Demand Forecasting And Planning
Expert-defined terms from the Professional Certificate in Business Calculations in Supply Chain Management course at Greenwich School of Business and Finance. Free to read, free to share, paired with a professional course.
A/B Testing refers to a method of comparing two versions of a product, service,… #
Related terms include hypothesis testing and experimentation. A/B testing is crucial in demand forecasting as it helps organizations to identify the most effective strategies for predicting and managing demand. For instance, an organization may use A/B testing to compare the effectiveness of different forecasting models or to evaluate the impact of different pricing strategies on demand.
ABC Analysis is a method of categorizing inventory into three classes based on t… #
Related terms include inventory control and classification. ABC analysis is essential in demand forecasting and planning as it enables organizations to prioritize their inventory management efforts and allocate resources more effectively. For example, an organization may use ABC analysis to identify the most valuable and high-demand products and focus their inventory management efforts on these items.
Accuracy refers to the degree of closeness between the forecasted value and the… #
Related terms include precision and error analysis. Accuracy is critical in demand forecasting as it directly impacts the effectiveness of supply chain operations. For instance, an organization with high forecasting accuracy can better manage its inventory levels, reduce stockouts and overstocking, and improve its overall supply chain efficiency.
Aggregate Forecasting refers to the process of forecasting demand at a high leve… #
Related terms include disaggregation and hierarchical forecasting. Aggregate forecasting is essential in demand forecasting and planning as it enables organizations to develop broad strategies and allocate resources effectively. For example, an organization may use aggregate forecasting to forecast demand at the product category level and then disaggregate the forecast to the individual product level.
Agile Methodology refers to an approach to project management that emphasizes fl… #
Related terms include scrum and kanban. Agile methodology is critical in demand forecasting as it enables organizations to respond quickly to changes in market conditions and customer demand. For instance, an organization may use agile methodology to develop and implement forecasting models that can be quickly updated and refined in response to changing market conditions.
ARIMA (AutoRegressive Integrated Moving Average) is a statistical model used for… #
Related terms include exponential smoothing and seasonal decomposition. ARIMA is a widely used forecasting model in demand forecasting as it can effectively capture trends and seasonality in time series data. For example, an organization may use ARIMA to forecast sales data that exhibits strong seasonality and trend components.
Backcasting refers to the process of using historical data to estimate th… #
Related terms include forecasting and estimation. Backcasting is essential in demand forecasting as it enables organizations to evaluate the performance of their forecasting models and identify areas for improvement. For instance, an organization may use backcasting to evaluate the accuracy of its forecasting model and identify biases or errors in the model.
Base Forecast refers to the initial forecast generated by a forecasting m… #
Related terms include naive forecast and seasonal adjustment. Base forecast is critical in demand forecasting as it provides a foundation for further analysis and refinement. For example, an organization may use a base forecast as a starting point for judgmental adjustments or quantitative refinements.
Batching refers to the process of grouping similar products or orders<… #
Related terms include lot sizing and production scheduling. Batching is essential in demand forecasting as it enables organizations to optimize their production and shipping operations. For instance, an organization may use batching to group similar products together for production and reduce setup costs and lead times.
Bias refers to a systematic error or distortion in a forecastin… #
Related terms include accuracy and error analysis. Bias is critical in demand forecasting as it can significantly impact the accuracy of forecasting models. For example, an organization may use techniques such as data normalization or feature scaling to reduce bias in its forecasting models.
Bill of Materials (BOM) refers to a list of components or materials</b… #
Related terms include product structure and material requirements planning. BOM is essential in demand forecasting as it enables organizations to plan and manage their inventory levels effectively. For instance, an organization may use a BOM to identify the components required to produce a product and forecast the demand for those components.
Bullwhip Effect refers to the phenomenon where small changes in demand ar… #
Related terms include supply chain dynamics and amplification. Bullwhip effect is critical in demand forecasting as it can significantly impact the stability and efficiency of supply chain operations. For example, an organization may use techniques such as smoothing or buffering to reduce the bullwhip effect and improve the stability of its supply chain.
Capacity Planning refers to the process of determining the production cap… #
Related terms include capacity management and resource allocation. Capacity planning is essential in demand forecasting as it enables organizations to plan and manage their production capacity effectively. For instance, an organization may use capacity planning to determine the production capacity required to meet peak demand and allocate resources accordingly.
Chaos Theory refers to the study of complex and dynamic systems that are… #
Related terms include complexity theory and nonlinear dynamics. Chaos theory is critical in demand forecasting as it can help organizations understand and model complex systems and phenomena. For example, an organization may use chaos theory to model the complex interactions between different variables in a supply chain and improve its forecasting accuracy.
Classification refers to the process of categorizing products or custo… #
Related terms include clustering and segmentation. Classification is essential in demand forecasting as it enables organizations to identify and target specific customer segments and products. For instance, an organization may use classification to categorize its products into different categories based on their sales patterns and profitability.
