Question Tag: Sales Forecasting

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PM – May 2021 – L2 – Q3 – Cost-Volume-Profit (CVP) Analysis

Forecast future sales using historical data and analyze which data period provides a better basis for forecasting.

Question

Some time ago, Robert launched a new product. Initially, sales were strong, but recent figures have raised concerns. Robert seeks a more accurate sales forecast to create detailed cash projections. The sales data below illustrates an underlying trend derived from an averaging method:

Year	Quarter	Trend Point (x)	Sales (Cartons) (y)
2016	3rd	1	10,000
2016	4th	2	10,760
2017	1st	3	10,920
2017	2nd	4	11,000
2017	3rd	5	11,050
2017	4th	6	11,080
2018	1st	7	11,085
2018	2nd	8	11,095
2018	3rd	9	11,120
2018	4th	10	11,130

On average, quarters 1 and 3 are 5% and 6% above the trend, respectively, while quarters 2 and 4 are 2% and 9% below it. Preliminary calculations for the 10 periods yield:

Linear Regression:
Slope: 82.67
Intercept: 10,472.33
Coefficient of determination: 0.535

Forecasting is needed for quarters 3 and 4 in 2019 and quarters 1 and 2 in 2020. There is a debate about using data from all 10 periods versus only the last 5. Analysis for the last five periods includes:

Results of last five periods‟ observations

(Note: y values are scaled down by 100 for ease of calculation.)

Required:
a. Forecast sales for the four quarters using the 10-period data. (8 Marks)
b. Prepare similar forecasts using the last five periods of data. (8 Marks)
c. Evaluate which data set provides the better forecast. (4 Marks)

Answer

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Tags: Coefficient of Determination, Linear Regression, Sales Forecasting, Seasonality, Trend Analysis
Level: Level 2

Topic: Cost-Volume-Profit (CVP) Analysis
Series: MAY 2021

PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

Question

Some time ago Robert launched a new product. At first, sales were good, but now the figures are causing concern. Robert wants a more accurate sales forecast to produce detailed cash forecasts.

Since there is some seasonality present in the raw data, the series for sales shown below represents the underlying trend based on an averaging process:

On average, quarters 1 and 3 are 5% and 6% respectively above trend, while quarters 2 and 4 are respectively 2% and 9% below trend.

Some preliminary calculations on the above ten observations have been carried out and the results are summarized below:

Required:
a. Forecast the sales for the next two years, adjusting for seasonality. (12 Marks)
b. Discuss the importance of seasonality adjustments in sales forecasting. (4 Marks)
c. Explain how Robert could use the sales forecasts to produce detailed cash forecasts. (4 Marks)

Answer

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Tags: Cash Forecasting, Regression, Sales Forecasting, Seasonality, Trend Analysis
Level: Level 2

Topic: Environmental and Social Performance Management
Series: MAY 2024

PM – Nov 2021 – L2 – Q4 – Performance Measurement Systems

Adjust sales for seasonal variations and discuss deseasonalised data, along with the challenges of participative budgeting.

Question

You work as the assistant to the management accountant for Henry Limited, a medium-sized manufacturing company. One of its products, Product P, has been very successful in recent years, showing a steadily increasing trend in sales volumes. Sales volumes for the four quarters of last year were as follows:

Quarter	1	2	3	4
Actual sales volume (units)	420,000	450,000	475,000	475,000

A new assistant has recently joined the marketing department and she has asked you for help in understanding the terminology used in preparing sales forecasts and analysing sales trends. She said: “My main problem is that I do not see why my boss is so enthusiastic about the growth in Product P’s sales volume. It looks to me as though the rate of growth is really slowing down and has actually stopped in quarter 4. I am told that I should be looking at the deseasonalised or seasonally adjusted sales data, but I do not understand what is meant by this.”

You have found that Product P’s sales are subject to the following seasonal variations:

Quarter	1	2	3	4
Seasonal variation (units)	+25,000	+15,000	0	-40,000

Required:
a.
i. Adjust for the seasonal variations to calculate deseasonalised or seasonally adjusted sales volume (i.e., the trend figures) for each quarter of last year. (5 Marks)
ii. Assuming that the trend and seasonal variations will continue, forecast the sales volumes for each of the four quarters of next year. (4 Marks)

b. Explain what is meant by seasonal variations and deseasonalised or seasonally adjusted data. Indicate how they can be useful in analysing a time series and preparing forecasts. (5 Marks)

c. State the arguments for and challenges arising from managers participating in setting their budget targets. (6 Marks)

Answer

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Tags: Deseasonalisation, Participative Budgeting, Sales Forecasting, Seasonal Variations, Trend Analysis
Level: Level 2

Topic: Performance Measurement Systems
Series: NOV 2021

QTB – Nov 2014 – L1 – SB – Q4 – Forecasting

Analyze sales data using the Least Squares Method and forecast future sales based on the trend.

