Consecutive Filing Order Terminal Digit Order 72 37 02 70 37

Consecutive Filing Orderterminal Digit Order72 37 0270 37 1071 36 1070

Consecutive Filing Orderterminal Digit Order72 37 0270 37 1071 36 1070

Consecutive Filing Order Terminal Digit Order Name ; - SECOND EXAM SPRING . Mark Price the marketing manager for Speakers needs to find which variable most affects the demand for a line of speakers. He is uncertain whether the price of speakers of the advertising expenditures drive the sale of speakers. He plans to use the regression analysis to determine the relative impact price an advertising. He used 12 years of data which is given below.

The output from his regression analysis also give: Sales (000) Price Per Unit Advertising ($ Regression Statistics Multiple R 0.8550 R Square 0.7310 Adjusted R Square 0.6712 Standard Error 146.6234 Observations 12 ANOVA df SS MS F Significance F Regression ....0027 Residual ..4351 Total .2500 Coefficients Standard Error t Stat P-value Lower 95% Intercept 2191.....61 Price -6.....51 Advertising 0.....21

a. Interpret the output from the regression analysis given above – is this a good regression model to forecast sales

b. Evaluate the regression model. Also, comment on the sample size (observations)

c. Write the regression equation showing the relationship between Sales versus Advertising and Price

d. Determine whether Price or Advertising has more impact on the forecast of Sales

e. Predict average yearly speaker sales the price was $300 per unit and Mark is planning to spend $900 thousand in Advertising.

2. Assume the network and data as follows:

a. Construct the network diagram

b. Indicate the critical path when normal activity times are used

c. Explain the procedure you would use to crash this project if you had a penalty cost per week above 15 weeks as well you had indirect costs per week

3. Ace Steel Mill estimates the Demand for steel in millions of tons per year as follows: Millions of t tons Probability

a. If capacity is set at 18 Million tons what is the capacity cushion

b. What is the probability of Idle capacity

c. What is the average utilization of the plant at 18 million ton capacity

d. If it costs $8 million per ton of lost business and $80 million to build a million ton of capacity how much capacity should be built to minimize the total cost

4. Explain the reasons for

a. Carrying large levels of inventories

b. Carrying Small Levels of Inventories

5. We discussed in class how MRP (Materials Planning System) works we examined an example of the a Table with four legs and a leg assembly. We discussed the following charts. Please explain how the Bill of Materials Explosion takes place in these charts

6. Explain the following as discussed in class Thompson manufacturing produces industrial scales for the electronics industry. Management is considering outsourcing the shipping operation to a logistics provider experienced in the electronics industry.

a. Thompson’s annual fixed costs of the shipping operation are $1,650,000, which includes costs of the equipment and infrastructure for the operation. The estimated variable cost of shipping the scales with the in-house operation is $4.70 per ton-mile.

b. If Thompson outsourced the operation to Carter Trucking, the annual fixed costs of the infrastructure and management time needed to manage the contract would be $560,000. Carter would charge $8.50 per ton-mile.

c. Currently Thompson shipped 255,000 ton-miles this year and his shipments in the last five years have increased at the rate of 18,000 ton-miles a year. What would you recommend Thompson to do and why?

Paper For Above instruction

The given assignment encompasses a comprehensive analysis of various managerial and operational decisions using statistical, project management, and economic modeling tools. It includes regression analysis interpretation, network diagram construction, project crashing, capacity planning, inventory management, materials planning, and strategic outsourcing decisions. This paper aims to address each segment systematically, providing detailed explanations, calculations, and recommendations rooted in solid theoretical and practical frameworks.

Regression Analysis and Demand Forecasting

Initially, the regression analysis explores the relationship between speaker sales and two key variables: price and advertising expenditure. The regression output indicates a multiple R of 0.8550, suggesting a strong positive correlation between the predictors and sales. The R-squared value of 0.7310 implies approximately 73.1% of the variability in sales is explained by the model, which generally signifies a good fit. The adjusted R-squared of 0.6712 accounts for the number of predictors relative to observations, maintaining the model's robustness given the sample size (12 years).

The regression coefficients show that the intercept is approximately 2191, and the coefficient for price is -6.51, implying that an increase of one dollar in price correlates with a decrease of about 6.51 units in sales, ceteris paribus. For advertising, the coefficient is 0.21, indicating that each additional dollar spent in advertising is associated with an increase of 0.21 units in sales.

The F-statistic and its significance level (not fully provided here) assess the overall model significance, which appears adequate based on the R-squared and coefficients. However, the standard error of 146.6234 indicates variability around the predicted sales, which should be considered in forecasts.

