Marketing Analysis: Sales Forecasting (Copy)

8.1 Forecasting And Managing Demand

8.1.3 Sales Forecasting

The Need To Forecast Sales

• Definition of sales forecasting
  • Sales forecasting is the process of estimating future sales revenue or sales volume for a given period.
  • It uses past data, market research, and external factors to predict how much a business is likely to sell.
• Reasons why sales forecasting is important
  • Production planning: ensures the business produces the right quantity of goods and services to meet demand without overproducing.
  • Human resource planning: forecasts help businesses plan how many workers to hire, train, or lay off depending on expected demand.
  • Financial planning: forecasts predict future revenue, enabling businesses to prepare budgets and cash flow forecasts.
  • Inventory management: ensures that stock levels match expected sales to avoid shortages or overstocking.
  • Investment decisions: forecasts help managers decide when to expand production capacity or launch new products.
  • Marketing strategies: businesses plan promotions, pricing strategies, and advertising campaigns based on sales forecasts.
  • Investor confidence: accurate forecasts help attract investors and maintain trust with shareholders.
• Examples
  • A clothing retailer forecasts high sales during the festive season and orders additional stock.
  • A smartphone company predicts rising demand for 5G devices and invests in new production facilities.

Time Series Analysis: Calculation And Use Of Four-Period Centred Moving Average Method

• Definition of time series analysis
  • A statistical technique that analyses data collected over time to identify patterns such as trend, seasonal fluctuations, and cyclical variations.
• Purpose
  • To predict future sales by examining historical data.
  • Helps identify long-term trends, seasonal demand, and irregular fluctuations.
• Key components of a time series
  • Trend (T): the general direction of sales over time (upward, downward, or stable).
  • Seasonal variation (S): regular fluctuations due to seasons, festivals, or recurring events.
  • Cyclical variation (C): long-term fluctuations due to economic cycles.
  • Random variation (R): unpredictable factors (natural disasters, strikes, sudden changes in demand).
• Moving average method
  • Averages sales over a fixed number of periods to smooth short-term fluctuations.
  • A four-period moving average averages sales over four consecutive periods.
  • The centred moving average (CMA) adjusts the average so it is aligned with the middle of the time period, making it more accurate.
• Steps for a four-period centred moving average (CMA)
  1. Add sales data for four consecutive periods.
  2. Divide the total by 4 to get the simple moving average.
  3. Repeat for the next 4 periods (shifting forward one period at a time).
  4. To centre, take the average of two consecutive 4-period moving averages and align them with the midpoint.

Worked Example

• Suppose a company has the following sales data (in units) for 12 quarters:
  • Q1: 120
  • Q2: 135
  • Q3: 160
  • Q4: 180
  • Q5: 130
  • Q6: 150
  • Q7: 170
  • Q8: 190
  • Q9: 140
  • Q10: 160
  • Q11: 185
  • Q12: 205
• Step 1: Calculate 4-period simple moving averages (MA₄):
  • (Q1+Q2+Q3+Q4) ÷ 4 = (120+135+160+180) ÷ 4 = 148.75
  • (Q2+Q3+Q4+Q5) ÷ 4 = (135+160+180+130) ÷ 4 = 151.25
  • (Q3+Q4+Q5+Q6) ÷ 4 = (160+180+130+150) ÷ 4 = 155.0
  • (Q4+Q5+Q6+Q7) ÷ 4 = (180+130+150+170) ÷ 4 = 157.5
  • (Q5+Q6+Q7+Q8) ÷ 4 = (130+150+170+190) ÷ 4 = 160.0
  • (Q6+Q7+Q8+Q9) ÷ 4 = (150+170+190+140) ÷ 4 = 162.5
  • (Q7+Q8+Q9+Q10) ÷ 4 = (170+190+140+160) ÷ 4 = 165.0
  • (Q8+Q9+Q10+Q11) ÷ 4 = (190+140+160+185) ÷ 4 = 168.75
  • (Q9+Q10+Q11+Q12) ÷ 4 = (140+160+185+205) ÷ 4 = 172.5
• Step 2: Calculate centred moving averages (CMA):
  • Average of first two MAs: (148.75+151.25) ÷ 2 = 150.0 (aligned to Q3)
  • (151.25+155.0) ÷ 2 = 153.125 (Q4)
  • (155.0+157.5) ÷ 2 = 156.25 (Q5)
  • (157.5+160.0) ÷ 2 = 158.75 (Q6)
  • (160.0+162.5) ÷ 2 = 161.25 (Q7)
  • (162.5+165.0) ÷ 2 = 163.75 (Q8)
  • (165.0+168.75) ÷ 2 = 166.875 (Q9)
  • (168.75+172.5) ÷ 2 = 170.625 (Q10)
• Interpretation of CMA values
  • The CMAs show the underlying trend of sales, smoothing out seasonal fluctuations.
  • Trend is rising steadily: from 150.0 (Q3) to 170.6 (Q10).
  • Business can forecast future sales by extending the trend line.
• Applications
  • Helps identify whether sales are increasing, stable, or declining.
  • Assists in setting production targets.
  • Can be combined with seasonal analysis to improve accuracy.

