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Total Marks 5
Starting Date Thursday, November 20, 2014
Closing Date Tuesday, November 25, 2014
Status Open

Topic: Forecasting

Learning Objective: 
The objective of this discussion is to make the students able to read and analyze the information on the concept of forecasting and to understand how to make an adequate demand forecast.

Discussion Question:

ABC Limited is currently dealing in the manufacturing and sales of the furniture equipment. The organization is currently facing severe financial troubles.  At the inquiry it was revealed that these troubles are majorly due to inadequate demand forecast. The sales manager who was responsible for making the demand forecast, when contacted for the explanation of the trouble reported that the major problem lies in the inaccuracy of the demand forecast. Sometimes, the company incurs huge expenses on inventory holding in situations of reduced demand while at others; the company has to face losses by giving up their valuable customers, when they are unable to meet the customer demand because of the shortage of the produced items.

Being a student of production/operations manager, how will you help the Sales Manager in monitoring the forecast errors and in improving the accuracy of their demand forecasts in order to ensure that the demand forecasts are performed adequately in future?


  •    Before attempting the GDB, gather and read required information about inventory management. After doing this, carefully        attempt GDB as per requirements.
  •    The length of your answer must not exceed 250 words.
  •     Avoid directly copying material from any source. Produce logical points in your own words.
  •       Adopt a critical approach while answering the given question.
  •     Only logical and rational points supported by good reasoning will be awarded marks.
  •    You are advised to be specific and avoid unnecessary details or explanations as it can have negative impact on your scores.
  •    Copied material will be marked as ZERO.

Other Important Instructions:

 1. Your discussion must be based on logical facts.

2. Do not copy your answer with other students or from internet sources. Two identical / copied comments will be marked Zero (0) and may damage your grade in the course.

3. Obnoxious or ignoble answer should be strictly avoided.

4. Questions / queries related to the content of the GDB, which may be posted by the students on MDB or via e-mail, will not be replied till the due date of GDB is over.

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MGT713 - Production / Operations Management Graded Discussion Board GDB 1 Solution Fall 2014 of Virtual University (VU)

Learning Outcomes


After going through this activity, the students would be able to use knowledge of conversion methodology of accounts from single entry system to double entry system in order to prepare financial statements of any business concern.




The following data has been provided by Mr. Entrepreneur regarding his small business for the year ending March 31, 2014:

Assets & Liabilities






Trade payables

Rs.  19,713

Rs.   15,500

Direct expenses payable



Misc. Assets






Cash & bank balances



Trade receivables







His cash and other transactions took place during the year are as follows:










Collections from receivables


Cash purchases


Sales returns & allowances


Cash expenses – ½ as to indirect expenses






Bad debts


Machinery purchased through cheque


Sales – cash and credit


Personal expense on bank account


Discount from trade payables


Paid into the bank


Returns to trade payables


Withdrawals from the bank


Capital invested during the year


Cash-in-hand as on 31-3-2014


Collections sent to the bank


Check issued to trade payable








Required Using the above data, prepare the following:



An income statement for the year ended March 31, 2014;

(8 Marks)


A balance sheet as on that date; &

(7 Marks)


Necessary working schedules

(5 Marks)

Dear Students Don’t wait for solution post your problems here and discuss ... after discussion a perfect solution will come in a result. So, Start it now, replies here give your comments according to your knowledge and understandings....

2.2 Forecasting Accuracy and Control

It is important to include an indication of the extent to which the forecast might deviate from the value of variable that actually occurs. This will provide the forecast user with a better perspective on how far off a forecast might be. This also provides a decision maker a measure of accuracy to use as a basis for comparison, when choosing among different techniques.

2.2.1 Forecast Accuracy

Forecasting error is defined as the difference between actual and forecast, i.e.,


Two commonly used measures are

·        Mean absolute deviation (MAD)

, and

·        Mean squared error (MSE)


The difference between these two measures is that MAD weights all errors evenly, and MSE weights errors according to their squared values.

For the usage of these measures, either MAD or MSE, a manager could compare the results of exponential smoothing with values of .1, .2, and .3, and select the one that yields the least MAD or MSE for a given set of data.

2.2.2 Forecasting Control

It is necessary to monitor forecast errors to ensure that the forecast is performing adequately over time. This is generally accomplished by comparing forecast errors to predefined values, or action limits, as illustrated below.

