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What should I check to ensure this is what I need? How do I download? Theodore Helmer, Edward B. Opperman, and James D. Holton Wilson and Steven J. Introduction to Business Forecasting To help make the entire supply chain function more smoothly, many companies have started to use collaborative forecasting systems in which information about the forecast is shared throughout the relevant portions of the supply chain.
One such component might be the leather carrying case. The manufacturer of the carrying case may have suppliers of leather, clear plastic for portions of the case, fasteners, dyes perhaps, and possibly other components. Each one of these suppliers has its own suppliers back one more step in the supply chain. Forecasting has come to play an important role in managing supply chain relationships.
If the supplier of leather phone cases is to be a good supply chain partner, it must have a reasonably accurate forecast of the needs of the cellular phone company. The cellular phone company, in turn, needs a good forecast of sales to be able to provide the leather case company with good information. To help make the entire supply chain function more smoothly, many companies have started to use collaborative forecasting systems in which information about the forecast is shared throughout the relevant portions of the supply chain.
Often, in fact, suppliers have at least some input into the forecast of a business further along the supply chain in such collaborative forecasting systems. At the beginning of the text, at the very start of page 1, you read the following quote from Al Enns, director of supply chain strategies, at Motts North America: I believe that forecasting or demand management may have the potential to add more value to a business than any single activity within the supply chain.
Without knowledge of demand, manufacturing has very little on which to develop production and inventory plans while logistics in turn has limited information and resources to develop distribution plans for products among different warehouses and customers. Simply stated, demand forecasting is the wheel that propels the supply chain forward and the demand planner is the driver of the forecasting process.
There is another issue that is partially related to where a business operates along the supply chain that is important to think about when it comes to forecasting. As one gets closer to the consumer end of the supply chain, the number of items to forecast tends to increase. For example, consider a manufacturer that produces a single product that is ultimately sold through discount stores. Along the way it may pass through several intermediaries.
That manufacturer only needs to forecast sales of that one product and, of course, the potentially many components that go into the product. But assume that Wal-Mart is one of the stores that sells the product to consumers throughout the United States.
Just think of the tens of thousands of stockkeeping units SKUs that Wal-Mart sells and must forecast. Clearly the methods that the manufacturer considers in preparing a forecast can be much more labor intensive than the methods that Wal-Mart can consider.
An organization like Wal-Mart will be limited to applying forecasting methods that can be easily automated and can be quickly applied. This is something you will want to think about as you study the various forecast methods discussed in this text.
Emphasis added. Introduction to Business Forecasting 13 In the simplest form the process is as follows: A manufacturer that produces a consumer good computes its forecast. That forecast is then shared with the retailers that sell that product to end-use consumers. The manufacturer then updates the forecast including the shared information. In this way the forecast becomes a shared collaborative effort between the parties.
Lower inventory and capacity buffers. The producer can push the forecast throughout the supply chain resulting in a better match of inventories for all participants. Fewer unplanned shipments or production runs. These unplanned shipments usually carry a premium price. Reduced stockouts. This will always have a negative impact on the seller due to lost sales and lower customer satisfaction.
Increased customer satisfaction and repeat business. Buyers know that they sometimes have unusual demand cycles. Better preparation for sales promotions. Promotions are special demand situations. No one wants to promote products that cannot be supplied. Meeting the needs of promotions is another positive input for customer service. Better preparation for new product introductions. New product launches can be very tricky as sellers attempt to establish the supply chain.
Meeting the needs of new product launches can maximize launch timing and increase speed to market. Dynamically respond to market changes. Sometimes markets change based on external factors popular culture, governmental controls, etc. Being able to respond dynamically to these special cases without overstocking or understocking is critical. Companies that have adopted collaborative forecasting programs have generally seen very positive results. Accessed February 9, This retailer operates at more than 20 locations each with multiple retail outlets including department stores, mass-merchandisers, and convenience stores.
As a result of sharing information in the supply chain the retailer achieved supply chain savings at the two biggest locations of about 15 percent and 33 percent.
