MBA 1ST Year Supply Chain Management Long Question Answers: – In this Post you will find MBA 1 year related to important questions related to the answer such as the Operation Concept Answer and many other important questions. Long Questions are answered in section C
LONG ANSWER QUESTIONS
Q.1. What are supply chain drivers? Explain.
Ans. Supply Chain Drivers
Drivers determine supply chain performance. For each driver, managers must make trade-offs between efficiency (cost) and responsiveness.
Performance of the supply chain can be determined with the help of the drivers. The drivers of the supply chain consists of three logistical drivers and three cross functional drivers.
Important Drivers: Following are the important drivers of the supply chain: I. Logistical Drivers
1. Facilities: Facilities are the physical locations where the products are stored or assembled. The major types of facilities are production sites and the storage sites. Economies of scales are used in centralisation of facilities to increase supply chain efficiency.
2. Inventory: Inventory consists of the raw materials, work-in-progress and the finished goods. Changing inventory policies can largely effect the efficiency and the responsiveness of the supply chain. Three basic decisions to be taken by the business regarding inventory are cycle, safety and the seasonal inventory decisions.
3. Transportation: Transportation refers to the modes and routes for moving inventory throughout the supply chain. Faster transportation ensures more responsiveness but less efficiency of supply chain. Transportation supports a firm’s competitive strategy. The different ways of transportation includes rail, road, sea water, pipe lines and the air ways. Electronic transport is the fastest and the efficient mode of transportation. Transportation decisions includes mode, routes and in house or outsourcing the transportation.
II Cross Functional Drivers
1 Information: Information connects various supply coordinate activities. Informat
ation connects various supply chain partners and allows them to ormation system can enable a firm to get a high variety of customised pro ation is crucial to the daily operations at each stage of the supply chain. An and to understand the chang Te a firm to get a high variety of customised products to customers rapidly ne changing customers tastes and preferences.
2. Sourcing: Sourcing is th e final products. Components of the sourcing decisions are the evalua is the process of purchasing the materials required for the production of ppliers, in house or outsourcing. ents of the sourcing decisions are the evaluation and the selection of the
3. Pricing: Pricing involves determining the charges for the goods or s manufacturers. The price of the product effects the buying patterns of the custom supply chain performance. errects the buying patterns of the customers thus effecting the
All the above stated drivers are very crucial in determining the success of the supply chanh management process.
MBA 1ST Year Supply Chain Management Long Question Answers Paper Set in English
Q.2. Write a note on:
1. Simple moving average method.
2. Weighted moving average method.
3. Exponential smoothing method.
Ans. 1. Simple Moving Average Method: This method of forecasting is a trend, which follows an indicator to smoothen a demand. Simple moving average is calculated by adding in the demand. Simple moving average is calculated by adding up the total demands in a fixed time period and dividing the sum total by the total number of time periods. In addition to looking for the average demand of a product, this technique also looks for a specific period of time, which could span over a week or a month.
A general trend, which is followed in moving average method of demand forecasting, is to take the average of previous weeks or months depending upon the nature of the business. It can also work on uarterly basis where fluctuations are not so volatile.
Moving Average Forecast = ( Demand in previous n neriods) / (n)
where n is the number of periods in the moving average.
Simple moving average analysis shows that this method is useful when the error rate is less and demand is somewhat constant all throughout a period. But its drawback is that it does not consider seasonal variations and upward changes in demands. If a company is using quarterly simple moving average to forecast its demand in the fourth quarter and the trend is that its demand rises in the third and fourth quarter of a year, the result in moving average outcome for the fourth quarter will be very low when compared to the actual demand.
A major drawback of the SMA is that it lets through a significant amount of the signal shorter than the window length. Worse, it actually inverts it. This can lead to unexpected artifacts, such as peaks in the smoothed result appearing where there were troughs in the data. It also leads to the result being less smooth than expected since some of the higher frequencies are not properly removed.
