And the deteriorating state of the
Economists use the term “price elasticity of demand” to describe the relationship between the quantity of a product that consumers will purchase and the price they pay for it: in general, the higher the price, the less quantity they will buy. (Answering “How much less?” is where the mathematics comes in.) Understanding the price elasticity of demand is one of the means to a very important end: determining the price for each product at which the food manufacturer maximizes its profits. For food products sold at retail through grocery stores and supermarkets, the relevant price is the price paid by the consumer at check-out, which is a function of both manufacturer and retailer actions. This means that, in order to understand the price elasticity of demand, the manufacturer must analyze point-of-sale (POS) data. Many food manufacturers are building specialized data bases called “demand signal repositories” to contain POS data and enable price elasticity of demand and other types of analysis. Understanding the price elasticity of demand is the first step in selecting the price that will maximize profits. The second step for the food manufacturer is to understand its own cost structure, particularly which of its costs are fixed and which are variable. Reducing the price in order to drive more volume makes the most sense when the majority of the cost is fixed, and therefore spreading it over a larger sales volume results in less cost per unit. Some fixed costs (such as those associated with manufacturing capacity) typically are fixed over a certain range of volume with “step function” changes in cost at the endpoints of that range; both the volume ranges and the associated cost function must be well understood in order to find the price/volume combination at which profit is maximized. Both of these types of analysis – price elasticity of demand, and fixed versus variable cost – require sophisticated mathematical techniques to be applied to large volumes of detailed data. They are well-suited to business intelligence solutions because BI applications can acquire the necessary data from multiple internal and external sources, organize it, manipulate that data using the appropriate algorithms, enable analysis across multiple dimensions (product, plant, customer, time, and so forth), present the resulting information in ways that are easy to understand, and support simulation of “what if” scenarios. Effective pricing is critical to optimizing profit; many food manufacturers could benefit from business intelligence to strengthen their capabilities in pricing. The Wall Street Journal (September 10, 2008) included an article describing how some retailers are using “workforce-management” or “human-capital management” software to better schedule the workers in their stores. These systems calculate performance metrics (average sales per hour, dollars per transaction, and so on) for each worker, and schedule the most productive workers to work the busiest hours. As you might expect, the use of such systems is controversial, and many workers dislike them. The more interesting aspect of these systems is that they are an example of leveraging information to improve decisions that matter. As an expert quoted in the article states, “The biggest controllable cost in retail is people.” Optimally scheduling the workers (how many people to have on duty, and which ones, during each part of the work-day and work-week) not only is key to controlling costs, but also greatly affects customers’ service experience. And that’s the essence of the problem/opportunity: determining the optimal staffing that minimizes labor cost while maximizing the customer experience. The companies mentioned in the WSJ article aren’t food retailers, but what’s true for AnnTaylor Stores, Limited Brands, and Gap Inc., also is true for supermarkets: labor costs for in-store personnel are a significant cost, and proper staffing drives the customer experience. Supermarkets know that not having enough checkers on duty during a surge of customer traffic means long check-out waiting lines, grumpy customers, and some customers who will defect to a competitor who has shorter lines and faster check-out. Workforce-management systems demonstrate a fundamental element of the power of BI: to transform data into useful information that enables better/optimal decisions to be made about an important aspect of the business. We who work in the food industry are accustomed to its unique requirements, and often don’t realize how demanding they truly are. Take traceability and the ability to do product recall. As food manufacturers, we must have the capability to take a single code-dated package in the hands of a consumer and trace it back through retail (the supermarket), the supermarket distribution center and/or distributor or wholesaler distribution center, our own distribution center(s), our own plant and/or contract manufacturer, through to a specific lot from a specific vendor, potentially for each ingredient in the recipe. We must accomplish this trace quickly, potentially in a matter of hours. And then, once we’ve identified the problem, we must explode our trace back down the supply chain to identify every case of potentially affected product at every stage of the supply chain, all the way back to the retail shelf. While the likelihood of having to execute a product recall is remote, the consequences of failure are huge, possibly including putting us out of business. It’s true: other industries, such as pharmaceuticals, have as stringent or more stringent product recall capability requirements. But pharmaceuticals have both high prices (often several dollars per pill) and high margins to absorb the cost of traceability systems and processes. We don’t have the luxury of their cost structure. The portion of traceability within our own walls is fairly straightforward. Even though code dating of finished goods and lot traceability of ingredients isn’t innately designed into most ERP systems, it usually can be accomplished using lot numbering or serialization functionality, perhaps modified. The challenge comes from the inventory that resides or already has flowed through our downstream partners in the supply chain: carriers, distributors/wholesalers, and retailers/customers. Because of the importance of managing inventory age (i.e., FIFO to reduce the risk of aged obsolescence), all food supply chain participants track code dates in their inventory systems. But each one of these many participants might use a different system and have implemented code date tracking in a different way. Obtaining the data we need to do product recall from our supply chain partners can be a challenge, but equally challenging is what to do with it, once we have it. We must be able to combine differently-defined and differently-structured data from multiple sources to provide a complete picture of the potentially-affected inventory. Luckily, integration and standardization of data from multiple disparate sources to provide a summarized view of a business situation is one of the things business intelligence/data warehousing (BI/DW) applications do well, making lot traceability and product recall a food manufacturer imperative well-suited to BI/DW. It’s a capability that we can’t afford not to have, and BI/DW can help us get it. An important aspect of increasing revenue is the day-to-day struggle to increase distribution of your products: to secure placement of more items, obtain more facings, and defend