The Home Depot is a retailer of construction and home improvement services and products, has stores in Mexico, America, China, and Canada. The present economy in the United States has not been friendly for most organizations. The housing market has been the basis of inventory needs of the Home Depot, but it has recently faced been fluctuating unpredictably. The level of inventory is the most critical aspect in the management of Home Depot because it is among the top five costs of doing business. It is a factor that should be kept optimal at all times because undersupply of oversupply could carry detrimental effect on the business performance.
Using effective forecasting, Home Depot will establish the correct amount of stock and ensure that customers’ demands are satisfied at all times. This will also ensure that the corporation is protected from bearing the cost of storing unsold goods due to overstock. Inventory understock will make the organization to lose prospective business to rivals because it is unable to handle demand. Inventory Overstock will make it suffer the costs of storing unsold goods and holds required capital, a situation the Home Depot is facing currently. Forecasting can help businesses to maintain their competitive advantage. The Home Depot does forecast through the use of the Gross National Product (GNP) (Bianchi, Constanza & Arnold 150).
GNP refers to the full value of the services and goods generated in a country. Forecasting the GNP is a detailed and convenient measure for determining the changes in economic status. It also provides a vital framework for comprehensive forecasts of some industries. GNP forecasting consists of an assessment of government, private and consumer spending. Home Depot uses the demand and supply data within government and private sectors and projects how such spending patterns would create demand for their stock. It is possible to determine government expenditures fairly accurately for up to a year beforehand by studying the current appropriations and budgets that have been modified to accommodate new economic and political developments. Contrary to government spending, private investment is difficult to forecast since it reflects several corporate and individual decisions that are either not recorded in the public or often change considerably (Smyth, David & Ash 362). The underlying assumption behind the use of GNP to predict inventory demand of an economy is based on the assumption that all the supply of goods and services will be at equilibrium with the demand of the same. If an economy projects to supply a certain amount in it use that as a base and combine it with its market share to determine the levels of stock they need to hold to fully satisfy its customers without overstocking.
The Home Depot uses economic theory and micro-economic data to forecast the products’ demand levels through the GNP. The challenge is the fact that demand is majorly based on the housing market. The current fluctuation in the housing market may force the organization to employ a different way of forecasting. Home sales have a direct impact on the Home Depot’s inventory needs (Bianchi, Constanza & Arnold 150). Furthermore, the economic crisis in the United States makes the prediction method currently used by the organization unreliable. Thus, Home Depot should consider adopting other methods of demand forecast because its inventory portfolio may not be absolutely dependent on microeconomic climate. Such additional methods of forecasting Home Depot should consider include Holt-Winters and Delphi Methods to take care of seasonality of some demands and use qualified experts opinions respectively.
Works Cited
Smyth, David J., and J. C. K. Ash. “Forecasting gross national product, the rate of inflation
and the balance of trade: The OECD performance.” The Economic Journal 85.338 (1975): 361-364.
Bianchi, Constanza C., and Stephen J. Arnold. “An institutional perspective on retail
internationalization success: Home Depot in Chile.” The International Review of Retail, Distribution and Consumer Research 14.2 (2004): 149-169.