2. Weighted Moving Average Method: Weighted average is a mean calculated by giving values in a data set more influence according to some attribute of the data. It is an average in which each quantity to be averaged is assigned a weight, and these weights determine the relative importance of each quantity on the average. Weights are the equivalent of having that many like items with the same value involved in the average.
In this method, the moving totals are multiplied by the weights assigned to them and the weighted moving average is obtained by dividing this product by the sum of the weights.
A weighted average is most often computed with respect to the frequency of the values in a data set. A weighted average can be calculated in different ways, however, if certain values in a data set are given more importance for reasons other than frequency of occurrence.
3. Exponential Smoothing Method: These models are well known and in of little data storage and computation management because of their ready availability and requirement of little
Uses of Exponential Smoothing: These are as follows:
1. Exponential models are accurate,
2. Formulating exponential model is easy,
3. The user can understand how the models work,
4. Little computation is required to use the model,
5. Computer storage requirements are small because of limited use of historical data,
6. Test for accuracy are easy to compute.
Single Exponential Smoothing: These are as follows:
The equation for creating a new or updated forecast uses two pieces of information:
1. Actual demand for the most recent period, and
2. The most recent demand forecast.
As each time period expires, a new forecast is made:
Demand forecast for Actual demand for most Forecast next period’s demand = a.
most recent period) I recent period
Fe = aDc-1 + (1 – a)Ft-1 where,
Osa s 1, and t is the period
After period t – 1 ends, the actual demand D – for period is t – 1. At the beginning of period t-1, you made a forecast F-1 of the demand during period t-1. Therefore, at the end oft-1, one have both pieces of information needed for calculating a forecast of demand for the next period Ft
Exponential Smoothing with Trends (Double Exponential Smoothing): An exponential smoothing over an already smoothed time series is called double exponential smoothing. Double exponential smoothing allows forecasting data with trends. This method is better at handling trends that are not stationary. Double exponential smoothing applies the process of exponential smoothing to a time series that is an exponentially smoothened series to account for linear trend in the forecasted value. The extrapolated series has a constant growth rate, equal to the growth of the smoothed series at the end of the data period.
An upward or downward trend in data collected over a sequence of one period causes the exponential forecast to always lag behind (be above or below the actual occurrence). Exponentially smoothed forecasts can be corrected somewhat by adding in a trend adjustment. To correct the trend, two smoothing constants are needed. Besides the smoothing constant, the trend equation also uses smoothing constant delta (8). The delta reduces the impact of the error that occurs between the actual and the forecast. If both alpha and delta are not included, the trend would over react to errors.
To get the trend equation going, the first time it is used the trend value must be entered manually. This initial trend value can be educated guess or a computation based on observed past data. The equation to compute the Forecasted Including Trend (FIT) is
FITt, = Ft+T
Ft = FITt-1+ a(At-1-FITt-1)
Tt = Tt-1+ a 8(At-1-FITt-1)
Ft = The exponentially smoothed forecasted for period t
Tt = The exponentially smoothed trend for period t
FITt, = The forecast including trend for period t
FITt – 1 = The forecast including trend made for the prior period
At-1 = The actual demand for the prior period
a = Smoothing constant
If a trend is present, a second smoothing constant is employed. The process of making a trend
Adjusted forecast sred forecast follows a sequence of steps:
1. Make a single exponential forecast, using the previous forecast, actual data and smoothing constante
F=F-1+ a(A.-1-7-1) 2.
Calculate the trend:
Tt = Tt-1 + B(ft-ft-ft-1)
. Add the trend to the forecast:
TAF = Ft+T
Triple Exponential Smoothing: In the case of non-linear
Trends, it might be necessary to extend it even to a triple exponential smoothing. Triple exponential smoothing is better at handling parabola trends and is normally used for such data.
While simple exponential smoothing requires stationary conditions in the demand parameters, she double exponential smoothing can capture when the demand is changing in a linear trend, and triple exponential smoothing can handle almost all other business time series.