The need to make or order products before demand is known with certainty is one of the basic challenges of supply chain operations. Too much product means that it has to be stored for a long time, incurring inventory carrying costs and likely being sold at a discount or a loss; too little inventory means lost sales and lost customers.
Typically, companies use forecasting techniques based on statistical models to predict demand and use the resulting forecast to produce or order the amount they anticipate their customers will demand. But even with the most sophisticated forecasting models, forecasts are inaccurate. And forecasting methods are even less useful for predicting low-probability events. Thus, it is instructive to study how companies facing significantly uncertain demand organize their operations. These companies minimize forecast errors by creating a more responsive supply chain so that they can react to demand fluctuations even when not anticipated.
In mid-2000, IBM revamped its laptop computer product line, the venerable ThinkPad, releasing the T20 and A20 models. After losing $800 million in 1998 and $571 million in 1999, the IBM PC division used a conservative forecast of the sales of the new machines and kicked off a major ad campaign in conjunction with the release. However, to the delight of the IBM marketing department and to the horror of its operations department, the new ThinkPads became an instant hit with consumers. Sales soared, leading immediately to product shortages. In mid-July, customers seeking 79 of the 108 ThinkPad configurations faced back orders of well over a month. The problem had no quick fix because component suppliers were geared for the original forecast and could not quickly ramp up their production of DVD and CD-RW components.
One of the most unfortunate problems with such “under-forecasting” is that IBM does not even know how many potential sales it lost to competitors, as would-be buyers were turned away by the publicity regarding the shortages. Erring on the side of overestimating demand, however, can also be detrimental, as items may have to be sold at discount, robbing the manufacturer or the retailer of its profit margin and even forcing it to sell at a loss. Such discounts are commonplace in both fashion apparel, which is subject to teenagers’ whims, and consumer electronics products, which lose their appeal when a new model or gadget comes on the market. But even in mature products, like automobiles, discounting is common. For example, in 2004 American manufacturers were offering $3,000-$4,500 rebates on sales of sport-utility vehicles when demand ran below forecast, in part because of high gas prices.
In some cases, products flop completely, as was the case with the Ford Edsel. After unprecedented consumer research and an extensive ad campaign, Ford introduced the car on September 4, 1957. It quickly became clear, however, that consumers preferred the silhouette photos in the marketing brochures to the actual cars. Instead of the 200,000 Edsels that Ford was geared to sell, only 63,110 were sold—with discounts. Even after a restyling for the 1959 model year, only 45,000 Edsels were sold. Ford pulled the plug on the car one month after the 1960 model was introduced, again to disappointing sales.
New product failures are not confined to automobiles. In the consumer packaged-goods industry, almost 80 percent of all new products and new product variations fail within the first two years. In the industrial products sector, 30 percent of all new products fail.1 And the results of such failures can be dramatic. In 2003 two pharmaceutical companies—Wyeth Inc. and MedImmune Inc.—jointly introduced a new flu vaccine that could be inhaled rather than injected. Unfortunately, it was not a consumer favorite even during the flu vaccine shortage of 2003.2 Wyeth was able to sell fewer than 400,000 FluMist doses out of 4 million produced and had to take a $20 million charge in January 2004 to destroy3 and write off the unsold stock. Wyeth has announced a plant closing and the elimination of several hundred jobs4 while MedImmune stayed in the market but produced only a few vaccines in 2004, contributing to the influenza vaccine shortage of 2004.5
The world of commerce is strewn with products that failed despite sophisticated customer research and lavish ad campaigns. The failure of New Coke, Crystal Pepsi, the Betamax VCR, the Apple Newton, and Microsoft’s Bob demonstrate vividly the difficulties of forecasting.
Forecasting has always been a challenge to business. In the last part of the twentieth century, however, it has become at once more difficult and more important because of several trends that characterize almost all supply chains:
Globalization As companies buy parts and sell products around the globe, their supply chains involve greater distances and larger numbers of partner companies on different continents and over many time zones. The result is longer lead times, a need to forecast further in advance of sales, and greater communication challenges.