Collaborative Planning, Forecasting, and Replenishment (CPFR) refers to a pro… #
Related terms include collaboration and partnership. CPFR is critical in demand forecasting as it enables organizations to share information and coordinate their efforts with their trading partners. For example, an organization may use CPFR to collaborate with its suppliers and retailers on forecasting and replenishment decisions and improve its supply chain efficiency.
Component Forecasting refers to the process of forecasting component dema… #
Related terms include component requirements planning and material requirements planning. Component forecasting is essential in demand forecasting as it enables organizations to plan and manage their component inventory levels effectively. For instance, an organization may use component forecasting to forecast the demand for components required to produce a product and manage its inventory levels accordingly.
Continuous Improvement refers to the ongoing process of identifying and <… #
Related terms include kaizen and total quality management. Continuous improvement is critical in demand forecasting as it enables organizations to refine and improve their forecasting processes and models over time. For example, an organization may use continuous improvement to refine its forecasting models and processes and improve its forecasting accuracy.
Correlation Analysis refers to the process of analyzing the relationsh… #
Related terms include regression analysis and causality analysis. Correlation analysis is essential in demand forecasting as it enables organizations to identify and analyze the relationships between different variables and factors that affect demand. For instance, an organization may use correlation analysis to analyze the relationship between weather patterns and sales data and improve its forecasting accuracy.
Cross #
Validation refers to the process of evaluating the performance of a forecasting model using multiple datasets or scenarios, often used in model validation and selection. Related terms include model evaluation and validation. Cross-validation is critical in demand forecasting as it enables organizations to evaluate and compare the performance of different forecasting models and techniques. For example, an organization may use cross-validation to evaluate the performance of different forecasting models and select the best model for its specific needs.
Cycle Time refers to the time it takes to complete a process or <b… #
Related terms include lead time and throughput time. Cycle time is essential in demand forecasting as it enables organizations to plan and manage their production and inventory levels effectively. For instance, an organization may use cycle time to plan its production schedule and manage its inventory levels to meet customer demand.
Data Mining refers to the process of discovering patterns and r… #
Related terms include machine learning and predictive analytics. Data mining is critical in demand forecasting as it enables organizations to discover and analyze complex patterns and relationships in large datasets. For example, an organization may use data mining to discover patterns in customer behavior and preferences and improve its forecasting accuracy.
Decomposition refers to the process of breaking down a time series… #
Related terms include trend analysis and seasonal decomposition. Decomposition is essential in demand forecasting as it enables organizations to analyze and understand the different components of a time series and improve their forecasting accuracy. For instance, an organization may use decomposition to analyze the trend and seasonal components of its sales data and improve its forecasting accuracy.
Demand Shaping refers to the process of influencing demand through… #
Related terms include demand management and revenue optimization. Demand shaping is critical in demand forecasting as it enables organizations to influence and manage demand and improve their revenue and profitability. For example, an organization may use demand shaping to influence demand through pricing and promotion strategies and improve its revenue and profitability.
Demand Signal Repository (DSR) refers to a database that stores and ma… #
Related terms include demand sensing and signal processing. DSR is essential in demand forecasting as it enables organizations to collect and analyze demand signals from various sources and improve their forecasting accuracy. For instance, an organization may use a DSR to collect and analyze demand signals from point of sale data and improve its forecasting accuracy.
Econometric Modeling refers to the process of using economic theor… #
Related terms include econometrics and macroeconomic modeling. Econometric modeling is critical in demand forecasting as it enables organizations to model and forecast economic variables that affect demand and improve their forecasting accuracy. For example, an organization may use econometric modeling to model and forecast economic indicators such as GDP and inflation and improve its forecasting accuracy.
Exponential Smoothing (ES) refers to a family of forecasting metho… #
Related terms include simple exponential smoothing and holt winters method. ES is essential in demand forecasting as it enables organizations to smooth and forecast time series data and improve their forecasting accuracy. For instance, an organization may use ES to smooth and forecast its sales data and improve its forecasting accuracy.
Forecast Error refers to the difference between the forecasted value and… #
Related terms include accuracy and error analysis. Forecast error is critical in demand forecasting as it directly impacts the effectiveness of supply chain operations. For example, an organization with high forecasting accuracy can better manage its inventory levels, reduce stockouts and overstocking, and improve its overall supply chain efficiency.
Forecast Horizon refers to the time period over which a forecast i… #
Related terms include short term forecasting and long term forecasting. Forecast horizon is essential in demand forecasting as it enables organizations to plan and manage their resources and operations over different time horizons. For instance, an organization may use a short term forecast horizon to plan its daily or weekly operations and a long term forecast horizon to plan its strategic initiatives.
Forecasting Model refers to a mathematical or statistical model us… #
Related terms include arima and exponential smoothing. Forecasting model is critical in demand forecasting as it enables organizations to forecast future values of a time series and improve their forecasting accuracy. For example, an organization may use a forecasting model to forecast its sales data and improve its forecasting accuracy.
Gantt Chart refers to a visual representation of a project schedul… #
Related terms include project management and scheduling. Gantt chart is essential in demand forecasting as it enables organizations to visualize and manage their projects and operations over time. For instance, an organization may use a Gantt chart to plan and manage its production schedule and ensure that it meets customer demand.