Possible sources of forecast errors:

·        the omission of an important variable,

·        a sudden or unexpected change in the variable (causing by severe weather or other nature phenomena, temporary shortage or breakdown, catastrophe, or similar events),

·        appearance of a new variable,

·        being used incorrectly,

·        data being misinterpreted, and

·        random variation.

Two common methods in forecast control / monitor are tracking signal and control chart.

Tracking Signal

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding MAD:


The tracking signal often ranges from  to . For the most part, we shall use limits of , which are roughly comparable to three standard deviation limits. Values within the limits suggest --- but do not guarantee --- that the forecast is performing adequately.

MAD can be updated using the following exponential smoothing equation:


Control Chart

The control chart sets the limits as multiples of the squared root of MSE. Basic assumptions are

·        Forecast errors are randomly distributed around a mean of zero, and

·        The distribution of errors is normal.

The square root of MSE is used in practice as an estimate of the standard deviation of the distribution of errors. That is,


For a normal distribution, 95% of the errors fall within , and approximately 99.7% of the errors fall within . Errors fall outside these limits should be regarded as evidence that corrective action is needed.

Plotting the errors with the help of a control chart can be very informative. A plot helps you to visualize the process and enables you to check for possible patterns, nonrandom errors, within the limit that suggests an improved forecast is possible.

The control chart approach is generally superior to the tracking signal approach. The major weakness of the tracking signal approach is its use of cumulative errors: individual errors can be obscured so that large positive and negative errors cancel each other.

2.3 Forecasting Method Selection and Usage

Two most important factors are cost and accuracy. Generally speaking, the higher the accuracy, the higher the cost, so it is important to weight cost-accuracy trade-offs carefully.

When deciding among forecasting alternatives, the operations manager need to consider

·        the historical performance of a forecast, and

·        the ability of a forecast to respond to changes.

Below are five topics to consider in your pursuit of forecast accuracy.

1. Forecast at the right level

It is advisable to periodically review your current level of forecast and decide whether it is appropriate given the forecast accuracy goals and review process. There are many options to consider when designing your forecast database. Is a forecast at SKU level appropriate? In many environments it is not and greater detail must be made available by segmenting history and forecast by channel, sales region, or even customer. In other cases companies are currently forecasting at customer level when a more accurate forecast could be achieved with less effort by working at a higher level.

2. Review forecasts at aggregate levels

When reviewing item level forecasts it is all to easy to “pad” each item’s forecast just in case. It is not until the item level forecast is aggregated and reviewed at a brand or product category level that the cumulative effect of this “padding” is exposed in the form of a clearly unachievable growth in forecast over history. Aggregate level forecast review is an essential part of the forecast review process because it allows for a “sanity check” of the forecast compared to history and preferable company budgets. Any anomalies must be identified and corrected before putting the forecast into the inventory planning system.

3. Involve the “right” people

The “art” of forecast management involves getting the people who have market information easy access to input their intelligence into the forecast. Their local market or product line knowledge must be tapped into because it will provide valuable information on upcoming demand spikes and troughs. Many of our clients use the Demand Solutions™ Feedback tool to solicit forecast information from sales, product managers, or even customers directly. Whatever mechanism is used to gather the forecast intelligence it must be timely, systematic, and allow for analysis of forecast accuracy.

4. Review forecast by exception

I always cringe when people tell me they review the forecast by starting at the first product and scrolling to the last. Why not use ABC analysis to focus on the products which are most important first while the mind is still fresh? Why not use deviation filters to identify the few products in the database which have unusually high or low trending forecasts when compared to history? This management by exception will keep you focused and allow for a much more efficient review of the forecast.

5. Measure and report forecast accuracy

Demand Solutions™ allows you to review and report on forecast accuracy in almost any way you can imagine. Standard reports offer analysis at detailed and summary levels to help you identify weak areas and opportunities for improvement.

A forecasting process will rarely be successful if the progress is not measured and the results reported to all stakeholders. Take advantage of the utilities available and start your forecast accuracy graph this month. Let everyone know how “good” or “bad” they are doing and what progress they have made over time. In my experience as soon as a forecast accuracy graph is started the overall trend is up for six months. After that time everyone is committed to the process and the real work can begin to fine tuning the already successful process.

For more information on how these ideas can be applied in your environment using the tools in Demand Solutions™ feel free to call our helpdesk and speak with a consultant…and most importantly remember:



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