• Unit 5: Demand Forecasting

    This unit covers the need for, different types of, and methods for forecasting demand. It is the first step in operations management and planning for changes in production and service capacity. Understanding statistical methods used in forecasting, and optimal levels of risk/uncertainty inherent in the analysis, is key to successful operations management.

    Completing this unit should take you approximately 9 hours.

    • 5.1: Forecasting the Fundamentals

      Demand management is a process for forecasting the anticipated demand for a product or service. This responsibility by the organization is highly planned, structured, and strategic so that the right amount of resources can be ordered, supplied, and managed appropriately. Moreover, data and information are vital components of forecasting as it provides objective reference points in order to predict future needs.

    • 5.2: Quantitative Forecasting

      Forecasting maintains a direct relationship with strategic planning. Predicting the future quantitatively helps a company manage its resources, navigate change, and mitigate negative market conditions. Generally speaking, this approach uses statistical confidence intervals and historical data to predict future trends.

      • 5.2.1: Time-Series Forecasting

        Time series is best known as a collection of data points gathered at equally spaced points in time. It can be useful in various applications such as forecasting, budgeting, and quality control. When used in forecasting it can assist companies in predicting future values based on previously collected values.

      • 5.2.2: Decomposition

        Decomposition is a time-series task that deconstructs time-series data into categories. Through this deconstruction, the data can be broken down and analyzed in smaller portions to find patterns or variations. Essentially it provides a structured way of thinking through a time-series forecasting problem.

      • 5.2.3: Linear Regression

        Linear regression strives to model a relationship between a dependent and independent variable. This analysis can be useful as a forecasting tool but also be used to quantify the strength of a relationship between two variables. Inferences can be made with the data because these relationships are not precise or perfect.

      • 5.2.4: Moving Average

        A moving average is also known as a rolling average or running average to analyze data points. This statistical approach is commonly used with time series data to identify trends. When used in conjunction with time series it can filter out high-frequency components and smooth out data.

      • 5.2.5: Exponential Smoothing

        Exponential smoothing is a type of moving average and refers to a technique that is applied to time-series data for forecasting. When applied, it allows one to estimate the demand for items accounting for intermittent and seasonal variations. It is relatively simple and is widely used and accepted for short-term forecasting.

    • 5.3: The Components of Demand

      Supply and demand are key concepts fundamental to economic analyses. The demand part of this relationship plays a huge role in the price of a good or service. While there are many elements of demand, all generate a force on the supply chain requiring each to adjust accordingly.

      • 5.3.1: Trends

        Trends in Demand indicate medium and long-term fluctuations when the desire for a product or service increases or decreases over time. All products and services cycle through peaks as well as drops in demand. This could happen due to factors that may include word of mouth, marketing campaigns, changes in demographics, or shifts in interest.

      • 5.3.2: Seasonality

        Seasonality is another component of demand which can include certain weeks, months, years, or any time period. Since this area is irregular and sporadic, it is further proof that seasonal forecasting must be as accurate as possible. Both historical data and real-time data are critical to anticipating this cyclical dynamic.

    • 5.4: Causal Relationships

      A causal relationship attempts to identify a connection between two specified factors. This quantitative forecasting tool suggests that one factor causes changes in the other factor. While the first event is known as the cause, the following event is known as the event.

    • 5.5: Forecasting Errors

      When it comes to statistics, forecasting error is defined as the difference between the real data value and the predicted data value. The error itself is calculated as the outcome minus the value of the original forecast. The greater the error in forecasting the greater potential to compromise economic and performance outcomes.

    • 5.6: Qualitative Forecasting

      Qualitative forecasting is unique in that it uses experiential, anecdotal, or other non-numerical means to predict demand. Forces that affect this can be attributed to weather, the economy, world events, or even catastrophes. This can be useful in large-scale resource planning and also conflict resolution.

    • Study Guide: Unit 5

      We recommend reviewing this Study Guide before taking the Unit 5 Assessment.

    • Unit 5 Assessment

      • Receive a grade