MBA 505 OPM Assignment - The problem is the Case Study called M&L Manufacturing found on page 132 of PDF

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The problem is the Case Study called M&L Manufacturing found on page 132 of the text (Stevenson). It appears in the Forecasting chapter, suggesting that it is a problem in Forecasting....

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Course Title: Operation Management Course Code: MBA 505 Assignment 1 Topic: M&L Manufacturing Case Solving

Submitted to: A. F. Wazir Ahmad Assistant Professor School of Business University of Liberal Arts Bangladesh

Submitted by: Name

ID No.

Hira Mohammad Abdullah Md. Mahbub Alam Md. Rubel Abedin Md. Abdul Karim Md Sazzadu Zzaman

172051017 172052001 171051063 171051075 171051011 Section: 02 Summer 2018

University of Liberal Arts Bangladesh August 13, 2018

Q1. What are some of the potential benefits of a more formalized approach to forecasting? A forecast is a statement about the future value of a variable of interest such as demand. Benefits of forecasting include more accurate planning. The potential benefit of using a formalized approach to forecasting is that it will be easier to utilize the computer and easier to quantify the information. A less formalized approach is more likely to utilize personal intuition. For small forecasting problems, intuition may involve personal bias, which may be reflected in the forecast. As the forecasting problem gets larger, it will be impossible to rely solely on a less formalized approach because a person’s intuition will be unable to process the large quantity of information. As mentioned in the case, the company has been experiencing stock out problems which is putting strains in the relationship with its store managers and most probably, customers. With a forecasted demand of its products, this will not happen as often as before. There are some others potential benefits of a more formalized approach to forecasting are:  They provide a wide range of decisions.  They form the basic inputs for many kinds of decisions in business organizations.  Fewer stock-outs in retail stores.  Reduce uncertainty factor by a considerable amount.  Proper inventory management and control over the stock levels.  Better knowledge regarding which products to continue selling and discontinue selling.  Examples of forecasting in Business organizations (Accounting, Finance, HR, Marketing, MIS, Operations, Product/Service Design) There are basically 2 uses for forecasts: 1. Plan the System( Short-term, Long-term ) 2. Help them plan the use of the system.

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Q2. Prepare a weekly forecast for the next four weeks of each product. Briefly explain why you chose the methods you used. Product 1: Product 1 reveals that, except for the unusual order of 90 units in the seventh week, the demand has risen over the period in a rising trend, which depicts that demand can be expected to rise further in the coming four week. Thus, the demand for the 7th week is considered an outlier. There are different ways of dealing with outliers. A simple and intuitive way is to replace the demand for the week in question with the average demand from the previous week and the next week in the time-series. Therefore in this case, the demand of 90 in week 7 will be replaced with 71.5 = [(67 + 76)/2].

t

y

ty

t2

1 2 3 4 5 6 7 8 9 10 11 12 13 14

50 54 57 60 64 67 71.5 76 79 82 85 87 92 96

50 108 171 240 320 402 500.50 608 711 820 935 1044 1196 1344

1 4 9 16 25 36 49 64 81 100 121 144 169 196

1020.5

8449.5

1015

∑ 105

𝑏=

𝑎=

n ∑ty − ∑t ∑y n ∑t2– (∑t )2 ∑y – b ∑t

𝑛

=

=

(14 ∗ 8449.5) − (105 ∗ 1020.5) = 3.5 (14 ∗ 1015) − (105 ∗ 105)

1020.5 − (3.5 ∗ 105)

14

= 46.64

The trend equation is: Ft = 46.64 + 3.5t

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The next four weeks forecasts are: F15 = 46.64 + 3.5*15 = 99.14 F16 = 46.64 + 3.5*16 = 102.64 F17 = 46.64 + 3.5*17 = 106.14 F18 = 46.64 + 3.5*18 = 109.64

Reason for using a linear trend method: • •

Linear trend shows a steady, straight line increase. It has an upward slopping trend and Predict Short-term demand for products.

Product 2: The demand for product two has complex patterns as there is a spike once every four weeks and the values between the spikes are fairly close to each other. From the data for Product 2 reveals, the data appear to be increasing at the rate of about one unit per week. Since, Intuitive Forecasting provides extended capabilities in forecast generation; we can use this approach to average of the three non-spike periods plus 1 to predict the next three non-spike periods. Therefore in this case, the forecast demand might be [(42+43+42)/3] = 42.33+1 = 43.33 to predict orders for period 15, and use the average of the values for periods 13, 14 and 15 plus 1 [{(43+44+43.33)/3}+1] = 44.44 as a forecast for periods 17 and 18. The values of the spikes also seem to be increasing. The initial increase was 1 and the second increase was 2. So we can assume there will be third increase with plus 3 units and forecasts for period 16 will be (49+3) = 52. Still, the fact that there is a limited amount of data makes this forecast more risky. Hence, the forecasts are:

Period

Forecast

15 16 17 18

43.33 52 44.44 44.44

Reason for using Intuitive approach: • • •

Nature of the spread of the data shows complex pattern. Improve the accuracy of forecasts. Improve planning and Decrease stockouts. Page | 3...