While considered a form of financial voodoo in many industries, accurate forecasting is vitally important in any industry that needs to make business decisions based on what the future holds. Forecasting demand is important to manufactures when determining how much of a product to produce, and equally as important to industries such as retailers when trying to predict how much product the demand bears. There are several methods of forecasting commonly used, with the choice primarily being guided by the demand one is trying to predict. The following are a few methods used with examples of how certain industries use them as well as how these methods are used in my current industry; home improvement retailing.
The first method is historical analogy. This is a qualitative method of forecasting meaning that it is fairly subjective and based on estimates and opinions. A common use for this method is when a firm is trying to forecast demand for a new product (Chase, 2006). A company could use past demand of a similar product to help predict future demand for the new product. Chase, et al used an example of a firm that produces toasters who wants to carry a coffee maker. They could reasonably use the toaster history as a possible growth model. While the two appliances have very different purposes, their similarity in other aspects is alike enough to make this method viable. For instance, they are both small countertop appliances. Their price points are relatively similar. A specific demographic with a demand for a toaster may have a similar demand for a coffee maker.
Seasonal forecasting is simply a time series method of forecasting that capitalizes on a seasonal component of demand. The component variation can be either additive or multiplicative. Differing from an historical analogy method of forecasting, a time series is used to predict future demand based on past data (Chase, 2006). This lends itself as a better choice for estimating demand of products with a long enough history to be relevant.
Noting that a time series can be defined as "chronologically ordered data that may contain one or more components of demand" including trend and seasonal demand, one can base their forecast on both components concurrently (Chase, 2006). The differences are in how these components relate to each other. An additive variation assumes that the variation is independent of the trend. Imagine a plot of a retail store's sales with the dollar amount on the y-axis and the time (in months or years) along the x-axis.
Cachon, Randall, and Schmidt gave various examples, including Proctor and Gamble, which is a very large, established firm that many would assume have complete control of demand forecasting; Proctor and Gamble experienced the bullwhip effect through high demand volatility despite having a relatively constant consumer demand for its products (457).
In addition, we divided our forecasted sales into retail and consumer. Our average demands per month for each quarter for retail sales are 22,082, 24,583, 26,432, and 27,846. For direct to consumer sales, our averages per month for each quarter came out to 1,790, 1,993, 2,143, and 2,258. After determining the average demand per month for each quarter, we used this data to calculate how much safety stock we need to keep per month for each different quarter as well. Safety stock is inventory that is kept to prevent a stock out due to uncertainty. For our product, Cosmic Brownies, we came to the conclusion that a service level of 95% would be sufficient for our retail customers, while a 90% service level was appropriate for direct to consumer. Since our retail sales make up about 92.5% of our total sales, we determined that it was important to have a higher service level for this portion of our
Planning challenges start with specification of client’s demand that must be met by the production plan. It is not easy to predict the exact future demand and thus sometimes future demand is not known (Graves 1999). This results to a firm relying on forecasting to predict the future demand. It is thus important for a company to formulate a plan that comes from the demand uncertainty. Alternatively it is important for a firm to revise the predicted figures frequently in order to update the forecast. This is done using the optimization models. It is very important for a firm to identify the relevant costs in a production planning. It is important to determine the variable costs of production, holding cost/carrying costs and set up costs (Graves 1999).
Target Corporation needs to increase product availability based on the customer needs using a forecasting and supply chain
The next model is the Quadratic Trend Model. The quadratic formula uses the least-squares method to forecast and can be written as Yi =b_0+ b_1 X_1+ b_2 X_2. In this formula the only difference is b_2 X_2 represents the estimated quadratic effect on Y. Figure 1-6 represents the comparison between the linear and quadratic
Three methods that L.L. Bean uses to determine past demand data and a specific item forecast to decide how many units of that to stock are: frozen forecast, A/F ratio demand, and forecast demand. Frozen forecast is based on items in the future period, which is done by the forecasting department and it involves book forecasting and past demand data. One advantage is that this forecast is used together with historical forecast errors, known as A/F ratios. A/F ratios are comprised of past season items and actual demand. Having this information, Bean will be able to estimate the range of inventory that the product will be in the upcoming season after converting the point forecast into a demand distribution. E.g., a 50% chance that the forecast
They store all of their parts in it factory store. The sales team takes the approach of forecasting sales by using the last two to three months of sales data and also compares that to the same months over the past couple of years. This method of predicting sales has been problematic from the start. Forecasting sales on limited and outdated data never produces accurate results.
Sales forecasting is an important part of business. Sales forecasts are crucial in developing business plans, production schedules, budgets, advertising and marketing plans, etc. as the forecasts drive decisions around sales prices, production costs, strategic operations and more (Hicham, Mohammed, & Anas, 2012). The Delphi Method as described by Dalkey and Helmer (1963) utilizes questionnaires to gather key information from a variety experts to form a consensus. Businesses are then able to use this information in their long-range forecasting (Sharp, n.d.). This work will discuss the Delphi Method and how it is utilized in sales forecasting in businesses today.
Bower, P. (2005). 12 MOST COMMON THREATS TO SALES AND OPERATIONS PLANNING PROCESS. Journal Of Business Forecasting, 24(3), 4-14
Doing research during the lesser holidays like Mother's day would be beneficial. This can be accomplished through data collection and data mining through a data warehouse and an analysis of the data that can recognize spending trends for customers and target those customers as valued customers and prioritized as such. Implementing this method would alleviate most of the surge day pressure. The data can be analyzed in conjunction with statistics to get an idea of when demand will increase and when to start capacity planning to use as a contingency plan.
In contrast, the Keynesian Economic Theory was presented in the 1930's, during the Great Depression, by a man named John Maynard Keynes (Classical vs. Keynesian). It relies on spending and aggregate demand which makes this theory demand driven. These economists believe that aggregate demand is influenced by public and private decisions. The public means the government, and the private means individuals and businesses. Aggregate demand sometimes affects production, employment, and inflation. When the economy starts to slack, they rely on the government to build it back up.
Business forecasting can be used in a wide variety of contexts, and by a wide variety of businesses. For example, effective forecasting can determine sales based on attendance at a trade show, or the customer demand for products and services (Business and Economic Forecasting, p.1). One of the most important assumptions of business forecasters is that the past acts as an important guide for the future. It is important to note that forecasters must consider a number of new information, including rapidly changing economic conditions and globalization, when creating business forecasts based on past sales.
Addressing the trials of operating in a continually changing environment and realizing forecasts can only
Buxey,G.(1993). Production planning and scheduling for seasonal demand. International Journal of Operations and Production Management, 13(7),4-21.
...om product forecasting exercise, this will help customers in getting a better deal from suppliers (Mellahi, K., Johnson, M., 2000).