Productivity
Labour Productivity: measures the output per unit of labor input
Machine Productivity: output produced per unit of machine time
Material Productivity: output of material input
Multifactor Productivity: broader measure and takes into account all inputs\\
Forecasting
- The process of predicting future events or trends by analyzing historical and
contemporary data
- Sales Forecasting: estimating a company’s future sales volume
- Market Potential: total projected industry-wide sales for a particular product category
within a given timeframe
- Sales Potential: the company’s projected maximum market share for the designated
timeframe
Horizons and Applications:
1. Long-term forecasting - more than 2 years; strategic level
2. Medium-term forecasting - several months to 2 years; allows for the utilization of
quantitative and qualitative forecasting (tactical decision-making)
3. Short-term forecasting - daily to a few months; operational decision-making processes
Types:
1. Economic Forecasts - address the overall business cycle and predict economic
indicators
2. Technological Forecasts - monitor the rates of technological progress and trends
3. Demand Forecasts - estimate consumers’ future demand for a company’s products or
services
Categories:
Qualitative - rely on subjective judgments and opinions rather than historical data
1. Executive Judgement (Top-down): leverages the expertise of high-level executives
within an organization.
2. Sales Force Opinions (Bottom-up): solicits individual sales forecasts from sales
personnel within their designated territories
3. Delphi Method: a panel of experts anonymously participates in a series of surveys;
based on the anonymized collective insights provided
4. Market Surveys: market research firms can be employed to conduct surveys that
gauge consumer sentiment toward products and future purchasing intentions
Quantitative - used to forecast future data as a function of past data
1. Associative Models/Causal Forecasting - used to predict future values of a variable by
analyzing its relationship with other related variable
Regression Analysis - involves constructing a mathematical equation that relates the
dependent variables to one or more independent variables
I. Simple Linear Regression
II. Multiple Linear Regression
Correlation Analysis - used to measure the strength and direction of the linear
relationship between dependent and independent variables
Applications:
● Demand forecasting
● Capacity planning
● Inventory management
● Resource allocation
● Supply chain management
2. Time Series Models - collects data points meticulously ordered along a time axis
Trend Analysis:
● Trend - consistent upward and downward
● Cyclical - recurring fluctuations that typically span more than a year
● Seasonal - predictable changes in demand that recur annually
● Irregular variations - Unforeseen events
● Random variations - also known as noise; unexplained fluctuations in demand
1. Naive method - using the actual value from the previous period as the forecast
for the next period without considering any other factors or patterns
2. Simple moving average - forecasting future values based on historical data
3. Weighted moving average
4. Exponential smoothing method - integrates the most recent actual demand and
the previous forecast to generate the forecast for the upcoming period
5. Seasonal index - a statistical tool used to quantify and account for recurring
seasonal patterns