A number of software developers offer programs that are designed to create that data link between parties. A company needs to be committed to an electronic data platform including available hardware, software, and support staff. Depending on the size of the company and the complexity of the integration, the amount of resources can vary greatly.
One of the most interesting problems to consider when establishing a collaborative relationship is how to deal with a nonparticipant. At the center of the issue is the preferential delivery of goods to the customer with the collaborative relationship. Companies with this dilemma have responded in several different ways. Some companies pass cost savings and reduced price structuring to all their customers. Some provide preferential delivery and pricing to the collaborative partner alone.
Others simply attempt to drive out the costs of excess inventory and stockouts while keeping their price structuring the same for all customers. Most of the time, information resides in public forums computer servers with only a software security system protecting it from outsiders. Production forecasts can often be tied to production capacity, which is very critical information, especially to competitors.
Other information surrounding product launches and special promotions is also very sensitive and could be at risk. Information breaches could be an oversight as well. With 32 Peter A. Today things are quite different. As early as a study reported that 68 percent of the companies surveyed used computers in preparing forecasts. Just over 30 percent of the marketing professionals surveyed who use a computer in developing forecasts relied solely on a personal computer.
The widespread availability of computers has contributed to the use of quantitative forecasting techniques, many of which would not be practical to carry out by hand. Most of the methods described in this text fall into the realm of quantitative forecasting techniques that are reasonable to use only when appropriate computer software is available. The use of personal computers in forecasting has been made possible by rapid technological changes that have made these desktop or laptop computers very fast and capable of storing and processing large amounts of data.
Wheelwright, and Victor E. McGee, Forecasting: Methods and Applications, 2nd ed. Holton Wilson and Hugh G. The dominance of PC forecasting software is clear at the annual meetings of the major forecasting associations. At these meetings various vendors of PCbased forecasting software packages display and demonstrate their products. The importance of quantitative methods in forecasting has been stressed by Charles W.
Chase, Jr. By and large, forecasts are driven 80 percent mathematically and 20 percent judgmentally. However, there is a rich history of forecasting based on subjective and judgmental methods, some of which remain useful even today.
In some situations a judgmental method may even be preferred to a quantitative one. Very long range forecasting is an example of such a situation. The computer-based models that are the focal point of this text have less applicability to such things as forecasting the type of home entertainment that will be available 40 years from now than do those methods based on expert judgments. In this section several subjective or judgmental forecasting methods are reviewed.
Sales Force Composites The sales force can be a rich source of information about future trends and changes in buyer behavior. If the information available from the sales force is organized and collected in an objective manner, considerable insight into future sales volumes can be obtained.
Members of the sales force are asked to estimate sales for each product they handle. Often a range of forecasts will be requested, including a most optimistic, a most pessimistic, and a most likely forecast.
Charles W. Scheduled production runs are shorter than they should be, raw-material inventories are too small, labor requirements are underestimated, and in the end customer ill will is generated by product shortages. The sales manager with ultimate forecasting responsibility can offset this downward bias, but only by making judgments that could, in turn, incorporate other bias into the forecast.
Robin Peterson has developed a way of improving sales force composite forecasts by using a prescribed set of learned routines as a guide for salespeople as they develop their forecasts. Review forecasts of gross domestic product. Review industry sales data for the preceding year.
Review company sales data for the preceding year. Review company sales forecasts for the previous years. Survey key accounts concerning their purchasing plans. Evaluate the pricing practices of competitors.
A script such as this can be developed, based on interviews with successful salespeople concerning procedures they have used in preparing their forecasts. This practice presumes that buyers plan their purchases and follow through with their plans. Such an assumption is probably more realistic for industrial sales than for sales to households and individuals.
It is also more realistic for big-ticket items such as cars than for convenience goods like toothpaste or tennis balls. Survey data concerning how people feel about the economy are sometimes used by forecasters to help predict certain buying behaviors. One of the commonly used measures of how people feel about the economy comes from a monthly survey conducted by the University of Michigan Survey Research Center SRC. The SRC produces an Index of Consumer Sentiment ICS based on a survey of individuals, 40 percent of whom are respondents who participated in the survey six months earlier and the remaining 60 percent new respondents selected on a random basis.