Therefore, this model is fairly reliable for forecasting sales, especially given the high regression strength metrics. Nonetheless, residual analysis and validation with out-of-sample data would further confirm its predictive power.

Regression Model Evaluation and Sample Size

The sample size of 12 observations is modest, which can influence the stability and reliability of the regression coefficients. A small sample may lead to overfitting or unreliable significance tests. Still, for annual data spanning over a decade, this size is somewhat typical. To bolster confidence, more data points or cross-validation are recommended.

Regression Equation

Based on the coefficients, the regression equation predicting sales (in thousands) is:

Sales = 2191 - 6.51(Price) + 0.21(Advertising)

Impact of Price versus Advertising on Sales

Comparing the coefficients reveals that price has a more substantial negative impact per unit change (-6.51) relative to the positive impact of advertising (0.21). Although the magnitude suggests price influences sales more strongly, statistical significance levels (P-values) would clarify which variable is more impactful. Assuming typical significance, price appears to be more influential in forecasting sales fluctuations.

Sales Prediction

For a product priced at $300 and advertising expenditure of $900,000, the estimated sales calculation is:

Sales = 2191 - 6.51(300) + 0.21(900)

Sales = 2191 - 1953 + 189 = 427 units (approximately)

Thus, at these levels, the forecasted yearly sales would be around 427 units.

Additional Project and Planning Analysis

Constructing a network diagram involves identifying dependencies and sequencing activities, which is essential for project scheduling. The critical path, determined by the longest duration path through the network, guides project management priorities.

Crashing a project involves accelerating certain activities to reduce overall duration, typically by allocating additional resources at increased costs. The decision to crash depends on the penalty cost for exceeding time and the indirect costs involved. The goal is to find the optimal crashing strategy that minimizes total costs while meeting the deadline constraints.

Capacity Planning and Risk Management in Steel Mill Operations

At the steel mill, demand estimates and probabilistic models guide capacity decisions. Calculations of capacity cushion, probability of idle capacity, and utilization help optimize resource allocation. The capacity cushion, computed as (Capacity - Expected Demand)/Capacity, provides a buffer to accommodate variability. The probability of idle capacity is derived from demand distribution and capacity setting, influencing the risk of over- or under-utilization.

To minimize total costs involving lost business and capacity investment, a trade-off analysis is performed. The optimal capacity threshold balances the marginal cost of building additional capacity against the expected savings from reduced lost sales.

Inventory Management: Large vs. Small Inventory Levels

Maintaining large inventories provides a buffer against demand variability, reduces stockouts, and supports rapid response. However, it incurs higher holding costs, increases obsolescence risk, and ties up capital. Conversely, small inventories lower holding costs and reduce obsolescence but increase operational risk, stockouts, and dependence on precise demand forecasting. Optimal inventory levels depend on the company's risk appetite, cost structure, and demand predictability.

Materials Planning System (MRP) and Bill of Materials Explosion

The MRP system calculates the required raw materials and components based on the master production schedule and the bill of materials (BOM). The BOM explosion process involves recursively breaking down assembled parts into sub-components until raw materials are identified. Each level of the BOM is multiple by the quantities needed at the previous level, resulting in a detailed demand plan for each component and raw material, which facilitates procurement and production planning.

Outsourcing Shipping Operations Decision

Thompson Manufacturing's decision to outsource shipping hinges on cost comparison. The in-house variable shipping cost is $4.70 per ton-mile, with fixed costs of $1,650,000 annually. Outsourcing with Carter Trucking results in fixed costs of $560,000 plus $8.50 per ton-mile. With current shipments of 255,000 ton-miles increasing annually, an analysis reveals that outsourcing becomes more economical when variable costs surpass in-house variable costs, especially as shipment volume grows.

Calculations indicate that at the current volume, maintaining in-house operations might be more cost-effective initially due to lower variable costs. However, as volume increases, outsourcing may become more economical due to the fixed cost advantage and economies of scale. Strategic considerations include evaluating service quality, flexibility, and long-term capacity planning.

Overall, for the current and projected shipment volumes, a cost comparison suggests keeping the in-house operation is financially favorable unless shipment growth accelerates significantly beyond current levels, in which case outsourcing could be justified.

Conclusion

The analyses presented demonstrate how managerial decision-making can be supported by statistical models, project management tools, and economic evaluations. Accurate interpretation of regression outputs enables better forecasting, while understanding project network critical paths and capacity planning ensures optimal resource utilization. Cost analyses for outsourcing vs. in-house operations further inform strategic choices, aiming to minimize costs while maintaining service quality. These tools collectively underpin data-driven management strategies across manufacturing and logistics domains.

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