Question

The sales of PQR Nigeria Plc. in thousands of Naira are listed in the table below for each quarter for years 2005 – 2008.

Sales of PQR in N’000s

Year	Quarter 1	Quarter 2	Quarter 3	Quarter 4
2005	22	35	82	37
2006	24	46	81	44
2007	25	40	87	49
2008	29	42	100	55

Required:

a. Calculate the trend in the above data using the Least Squares Method. (12 Marks)
b. Estimate the sales for each quarter using the trend line. (4 Marks)
c. Calculate the percentage variation for each quarter’s actual sales from the estimate obtained in (b) above. (4 Marks)

(Total: 20 Marks)

Answer

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Tags: Least Squares, Sales Forecasting, Time series, Trend Estimation
Level: Level 1

Topic: Forecasting
Series: NOV 2014

QTB – May 2016 – L1 – SA – Q18 – Statistics

Calculating the probability of total sales of 6 in two consecutive days given daily sales probabilities.

Question

The daily sales record of a company with their corresponding probabilities are tabulated as follows:

The probability of total sales of 6 in two consecutive days is

A. 0.1039
B. 0.1309
C. 0.1930
D. 0.3019
E. 0.3109

Answer

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Tags: Compound Events, Probability, Sales Forecasting
Level: Level 1

Topic: Statistics
Series: MAY 2016

QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

Question

A sales manager of a manufacturing company is to predict the sales of some products for given expenditures on advertising using a simple linear regression analysis. In the fitted regression equation, the sales and advertising expenditure are respectively ____________ and ________________ variables.

A. Independent, dependent
B. Dependent, independent
C. Fixed, dependent
D. Dependent, fixed
E. Nominal, categorical

Answer

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Tags: Regression, Sales Forecasting, Variables
Level: Level 1

Series: MAY 2015

MA – May 2020 – L2 – Q2b – Cash budgets and master budgets

Prepare a cash budget for Emefa Ltd for October 2019 based on the given sales and cost data.

Question

b) Emefa Ltd (Emefa) is in the process of preparing its budget for the month of October 2019 for its product, YEK. The Company expects to sell the product for GH¢75 but this price is expected to increase in the last quarter of 2019 by 5%. The following are the expected sales in units for the last six months in 2019.

Month	Units
August	7,000
September	8,000
October	9,000

In October 2019, a total of 9,150 units of product YEK are expected to be produced to meet demand.

Typically, cash sales represent 20% of sales. Credit sales terms are 2/10, n/30. Emefa bills customers on the first day of the month following the month of sale. Experience has shown that 60% of the billings will be collected within the discount period, 25% by the end of the month after sales, 10% by the end of the second month after the sale, and 5% will ultimately be uncollectible. The firm writes off uncollectible accounts after 12 months.

The firm uses two materials for production, Mat and Pat. The purchase terms for materials are 2/15, n/60. Experience has shown that 80% of the purchases are paid in the month of the purchase and the remainder is paid in the month immediately following. In September 2019, the firm budgeted purchases were GH¢32,000 for Mat and GH¢20,000 for Pat.

The firm’s budgeted direct material and labour budgets are as follows:

Direct Materials Purchases Budget (in Cedis) For October 2019

Material	Budgeted Purchases (Pounds)	Expected Purchase Price per Unit (GH¢)	Total (GH¢)
Mat	45,000	2.00	90,000
Pat	25,000	3.00	75,000
Total Budgeted Purchases			165,000

The production process requires direct labour at two skill levels (SL). The rate for labour at the SL1 level is GH¢45 per hour and for the SL2 level is GH¢25 per hour. The SL1 level can process one batch of YEK per hour while SL2 uses two (2) hours for the same output. Each batch consists of ten (10) units. The manufacturing of YEK also requires one-fifth of an hour of SL2 workers’ time for each unit manufactured.