This index has its base period in , when the index was High values of the ICS indicate more positive feelings about the economy than do lower values. Thus, if the ICS goes up, one might expect that people are more likely to make certain types of purchases.
Jury of Executive Opinion The judgments of experts in any area are a valuable resource. Based on years of experience, such judgments can be useful in the forecasting process. To provide a breadth of opinions, it is useful to select these people from different functional areas. The person responsible for making the forecast may collect opinions in individual interviews or in a meeting where the participants have an opportunity to discuss various points of view.
The latter has some obvious advantages such as stimulating deeper insights, but it has some important disadvantages as well. The Delphi Method The Delphi method is similar to the jury of executive opinion in taking advantage of the wisdom and insight of people who have considerable expertise about the area to be forecast. It has the additional advantage, however, of anonymity among the participants. The Delphi method can be summarized by the following six steps: 1.
Participating panel members are selected. Questionnaires asking for opinions about the variables to be forecast are distributed to panel members.
Results from panel members are collected, tabulated, and summarized. Introduction to Business Forecasting 19 4. Summary results are distributed to the panel members for their review and consideration. Panel members revise their individual estimates, taking account of the information received from the other, unknown panel members.
Through this process there is usually movement toward centrality, but there is no pressure on panel members to alter their original projections. Members who have strong reason to believe that their original response is correct, no matter how widely it differs from others, may freely stay with it. Thus, in the end there may not be a consensus. The processes of sending out questionnaires, getting them back, tabulating, and summarizing can be speeded up by using advanced computer capabilities, including networking and e-mail.
As future business professionals, like yourself, become better trained in quantitative forms of analysis, this advantage will become less important. Historically, another advantage of subjective methods has been their wide acceptance by users. This subjectivity is nonetheless the most important advantage of this class of methods.
There are often forces at work that cannot be captured by quantitative methods. They can, however, be sensed by experienced business professionals and can make an important contribution to improved forecasts. When the less accurate subjective method was combined with the quantitative methods, errors were further reduced to about 40 percent of the level when the subjective method was used alone.
It is clear from this result, and others, that there is often important information content in subjective methods. The disadvantages of subjective methods were nicely summarized by Charles W. Often judgmental methods are better suited to forecasting new-product sales because there are many uncertainties and few known relationships.
However, there are ways to make reasonable forecasts for new products. These typically include both qualitative judgments and quantitative tools of one type or another. Surveys of potential customers can provide useful preliminary information about the propensity of buyers to adopt a new product.
Test-market results and results from the distribution of free samples can also provide estimates of initial sales. On the basis of predictions about the number of initial innovators who will buy a product, an S-shaped market-penetration curve can be used to forecast diffusion of the new product throughout the market.
Terry Anderson has described a process for new-product forecasting at Howmedica that is based on various judgmental factors. A customer usage rate is derived based on experience with past new introductions. Inventory requirements are also included in making projections. Whitlark, Geurts, and Swenson have used customer purchase intention surveys as a tool to help prepare forecasts of new products.
Whitlark, Michael D. Geurts, and Michael J. Probabilities are then assigned to each of the intention-to-buy categories, using empirical evidence from a longitudinal study of members of the target market covering a length of time comparable to the length of time for the proposed forecast horizon. An example of these probabilities for a three- and a six-month time horizon is shown in Table 1.
Note that the probabilities of purchase increase as the time horizon increases. Applying this method to two products produced good results. In the six-month time horizon the forecast and actual rates were Similar results were found for a second product.
In the three-month horizon the forecast and actual percents were 2. Near the end of the growth stage, sales growth starts to level off substantially as the product enters the maturity stage. Businesses often employ marketing strategies to extend this stage as long as possible.
