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Introduction

When it comes to most demand forecasting processes the statistical modeling that is  used is based on the downstream sales order that or the information that comes from shipment. In many situations the sales orders are not all shipped out due to running out of stocks or problems related to production. Managers focusing on demand analysis experience this first hand realize that the sales orders do not necessarily represent the consumer demand. As a result, most companies have opted to integrate the statistical models with the upstream POS data. Some of the long term forecasts are also available at two of the federal agencies, for example the Bureau of Labor Statistics, which publishes low, medium and high 12 to 15 year forecasts on numerous economic variables (U.S. Department of Labor, Bureau of Labor Statistics, 1994).

Demographic and independent variables relevant to complete a demand analysis

The project is for Dominos Pizza which is attempting to conduct a market research of the area. In this way, there are various variables and statistics the company should pay attention to while entering the market or creating a demand forecast for success (Chase, 2009). Companies should attempt to determine the frequency of new demand data based on the availability and the business needs related to their immediate market environment. An example is if Dominos Pizza receives sales on a daily basis, then there is a need to restate or refresh the actual sales so that it can work with the appropriate information. Relying on experience it is not practical to reforecast the data that one has obtained on a daily basis or even weekly in some cases.

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The presence and number of children may have a significant impact to the success of family business owners, as well as the market entrance for fresh enterprises. It is therefore, quite important to note that in addition to the societal culture and the background of the company members and age and size of Dominos Pizza. As the process of demand shaping becomes prevalent within Dominos Pizza, then role of sales and marketing in the demand driven forecasting process will increase as concerns jurisdiction as well as accountability (Chase, 2009). The departments of sales and marketing forecasting will become an additional input to the consensus forecast process, thus providing valuable information as to improving the final unconstrained demand forecast. In some companies, the sales department’s demand forecast input might need to be converted from a revenue based demand forecast into a unit based demand forecast.

If anything, the sales and marketing department forecasts on demand usually have the greatest bias. The some experts have prescribed that the solution would be to ask the sales and marketing departments to look at the macro-trends in the market place by virtue of account and channel and hold them accountable for measuring and improving the forecast accuracy (Storey, 1994). Demand shaping happens to be the process through which varying elements influence the demand volumes and corresponding revenue and profits for the products sold by a company based on the internal sales and marketing strategies as well as external factors of the market place.

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The external factors may refer to the Domino’s competitor’s activities, or weather related events, not forgetting interest rates and other economic factors. They thing behind demand shaping is the functional collaboration that goes between sales and marketing and the other members that belong to the supply chain. The purpose of these demand shaping programs is to drive the unit volume and profitability that comes from the company’s brands and products. In the first place, these activities are mostly monitored and managed on an independent basis whereby the functional details such as strategic marketing and product management (Chase, 2009).

At a tactical level, the aim is to understand consumer demand patterns and proactively try to influence the demand to meet the available supply using the marketing mix to the level price, and product awareness so as to improve the overall outlook of the product portfolio. As such, demand shaping has become an essential part of the sales and operations plot process. In the event that most companies use demand forecasting to plan for the customer demand, they also need the influence of demand shaping when it comes to closing the gap between unconstrained demand expectations and availability of supply (Maddala and Ellen, 1989).

Estimating accuracy of regression statistics

Upon estimating the parameters, the depth of the bond between the dependent and independent variables can be computed in two ways (Maddala and Ellen, 1989). The first method uses a measure known as the coefficient of determination, which is demoted as R squared. This computes the healthy nature of how the overall equation explains the relevant alterations as concerns the dependent variable. A subsequent method relies on a variable known as the t-statistic for the strength of the bond between the independent and dependent.

The value of the R squared ranges between the values of 0 and 1. For the part that the equation explains the fluctuation; (full explained variation = total variation), the coefficient will be determined. The value of R2, has a direct correspondence to the quality when it comes to the regression equation. Fit is the term often used to describe the power of the estimated equation. Therefore, when the R-squared is high then the computation should fit the data. A low value of the R2 shows a poor fit. There are a quite a few considerations that one should consider when infiltrating a test market. The location should first be of a controllable proportion. An area that is too big may be too expensive to maintain the area or serve the demand. The Bureau of economic analysis would be useful in this effect as it develops 50 year regional projections of the population and personal income, as well as employment and earnings of the industrial sector as per individuals (U.S. Department of Commerce, Bureau of Economic-Analysis, 1990).

In order to calculate the demand forecast during the next for month in the area. We must rely on the regression coefficient as the starting point of reference for the equation

D0 + Bt = Dt               

D here represents the demand function in the area, while t represents the time frame of forecast and B is the constant which is the regression coefficient. Taking D0 as 1, over a period of four months the equation will be

1 + (-1.852)4 = 11. 764

Demand function is positive therefore, the sales projection will be positive. The regression coefficient is negative, as shown in the graphical and trend analysis in the excel sheet, which is similarly depicted above. The coefficient being negative suggests that the more the independent variable increases, then the more the dependant variable tends to decrease. The meals served per day represent the independent variables while the dependent variable is the cost for unit production. Therein lays the statistical significance. An increase of meals served represents a corresponding decrease in the cost per unit of production, representing a profitable use of the organization’s resources on the overall.

Recommendations

The regression equation done for Dominic’s Pizza shows that the introduction of the chain to the area would result in demand for the company product right from introduction. This will result from brand name reputation spread through word of mouth, internet social media and so forth. Therefore, the company should go ahead with the project.

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