Product variety and life cycle As companies introduce more new products, forecasting becomes more challenging because such products have no “history.” In addition, the sheer number of new products and the increasing variety of exisitng products mean that each is sold to an ever-smaller market segment, which is more sensitive to random variations (see further explanation below).
Demand homogeneity As CNN and the Internet bring new trends and innovations to every corner of the globe, consumer tastes become similar. In other words, if Japanese teenagers turn thumbs down on a new gadget, it is not likely to catch fire in Kansas City or Barcelona. When markets “move together” like this, the amplitudes of their uncertain fluctuations are larger. Moreover, this trend makes it hard to dump obsolete inventory into secondary markets.
At the same time, forecasting is more important because most companies are facing fierce competition around the globe. Faster product cycles mean that inventory becomes obsolete more quickly, exacerbating inventory carrying costs. Companies that fail to deliver on time are in danger of losing their markets to competitiors who are ready to take their place. Thus, forecasting mistakes may have long-term impact.
Many companies have responded to these trends by investing in refined forecasting tools based on statistical models. By and large, these software applications use data on past sales and, with sophisticated algorithms, try to forecast future sales.
Regardless of the sophistication of the underlying approach, the characteristics shared by all supply chain forecasts include the following:
Inaccuracy The most glaring attribute of all (point) forecasts6 is that they are invariably wrong. This is simply a statistical reality. For example, forecasting the monthly sales of a certain yellow women’s blouse in size 8 at a given price is bound to be wrong because there is a certain probability that it will equal almost any number. Since the forecast is a single number, the probability of the actual sales matching exactly the forecasted demand is practically nil.
Improvement with aggregation A second characteristic is that aggregate forecasts are more accurate than disaggregate forecasts. Forecasts can be aggregated, for example, over time, geography, or products. With aggregate forecasts, errors tend to cancel each other out, leading to more accurate forecasts.7 While it is difficult to forecast the sales of a blue men’s blazer size 42R on a given day in a given Boston store, it is easier to forecast the monthly sales of that blazer in that store, and even easier to forecast the monthly sales of that blazer throughout New England.
Time Horizon As anybody who follows weather forecasting knows, long-range forecasts are less accurate than short-range ones since fewer factors are known the longer the time frame is. Likewise, sales trajectories can diverge further and further from a projected forecast as time progresses. New fashions, economic changes, and competitors’ actions make the distant future murkier than the near-term future. But many supply chain operations require long-term demand forecasts since orders involve long lead times.
Reliance on history Forecasting methods use historical data and experience. The competitive environment drives manufacturers and suppliers to introduce new products and new versions of old products continuously. In these cases, and when companies enter new markets, data are scarce, making it difficult to forecast.
Reliance on trading partners History, however, is not the only source of data; trading partners often have information that can help in forecasting and planning. For example, retailers can give their suppliers data on sales patterns throughout their stores so the suppliers can base their forecasts on actual consumer behavior rather than the retailers’ order pattern.
Risk sharing While the sharing of data may lead to more accurate forecasting, companies can also share the risk of forecasting. Even though this will not improve the forecast itself, the practice can help supply chain partners mitigate the consequences of wrong forecasts and increase the profits of all trading partners.
Companies facing uncertain demand are doing more than investing in better forecasting tools. Acknowledging the inherent variability of demand and the limitations of statistical forecasting, they use these characteristics to design their supply chains to be flexible and to respond to ever-changing demand patterns, thus making them less dependent on demand forecasting. The flexibility to respond to demand fluctuations, created by these supply chain designs, also increases these companies’ resilience to disruption—be it an unexpected demand surge or unexpected problem with their supply lines.
The next sections describe how companies build flexibility into their operations based on each of these six forecast characteristics.
Instead of forecasting a single demand figure, progressive companies have turned to forecasting a range of potential outcomes. The range is used as a guide for supply contracting terms and contingency plans: what to do if demand is on the high end or low end of the range. More important, the use of range forecasting conditions the company to think in terms of uncertain outcomes or a range of possible realizations.