However, all products eventually reach the stage of decline in sales and are, at some point, removed from the market such as Oldsmobile cars, which had been in the automobile market for a century. This notion of a product life cycle can be applied to a product class such as personal passenger vehicles , to a product form such as sport utility vehicles , or to a brand such as Jeep Cherokee—whose life cycle ended after many years and was replaced with the Jeep Liberty.
Product life cycles are not uniform in shape or duration and vary from industry to industry. The Jeep example illustrates a relatively long life cycle. For high-tech electronic products, life cycles may be as short as six to nine months. An example would be a telephone that has a design based on a movie character. The forecasting approach that is best will vary depending on where a product or product class is in the life cycle.
The real forecasting problems occur in the introductory stage or in the preintroductory product development stage. Analog Forecasts The basic idea behind the analog method is that the forecast of the new product is related to information that you have about the introduction of other similar products in the past. Two years ago for the Christmas season you introduced a toy that was based on a popular animated Christmas movie. The 48 See, for example, Scott E. Pammer, Duncan K.
Fong, and Steven F. Aaker, V. Kumar, and George S. Both unit sales and market penetration are illustrated. Now you have a new toy to bring to market this Christmas season, and you need some estimate of sales. Suppose that this new product appeals to a narrower age range such that the likely percentage of households that would purchase the product is 1. Assuming that the only change is the percentage of households likely to purchase the product, the relation of sales of the new product to the old one would be 1.
If the size of the relevant population, the percentage of stores stocking the product, or the promotional effort changes, you would adjust the forecast accordingly. Test Marketing Test marketing involves introducing a product to a small part of the total market before doing a full product rollout.
The test market should have characteristics that are similar to those of the total market along relevant dimensions. For example, usually we would look for a test market that has a distribution similar to the national market in terms of age, ethnicity, and income, as well as any other characteristics that would be relevant for the product in question.
For example, Kansas City, Missouri, would not usually be a good test market because there would be a good deal of crossover between Kansas City, Missouri, and Kansas City, Kansas. Indianapolis, Indiana, on the other hand, might be a better choice of a test market for many types of products because it has a demographic mix that is similar to the entire country and is relatively isolated in the context discussed here. If, in the total market, there are million households, we might project sales to be 17, units [1.
Part of this evaluation would normally include some measure of likelihood to purchase the product. From these results a statistical probability of purchase for the population can be estimated and used to predict product sales. The use of in-home product evaluations is a similar process. A panel of consumers is asked to try the product at home for an appropriate period of time and then is asked to evaluate the product, including an estimate of likelihood to purchase.
Type of Product Affects New-Product Forecasting All products have life cycles and the cycles have similar patterns, but there may be substantial differences from one product to another. Think, for example, about products that are fashion items or fads in comparison with products that have real staying power in the marketplace.
Fashion items and products that would be considered fads typically have a steep introductory stage followed by short growth and maturity stages and a decline that is also very steep. High-tech products often have life cycles that are relatively short in comparison with low-technology products. Its importance is highlighted by the fact that it was republished in Management Science in December The Bass model was originally developed for application only to durable goods.
However, it has been adapted for use in forecasting a wide variety of products with short product life cycles, and new products with limited historical data.
Lynn, Steven P. Schnaars, and Richard B. Yt Number of previous buyers at time t. The values for p, q, and m can be estimated using a statistical tool called regression analysis, which is covered in Chapters 4 and 5 of this text. The algebraic form for the regression model is: St a bYt cYt 1 2 1 From the regression estimates for a, b, and c the values of p, q, and m can be derived. Cell phones would be a good example. New cell phones with a variety of enhancements seem to appear almost weekly.
Such products may have a life cycle of perhaps 12 to 24 months, which means that there is little time to gather historical data upon which to base a forecast. It also means that the initial forecasts are exceptionally important because there is less time to recover from either over- or underprojecting sales.
The life cycle for this type of situation may look something like that shown in Figure 1. We illustrate this in Figure 1. The data shown in such a graph can frequently be developed by looking at the historic PLC for similar products, such as past generations of cell phones. Based on this knowledge, a natural bump to sales can be expected during the back-to-school period in August and September, followed by increased buying during the holiday season, and another bump when people get tax returns in March.
