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7 Steps of Problem Solving, Essay Example

Pages: 5

Words: 1398

Essay

Introduction

Every business, irrespective of its size or industry, has specific target goals to achieve. Thus, goal achievement entails managers making strategic decisions based on several techniques such as decision making, forecasting, optimization, linear programming, inventory, and simulation. The purpose of utilizing such forecasting and data analysis techniques is to attain more efficient resource allocation, costs reduction, and effective production planning. Various qualitative and quantitative approaches are used to analyze a particular problem to establish the objective functions, constraints, and other uncontrollable inputs. Therefore, using small business an example, bakery, the paper undertake a 7 step decision-making approach using techniques like forecasting and time scale, linear programming, decision matrix, and production optimization.

7 Step Decision-Making Approach

Problem Definition and Of Alternatives Solution Set Determination                                                      

Charlie, The Sugar Bakery Company owner, together with his business partner, Molly, his wife have decided to make some changes in the product line and house offerings. Although Sugar Bakery manufactures wide product range of bread, cakes, and pretzels, only individual products like cupcakes, pastries, donuts and cookies were chosen for the trial. Nonetheless, the partnership has proposed to expand each product range into two new product types that best improves revenues streams and ensures long-run business sustainability.

After thorough market observation, Charlie has suggested that supersizing products create a sustainable competitive advantage over its competitors. Bowman’s strategy clock explains that supersized production sets apart a firm from its rivals as it attains a differentiated position (Johnson et al., 2014). Molly as a business strategist suggested that available production constraints only permits implementation of one supersized product range. This creates a problem of right option selection and optimal production mix within the new range.

Criteria for Evaluating Alternatives Determination

A careful examination of alternatives will be undertaken using the company’s 25-month historical sales data. Based on total past sales, potential future sales will be computed for each product type. Firstly, the product type with the best future potential return will be determined using various Forecasting and time series methods, like naive, moving average, weighted moving average, exponential smoothing, and arithmetic average. Notably, the optimum method that shows a level of significance difference will be chosen.

Secondly, the forecasting findings will be critically analyzed and organize into a structured decision analysis format using a Decision Matrix as the best performance criteria choice. Since, Murphy (1979) argued that the Decision Matrix allows decision makers to methodically study information sets’ strength. The Decision Matrix null model uses five criteria each with a 1-10 scale to analyze the four product types. These criteria include Project risk, expected return on investment, production processes compatibility, source of competitive advantage, and Staff training required.

Performance Criteria Donut Cookies Pastries Cupcakes
Project Risk        
Expected Return on Investment        
Compatibility with current
manufacturing processes
       
Source of Competitive Advantage        
Staff Training Required        

After the clear product range selection using decision matrix, an optimal mix of different inputs for profits maximization will be determined.

Alternatives Evaluation

The forecasts’ mean average error (MAE) was computed from the absolute error to determine the method that provides the closest sales forecast to actual sales data. Hence, the 3 month average generated the smallest MAE making it fit compared to other measurements. Using time series forecasting method, we calculated the 3 month average forecast and seasonal ratio that compares actual sales and forecast. Then, we computed the average directional trend through summing up initial sales and final sales movements, then, dividing by 24 movements. Later, we projected the next 13 month future sales using November 2015 as the initial point. In order to factor in seasonality factor into account, the actual monthly sales were by the corresponding 3 month forecast and dividing by 2 where two ratios existed to obtain the average seasonality per month. Finally, the seasonality average was multiplied by the future trend projection to obtain final sales projection.

Afterward, using the Decision Matrix as the Decision Analysis tool, various weights were allocated based their overall importance. The expected return on investment was allocated the highest weight of 0.35 about the company’s revenue maximization goal. Other criteria like Project risk and compatibility were each allocated 0.2 while, competitive advantage and staff training were allocated lower weights of 0.15 and 0.1 respectively. Each product type’s scores was paired with individual weights and an overall total obtained. The product type that produces the highest maximum value was chosen as new product range for The Sugar Bakery.