what you already have from competitors. Account reps spend a lot of time working to increase distribution. But how do they know exactly what to propose to the buyer? Let’s say that you are selling a line of cake mixes. The line contains twelve varieties, of which this account currently carries four. You want to convince the buyer to take another two varieties. Which two of the eight should you propose? The obvious answer: the two, that, when combined with the existing four, will yield the most unit sales, revenue, and profit for both you and the account. But determining the specific answer to that question for that account can be analytically challenging. It definitely is not something we want the account rep doing on the back of an envelope while in the customer’s lobby, waiting for his/her appointment with the buyer. In some product categories, the relative ranking of varieties is well established. In ice cream, vanilla, chocolate, and strawberry have a strong lock on the top three flavor spots. But other categories are less well structured. Cake mix, for example, has regional variation, with pound cake being more popular in the South than in any other region of the country. The optimal number and selection of varieties or flavors for a specific account depends on a number of factors: • The number of varieties in our product line • Which varieties the account already carries • The relationships (substitutability, complementariness, etc.) among varieties (If two of the four varieties the account already carries are chocolate-based, say Devil’s Food and Chocolate Fudge, then perhaps pitching another one (German Chocolate) is not the way to go.) • Number of items and facings for the category for this account • The variety lineups of our competitors, and the extent to which they match ours • The number of competitive items carried by this account, and which ones they are (If we’re selling cake mix, and the account already carries Yellow cake mix from both Duncan Hines and Betty Crocker, perhaps we should pitch Angel Food rather than our Yellow.) Business Intelligence (BI) can help us optimize our assortment with each account. Whether all we have is our own shipment data (good), or we have POS data for our items sold by this account (better), or we have POS data for all items in this category for all accounts (best), BI can support fact-based understanding of which numbers and combinations of which varieties maximize unit sales, revenue, and profit. “Our other accounts that carry six varieties, including Angel Food, have x% more unit sales per week than those that only carry four varieties” is a compelling argument that BI can enable us to make to the buyer. Individual account category management is a data intensive and analytically complex undertaking, making it a good candidate for a BI application. Much has been said in the media recently about how the economy is affecting consumers’ shopping habits for food. Grocery retailers are experiencing significant paycheck-to-paycheck seasonality in sales patterns, and are responding by varying assortments throughout the month: more discretionary and indulgent products after payday, and low-cost staples like rice and beans before payday. What do these within-month assortment changes mean for upstream players in the food value chain, like wholesalers and manufacturers? They mean smaller order quantities, more frequent orders, less time to remedy retail out-of-stocks, increased risk of returns: in short, more change, more complexity, and more stress on the supply chain. What must wholesalers and manufacturers do to cope with frequently changing assortments at retail? Fundamentally, they must uptempo the process by which they sense and respond to demand. The manufacturer of an indulgence product that is “two weeks on, two weeks off” at retail cannot survive with a monthly replenishment process. It must analyze both retail off-take from POS data and store-level inventory in order to supply just the right amount of product at the beginning of the two-week “on shelf” period so that the inventory all sells through before the two-week “off shelf” period begins. Too little, and the product will be out-of-stock during a portion of the already limited selling period; too much, and the retailer might return the remainder. The more frequently and quickly a planning process must be executed, the more important it is for it to be automated. If a “sense and respond” replenishment process must be completed multiple times per week, there’s no time for semi-manual data acquisition, manual data cleansing, manual manipulation using spreadsheets, custom data integration by analysts and planners, or similar methods still ubiquitous in even large sophisticated food companies. Accurate, integrated, useful, actionable information must be automatically presented to planners so that they can spend their time solving the business problem (right quantity of product to the retail shelf at the right time) rather than laboriously turning the crank on the process. And that’s what a well-designed business intelligence (BI) application for replenishment planning can do: distill mountains of POS and store-level inventory data into information the planner can use. For today’s piece we’ll start with a historical perspective, but first a quick definition of BI is in order. While the words might vary a bit, there is a general consensus that BI is a business process improvement and performance management tool that leverages transactional business information from companies’ transactional systems to feed analytical tools that provide better understanding of complex business situations and company options, thereby allowing the company to improve the effectiveness and efficiency of the core processes that drive its business results. A great example of BI use in the food industry Kroger’s use of loyalty card data (transactional information) to segment shoppers based on their buying behavior (analysis) in order to develop customized offers for the individualized needs of various shopper segments and better target its promotional offers toward its most profitable customers (increased revenues and profits). As to the historical perspective, food industry companies in particular -- and supply chain experts in general -- have been talking for years about leveraging demand data to create a more efficient and responsive supply chain. And some companies have been doing just that, i.e. using point-of-sale (POS) data to drive store replenishment. That being said, the food industry has been slow to change when it comes to leveraging demand information, even though retailers are increasing competing on the basis of optimizing the retail shelf (increased focus on preventing out-of-stocks, category management, customized assortments, and other efforts) and thus moving from the old supply push industry model to a demand pull industry model. The extent to which this shopper-centric model will be adopted by all participants in the food value chain remains to be seen, but there is no doubt that better use of demand data (transactional information) can feed sophisticated analytical applications (analysis) across core business processes such as assortment optimization, pricing, category management, distribution, inventory management, and supplier management (increased revenues, decreased costs, increased profits). In other words, BI can make a difference in the food value chain, just as it has in many other industries. |
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