Histogram refers to a visual representation of the distribution of… #
Related terms include data visualization and statistical analysis. Histogram is critical in demand forecasting as it enables organizations to visualize and understand the distribution of their data and improve their forecasting accuracy. For example, an organization may use a histogram to visualize the distribution of its sales data and identify trends and patterns.
Information Sharing refers to the process of sharing information a… #
Related terms include collaboration and coordination. Information sharing is essential in demand forecasting as it enables organizations to share and analyze data and improve their forecasting accuracy. For instance, an organization may use information sharing to share point of sale data with its suppliers and improve its forecasting accuracy.
In #
Transit Inventory refers to the inventory that is in transit from the supplier to the customer, often used in inventory management and logistics. Related terms include inventory management and supply chain management. In-transit inventory is critical in demand forecasting as it enables organizations to manage their inventory levels and reduce stockouts and overstocking. For example, an organization may use in-transit inventory to manage its inventory levels and ensure that it meets customer demand.
Inventory Control refers to the process of managing and controlling</i… #
Related terms include inventory management and supply chain management. Inventory control is essential in demand forecasting as it enables organizations to manage their inventory levels and reduce stockouts and overstocking. For instance, an organization may use inventory control to manage its inventory levels and ensure that it meets customer demand.
Inventory Management refers to the process of managing and controlling… #
Related terms include inventory control and supply chain management. Inventory management is critical in demand forecasting as it enables organizations to manage their inventory levels and reduce stockouts and overstocking. For example, an organization may use inventory management to manage its inventory levels and ensure that it meets customer demand.
Just #
In-Time (JIT) refers to a production system in which products are produced and delivered just in time to meet customer demand, often used in production planning and inventory management. Related terms include lean manufacturing and agile manufacturing. JIT is essential in demand forecasting as it enables organizations to produce and deliver products just in time to meet customer demand and reduce inventory levels. For instance, an organization may use JIT to produce and deliver products just in time to meet customer demand and reduce its inventory levels.
Kaizen refers to a philosophy of continuous improvement , often use… #
Related terms include continuous improvement and total quality management. Kaizen is critical in demand forecasting as it enables organizations to refine and improve their forecasting processes and models over time. For example, an organization may use kaizen to refine its forecasting models and processes and improve its forecasting accuracy.
Lead Time refers to the time it takes to produce and deliver a … #
Related terms include cycle time and throughput time. Lead time is essential in demand forecasting as it enables organizations to plan and manage their production and inventory levels effectively. For instance, an organization may use lead time to plan its production schedule and manage its inventory levels to meet customer demand.
Lean Manufacturing refers to a production system that aims to minimize <i… #
Related terms include just in time and agile manufacturing. Lean manufacturing is critical in demand forecasting as it enables organizations to produce and deliver products efficiently and reduce inventory levels. For example, an organization may use lean manufacturing to produce and deliver products just in time to meet customer demand and reduce its inventory levels.
Linear Regression refers to a statistical method used to model the… #
Related terms include multiple linear regression and nonlinear regression. Linear regression is essential in demand forecasting as it enables organizations to model and forecast the relationship between different variables and improve their forecasting accuracy. For instance, an organization may use linear regression to model the relationship between price and demand and improve its forecasting accuracy.
Machine Learning refers to a field of study that focuses on the developme… #
Related terms include deep learning and neural networks. Machine learning is critical in demand forecasting as it enables organizations to develop and improve their forecasting models and algorithms over time. For example, an organization may use machine learning to develop a forecasting model that can learn from data and improve its forecasting accuracy.
Mean Absolute Error (MAE) refers to a metric used to measure the <… #
Related terms include mean squared error and root mean squared error. MAE is essential in demand forecasting as it enables organizations to evaluate and compare the performance of different forecasting models and algorithms. For instance, an organization may use MAE to evaluate the performance of its forecasting model and identify areas for improvement.
Mean Squared Error (MSE) refers to a metric used to measure the <i… #
Related terms include mean absolute error and root mean squared error. MSE is critical in demand forecasting as it enables organizations to evaluate and compare the performance of different forecasting models and algorithms. For example, an organization may use MSE to evaluate the performance of its forecasting model and identify areas for improvement.
Monte Carlo Simulation refers to a method used to model and ana… #
Related terms include simulation modeling and stochastic processes. Monte Carlo simulation is essential in demand forecasting as it enables organizations to model and analyze complex systems and processes and improve their forecasting accuracy. For instance, an organization may use Monte Carlo simulation to model and analyze the impact of different scenarios on its supply chain and improve its forecasting accuracy.
Moving Average (MA) refers to a statistical method used to smooth … #
Related terms include exponential smoothing and autoregressive integrated moving average. MA is critical in demand forecasting as it enables organizations to smooth and forecast time series data and improve their forecasting accuracy. For example, an organization may use MA to smooth and forecast its sales data and improve its forecasting accuracy.
Naive Forecast refers to a simple forecasting method that uses histori… #
Related terms include random walk and moving average. Naive forecast is