What Is Forecasting in Operations Management?

Jun 14, 2022

Forecasting in operations management can help plan production of goods and services in advance. By using predictive analytics, businesses can save resources and reduce expenses.

Shradha GoyalTeam Lead
What Is Forecasting in Operations Management?

What we'll cover

Planning production operations without an estimation of how much you’d be able to sell or how many resources will be at work at all times is probably one of the biggest mistakes any operations manager could make. 

A poor estimate of demand or availability of workforce can lead to either shortage of products or a pile up of inventory, both of which can be detrimental to any business’s growth plans. Therefore, forecasting is a key skill that operations managers responsible for deciding a company’s production quantity and schedule need to nurture. Forecasting in operations management is complex, but it aids in decision making and planning based on predictive data analytics.

Here’s everything you’d need to know about forecasting to avoid any misjudgements in the production planning process.

What are the different forecasting models?

Forecasting in operations varies according to the available data, industry size and respective goals. The three common forecasting models we discuss here use both qualitative and quantitative data. Although they are useful in making educated predictions, there is usually some degree of forecast error involved, which makes it important to use them with caution.


3 common types of forecasting models

Qualitative forecasting method

1. Judgmental: This type of forecast uses subjective inputs. This includes the Delphi method wherein subject matter experts predict trends based on their knowledge and experience. A few other techniques involve scenario building, and statistical surveys which are also based on intuition and a collection of opinions from managers and experts.


Quantitative forecasting method

2. Time series: 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. This method is particularly useful while doing demand forecasting.

3. Associative models: Associative models attempt to identify different variables and understand their association with each other. One of the most common associative models used is regression analysis, by which you can understand the relationship between two variables in a dataset. For example: the impact of inventory stock on profitability. You could use statistical analysis software to conduct such quantitative forecasts. You can effectively use this method for conducting business forecasting. 

Another forecasting technique which businesses can use is to predict events over different time horizons:

  1. Short range: These forecasts are usually 3 months into the future. They are most often used for making decisions regarding hiring, scheduling, and determining production levels.

  2. Medium range: This is usually 3 months to a year into the future. These forecasts are used for budgeting, sales and demand planning.

  3. Long range: These forecast 3 years or more into the future. It's used for capital expenditures, relocation and expansion, and research and development.


What are the different types of forecasts?

Economic forecasts: Make predictions related to inflation, money supplies, and other economic factors that can affect businesses and production schedules. 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, you may want to invest in new facilities, equipment and processes. These forecasts impact long-range planning.

Demand forecasts: Estimate consumer demand for a business' products or services. You can use a demand forecast to estimate production and all relevant inputs including the quantity of raw materials or number of workforce required. These forecasts can impact short, medium, and long-term planning. These are also referred to as sales forecasts.


Which forecasts are used in operations management?

Below are the commonly used forecasting methods in operations, which differ in accuracy and popularity amongst organizations.

  1. Run rate/Historical forecasting: The method uses historical data to predict trends. This is useful to extrapolate demand patterns, availability of resources at different times, and financial liabilities at different stages of the production process. It is one of the most commonly used forecasting methods by organizations, as per a report by Gartner (full report available for Gartner clients).

  2. Driver-based forecasting: This method uses key operational metrics to predict the obtainable output. For example, throughput, i.e. the rate of production of a plant or a machine can give a fair idea of the capacity to meet supply requirements. A few other metrics which you can use to do driver-based forecasting include share of accurate production (first-pass yield), average time taken to produce a unit (cycle time) and average time a machine stays unavailable for use (downtime). 

  3. Risk-based forecasting: This approach assesses the possible risks that the process could run into, such as strikes, machine failures, budget outruns, etc, and plans ahead in time to mitigate, if not all, some of these factors. 

Using one or more of the above-mentioned methods, these are the two most important metrics which you might want to consider for forecasting to ensure a seamless production process.

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, you can use a forecast to ensure there are enough front office employees to meet the 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 you to manage inventory, determine if any additional materials are needed, and schedule production.


How to implement forecasting in operations management?

Take note of these important tips to ensure you forecast accurately.

  1. Conduct short-term forecasts for more accurate results: 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.

  2. Acknowledge that all forecasts carry some level of error: 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.

  3. Prefer aggregate forecasts to disaggregate forecasts: 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. Therefore, try grouping datasets (sales of multiple stores) to create aggregate forecasts, which are relatively more accurate. 

  4. Keep it simple: 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.

  5. Use software to analyze datasets: Analyzing data to create trends which can be extrapolated for the future can be complex. Invest in a suitable predictive analytics software to make more accurate predictions to ensure success for your business.

About the author

Shradha Goyal

Team Lead
Shradha Goyal is team lead—content at GetApp, covering industry specific trends for small businesses. In her 10-years+ writing career, she has written extensively on real estate in The Times of India, Economic Times, and 99acres. She is an MBA graduate from the Indian Institute of Management, Kozhikode, and holds a keen interest in marketing. Besides work, she loves traveling and cuddling with her Lhasa Apso.
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