Variable manufacturing overhead is GH¢100 per batch plus GH¢75 per direct labour-hour. In addition to variable overhead, the firm has a monthly fixed factory overhead of GH¢60,000, of which GH¢18,000 is depreciation expense. The firm pays all manufacturing labour and factory overhead when incurred.

Total budgeted marketing, distribution, customer service, and administrative costs for the 2019 annual budget are GH¢3,000,000. Of this amount, GH¢2,000,000 is considered fixed and includes depreciation expense of GH¢400,000. All marketing and administrative costs are paid in the month incurred.

Management desires to maintain an end-of-month minimum cash balance of GH¢100,000. The firm has an agreement with a local bank to borrow its short-term needs in multiples of GH¢10,000 up to GH¢1,000,000 at an annual interest rate of 26%. Borrowings are assumed to occur at the end of the month. Bank borrowing at October 1 was GH¢0.

Required:

Prepare the cash budget for October 2019 for Emefa Ltd. (10 marks)

Answer

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Tags: Cash Budgeting, Cash Flow Management, Payables, Receivables, Sales Forecasting
Level: Level 2

Topic: Cash Budgets and Master Budgets
Series: MAY 2020

IMAC – MAR 2024 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Calculate daily variations using moving averages and explain interrelationships between material price and usage variances, and labor rate and efficiency variances.

Question

a) BB Importers Ltd has been importing electrical gadgets through the port of Takoradi over the past ten years. Management is aware that the business has been facing seasonal fluctuations but there is no scientific basis for the determination of such variations that can be used to predict future revenue. As a newly recruited Cost Accountant, you have been provided with some past daily sales performance over a three-week period. Details of the sales performance are shown below:

Sales	Monday	Tuesday	Wednesday	Thursday	Friday
Week 1	780	830	890	850	850
Week 2	880	930	990	950	950
Week 3	980	1030	1090	1050	1050

Required:
Using daily moving averages, calculate the daily variation for the company. (15 marks)

b) The reasons for variances might be connected, and two or more variances may arise from the same cause. For example, a favorable variance and an adverse variance might have the same cause.

Required:
Explain the interrelationships between:
i) Material price and usage variances (2.5 marks)
ii) Labor rate and efficiency variances (2.5 marks)

Answer

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Tags: Material Price Variance, Moving Averages, Sales Forecasting, Usage Variance
Level: Level 1

Topic: Forecasting, Standard Costing and Variance Analysis
Series: MAR 2024

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

Question

a) A company operates from Monday to Friday. Sales data for the most recent three weeks as well as the moving total are as follows:

Day	Sales	Moving Total
Day 1	78
Day 2	83
Day 3	89	420
Day 4	85	430
Day 5	85	440
Day 6	88	450
Day 7	93	460
Day 8	99	470
Day 9	95	480
Day 10	95	490
Day 11	98	500
Day 12	103	510
Day 13	109	520
Day 14	105
Day 15	105

Required:
i) State the length of the cycle. (2 marks)
ii) Using the moving averages, establish the trend of the historical data above. (6 marks)
iii) Calculate the seasonal variation for each day of the week. (7 marks)

b) The value of variances as a control technique for management depends on the reliability and accuracy of the standard costs. If the standard costs are inaccurate, comparisons between actual cost and standard cost will have no meaning.

Required:
Explain TWO (2) factors to be considered by the purchasing department in estimating the direct material costs per unit of raw material. (5 marks)

Answer

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Tags: Direct Material Costs, Moving Averages, Sales Forecasting, Seasonal Variation
Level: Level 1

Topic: Forecasting, Standard Costing and Variance Analysis
Series: NOV 2023

IMAC – JULY 2023 – L1 – Q5 – Budgeting | Forecasting

Prepare seasonally adjusted sales forecast and production overheads budget, and explain key components of time series analysis.

Question

Question:
a) Balan Ltd is engaged in manufacturing and selling a single product and is in the process of preparing its budget for 2024. The following information is available:

Sales:
Balan Ltd has developed the linear relationship Y = 20,000 + 4,000X, where X represents the time period (X = 1 for the 1st quarter of 2024) and Y represents the sales trend in units.

The following seasonal variations are required to be adjusted to the sales trend in order to arrive at the sales forecast:

Quarter	Jan-Mar	Apr-Jun	Jul-Sep	Oct-Dec
Index value	110	90	80	120

A unit of product can be sold at GH¢1,000 during the first three quarters, and this price will be increased by 10% in the fourth quarter of 2024.