Companies can and do use range forecasting for more than just estimating future demand volume. For example, Agilent, Inc., develops a range forecast not only for future demand but also for supply volumes and prices for all of its products. Agilent is a $6 billion manufacturer of scientific instruments and analysis equipment, spun off from Hewlett-Packard in 1999. After developing all the possible outcomes for each new product, it assigns probabilities to each outcome that has more than a 10 percent likelihood of materializing and develops contingencies for these outcomes. Thus, Agilent’s planning covers 80 percent of the potential outcomes.
Range forecasts are used in flexible contracting, procurement strategies, and financial planning. The objective is to increase the company’s flexibility, as the range forecasts prepare the company for changing market conditions.
Many companies use their range forecast as an integral part of flexible contracts with their suppliers. These contracts specify a range of performance expectations, giving the company built-in flexibility to ramp production up or down as demand materializes. In a typical supply contract, Hewlett-Packard specifies that its suppliers should be able to ramp up production by 50 percent with two weeks’ notice and by 100 percent given one month’s notice. Similarly, Jabil Circuit Inc., an electronic manufacturing services company, requires its suppliers to increase deliveries by 25 percent on a week’s notice and 100 percent above normal on four week notice.8 Such contracts communicate to the supplier the importance of flexibility.
When contracting for transportation services, it is common for U.S. companies to bid for capacity on certain origin-destination movements (“lanes”) and then bid separately for “surge capacity.” Surges occur when a company’s business grows unexpectedly in certain regions of the country (as a result of weather, for example, or because of an unanticipated large order). Transportation carriers cannot be expected to have trucks or rail cars in reserve everywhere “just in case.” They can, however, put in place certain operational procedures to identify available resources and move them around, helping them respond to surges. Such surge capacity is typically priced higher, in acknowledgement of the extra equipment repositioning required by the carriers to respond to the increased demand.
Rather than rely on specific contracts, Helix Technology Inc., a vacuum products company, relies on its long-term relationships with its suppliers, and a continuing assessment of the suppliers’ capacities to ensure resilience and flexibility. In particular, Helix uses “quarterly capacity reports” via site visits to the supplier facilities to assess the supplier’s capability to support it in case of a disruption or a significant change in demand patterns. This also contributes to “socializing” the suppliers so they can better understand the importance that Helix places on flexibility.9
Typically, the forecasting range can be separated into an uncertain portion and a more predictable portion. Companies can be confident that they will sell at least at the low end of the forecast range while being less sure of, but prepared to sell at, the high end of the range.
For example, Hewlett-Packard manufactures DeskJet printers for North America in Singapore and Vancouver.10 The plant in Vancouver is more flexible, faster, and closer to the market, but manufacturing costs in Singapore are lower. So HP assigns the more stable, high-volume production to Singapore while using the Vancouver plant for the uncertain portion of the demand range. The Vancouver plant is used for short production runs to satisfy temporary demand surges. It is also used during each printer’s endof-life, when demand is falling off and becoming very uncertain (as a result of a new model introduction, a competitor’s offering or other changes in the marketplace). At that point, HP can even stop production in Vancouver altogether without being burdened with several weeks’ worth of inventory in the pipeline from Singapore.
Ford Motor Company, like other manufacturers in the automotive and heavy manufacturing sectors, cannot easily change its automotive production capacity. While range forecasting is used to let suppliers understand the variety of possible outcomes that Ford foresees, Ford uses the range forecasting internally for financial planning. Ford knows that at the low end of the demand range it will have to offer rebates to potential buyers, while at the high end it may have to use overtime to manufacture the extra vehicles that consumers demand. Analyzing the possible financial outcomes helps Ford hedge and plan its borrowing, cash and dividend policies.
In general, the value of range forecasting is that it conditions all the organizations involved in a particular supply chain to the likelihood that the demand, and therefore orders, staffing levels, capacity utilization, and other factors may defer from the expected forecast number.
