DEMAND FORECASTING FOR WHOLESALE BUSINESSES

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1 Page 1 of 7 DEMAND FORECASTING FOR WHOLESALE BUSINESSES John B. Vinturella, Tammany Supply, Inc., Covington LA Ernest R. Nordtvedt, Loyola University, New Orleans LA ABSTRACT A model for forecasting demand for wholesale businesses is proposed which builds on existing theories developed in a retail context. The model is then tested on an actual wholesale building supplies firm, with promising results for wider application. BACKGROUND Introduction Demand forecasting is widely used by retail firms in evaluating prospective sites for a new business location, and in the planning process for existing locations. Shopping malls, population concentrations, presence of competition, single site V. convenience shopping, rural v. urban retail settings, are among the factors which have been studied. While considerable work has been done on identification of trading areas and forecasting of demand for retail business, the same type of information has not generally been available to wholesalers. Wholesale distribution success or failure is impacted by many of the same factors that impact the retailer. The primary differences lie in definition of market demand, customer, and market structure. This study will examine the methodology used in determining retail forecasts, extend analytic models to the wholesale distribution function, and validate the result through a case study comparing projected and actual demand over a twelve-year period of operations. Application discussions will emphasize the use of the model in support of the startup decision. The resultant model can then be used by entrepreneurs to predict an identified market's demand for their services. Given the price structure of the proposed product line, a decision can then be made as to current entry feasibility, or when or whether the business might become feasible within the range of predictive confidence of the model. Distribution Industry Characteristics. Because of sales volume requirements, distributors tend to cluster in the heart of a

2 Page 2 of 7 metropolitan area while serving a larger geographic area. As sections of the area build in activity, a local distributor or branch of a larger distributor might open "convenience" locations, such as satellite showrooms, "pick-up counters," or sub-branches ("twigs") with very limited inventory. Such operations recognize a concentration of demand, but do not constitute the opening of a new market. Our focus will be on determining when a rural area or segment of a larger metropolitan area has reached a size and activity level ("threshold") sufficient to support a full-service distributor. Such a distributor would order material in sufficient quantity to buy directly from manufacturers, and provide support services such as showrooms, delivery, and an outside sales force. This distinction is important because the full-service operation frequently represents a location decision reflecting the "arrival" of a market, and has considerable implications for entrepreneurial activity. Some could be direct (encouragement of tradesmen to form their own companies), and others indirect (provision of information to other entrepreneurs in related/competitive businesses). LITERATURE REVIEW Market Structure Central Place Theory presents a widely accepted explanation of market size, location and spacing [1]. The first of two variables, threshold, suggests that a minimum consumer demand is necessary to support a retail operator. The second, range, suggests that the consumer will travel only so far in shopping for a specific good. Other concepts within this theory that apply to our problem are: a.those communities most centrally located within a region attract the most specialized and largest number of firms. b.smaller communities emerge around the dominant one and offer lower-level merchandise. C.The more highly specialized the product or service being offered, the larger the community must be before a business can be supported. d.in rural areas, where market areas are necessarily larger, thresholds tend to be lower and competition weaker. e.consumers prefer a single location for a variety of related goods over a separate place for

3 Page 3 of 7 each. Reilly's intercity model [2] addresses issues of customer indifference when deciding between or among two or more shopping opportunities. Driving time and distance are important consumer considerations, while population density will determine the number of shopping choices available. The model estimates the "breakpoint" between trade centers a and b, as that distance (or driving time) from center b at which customers are indifferent relative to the two trade centers: Breakpoints can be calculated between the center being evaluated and adjacent trading centers to outline the boundaries of the subject trading area. Another variation might be to substitute store inventory for population. Market Potential Huff's gravity model (3] was originally intended as a tool to forecast trading area size. Extensions to the model are commonly made to change the forecast output to retail demand. Such extensions include the estimation of numbers of consumers, expenditure levels, and the number of trips made by the consumer to purchase goods within a specified time frame. Consolidating Huff's probability and market size variables into a market share produces a more intuitive form of the model. MODEL DEVELOPMENT Research Objectives and Methodology In contrast to the retail function, little effort has been expended in studying similar factors involved in wholesale distribution location decisions or marketing potential. The objective of this study is to propose a structure for determining the point at which a potential market for this service is created. Methodology will be based on the Huff gravity model, extending it by adding variables that forecast wholesale demand in the same way that the current model forecasts retail demand. A distributor of homebuilding and related materials will be the case study used to validate the results. Application to Wholesale Building Supplies

4 Page 4 of 7 Our hypothesis is that the gravity model can be applied to a wholesale building supply business by extending it in the following way: This equation, to be referred to as the VintNord model, stated that demand for the offerings of a building products supplier consists of two components: (1) a population based demand (replacement and repair parts, etc.), and; (2) a building activity based demand (essentially a demand by consumers not yet reflected in population figures). In the first, or "retail," part of the model, the number of consumers may be considered to be the entire population, and the average amount spent a per capita figure. In the second, or "wholesale" term, each building project requires a permit, and permits to be issued in each submarket can be forecast; an average amount spent per permit can be obtained from historical data on existing firms. Since the firm competes with far more outlets (e.g. hardware and discount stores) for the retail component of the business, market share "L" will in general be smaller than market share "M." Operationalization of Variables The usefulness of the VintNord model is dependent on the user's ability to provide meaningful estimates of the items in the equation. Some are reasonably straightforward, e.g., average per capita and per building permit expenditure on the product line can be calculated with some confidence; population and building activity can be projected from historical data with some judgment about current trends. A more difficult problem arises in identifying the various sub-markets and assigning an expected retail and wholesale market share to each. This problem is not unique to the VintNord model; it is the crux of any forecasting method, "gravity-based" or otherwise. In the course of developing a business plan for wholesaler start- ups, entrepreneurs make many decisions which rely heavily on their business experience and judgment. Once a site is under consideration, estimates must be made of minimum inventory level, personnel, and floorspace required to compete effectively in that market. From these basic estimates, the expense side of the pro-forma income statement can be developed. On the revenue side, there is generally a fairly strong sense of the margin achievable within the subject industry. The VintNord model then assists entrepreneurs in completing the income picture by reducing the process of "guessing" at sales figures to estimating those figures as a product of more tangible items for which they might have a considerably stronger "feel."