Written and Compiled By Sir Hunain Zia, World Record Holder With 154 Total A Grades, 7 Distinctions and 11 World Records For Educate A Change A2 Level Business Full Scale Course

Qualitative Sales Forecasting

• Definition
  • Uses non-numerical information, judgement, and opinions instead of statistical data.
  • Often used when historical data is limited or when entering new markets.
• Methods
  • Delphi technique
    • A panel of experts answer questionnaires in multiple rounds.
    • Responses are aggregated and shared until consensus is reached.
    • Useful for forecasting technology trends or future consumer behaviour.
  • Market research
    • Collecting information directly from consumers through surveys, focus groups, or interviews.
    • Helps estimate demand for new products.
  • Sales force composite
    • Sales staff provide estimates based on their knowledge of customers.
    • Useful for short-term forecasts in local markets.
  • Executive opinion
    • Senior managers use experience and judgement to predict sales.
    • Quick and low-cost, but may be biased.
  • Historical analogy
    • Comparing new products to similar products launched in the past.
    • Example: forecasting sales of a new smartphone by analysing previous launches.
• Advantages
  • Useful where no historical data exists.
  • Can provide insights into consumer behaviour.
  • Involves experts and employees in the forecasting process.
• Disadvantages
  • Subjective and prone to bias.
  • Less accurate than quantitative methods.
  • Time-consuming and costly in case of large-scale research.

The Impact Of Sales Forecasting On Business Decisions

• Production decisions
  • Helps determine how much to produce to meet demand.
  • Prevents overproduction (wasted stock) or underproduction (lost sales).
  • Example: Ice-cream manufacturers increase production in summer based on forecasted sales.
• Financial decisions
  • Sales forecasts provide basis for budgeting and financial planning.
  • Helps plan investment in equipment, machinery, or new projects.
  • Banks and investors require forecasts before providing loans.
• Human resource decisions
  • Forecasts help decide workforce requirements.
  • Example: Retailers hire temporary staff during holiday seasons based on sales forecasts.
• Marketing decisions
  • Forecasting helps businesses plan promotions and advertising campaigns.
  • Example: Smartphone companies launch marketing campaigns before festive seasons when sales are expected to rise.
• Strategic decisions
  • Helps businesses decide when to expand into new markets or introduce new products.
  • Example: Car manufacturers forecast demand for electric vehicles to plan future investments.
• Risk management
  • Forecasting helps businesses prepare for downturns or seasonal slumps.
  • Example: Airlines use sales forecasting to adjust ticket pricing and manage capacity.
• Consequences of inaccurate forecasting
  • Overestimating demand → wasted resources, excess stock, storage costs.
  • Underestimating demand → stockouts, lost sales, dissatisfied customers.
  • Financial instability due to poor budgeting.

Written and Compiled By Sir Hunain Zia, World Record Holder With 154 Total A Grades, 7 Distinctions and 11 World Records For Educate A Change A2 Level Business Full Scale Course