Course Info

Date:

2025-09-08

Length:

5 Days

Type:

Online

Fees:

£ 1760
Available DatesPrice
2025-05-12£ 1760
2025-06-09£ 1760
2025-07-14£ 1760
2025-08-11£ 1760
2025-10-13£ 1760
2025-11-10£ 1760
2025-12-08£ 1760

Course Details

  • Introduction

  • Objective

  • Who should attend

Demand management and Forecasting is one most important aspect of any organization where demand for its services or products are determined. Planning for demand is also required internally so that there is a product available on time without an excess of inventory.

 

Demand planners are more challenged with increased uncertainties of market demand. To achieve accurate demand planning and forecasting, demand planners are required to combine the forecasting science with effective judgment on both internal and external factors. This is not an easy task.

 

The management of demand is a function of how an organization controls its capacity whether it is in terms of its resources, inventory, or human resource. Demand Management is essential in order that the organization is cost-effective, cost-focused, and to be lean in its operation.

 

Demand Management and Forecasting training Course covers the fundamentals of demand management and planning, including roles/responsibilities and accountabilities, definitions, process flows, levels, timing and horizons, technologies, and mechanics.

Course Outline

5 days course
  • Day 1
  • Day 2
  • Day 3
  • Day 4
  • Day 5
Keys to successful forecasting process and function
 
  • Problems and Needs

  • Goals and Objectives

  • Coordination and Leadership

  • Process Management

  • Forecast Model Development

  • Communication with Participants and Users

  • Four Forecasting Process Approaches

  • Independent

  • Concentrated

  • Consensus

  • One Number

  • Myths About Forecasting

  • Silo Forecasting Impact on Organization

 

 Data patterns and demand variability
 
  • The Bullwhip Effect

  • Demand Variability and the Methods to Minimize It

  • Recognition of Data Patterns

  • What kind of problems to look for in the data and how to treat them

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