Based on knowledge from past product introductions, the seasonal indices are estimated to be: August, 1. A complete list of the seasonal indices SI for this product are shown in Table 1.
We can also incorporate the marketing plans for the product into the PLC forecast. Suppose that the marketing mix for the product calls for a skimming introductory price followed by a price cut three months after the product launch.
A similar price cut is planned for the following July to help prop up sales as the life cycle moves into a more rapid rate of decline. Typically a price cut this late in the PLC has less effect, as can be seen in Table 1. These seasonal and marketing mix constructs are used to adjust the baseline new-product life cycle as illustrated in Table 1. Additional marketing mix relationships, such as distribution and awareness strategies, could be included in a similar manner.
The baseline forecast and the seasonally adjusted forecast are shown in the top of Figure 1. The bottom graph shows the baseline forecast and the forecast after adjustment for seasonality, and marketing mix strategies including pricing P , a holiday promotion H , and a backto-school promotion S. You may have used this method today in deciding what clothes to wear. If yesterday was snowy and cold, you might expect a similar wintry day today.
In fact, without evidence to suggest otherwise, such a weather forecast is quite reasonable. For this example we use data from January through December These data are given and shown graphically in Figure 1.
In both forms of presentation you can see that the UMICS varied considerably throughout this period, from a low of You should develop the habit of observing data in graphic form when forecasting.
The simplest naive forecasting model, in which the forecast value is equal to the previous observed value, can be described in algebraic form as follows: Ft At 1 where Ft represents the forecast value for time period t and At 1 represents the observed value one period earlier t 1. We call this Naive forecast 1 because we will very shortly look at another naive forecast. The results are shown in Table 1. These measures will be discussed shortly.
Note that each forecast value simply replicates the actual value for the preceding month. These results are presented in graphic form in the upper graph of Figure 1. The forecast for every month is exactly the same as the actual value for the month before. We might argue that in addition to considering just the most recent observation, it would make sense to consider the direction from which we arrived at the latest observation.
If the series dropped to the latest point, perhaps it is reasonable to assume some further drop. Alternatively, if we have just observed an increase, it may make sense to factor into our forecast some further increase. The naive forecast2 takes into account the change between previous periods. In general algebraic terms the model becomes Ft At P At 1 1 At 2 where Ft is the forecast for period t, At 1 is the actual observation at period t 1, At 2 is the observed value at period t 2, and P is the proportion of the change between periods t 2 and t 1 that we choose to include in the forecast.
This is illustrated with P 0. Let us look closely at the circled value in Table 1. To get the forecast for March denoted as MAR06 , we take the observed value for February Feb and adjust it by including some information from the most recent trend. You have now looked at two alternative forecasts of the University of Michigan Index of Consumer Sentiment.
Which forecast is best depends on the particular year or years you look at. See Table 1. In retrospect it is easy to say which forecast was better for any one period.
But we need some way to evaluate the accuracy of forecasting models over a number of periods so that we can identify the model that generally works the best. To illustrate how each of these is calculated, let At Ft n Actual value in period t Forecast value in period t Number of periods used in the calculation 1. The mean error is calculated as: ME At Ft n 2. For criteria one through six, lower values are preferred to higher ones. If U 1, the model forecasts better than the consecutive-period nochange naive model; if U 1, the model does only as well as the consecutiveperiod no-change naive model; and if U 1, the model does not forecast as well as the consecutive-period no-change naive model.
The values for these measures, for both forecasts of the University of Michigan Index of Consumer Sentiment, are shown in Table 1. Often you can expect mixed results, in which no one model performs best as measured by all seven measures.
Mean error ME and mean percentage error MPE are not often used as measures of forecast accuracy because large positive errors At Ft can be offset by large negative errors At Ft.
ME and MPE are, however, very useful as measures of forecast bias. For example, a sales series may be in thousands of units, while the prime interest rate is a percentage. The RMSE is easy for most people to interpret because of its similarity to the basic statistical concept of a standard deviation, and it is one of the most commonly used measures of forecast accuracy.