Production Overheads:
Based on the past 6 quarters’ results, the following statistical data has been gathered:
n = 6 (where ‘n’ is the number of quarters considered)
∑X = 306 (where ‘X’ is the production quantity in thousands of units)
∑Y = 146,400 (where ‘Y’ is the total production overheads in GH¢ thousands)
∑XY = 7,578,400
∑X² = 15,886

Balan Ltd estimates a linear relationship between output (X) and production overheads (Y), and the total production overheads can be expressed as Y = a + bX.

Balan Ltd does not maintain finished goods inventories. You are one of the Management Accounting executives of Balan Ltd and have been requested to assist in compiling budget figures.

Required:
i) Prepare the seasonally adjusted quarterly sales forecast in unit and in value for the calendar year 2024. (4 marks)
ii) Prepare the quarterly production overheads budget for the calendar year 2024. (5 marks)

b) Explain the following terms:
i) Trend-Cycle (C) (2 marks)
ii) Seasonal Components (S) (2 marks)
iii) Irregular Components (I) (2 marks)

c) The purpose of Management Accounting is to provide information for planning, control, and decision making. Information for control of the performance is an important management task.

Required:
Outline THREE (3) activities involved in providing information for control purposes. (5 marks)

Answer

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Tags: Irregular Components, Production Overheads Budget, Sales Forecasting, Seasonal Components, Trend-Cycle
Level: Level 1

Topic: Budgeting, Forecasting
Series: JULY 2023

PM – May 2021 – L2 – Q3 – Cost-Volume-Profit (CVP) Analysis

Forecast future sales using historical data and analyze which data period provides a better basis for forecasting.

Question

Year	Quarter	Trend Point (x)	Sales (Cartons) (y)
2016	3rd	1	10,000
2016	4th	2	10,760
2017	1st	3	10,920
2017	2nd	4	11,000
2017	3rd	5	11,050
2017	4th	6	11,080
2018	1st	7	11,085
2018	2nd	8	11,095
2018	3rd	9	11,120
2018	4th	10	11,130

On average, quarters 1 and 3 are 5% and 6% above the trend, respectively, while quarters 2 and 4 are 2% and 9% below it. Preliminary calculations for the 10 periods yield:

Linear Regression:
Slope: 82.67
Intercept: 10,472.33
Coefficient of determination: 0.535

Results of last five periods‟ observations

(Note: y values are scaled down by 100 for ease of calculation.)

Answer

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Tags: Coefficient of Determination, Linear Regression, Sales Forecasting, Seasonality, Trend Analysis
Level: Level 2

Topic: Cost-Volume-Profit (CVP) Analysis
Series: MAY 2021

PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

Question

Some time ago Robert launched a new product. At first, sales were good, but now the figures are causing concern. Robert wants a more accurate sales forecast to produce detailed cash forecasts.

Since there is some seasonality present in the raw data, the series for sales shown below represents the underlying trend based on an averaging process:

On average, quarters 1 and 3 are 5% and 6% respectively above trend, while quarters 2 and 4 are respectively 2% and 9% below trend.

Some preliminary calculations on the above ten observations have been carried out and the results are summarized below:

Answer

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Tags: Cash Forecasting, Regression, Sales Forecasting, Seasonality, Trend Analysis
Level: Level 2

Topic: Environmental and Social Performance Management
Series: MAY 2024

PM – Nov 2021 – L2 – Q4 – Performance Measurement Systems

Adjust sales for seasonal variations and discuss deseasonalised data, along with the challenges of participative budgeting.

Question

Quarter	1	2	3	4
Actual sales volume (units)	420,000	450,000	475,000	475,000

You have found that Product P’s sales are subject to the following seasonal variations:

Quarter	1	2	3	4
Seasonal variation (units)	+25,000	+15,000	0	-40,000

b. Explain what is meant by seasonal variations and deseasonalised or seasonally adjusted data. Indicate how they can be useful in analysing a time series and preparing forecasts. (5 Marks)

c. State the arguments for and challenges arising from managers participating in setting their budget targets. (6 Marks)

Answer

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Tags: Deseasonalisation, Participative Budgeting, Sales Forecasting, Seasonal Variations, Trend Analysis
Level: Level 2

Topic: Performance Measurement Systems
Series: NOV 2021

QTB – Nov 2014 – L1 – SB – Q4 – Forecasting

Analyze sales data using the Least Squares Method and forecast future sales based on the trend.

