OBJECTIVES
The training course DEMAND PLANNING has the following objectives:
- Introduce the attendees in the context of Demand Planning, as activities of planning, management and control of the demand for the products and services provided by companies, inside the Supply Chain process of Sales & Operations Planning.
- Explain the processes, the input and output data of Demand Planning, and also the organizational roles and functions, the typical workflows and the methodologies for Collaborative Demand Planning.
- Underline the functional coverage to the Demand Planning processes coming from software packages like ERPs, Data Warehousing, Advanced Planning & Scheduling (APS).
- Classify the combinations item-customer into homogeneous forecasting families, and associate the best forecasting methods to them.
- Explain the methods and the algorithms to forecast the item demand, depending on the presence / absence of historical series of demand and on the morphological shape of available historical series.
- Define the typical steps in a Demand Planning Reengineering Project (review and rebuild of processes and workflows).
- Forecasting Assessment: explain how it works.

TARGET QUESTIONS
The training course DEMAND PLANNING aims to give answers to the following questions:
- Which are the processes, the steps and the typical workflows of “Demand Planning”?
- Which links exist between Demand Planning and Supply Planning?
- Which are the “demand plans” to be periodically elaborated during the Demand Planning activities? By which kind of users? How often?
- Which are the input data to provide to Demand Planning processes? Which kind of “demand” has to be planned and forecasted?
- Which algorithms and mathematical methods are available to generate accurate and reliable products’ forecasts? How to monitor and control the quality of demand forecasts?
- In which way trade promotions are taken into account, in demand planning?
- How to measure forecast accuracy of algorithms and demand planners?
- How to perform a project for implementing an APS package for Demand Planning? What is a Forecasting Assessment? How to conduct it?
- How to forecast sales for new items, for regular items? How to model the sales forecast for slow moving items?
- How to correlate historical demand with external variables (pricing of products, temperature, advertising campaigns)?

AGENDA
1. Demand Planning in the context of Sales & Operations Planning
- Demand Planning in the modern Supply Chain Networks
- Demand Planning, Sales Execution, Demand Analytics
2. Typologies of demand plans
- Definition of Sales Budget, Demand Forecast, Consensus Forecast, Sales Target, Promotion Plan, Constrained Demand Plan
- Differences and numerical examples
3. Demand Planning processes
- Demand Forecasting, Demand Analytics, Demand Intelligence, Marketing Intelligence
4. Collaborative models for Demand Planning
- Methods for brainstorming and coordination (Delphi, jury of executive, focus forecasting, …)
- The role of Demand Planner / Data Scientist in the company
- Meetings MFR (Monthly Forecast Review), S&OP
- Collaborative models: VMI (Vendor Managed Inventory) and CPFR (Collaborative Planning, Forecasting & Replenishment)
5. Demand Analytics & Business Intelligence
- Demand Cubes: entities, hierarchies, measures, KPIs
- Methods for browsing multidimensional demand data
- Splitting rules across products, markets and time periods
- Business Intelligence for Demand Planning: building KPI Dashboards for the Demand Forecasting and Sales Budgeting processes
- TSAC algorithm (Time Series Automatic Classification): recognize and classify demand patterns into new items, early sales items, continuous vs intermittent items
- Forecast Accuracy KPIs
6. Methods for Sales Cleaning and Trade Promotion Management
- Algorithm for outliers detection and removal from historical series
- Analysis of historical trade promotions
- Promotions Planning: pattern matching algorithm
7. Continuous items forecasting
- Trend and seasonalty detection into historical series
- Exponential smoothing and series decomposition models
8. Sporadic items forecasting
- Croston’s model
- Poisson’s model
- Normalization of outliers into intermittent historical series
9. New items forecasting
- Patterns methodology
- Analogy forecasting
- In-season reforecasting algorithm
10. Big Data Analytics for Demand Forecasting
- Linear regression models (single and multiple correlations)
- Clustering algorithms
- Cross-selling and cannibalization among items
- Forecastability and ABC Pareto Classification
11. Information Technology for Demand Planning
- Criteria for APS / Demand Planning software selection
- «Forecasting as a Service» models