The Pizza Company is considering entering the marketplace in your community. By conducting a demand analysis and forecast for pizza, you will be able to make a decision whether The Pizza Company should establish a presence in your community.
- Estimated regressions:
Demand analysis of the forecast for pizza in 30 markets is shown in graphical illustration (Graph).
Supply of pizza products and services to customers is examined in comparison where the company’s pricing structure is exhibited in Price (P) frequencies, and competitor pricing (Px) frequencies in multi-linear analysis of median cost to customers (Graph).
- Then, from the calculation provided, interpret the coefficient of determination, indicating how it will influence your decision to open the pizza business in your town or community. Explain any additional variables that may improve the coefficient of determination.
Co-efficient dyads, Income: Market when subject to linear regression analysis of median frequencies in company revenue, report comparison of per market performance across the target region; the determinant to the final decision to open a pizza business in a town or community (Graph).
- Test the statistical significance of the independent variables and the regression equation, indicating how it will impact your decision to open the pizza shop.
The illustration of proportionality of advertising circulation to proportionality of income offers insight into marketing return on investment (ROI) per market (Graph).
- Forecast the demand for pizza for the next four (4) periods using the regression equation.
This question requires additional data. No quarterly or annual sales figures are provided. Income figures correspond to markets rather than reporting cycles. Regression analysis would require revenue reporting for several periods. Without reporting, no median frequencies may be evaluated for sales performance forecasting over time.
- Based on the forecasted demand, determine whether The Pizza Company should establish an operation in your community. Provide a rationale and support for the decision. specific course learning outcomes associated with this assignment are:
• Apply the concepts of supply and demand to determine the impact of changes in market conditions in the short run and long run, and the economic impact on a company’s operations.
Analysis of the pizza company managerial economics demand analysis is a consumer centred strategy that prioritizes price as the most key mechanism to growth. The limits of this microeconomic theory are evidenced in the regression analysis of co-efficient dyads reported in the company’s feasibility study of targeted consumer markets. The skewed focus on demand, rather than the traditional macroeconomic perspective of aggregate supply (AS) and aggregate demand (AD), constrains modelling to demand, and does not account for supply side issues relevant to market flux (Benigno, 2009).
Launching a new pizza company business requires more than a simple cash/credit dynamic. Environmental factors of macroeconomics must be taken into account in microeconomic decision. Managerial economics is closely tied to sustained investor support of the venture in response to performance (Douma and Schreuder, 2013). The pizza company prospectus reviews a convenience sample of thirty (30) consumer markets as part of its market entry strategy. Regression analysis of demand, income, prices, competitor prices and advertising across those markets offers feasibility study of forecasted performance in those markets.
Neo-classical economics theory provides that disequilibrium in prices and wages is indicator of inflationary response. If prices exceed consumer demand rates, the profitability may decline. If external market forces lead to a retraction in consumer spending, this will also be succeeded by a downward trend in sales performance. While the latter scenario is outside the control of the pizza company, the organization must consider standard deviation of projected income from median performance as an actual risk. The planned market expansion of the pizza company will only be realized if risk is sufficiently constrained in this regard.
The demand analysis is also limited by undisclosed cash flow. With no knowledge of liquidity, the company is restricted in reporting of solvency; and also leveraged finance that might impact competitive advantage in the segment. Restricted cash flow generally translates into limited growth. Standard economics theory defines this stagnation; a limitation of the robust potential of the AS-AD dynamic (Benigno, 2009).
The AS-AD is a direct effect on trend analyses of the restaurant and related vendor sectors. For example, product segments sold by vendors may diversify substantially, but not affect aggregate consumer demand for the same or similar food products. Consumer and product segmentation studies reviewed in the interest of forming adequate managerial decision, look to the microeconomics of AS-AD as an assumption in purchase and pricing of products (Benigno, 2009, Douma and Schreuder, 2013).
Wages are generally present in the theorem, yet price insertion according to the Calvo Pricing model affords a baseline disequilibrium of the AS-AD model; and this is the result of wages in correspondence with inflation or the IS curve dynamic (Benigno, 2009). Sticky wages and prices, then, are the basis of traditional economist predictions of inflation rates. Managerial economics, illustrate this phenomenon in direct correlation with sustainability (Douma and Schreuder, 2013). If the price of goods to the pizza company goes up, consumer prices must also rise. If wages remain the same, the company will still be forced to make decisions about retention of stores.
At the store level, the application of Learning Curve Theory (LCT) would provide a probabilistic measurement of return on investment (ROI) derived from basic operational processes (Chase, Jacobs and Aquilano, 2006). The number of times operational processes are repeated results in an indicator of increase or decrease in efficiency, LCT rate is a predictor of financial loss (Chase, Jacobs and Aquilano, 2006). LCT is comprised of three elements: 1) task repetition correlates to amount of time required to perform a task that also decreases; 2) units increase means improvement decreases; and 3) rate of improvement is significant enough that consistency is a prediction tool (Chase, Jacobs and Aquilano, 2006).
For instance, if a store is not turning tables over quickly enough in an environment known for proficiency in service, the LCT simulation captures those inefficiencies (Chase, Jacobs and Aquilano, 2006). Productivity measures such as scheduling number of wait staff, and potentially distribution of tables may be implemented immediately in response if it is determined that there is an imbalance between the demand of service(s) and the capabilities of the system to meet those criteria.
Targeted objectives such as reduction of client wait time is subject to LCT analysis, with deviation from projected mean average wait is identified, and evaluated for interference in this goal (Chase, Jacobs and Aquilano, 2006). LCT is beneficial for identification and determination of obstacles and potential solutions correspondent to the steps of the operational process (Chase, Jacobs and Aquilano, 2006).
Outcomes to the testing are reliable insights for determination of strategic management decisions in operating processes. Attribution of customer satisfaction in the model is aligned with waiting times in the case study, so that P&L projections are adjusted according to fluctuations in the learning curve. Precision in improvements to the processes observed in the simulation test is the objective (Chase, Jacobs and Aquilano, 2006).
The co-efficient analysis of the pizza company’s income, advertising ROI and price comparison with competitors in the business provides a demand analysis of the business and its markets. Evaluation of supply side product and price economics will also affect managerial economics in this scenario. The aggregate supply (AS) and aggregate demand (AD) models of market flux support this strategy, in illustration of sticky prices to other factors not accounted for such as wages, as well as microeconomic insights into consumer confidence and preference.
Application of LCT as part of the applied managerial economics strategy in a pizza store operations simulation offers a simple test environment for analysis of ebbs and flows in customer volume. This will further define the projections of demand and ultimately profits. Frequency in product output, as scaled in the LCT proposes correlates with longitudinal performance in operational tasks. The Pizza Store Layout simulation use of LCT is a metric of profit and loss, so that income is addressed by way of demonstrated activities within the business environment.
Chase, Richard B., Jacobs, R. & Aquilano, N.J. (2006). Operations Management for Competitive Advantage, 11/e. Columbus, OH: McGraw Hill.
Benigno, P. (2009). New-Keynesian Economics: An AS-AD View. NBER Working Paper 14824, March 2009. Retrieved from: http://www.nber.org/papers/w14824
Douma, S. and Schreuder, H. (2013). Economic Approaches to Organisations, 5/E. Upper Saddle River, NJ: Pearson.