Question

The sales of PQR Nigeria Plc. in thousands of Naira are listed in the table below for each quarter for years 2005 – 2008.

Sales of PQR in N’000s

Year	Quarter 1	Quarter 2	Quarter 3	Quarter 4
2005	22	35	82	37
2006	24	46	81	44
2007	25	40	87	49
2008	29	42	100	55

Required:

(Total: 20 Marks)

Answer

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Tags: Least Squares, Sales Forecasting, Time series, Trend Estimation
Level: Level 1

Topic: Forecasting
Series: NOV 2014

QTB – May 2016 – L1 – SA – Q18 – Statistics

Calculating the probability of total sales of 6 in two consecutive days given daily sales probabilities.

Question

The daily sales record of a company with their corresponding probabilities are tabulated as follows:

The probability of total sales of 6 in two consecutive days is

A. 0.1039
B. 0.1309
C. 0.1930
D. 0.3019
E. 0.3109

Answer

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Tags: Compound Events, Probability, Sales Forecasting
Level: Level 1

Topic: Statistics
Series: MAY 2016

QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

Question

A. Independent, dependent
B. Dependent, independent
C. Fixed, dependent
D. Dependent, fixed
E. Nominal, categorical

Answer

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Tags: Regression, Sales Forecasting, Variables
Level: Level 1

Series: MAY 2015

MA – May 2020 – L2 – Q2b – Cash budgets and master budgets

Prepare a cash budget for Emefa Ltd for October 2019 based on the given sales and cost data.

Question

Month	Units
August	7,000
September	8,000
October	9,000

In October 2019, a total of 9,150 units of product YEK are expected to be produced to meet demand.

The firm’s budgeted direct material and labour budgets are as follows:

Direct Materials Purchases Budget (in Cedis) For October 2019

Material	Budgeted Purchases (Pounds)	Expected Purchase Price per Unit (GH¢)	Total (GH¢)
Mat	45,000	2.00	90,000
Pat	25,000	3.00	75,000
Total Budgeted Purchases			165,000

Required:

Prepare the cash budget for October 2019 for Emefa Ltd. (10 marks)

Answer

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Tags: Cash Budgeting, Cash Flow Management, Payables, Receivables, Sales Forecasting
Level: Level 2

Topic: Cash Budgets and Master Budgets
Series: MAY 2020

IMAC – MAR 2024 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Calculate daily variations using moving averages and explain interrelationships between material price and usage variances, and labor rate and efficiency variances.

Question

Sales	Monday	Tuesday	Wednesday	Thursday	Friday
Week 1	780	830	890	850	850
Week 2	880	930	990	950	950
Week 3	980	1030	1090	1050	1050

Required:
Using daily moving averages, calculate the daily variation for the company. (15 marks)

b) The reasons for variances might be connected, and two or more variances may arise from the same cause. For example, a favorable variance and an adverse variance might have the same cause.

Required:
Explain the interrelationships between:
i) Material price and usage variances (2.5 marks)
ii) Labor rate and efficiency variances (2.5 marks)

Answer

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Tags: Material Price Variance, Moving Averages, Sales Forecasting, Usage Variance
Level: Level 1

Topic: Forecasting, Standard Costing and Variance Analysis
Series: MAR 2024

IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

Question

a) A company operates from Monday to Friday. Sales data for the most recent three weeks as well as the moving total are as follows:

Day	Sales	Moving Total
Day 1	78
Day 2	83
Day 3	89	420
Day 4	85	430
Day 5	85	440
Day 6	88	450
Day 7	93	460
Day 8	99	470
Day 9	95	480
Day 10	95	490
Day 11	98	500
Day 12	103	510
Day 13	109	520
Day 14	105
Day 15	105

Required:
Explain TWO (2) factors to be considered by the purchasing department in estimating the direct material costs per unit of raw material. (5 marks)

Answer

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Tags: Direct Material Costs, Moving Averages, Sales Forecasting, Seasonal Variation
Level: Level 1

Topic: Forecasting, Standard Costing and Variance Analysis
Series: NOV 2023

IMAC – JULY 2023 – L1 – Q5 – Budgeting | Forecasting

Prepare seasonally adjusted sales forecast and production overheads budget, and explain key components of time series analysis.