All quantitative forecasting models are developed on the basis of historical data. This helps us in selecting an appropriate model. When forecasting sales or some other business or economic variable, it is usually a good idea to consider more than one model.
We know it is unlikely that one model will always provide the most accurate forecast for any series. In our example of forecasting the University of Michigan Index of Consumer Sentiment, using the two naive models described in previous sections, we could take the lowest forecast value in each month as the most optimistic, the highest as the most pessimistic, and the average value as the most likely.
The latter can be calculated as the mean of the two other forecast values in each month. The purpose of a number of studies has been to identify the best way to combine forecasts to improve overall accuracy. For now, we just want to call attention to the 55 Pamela A. Some forecasting techniques require only the data series that is to be forecast.
These methods include the naive methods discussed in previous sections as well as more sophisticated time-series techniques such as time-series decomposition, exponential smoothing, and ARIMA models, which will be discussed in subsequent chapters of this text. On the other hand, multipleregression methods require a data series for each variable included in the forecasting model.
This may mean that a large number of data series must be maintained to support the forecasting process. The most obvious sources of data are the internal records of the organization itself. Such data include unit product sales histories, employment and production records, total revenue, shipments, orders received, inventory records, and so forth. However, it is surprising how often an organization fails to keep historical data in a form that facilitates the development of forecasting models.
Another problem with using internal data is getting the cooperation necessary to make them available both in a form that is useful and in a timely manner. As better information systems are developed and made available through computer technology, internal data will become even more important and useful in the preparation of forecasts.
Various trade associations are a valuable source of such data, which are usually available to members at a nominal cost and sometimes to nonmembers for a fee. But the richest sources of external data are various governmental and syndicated services. As you will see, there is a fair amount of variability in how well different methods work for this very important economic series. The data represent sales in thousands of units and have not been seasonally adjusted.
As seen in Table 1. Thus, our forecast for December is Table 1. Figure 1. Chapter 3 presents moving-average and exponential smoothing techniques. These methods are widely used, quite simple from a computational standpoint, and often very accurate. Chapter 4 provides an explanation of simple linearregression analysis and its applications to business forecasting.
Both simple trend models and simple two-variable causal models are presented. In Chapter 5 the simple regression model is expanded to include more than one independent variable. Classical time-series decomposition, discussed in Chapter 6, provides accurate forecasts for many series.
In addition, it can be used to develop seasonal indices that help identify the degree of seasonality in the data. These indices can also be used to deseasonalize the data series.
Chapter 8 contains a discussion of alternative methods for combining individual forecasts to take advantage of information contained in different methods to improve forecast accuracy. In Chapter 9, a new chapter in this edition, we present data mining as another useful forecasting tool. Often businesses have huge amounts of data that can be used to predict sales or other outcomes but the data are not amenable to standard forecasting methods.
Data mining is a tool that has evolved to help us deal with such situations. Chapter 10 focuses on how to select appropriate forecasting methods for a particular situation and how to establish an effective forecasting process. The Comments from the Field N.
Forecasts are critical inputs to a wide range of business decision-making processes. We can see the future coming if we know what to look for because many things often progress in an astonishingly orderly manner over time. This consistent progress provides a basis for forecasting.
At the same time, many things respond to needs, opportunities, and support resources. In addition, a set of exercises in each chapter will help you validate your understanding of the material. In these cases, concepts from the chapter will be applied to this sales series.
In this chapter we will apply concepts as well as provide an overview of the company. The Gap has managed to successfully market its retail stores and the apparel it carries. In , The Gap was the number two clothing brand in America, and in it placed in the top 25 of the 50 most recognizable brands in the United States.
He felt that there was a need for a store that would sell jeans in a full array of sizes. The store was an instant success, and The Gap stores were on their way to national prominence.
This changed in , however, when the Federal Trade Commission prohibited manufacturers from dictating the price that retailers could charge for their products.