One of the largest geographic markets for Cadillac, the upscale division of General Motors Corporation, is Florida. Customers’ experience when buying a Cadillac in Florida used to be similar to the less-than-satisfactory experience of car buyers everywhere. The process involved negotiations in which the dealers tried to sell what they had on their lot and the customers tried to compensate for buying the set of options and color they did not really want by haggling over price. If customers insisted on buying exactly what they wanted they had to wait about two months11 for the vehicle to be built to their specifications.
To enhance customer service, Cadillac decided in the late 1990s to move to a new sales regime. Instead of letting dealers order whichever cars they thought customers desired, Cadillac shipped only demonstration vehicles to the dealers, giving customers the opportunity to test-drive the cars. Cadillac itself used its Florida-wide database to forecast sales of different vehicle combinations and shipped them to a central Florida distribution center (DC). When a customer placed an order at any dealer, Cadillac shipped the car from the DC immediately, getting it to the dealer overnight.
Thus, instead of the dealers ordering from the factory, they were ordering from the DC. The DC had exactly the vehicle that the customer wanted about 75 percent of the time; a significant achievement given the large number of models, colors, and options available. When the right vehicle was not available at the DC, Cadillac was able to build the desired vehicle within three weeks, which is less than half the industry average, because it had to deal with a smaller number of special orders.12
In the example above, Cadillac had its customers’ desired configurations “in stock” most of the time because its forecast for all of Florida was inherently more accurate than any individual dealer’s forecast. Assume a customer wanted a crimson pearl Cadillac DeVille with dark gray interior, the Bose music system, and a touch-screen GPS, but no XM radio; memory mirrors, but no night-vision display or rain-sensing wipers; lumbar support for the driver seat but no heated/cooled front seats; with a trunk mat, but without a universal garage door opener; and so on. The chances of a customer finding that particular car at a local dealership are very low. The chances that such a car may be available centrally in Florida, however, are high because Cadillac only had to forecast that somebody, somewhere in Florida, may want such a car. Thus, Cadillac pooled the forecast risks across all its Florida dealers, forecasting Florida demand in the aggregate. As mentioned in the discussion of forecast characteristics, aggregate forecasts are more accurate than individual forecasts, because random forecast errors tend to cancel each other out. Harley Davidson, the American motorbike manufacturer, has instituted a similar system in Europe, leading to significantly improved customer service and reduced inventories.
Another common risk pooling strategy is to reduce the number of components used to make products. When multiple products share common components, the company can aggregate the forecasts for the products to create a more accurate forecast for the common components. For example, Intel’s Systems Group found it possible to simplify their parts usage by using common components, reducing 20,000 different part types to 500. In one case, they were using a mix of 2,000 different types of resistors, capacitors, and diodes; after analyzing their designs, they found that they needed only 35 types.13
Using a component or a part in a large number of finished products pools the demand risk. The demand for each part is the aggregate demand for all the finished products that use it. If there are many products, each facing its own independent demand pattern, a change in the demand for one product is likely to be offset by an opposite change in the demand for another. Such aggregation makes forecasting more accurate, enabling companies to keep all the parts they use in stock without excessive levels of inventory.
A large number of product configurations increases forecasting difficulty. For example, the 2000 Mercedes E Class allowed customers in the U.K. to specify any combination of two body styles, nine power trains, 121 paint/trim combinations, and 41 include/ exclude options, for a total of 3.9 trillion possible car varieties. Clearly, a company cannot forecast and stock trillions of car varieties. Some manufacturers combat these forecasting difficulties by restricting the number of options they offer. Thus, many automobile manufacturers put forward “packages” of options rather than offer a smorgasbord of add-on features. Honda, in contrast to Mercedes, allowed buyers of its popular Accord model in 2000 to choose among two body styles, four power trains, 30 paint/trim combinations, and two options, for a total of only 529 permutations. One can imagine the problem facing Mercedes dealers when trying to decide what to buy in order to stock their lot. The smaller number of packages, compared to an exponential explosion of fully customized configurations, allows for better risk pooling, lower variability, and thus better forecasts, resulting in higher customer satisfaction and lower inventory carrying costs.