5 Page 5 of 7 Sales figures generated by the model can then be used in the pro-forma statements, producing the financial data on which the startup decision is based. This analysis might include outcomes for a "pessimistic" set of estimates, thereby defining a worst- case scenario, as well as a "most likely" set of estimates. MODEL VALIDATION Case Study In 1977, St. Tammany Parish was in the early stages of the transition from a rural area to a suburb of New Orleans (see Figure 1). The city of Slidell and its environs, at the eastern end of the parish, were already experiencing the beginning of a building boom. The western end of the parish was slower in developing, largely because the commuter was still several miles from the New Orleans central business district after driving 24 miles across the take Pontchartrain Causeway. At that time, Southlarid Plumbing Supply made the decision to open a branch in Covington, the parish seat and business center of west St. Tammany. While several other "southshore" plumbing wholesalers were rumored to be considering a Covington branch, the consensus seemed to be that the area was not quite large enough yet. The decisions to open the branch, and to spin it off as a separate operation (Tammany Supply, Inc., or TSI) a year later, were based on the perception that the area was rapidly approaching the market threshold. TSI immediately assumed a dominant market position in west St. Tammany, and remains (1991) the only plumbing wholesaler in the area. To validate the VintNord model, we assembled data on TSI performance from 1979, its first full year of operation, through For each year actual sales were then compared to sales predicted by the model. Application of the Model to TSI The following assumptions and estimates were made in testing the model on TSI data: 1. The trading area was determined to be the "northshore," with the following sub-markets: Primary: West St. Tammany Secondary: East St. Tammany Tertiary: Washington Parish Quarternary: Tangipahoa Parish 2. The Reilly model indicated, and our judgment affirmed, that retail business was drawn from

6 Page 6 of 7 the primary market only. 3. Market shares of new construction were estimated for each year and each sub-market based on our knowledge of the market, the major contractors and construction projects, and the competitive climate. 4. Retail market share was estimated to be two-thirds of that for wholesale. 5. Population figures between census years used interpolation and intermediate estimates from Maruggi [4]. 6. West St. Tammany population was estimated to be 40% of parish population. 7. Permit figures are actual numbers of new residential permits, from Department of Commerce reports. Commercial permits were not considered because an insignificant amount of commercial business was done by TSI. 8. Consumer spending was estimated based on experience and Bureau of Labor Statistics data. Per permit spending was based on TSI sales data. 9. Spending estimates were made for 1990, then recomputed in constant dollars to 1979 using the Consumer Price Index. In 1988 the company took on several new retail lines (cabinets and appliances, casual furniture, water treatment products), and two new wholesale lines (air conditioning supplies, irrigation equipment). For the years , nominal values for actual sales of these items were added to predicted sales of our basic plumbing product line in order to keep the application of the model consistent. Results A comparison of actual to predicted sales is shown in Figure 2; supporting data appears in Table 1. Predicted sales are remarkably close to actual, while accurately depicting the growth and decline experienced in local economic and building activity. The comparison is weakest in the last three years, after the new product adjustment was introduced. This could indicate that the model should reflect individual classes of products with market shares and average expenditures estimated separately. FUTURE RESEARCH

7 Page 7 of 7 Examination of the literature made it clear that more work is necessary to develop satisfactory models for forecasting wholesale business demand. Combining this current investigation methodology with previous retail context research to forecast outcomes in other wholesale business fields would appear to define appropriate directions for future research. Generalizations may become possible through data gathered from additional case studies of successful and unsuccessful wholesale market entries. We believe that the form of the VintNord model has a strong theoretical base which will forecast demand over a wide range of applications, both retail and wholesale. We are particularly interested in its value to the entrepreneur in making decisions to enter a product or service into a new market. While the market share variable is a useful concept, further work should be done to improve the accuracy of its estimation. From an entrepreneur's viewpoint, the problem is not only in estimating how large the existing demand is, but also how much of it can be captured, and how much new demand will be created if the new business is started. Additional factors to be considered would include depth and breadth of inventory, commitment to service, and convenience of location. Once the first wholesale business of a certain type locates in a trading area, it would be useful to examine growth (if any) in the rate of new business formation within its customer population and related business fields. REFERENCES [1] Mason, J. Barry, and Mayer, Morris L., Modern Retailing, 4th ed., Business Publications, Inc., Plano, TX, [2] Reilly, William J., "Methods for Study of Retail Relationships," Research Monograph No. 4, University of Texas Bulletin No. 2944, University of Texas Press, Austin, TX, [3] Huff, David L., "A Probabilistic Analysis of Shopping Center Trade Areas," Land Economics, No. 39, Board of Regents of the University of Wisconsin System, Madison, WI, 1963, p. 86. [4] Maruggi, Vincent, ed., Statistical Abstract of Louisiana, University of New Orleans, Division of Business and Economic Research, New Orleans, LA, 1990.

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