Performance  Criteria                        Donuts            Cookies              Pastries         Cupcakes
Project Risk                                           8                        8                         7                   6
Expected Return on Investment            9                        5                         4                    6
Compatibility with current
manufacturing processes                       8                        8                         8                    8
Source of Competitive Advantage        9                       3                          5                   2
Staff Training Required                         7                       5                          4                  7

Weighting

Criteria                                                                                                      Weight
Project Risk                                                                                                  0.2
Expected Return on Investment                                                                   0.35
Compatibility with current manufacturing processes                                  0.2
Source of Competitive Advantage                                                               0.15
Staff Training Required                                                                                0.1

Calculations

Product                    Calculation                                                                      Total score
Donuts                     (0.2×8)+(0.35×9)+(0.2×8)+(0.15×9)+(0.1×7)                      8.4
Cookies                    (0.2×8)+(0.35×5)+(0.2×8)+(0.15×3)+(0.1×5)                        5.9
Pastries                    (0.2×7)+(0.35×5)+(0.2×8)+(0.15×5)+(0.1×4)                        5.9
Cupcakes                 (0.2×6)+(0.35×6)+(0.2×8)+(0.15×2)+(0.1×7)                        5.9

As the tabular results show new donuts recorded the highest maximum score of 8.4 out of 10 suggesting that the company should pursue newly developed supersized donuts production. As Kim and Mauborgne (2004) suggested a company should select the option that provides first mover advantage in its positive results.

Optimization, Alternatives Selection and Feedback

 In order to create a sustainable competitive advantage, the new products range should best fits available resources (Barney, 1991). The new range of supersized highly demanded varieties Donuts proposed for production are the supersized ring (Product A) and supersized cream donuts (Product B). Therefore, A will require 2 kilos of raw materials and 3 man hours for every batch. While, B will need 3 kilos and 2 man hours only for every batch. When maximum resources of 50 kilos of raw material and 40 man hours every week are utilized in the production, A and B will generate €48/batch and €34/batch respectively. The following linear programming equations will be generated: x ≥ 0,   y ≥ 0,  3x + 2y ≤ 40,   2x + 3y ≤ 50

 Generating Points on the Graph:

X=0, Y=0:                                                           Point: (0, 0)

3x + 2y ≤ 40              X=0, Y= 20                     Point: (0, 20)

                                    Y=0, X= 13.33               Point: (13.33, 0)

2x + 3y ≤ 50               X=0, Y= 16.66               Point: (0, 16.66)

                                     Y=0, X= 25                    Point: (25, 0)

From the graph shown, the feasibility region seems to be between points (0, 0), (0, 16.66), (13.33, 0) and (4, 14). A further analysis, as displayed in the table below, using equation P= 48x + 34y will establish the point that the company maximizes profits.

(X, Y) 48x+34y Profit
(0, 0) 48(0)+34(0) €0
(0, 16.66) 48(0)+34(16.66) €566.44
(13.33, 0) 48(13.33)+34(0) €639.84
(4, 14) 48(4)+34(14) €668

Evidentially, 4 batches of Product A and 14 batches of Product B seem are the optimal production mix every week. This mix of supersized donuts varieties, subject to the same constraints, yields a higher gross profit of €668 every week when compared to other production mixes.

Conclusion

A 7- step decision model was adopted to determine the company’s sales strategy. Hence, Bakery company management needed a crucial decision on the product range to produce. Future sales were projected based on the past sales data of the four product lines using the 3 months average forecasting method. Furthermore, forecasting findings analysis using decision matrix showed that donuts have the highest potential return. In order to assess the viability of the two donuts proposed, profit optimization was carried taking into consideration various production constraints. Finally, the optimal profitability production mix was determined, implemented and proved to generate the highest profit.

Works cited

Johnson, G., Whittington, R., Angwin, D., Regner, P., Scholes, K., & Pyle, S. Exploring strategy: text and cases. 10th Edition Pearson. (2014). Print.

Murphy, K.R. ‘Comment on Pugh’s method and model for assessing environmental effects’. Organizational Behavior and Human Performance, (1979), 23(1), pp. 56­59.

Kim, W. C. & Mauborgne, R., Blue Ocean Strategy. Harvard Business Review, 1(2004). Print.

Barney, J., ‘Firm resources and sustained competitive advantage’. Journal of management. (1991)17(1), 99­120.

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