The risk pooling in this strategy aggregates the desires of a large number of potential customers into a smaller number of product variants. The challenge with this strategy is to make sure that the vast majority of customers can be satisfied by a more limited range of options. Very few manufacturers today are in the position of Henry Ford, who suggested that customers can have its venerable Model T “in any color as long as it is black.”14
Reducing the lead time from product conception to its introduction into the marketplace shrinks the time lag over which the firm needs to forecast customer needs. The main steps involved are described in figure 6.1. Each of these steps involves multiple processes. For example, product development encompasses first the development of the product idea, market research, and final specification. Then the engineering process involves multiple cycles of building a prototype, testing it, sending it over to the manufacturing department, studying their objections, and repeating the process with a new prototype. Once the design is agreed upon, the ramp-up phase may involve tooling for the new product, manufacturing test batches, and finally production. In many cases, when nonstandard parts are involved, one waits for the suppliers to go through the same process. Production and logistics involve procuring and processing the parts and raw material, manufacturing the product, and distributing it.
In order to shorten the lead time involved, quick response manufacturing (QRM)15 processes involve rapid prototyping to speed up product development, rapid tooling to speed the ramp-up process, and rapid manufacturing to speed the production process. These methods are based on concurrent engineering—performing many of the development tasks in parallel, as shown in figure 6.2, in order to reduce the overall lead time through more efficient coordination between the various departments involved. To this end, the engineering iterations are conducted both with suppliers and with manufacturing personnel, with input from the procurement and logistics departments.
Companies who practice concurrent processes can react quickly to market changes when planning a new product. In addition, they can better respond to disruptions when products have to be changed quickly, parts rerouted, or products redistributed.
Some companies, such as Lucent Technologies, established such concurrency by creating a single supply chain organization. Others used physical proximity. For example, Luen Thai Holding Ltd. has been cobbling together a textile “supply chain city” in the southern Chinese city of Dongguan. It is a vast campus that represents all stages of the textile supply chain, aimed at speeding the time to market for customers like Liz Claiborne Inc. Instead of working with factories around the globe, Liz Claiborne designers are colocated with engineers from the fabric mills and apparel manufacturers. The aim is to shrink the iterative design process and reduce the time from concept to retail store from the current 10-50 weeks to just 60 days.
Nine West, a subsidiary of Jones Apparel Group, has emerged as a leader in specialty women’s shoe retail through a multistep supply chain design. The new process reduces downside risks and increases upside potential.
Fashion retailers such as Nine West face a common forecasting challenge. Each time a new style of shoe is introduced, Nine West faces unknown demand. Each shoe model is unique and thus it has no historical sales pattern to support demand forecasting. Typically, the result was widely missed forecasts, missed sales targets, and missed profits. When Nine West had a style that its customers really wanted, it ran out of stock. But at the same time, it had too much stock of slow-moving styles and had to discount them. It even developed a well-oiled process of getting rid of the unwanted inventory through outlet stores and discount retailers.
To improve its forecasts and be able to base its manufacturing decisions on sales data, Nine West developed a new process. After placing an order with a supplier, the first 1,000 pairs of shoes of a new style were flown to five representative U.S. stores where their sales were monitored closely for a few days. That information was then used as an indicator to forecast the sales of the entire line, increasing production if the sales were above expectations and decreasing it if the sales were lower than expected. If a new shoe “bombed” completely during the tests, Nine West would not only halt production; it would also send any already-produced shoes directly to outlet stores and discounters, saving the transportation to and from its own facilities.
Nine West is not the only manufacturer/retailer using this approach. A similar accurate response system used by the fashion skiwear manufacturer Sport Obermeyer cut the cost of overproduction and under-production by half; enough to increase profits by 60 percent.16 While such accurate response systems were designed to help companies respond to demand/supply imbalances caused by demand fluctuations, they allow companies to react quickly to imbalances rooted in supply problems as a result of a disruption, since they entail flexible contracts and close working relationships with suppliers.
