Chemical Engineering Department

Production and Project Management

1. Forecasting
Forecasting is a process of estimating a future event by casting forward past data. It can be defined as attempting to
predict the future by using qualitative or quantitative methods. In an informal way, forecasting is an integral part of all
human activity, but from the business point of view increasing attention is being given to formal forecasting systems which
are continually being refined1.

Forecasting can be described as the act of giving advance warning in time for beneficial action to be taken. The value and
importance of advance information is a cornerstone of planning activity. Forecasts cannot be totally exact; management
must be aware of this, and decide on the degree of inexactitude that can be tolerated when planning the future 2.

Forecasting occurs at different levels: internationally, nationally, by industry, etc., until we ultimately reach a specific
product forecast. Forecasting is an important component of strategic and operational planning 3. It is also an inference of
what is likely to happen in future. Types of forecasting include technological forecast, economic forecast and demand
forecast4.

 Economic forecasts- Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc.
 Technological forecasts -Predict rates of technological progress and innovation.
 Demand forecasts -Predict the future demand for a company’s products or services

1.1. Applications/importance of forecasting

The importance forecasting varies based on the types of the organizations.

Sales Forecasting: any company in selling goods needs to forecast the demand for those goods. Manufactures need to
know how much to produce. Wholesalers and retailers need to know now much to stock.

Marketing managers: use sales forecasts to determine optimal sales force allocations set sales goals, and plan
promotions and advertising.

The personnel department requires a number of forecasts in planning for human resources.

Managers of nonprofit institutions and public administrators also must make forecasts for budgeting purposes.

Universities forecast student enrollments, cost of operations, and, in many cases, the funds to be provided by instruction
and by government appropriations.

Set by: Tekleweyni G. MSc. in Material Science and Engineering

The bank has to forecast: Demands of various loans and deposits Money and credit conditions so that it can determine the
cost of money it lends.

Manufacturers also forecast worker absenteeism, machine availability, material costs, transportation and production lead
times, etc.

1.2. Forecasting ranges

Short-range forecasts

Short-term forecasts are usually made for tactical reasons that include production planning and control, short-term cash
requirements and adjustments that need to be made for seasonal sales fluctuations. This latter factor can be very
important for production, whereas the general trend may be of less consequence. Such forecasts are for periods of less
than one year, with a normal range between one and three months.

Medium-range forecast

A forecast of this length is generally more closely related to a yearly production plan and will reflect such items as peaks
and valleys in demand and the necessity to secure additional resources for the upcoming year.

Long-term forecasts

A long-range forecast typically encompasses a period longer than 1 or 2 years. Long-range forecasts are related to
management's attempt to plan new products for changing markets, build new facilities, or secure long-term financing. They
deal with general rather than specific items.

Set by: Tekleweyni G. MSc. in Material Science and Engineering

1.3. Steps of forecasting
1. Determine the use of the forecast—what objective are we trying to obtain?
2. Select the items or quantities that are to be forecasted.
3. Determine the time horizon of the forecast—is it 1 to 30 days (short term), 1 month to 1 year (medium term), or more
than 1 year (long term)?
4. Select the forecasting model or models
5. Gather the data or information needed to make the forecast
6. Validate the forecasting model
7. Make the forecast
8. Implement the results
1.4. Forecasting models

Forecasting models that can be classified into one of three categories: time-series models, causal models, and qualitative
models.

Time-Series Models

Time-series models attempt to predict the future by using historical data. These models make the assumption that what
happens in the future is a function of what has happened in the past. In other words, time-series models look at what has
happened over a period of time and use a series of past data to make a forecast.

Causal Models

Causal models incorporate the variables or factors that might influence the quantity being fore- casted into the forecasting
model. For example, daily sales of a cola drink might depend on the season, the average temperature, the average humidity,
whether it is a weekend or a weekday, and so on. Thus, a causal model would attempt to include factors for temperature,
humidity, season, day of the week, and so on. Causal models may also include past sales data as time- series models do, but
they include other factors as well.

Qualitative Models

Whereas time-series and causal models rely on quantitative data, qualitative models attempt to incorporate judgmental or
subjective factors into the forecasting model. Opinions by experts, individual experiences and judgments, and other
subjective factors may be considered. Qualitative models are especially useful when subjective factors are expected to be
very important or when accurate quantitative data are difficult to obtain.

Set by: Tekleweyni G. MSc. in Material Science and Engineering

1.5. Factors consider during model selection
1. The amount & type of available data; Some methods require more data than others
2. Degree of accuracy required; Increasing accuracy means more data
3. Length of forecast horizon; Different models for 3 month vs. 10 years
4. Presence of data patterns; Lagging will occur when a forecasting model meant for a level pattern is applied with a
trend

References
1. Gor RM. FORECASTING TECHNIQUES. In: INDUSTRIAL STATISTICS AND
OPERATIONAL MANAGEMENT.

2. Sales Forecasting.

3. TELSANG MT. Industrial Engineering And Production Management.; 2015.

4. Mishra RC, Soota T. Modern Project Management. New Age International (P) Ltd.,
Publishers; 2005.

Set by: Tekleweyni G. MSc. in Material Science and Engineering