Learning Outcome Describe The Concept Of Operations Functions Suppl ✓ Solved

Learning Outcome: · Describe the concept of operations functions, supply chain strategy, process selection, forecasting, capacity planning, production forecast methods and schedule operations. · Demonstrate process-flow analysis, process design solutions, operations strategies, Inventory Control System and customer services in the business operation. Assignment Question(s) : (Marks 5) Question 2: An Electronic Company estimates the annual demand for a certain product as follows: Week Demand a. Forecast the demand for week 7 using a five-period moving average? (Marks 1.5) (word count maximum:100) b. Forecast the demand for week 7 using a three-period weighted moving average. Use the following weights: W1 = .4, W2 = .4, W3 = .2 (Marks 1.5) (word count maximum:100) c.

Forecast the demand for week 7 using exponential smoothing. Use α value of .1 and assume the forecast for week 6 was 602 units? (Marks 1.5) (word count maximum:100) d. What assumptions are made in each of the above forecasts? (Marks 0.5) (word count maximum:150)

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Introduction
The operations function in business encompasses processes that transform inputs (raw materials and labor) into outputs (finished goods or services). This involves a series of practices such as supply chain strategy, process selection, forecasting, capacity planning, production scheduling, and inventory control. In this context, forecasting demand is essential for ensuring that companies can meet customer needs while optimizing resources (Chopra & Meindl, 2019). This assignment involves forecasting demand for an electronic company over seven weeks using various methods, including a five-period moving average, a three-period weighted moving average, and exponential smoothing.

A. Five-Period Moving Average

To calculate the five-period moving average (MA5) for week 7, we require the demand for the previous five weeks (weeks 2 to 6). The moving average is computed by adding the demands over the designated period and dividing by the number of periods.
Formula:
\[ MA5 = \frac{D_{week2} + D_{week3} + D_{week4} + D_{week5} + D_{week6}}{5}\]
Assuming the previous week's demands are:
- Week 2: 490 units
- Week 3: 500 units
- Week 4: 520 units
- Week 5: 550 units
- Week 6: 600 units
The calculation is:
\[ MA5 = \frac{490 + 500 + 520 + 550 + 600}{5} = \frac{2660}{5} = 532 \text{ units}\]
Thus, the forecast for week 7 using a five-period moving average is 532 units.

B. Three-Period Weighted Moving Average

In a three-period weighted moving average (WMA3), we assign weights to the previous three weeks based on their importance. Let’s assume the demands for weeks 5, 6, and 7 are 550, 600, and a presumption of demand for week 7, respectively.
Formula:
\[ WMA3 = (W1 \times D_{week5}) + (W2 \times D_{week6}) + (W3 \times D_{week7}) \]
Where:
\(W1 = 0.4, W2 = 0.4, W3 = 0.2\)
Using the data for weeks 5 and 6:
- Week 5: 550 units
- Week 6: 600 units
Assuming forecast demand for week 7 is x:
\[ WMA3 = (0.4 \times 550) + (0.4 \times 600) + (0.2 \times x) \]
To forecast week 7, let’s assume the projected demand (using previous calculations) is approximated to 600 to maximize previous trends. This gives us:
\[ WMA3 = (0.4 \times 550) + (0.4 \times 600) + (0.2 \times 600) \]
\[ WMA3 = 220 + 240 + 120 = 580 \text{ units} \]
Thus, the forecast for week 7 using a three-period weighted moving average is 580 units.

C. Exponential Smoothing

Exponential smoothing is a forecasting technique that uses a smoothing constant (α) to weigh the previous forecast and the most recent actual demand.
Formula:
\[ F_{t+1} = \alpha D_t + (1 - \alpha) F_t \]
Where:
- \( F_{t+1} \) is the forecast for the next period,
- \( D_t \) is the actual demand of the current period,
- \( F_t \) is the forecast for the current period.
Given:
- \( α = 0.1 \),
- \( F_6 = 602 \) (forecast for week 6),
- \( D_6 = 600 \).
Calculating the forecast for week 7:
\[ F_7 = (0.1 \times 600) + (0.9 \times 602) \]
\[ F_7 = 60 + 541.8 = 601.8 \text{ units} \]
Thus, the forecast for week 7 using exponential smoothing is approximately 602 units.

D. Assumptions Made in Forecasts

Each of the forecasting methods has inherent assumptions that impact their reliability:
1. Five-Period Moving Average: The method assumes that past demand patterns will continue into the future without accounting for potential fluctuations or trends (Hyndman & Athanasopoulos, 2018). Seasonal shifts, sudden demand spikes, or drops are not considered, which can affect accuracy.
2. Three-Period Weighted Moving Average: This method assumes that more recent demand is a better predictor of future demand than older demand. The weights assigned (0.4, 0.4, 0.2) indicate belief in the significance of the last few periods but do not address potential changes or shifts in demand drivers evenly.
3. Exponential Smoothing: In this approach, the primary assumption is that the most recent demand data has the greatest influence on the forecast. It assumes the pattern will remain stable and that the smoothing constant (α) accurately reflects this relationship. It also presumes that the forecast error is random and can be minimized through the appropriate choice of α (Makridakis, Snyder, & Anderson, 2021).

Conclusion

In conclusion, forecasting demand is a critical function of operations within a business. Using methods like moving averages and exponential smoothing, businesses can create strategies that align production with customer needs. While these methods offer estimates, the assumptions behind them highlight the inherent uncertainties in demand forecasting, emphasizing the importance of flexibility and responsiveness in operational strategies.

References

1. Chopra, S., & Meindl, P. (2019). Supply Chain Management: Strategy, Planning, and Operation. Pearson.
2. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
3. Makridakis, S., Snyder, D. L., & Anderson, A. (2021). Forecasting Methods and Applications. Wiley.
4. Heizer, J., Render, B., & Munson, C. (2017). Operations Management. Pearson.
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6. Schmidgall, R. (2020). Operations Management in the Hospitality Industry. Pearson.
7. Stevenson, W. J. (2018). Operations Management. McGraw-Hill Education.
8. Gattorna, J. (2016). Dynamic Supply Chain Alignment: A New Business Model for Peak Performance in Enterprise Logistics and Supply Chain Management. Gower Publishing, Ltd.
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