Collaborative efforts based on lean supply chain principles—such as vendor-managed-inventory (VMI) and the Kanban system—are aimed primarily at reducing the bullwhip effect. They all involve information sharing. Wal-Mart Stores recognized that providing information to its suppliers can help these suppliers become more efficient, reducing their cost and therefore their prices. To this end, Wal-Mart developed in the early 1990s a computer application known as Retail Link. The tool, which is currently Internet-based, provides secure access to detailed daily sales data, trend analysis and more, to Wal-Mart’s many suppliers and partners. “Retail Link provides rapid access to near real-time information,” said Linda Dillman, Wal-Mart’s CIO. She added that the system’s information capabilities range “from predefined management reporting to fully customizable inquiries.”17
Actual sales data help suppliers understand the real demand for their product, regardless of the retailer’s order pattern, negating one cause of the bullwhip effect. Such data can help suppliers better plan their production, promotions, and product introductions.
Tackling the difficulties in forecasting, however, requires more than data sharing; it requires a cooperative process of identifying discrepancies and fixing the trading partners’ forecasts so that their actions can be aligned. Many such processes have been developed over the years. The Collaborative Planning, Forecasting, and Replenishment (CPFR) process is one of the most comprehensive such methods.
Superdrug Plc operates more than 700 stores throughout the United Kingdom, offering its customers an average of more than 6,000 product lines. Despite selling health and beauty aids—items for which the demand is relatively stable and therefore should be known—inventory levels at Superdrug were running in peaks and valleys and didn’t always match sales levels. These supplydemand mismatches created out-of-stock situations in some cases and excess inventory in others. Not surprisingly, most of the problems occurred during sales promotions and new product introductions.
To solve the problem, Superdrug turned to CPFR, a process developed by an industry consortium of retailers and consumer packaged goods manufacturers.18 Its partner in the collaboration process was Johnson & Johnson (J&J), the giant health-care products manufacturer and one of Superdrug’s most important suppliers. Both trading partners had a keen interest in increasing forecast accuracy.
A pilot process involving several J&J products was launched in August 2000 and ran through the end of that year. Each week, the two trading partners would exchange their sales forecasts and orders forecasts. Next, a special-purpose CPFR software engine19 would process the data and return any discrepancies between the data sets. A joint group then decided which forecast was correct and who should adjust. The data were adjusted and the process repeated the following week.
CPFR resulted in a 13 percent reduction of Superdrug’s inventory levels, while improving in-store availability by 1.6 percent. Superdrug cited many other, less quantifiable but possibly even more important benefits of its collaboration process, including better relationships with J&J, a factor that can create flexibility to respond to future disruptions.
The potential of close supplier relationships to enhance resilience was demonstrated by Toyota’s suppliers in the aftermath of the fire in the sole plant of Aisin, Toyota’s P-valve supplier, discussed in chapter 13.
While sharing the risk of an erroneous forecast with supply chain partners cannot improve the forecast itself, it can mitigate the consequences of forecast mistakes and lead to higher expected profits for all trading partners. Risk sharing can be imbedded in supply contracts in many forms, including buybacks, revenue sharing, and real option-based contracts.
One of the most common forms of risk sharing arrangements is the buyback agreement. In the book industry, for example, publishers buy back unsold books from retailers.20 By doing so, publishers share the risk of having too much inventory rather than let the retailers shoulder the entire risk. This arrangement encourages the retailers not to be unnecessarily conservative in ordering books and it can increase the profits of both the supplier and the retailer.
Similarly, the practice of “price support” provides reimbursements from the manufacturer to the retailer when the price of a product falls as a result of the introduction of new models. This practice, prevalent in the consumer electronics industries, is similar to a buyback agreement in its effect on both trading partners.
For the retailer, such arrangements are advantageous because having higher inventory allows it to sell more if demand is high. At the same time, the retailer gets financial support from the manufacturer if the product is not selling and must be discounted below cost. For the manufacturer, these arrangements work because the manufacturer sells more up front with a better chance of higher sales. Even if it must bear some of the risk of low sales, its expected profits are higher.