Question

Question:
a) Balan Ltd is engaged in manufacturing and selling a single product and is in the process of preparing its budget for 2024. The following information is available:

Sales:
Balan Ltd has developed the linear relationship Y = 20,000 + 4,000X, where X represents the time period (X = 1 for the 1st quarter of 2024) and Y represents the sales trend in units.

The following seasonal variations are required to be adjusted to the sales trend in order to arrive at the sales forecast:

Quarter	Jan-Mar	Apr-Jun	Jul-Sep	Oct-Dec
Index value	110	90	80	120

A unit of product can be sold at GH¢1,000 during the first three quarters, and this price will be increased by 10% in the fourth quarter of 2024.

Balan Ltd estimates a linear relationship between output (X) and production overheads (Y), and the total production overheads can be expressed as Y = a + bX.

Balan Ltd does not maintain finished goods inventories. You are one of the Management Accounting executives of Balan Ltd and have been requested to assist in compiling budget figures.

b) Explain the following terms:
i) Trend-Cycle (C) (2 marks)
ii) Seasonal Components (S) (2 marks)
iii) Irregular Components (I) (2 marks)

c) The purpose of Management Accounting is to provide information for planning, control, and decision making. Information for control of the performance is an important management task.

Required:
Outline THREE (3) activities involved in providing information for control purposes. (5 marks)

Answer

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Tags: Irregular Components, Production Overheads Budget, Sales Forecasting, Seasonal Components, Trend-Cycle
Level: Level 1

Topic: Budgeting, Forecasting
Series: JULY 2023

Question Tag: Sales Forecasting

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PM – May 2021 – L2 – Q3 – Cost-Volume-Profit (CVP) Analysis

Forecast future sales using historical data and analyze which data period provides a better basis for forecasting.

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PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

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PM – Nov 2021 – L2 – Q4 – Performance Measurement Systems

Adjust sales for seasonal variations and discuss deseasonalised data, along with the challenges of participative budgeting.

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QTB – Nov 2014 – L1 – SB – Q4 – Forecasting

Analyze sales data using the Least Squares Method and forecast future sales based on the trend.

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QTB – May 2016 – L1 – SA – Q18 – Statistics

Calculating the probability of total sales of 6 in two consecutive days given daily sales probabilities.

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QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

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MA – May 2020 – L2 – Q2b – Cash budgets and master budgets

Prepare a cash budget for Emefa Ltd for October 2019 based on the given sales and cost data.

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IMAC – MAR 2024 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Calculate daily variations using moving averages and explain interrelationships between material price and usage variances, and labor rate and efficiency variances.

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IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

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IMAC – JULY 2023 – L1 – Q5 – Budgeting | Forecasting

Prepare seasonally adjusted sales forecast and production overheads budget, and explain key components of time series analysis.

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PM – May 2021 – L2 – Q3 – Cost-Volume-Profit (CVP) Analysis

Forecast future sales using historical data and analyze which data period provides a better basis for forecasting.

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PM – May 2024 – L2 – SB – Q4 – Environmental and Social Performance Management

Forecast sales with seasonality adjustments and regression analysis to produce cash forecasts.

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PM – Nov 2021 – L2 – Q4 – Performance Measurement Systems

Adjust sales for seasonal variations and discuss deseasonalised data, along with the challenges of participative budgeting.

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QTB – Nov 2014 – L1 – SB – Q4 – Forecasting

Analyze sales data using the Least Squares Method and forecast future sales based on the trend.

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QTB – May 2016 – L1 – SA – Q18 – Statistics

Calculating the probability of total sales of 6 in two consecutive days given daily sales probabilities.

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QTB – May 2015 – L1 – SA – Q9 – Statistics

Identifying the dependent and independent variables in regression analysis.

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MA – May 2020 – L2 – Q2b – Cash budgets and master budgets

Prepare a cash budget for Emefa Ltd for October 2019 based on the given sales and cost data.

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IMAC – MAR 2024 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Calculate daily variations using moving averages and explain interrelationships between material price and usage variances, and labor rate and efficiency variances.

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IMAC – NOV 2023 – L1 – Q5 – Forecasting | Standard Costing and Variance Analysis

Determine the trend and seasonal variation in sales using moving averages and calculate factors for estimating direct material costs.

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IMAC – JULY 2023 – L1 – Q5 – Budgeting | Forecasting

Prepare seasonally adjusted sales forecast and production overheads budget, and explain key components of time series analysis.

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