Fisher recognized the need to expand his product offerings to include higher-margin items, and therefore began to offer private-label apparel.
The Gap already had formed strong relationships with manufacturers from its earlier entry into the privatelabel business. This enabled it to monitor manufacturing closely, which kept costs low and quality high. Drexler paid equally close attention to the visual presence of the stores. As new merchandise came in, store managers were given detailed plannograms, which told them precisely where the items would go. With this control, Drexler ensured that each Gap store would have the same look, and would therefore present the same image to the customer.
The idea originated after Drexler was disappointed with the lack of selection he found while shopping for his own child. In , The Gap bought Banana Republic, which featured the then-popular safari-style clothing. This trend toward khakis had been brought on by the popularity of movies such as Raiders of the Lost Ark and Romancing the Stone.
Banana Republic was repositioned as a more upscale Gap, with fancier decor as well as more updated fashions that offered a balance between sophistication and comfort.
Although these other Gap divisions had grown and prospered, the traditional Gap stores began to falter in the early s. Coupled with the effects of a retailing recession, their strong emphasis on basic styles had made them a target of competition. Drexler and his team recognized that several major changes were taking place in the retailing environment, and they needed to identify ways to respond to this competition if they were to continue to grow.
One way The Gap responded to increasing competition was to revise its merchandise mix. To respond to this trend, The Gap took advantage of aggressive changes already under way in its inventory management programs, Introduction to Business Forecasting which gave it faster replenishment times. Another way that The Gap responded to increased competition and changing retailing trends was by entering into strip malls. This move has been facilitated in part by the reduction of available spaces in large malls.
As fewer spaces became available, retailers wishing to expand have had to explore other possible options. Many strip centers have been upgraded in response to this trend, and retailers found that they could offer their customers easier access to stores and more convenient parking than they could in their traditional mall locations.
With carefully placed geographic locations, retailers also discovered that they could often do the same volume that they could in the large malls. Additionally, strip-center rents are substantially lower than those of their mall counterparts.
Their common charges are sometimes a mere 25 percent of what they would be in a typical large mall. By doing so, The Gap capitalized on the new surge of price-conscious consumers. Gap Warehouse stores offer Gap-type styles at prices about 30 percent lower than The Gap apparel. Its success with this discount concept led to the launch of the Old Navy Clothing Co.
Old Navy stores carry a different assortment of apparel than traditional Gap stores. There are other ways in which The Gap differentiates its Old Navy stores from its traditional Gap stores, however.
Old Navy stores are further positioning themselves as one-stop-shopping stores by offering clothing for the whole family in one location. Forth and Towne was not well received by the market. Amazon Restaurants Food delivery from local forecastingg. Showing of 12 reviews. This Excel-based tool effectively uses wizards and many tools to make forecasting easy and understandable. Moreover, students learn how to interpret and communicate the most important components of each forecasting method.
The software accompanying the book also includes all the data used in the text examples and chapter ending problems. This made it difficult to know whether I was even performing my forecasting homework correctly or not. Please try again later.
Unless you need this book for a class, I would advise against purchasing it to learn more about business husiness. Amazon Second Chance Pass it on, trade it in, give it a second life. Holton Wilson holds a B. Amazon Advertising Find, attract, and engage customers. The book was a great companion for the materials. Discover Prime Book Box for Kids. I had to use this book for a forecasting class in college. It was not my favorite textbook, as some of the concepts were not explained thoroughly enough, which left me feeling lost.
Mac users beware though, the software is geared towards Windows users so a Windows PC or Laptop will be necessary. Try the Kindle edition and experience these great reading features: Forcasting ordered this book Friday in the evening and received it Monday when I got home from work.
These items are shipped from and sold by different sellers. If you are a seller for this product, would you like to suggest updates through seller support? It would also have been great to have some solutions provided with the CD but this is not the case.
Add both to Cart Add both to List. Read reviews that mention business forecasting real world using the forecastx easy to use learn about business book also great book software examples businesss text chapter concepts course data excel practical provides solutions.
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