Until 1998, video-rental stores bought movie tapes from the major studios for about $66 a tape and rented them to customers at $3 a tape. This meant that it took 22 rentals just to cover the tape’s direct cost and a lot more in order to return a profit. Unfortunately for the retailers and the studios, consumers’ interest in movie rentals peaks at the time of the release and wanes shortly thereafter as newer movies are released. Because tapes required so many rentals to reach profitability, retailers purchased tapes in accordance with long-term, not peak, rental patterns. Thus, retailers were nearly always out of the most popular movies during peak demand, leaving consumers frustrated and both stores and studios losing potential income.
In the summer of 1998, Blockbuster Video solved this problem by restructuring its supply contracts. Instead of paying $66 a tape, it negotiated with Paramount Studios to buy each tape for only $9 but give the studio 50 percent of the rental revenues. Even though Blockbuster gave away 50 percent of the $3 rental fee, the remaining $1.50 let them cover the direct costs in just six rentals. At this price, Blockbuster could justify buying many more copies of tapes, because the risk of not being able to recover the tape purchasing costs during the peak demand period was greatly reduced. The larger purchases led to many more rentals and satisfied customers. In fact, the average inventory of tapes of major movie releases in Blockbuster stores increased from 24 to 124 tapes per title, as compared with an average inventory of 12 tapes at independent stores.
The results were spectacular. Blockbuster’s market share increased by five percentage points in the first year following the establishment of the new supply contracts (an amount that roughly equaled the share of the number two retailer, Hollywood Studios). Blockbuster’s market share continued to increase from 24 percent in 1999 to 40 percent in 2002. The new arrangement was very profitable for the studios as well, because minting more tapes costs very little; the largest cost is in creating the content in the first place.
Two outcomes of this revenue-sharing scheme stand as testament to its success. First, the practice became widespread throughout the industry for all the large chains and studios. The second outcome was that Blockbuster and the studios were sued for unfair trade practices by independent retailers—a sure sign that the scheme provided Blockbuster with a competitive advantage.22
Companies facing uncertain demand cannot rely only on statistical forecasting, even when using increasingly sophisticated algorithms. As the examples in this chapter demonstrate, they must use inherently flexible supply chain designs, allowing them to anticipate and respond to inevitable demand changes instead of planning for a fixed forecasted amount.
The major benefit of range forecasts and flexible contracts is that they condition all the supply chain partners to think and act in terms of multiple possible scenarios. Such nimbleness is likely to earn its rewards also when responding to a disruption.
Reducing the number of parts and product variants allows companies to use aggregate forecasting, which is more accurate. Aggregation creates inherent flexibility—inventory can be deployed to serve multiple products, multiple markets, or multiple retail outlets. It allows a company to move parts and products from surplus areas to areas where they are needed. The same procedures and mindsets can be used when responding to disruptions.
Reducing time-to-market means that a firm can better respond to changing market conditions because it forecasts over a shorter horizon. It also means that it can recover faster from a disruption because its business processes are concurrent and the various functions are accustomed to cooperating with each other.
Using test batches to gauge market demand reduces uncertainty and allows firms to make manufacturing and distribution decisions based on actual data. The benefits of such practice are that firms can re-route their goods to where they are actually needed. For disruption-planning purposes, however, one of the main benefits of this approach is that it conditions the firms and its suppliers to react quickly and to test continuously for uncertain outcomes.
The benefits of collaborative relationships and risk sharing contracts vis-à-vis disruptions are similar. Collaboration strengthens the relationships between the trading partners, making it easier to respond to disruptions. Risk-sharing contracts ensure that trading partners understand each other’s processes and share data, leading to faster response in case of disruptions.
These examples demonstrate how firms can create supply chains that are responsive and can react nimbly to supply/demand imbalances in an environment of high demand uncertainty. Such supply chains are also more resilient and can withstand supply disruptions better. Most companies invest in these and related schemes designed to make them more flexible and less prone to forecasting errors. The increased resilience offered by such supply chain designs may provide an extra impetus for moving in these directions.
Another system for creating flexibility—based on postponement and late customization—combines many of the principles outlined in this chapter to create flexible organizations that can withstand significant disruptions. This topic is discussed in chapter 12.