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Introduction
Lawn King is a medium-scale enterprise that manufactures mowing equipment in the US. Currently, the company is experiencing difficulties with creating a sales forecast for the next fiscal year that started in the beginning of September. During the last board meeting John Connor suggested that the original forecast of 98,000 units should be increased to 110,000 because the company lost revenue due to an increased number of backorders, which were created due to the company’s inability to fulfill the orders in time. This paper focuses on developing a forecast to use as a basis for Sales and Operations Planning (S&OP). Additionally, the paper provides a S&OP plan by month for fiscal year 2020 based on different strategies.
Sales Forecast
Selecting the Forecast Method
The sales department conducted market analysis and arrived at the conclusion that at Lawn King will be able to sell 110,000 units in FY2020. However, the forecast was based purely on qualitative approach, as sales department failed to provide any quantitative evidence to confirm their forecast. According to Schroeder and Goldstein (2020), there are two general types of forecasting, including qualitative and quantitative forecasting. Qualitative methods include Delphi method, market surveys, life-cycles analogies, and informed judgement (Reefke & Gardiner, 2019). Lawn King used informed judgement as the central approach to forecasting, as John Connor mentioned only that the marketing department agrees with the prognosis without any supporting data. Schroeder and Goldstein (2020) state that informed judgement is an unreliable tool, as the quality of forecast can vary significantly depending a wide variety of factors.
Quantitative approach to forecasting includes time series forecasting, moving average, and exponential smoothing as the primary forecasting approaches (Lestari et al., 2017). Additionally, there are causal forecasting methods that presuppose dependence of sales on external factors (Stevenson, 2020). According to the sales department, there are two variables that can contribute to the variance in sales, including weather and economic conditions. The weather impacts when the mowing season starts, while the economic conditions determine which models the customers are more likely to buy.
Even though all the described forecasting methods are appropriate for Lawn King, there is not enough data to use the majority of these forecasting techniques. Lewis (2019) state that when selecting a forecasting method, it is crucial to understand what data can realistically be collected. The most appropriate method for the case study would be time series analysis, as Lawn King’s sales are highly seasonal. However, the information on monthly sales are provided only for one year, which makes it impossible to calculate the trend and seasonal variation. Moreover, sales information is available only for two previous years, which makes it difficult to make quantitative predictions based on the trend in annual sales. Thus, it was selected to use a combination of qualitative and quantitative methods. In particular, straight line forecasting in combination with informed judgement of the sales department, as there is not enough data to use any other forecasting methods reliably.
Sales Forecast for 2020
The annual forecast for 2020 was calculated by determining the trend in annual sales change and adding it to previous year’s sales. The results of the calculation are provided in Table 1 below.
Table 1. Annual Sales Forecast
Since the results of the quantitative analysis were in accord with the informed judgement of sales department, it was decided to use the annual forecast provided in Table 1 as the basis for S&OP. The percentage change by model was used to develop monthly sales forecast, which is provided in Table 2 below.
Table 2. Monthly forecast by model
Monthly Production Plan
A total of three strategies were considered for monthly production. Strategy 1 was based on the current strategy without any overtime to meet the demand and using inventory to meet peak demands. Strategy 2 was based on the current strategy, which used level workforce plus overtime when needed. Strategy 3 is the chase strategy, which is based on laying off workers and hiring new ones based on the demand. These strategies were calculated using the following assumptions.
- Monthly production rate (units/worker/month): 65. It was estimated by divided the total number of units produced during month without overtime by the number of employees,
- Beginning inventory units: 16,460
- Number of workers: 100
- Hiring cost per worker: $800
- Laying off cost per worker: $1,500
- Inventory cost per unit per month: $51.30. It was calculated by dividing cost of goods sold by the total number of units produced and then multiplying by 30%.
- Regular hourly wage: $15.00. It is the average hourly cost per employee.
- Overtime hourly wage: $22.50. Average hourly wage multiplied by 150%.
- Hours per month: 176 (22 working days multiplied by 8 hours).
All the strategies aimed at minimizing were based on the idea to reduce inventory and avoid inventory shortage cost, as they would be difficult to estimate. The calculations of the strategies are provided in Appendices A-C. The calculations do not take into consideration the natural turnover in employees, as it was assumed as a constant in all three strategies, which would not affect the strategy choice. Moreover, the calculations did not take into consideration which models would be produced during which months, and it was assumed that the current production strategy was optimal and the switchover costs would be the same in all the strategies.
Recommendation
The cost analysis demonstrates that the chase strategy (Strategy 3) was the most appropriate for the Lawn King as it was associated with the least total cost. The total cost of the strategy was $14,343,763, while the total cost of the level workforce strategy (Strategy 1) was $17,761,098, and the total cost of the level workforce strategy plus overtime (Strategy 2) was $16,103,529. Thus, based only on the cost, Strategy 3 should be accepted. However, qualitative analysis of possible ethical issues of laying off so many employees should be conducted before making the final decision.
References
Lestari, F., Anwar, U., Nugraha, N., & Azwar, B. (2017). Forecasting demand in blood supply chain (case study on blood transfusion unit). In Proceedings of the World Congress on Engineering (Vol. 2). Web.
Lewis, M. A. (2019). Operations Management: A Research Overview. Taylor & Francis.
Reefke, H., & Gardiner, D. (2019). Operations Management for Business Excellence: Building Sustainable Supply Chains. Taylor & Francis.
Schroeder, R, & Goldstein, S. (2020). Operations management in the supply chain (8th ed.). McGraw Hill.
Stevenson, W. J. (2020). Operations Management. McGraw-Hill Education.
Appendix A: Strategy 1 (Level Workforce)
Appendix B: Strategy 2 (Level Workforce + Overtime)
Appendix C: Strategy 3 (Chase Strategy)
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