Organizations use forecasting methods to predict business outcomes. Forecasts create estimates that can help managers develop and implement production strategies. Operations managers are responsible for the processes that deliver the final product. This where forecasts can help: They aid decision making and planning around possible events.
The method of forecasting will vary according to available data and industry size and respective goals. Forecasts are developed using both qualitative and quantitative data. Although they are useful in making educated predictions, they are not always accurate, so they should be used with caution.
Forecasts can be used to predict events over different time horizons:
Short range: These forecasts are usually 3 months into the future. They are most often used for hiring, scheduling, determining production levels of planning processes.
Medium range: This is usually 3 months to a year into the future. These forecasts are used for budgeting, sales and demand planning.
Long range: These forecast 3 years or more into the future. It's used for capital expenditures, relocation and expansion, and research and development.
Economic forecasts: Make predictions related to inflation, money supplies, and other economic factors that can affect businesses. These forecasts often influence medium to long range planning
Technological forecasts: Keep track of rate of technological progress. As technologies mature and become more applicable to business use cases, these may require new facilities equipment and processes. These forecasts inform long range planning
Demand forecasts: Estimate consumer demand for a business' products or services. A demand forecast will be used to estimate production and all relevant inputs. These forecasts can inform short, medium, and long term planning. Also referred to as sales forecasts.
Depending on the data you have available and time horizon you are using, you will want to apply different forecasting models. While there are a lot of models one could use, these fall into three broad types:
Judgmental (qualitative): This type of forecast uses subjective inputs. This includes the Delphi method, scenario building, and statistical surveys which are based on intuition and a collection of opinions from managers and experts.
Time-series (quantitative): Time series forecasts use historical data to make predictions about the future. Data corresponds to a specific period, such as monthly inputs over a span of ten years. These forecasts rely on the assumption that past patterns will repeat in the future, so these data inputs are used to create long term forecasts.
Associative models (quantitative): Associative models use data corresponding to various underlying factors to predict the desired variable. These models assume a relationship between the factors and the variable to predict more complex patterns. Some methods include regression analysis and autoregressive moving averages.
Scheduling: Staff and inventory scheduling are critical functions to meet demand. This process involves organizing, selecting, and allocating the necessary resources to complete the desired outputs over a period of time.
In a service business, for example, a forecast could be used to ensure you have enough front office employees to meet fluctuating demand that often involves attending to immediate customer service requests.
Material requirements planning (MRP): This system is used to calculate the materials needed to manufacture a final product. MRP requires operations managers to take inventory, determine if any additional inventory is needed, and scheduling production.
It's important that you consider these points to help you forecast accurately.
Short term forecasts use more quantitative data and require a lot less foresight than long term forecasts. More time allows for more unpredictable events, like changes in competition.
No one has the key to predicting the future. No matter how much data your business has, and how accurate it is, forecasts include assumptions, leaving room for error. Take this into account in your planning.
The larger the dataset, the more likely that anomalies can be smoothed out. For example, sales in a single store are more difficult to predict than sales in the whole state.
If you have a forecast that uses the data you have and is pretty accurate, it's probably worth sticking with it. A more complex forecast will not necessarily yield better results.
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