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Final year project - Presentation

Investigating the relationship between consumer demand forecast accuracy and cost in a FMCG retail company
by

Anja Janse van Rensburg

on 19 November 2012

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Transcript of Final year project - Presentation

Final Year Project Investigating the relationship between consumer demand forecast accuracy and cost in an FMCG retail company Objective Background Methodology Results: forecast error cost Company X: overview Results: over-forecast product analysis ? Questions? presented by Anja Janse van Rensburg Overview Problem statement
Objective
Background
Methodology
Company X: an overview
Results
Conclusions
Recommendations
What I have learned Consumer demand increasing at rapid pace
Demand forecasting plays key role in FMCG industry
Difficult to accurately predict future demand
Leads to over- and under-stocking of goods Question: What is the actual cost of improving forecast accuracy? Studying the relationship between accuracy of demand forecasts and cost in a specific South African FMCG retailer Ho: an increase in demand forecasting accuracy results in a decrease in cost for a large FMCG retailer FMCG industry: an overview Conclusions Definition: industry that provides vast variety of consumable goods
In South Africa, four major retailers
Industry's turnover in 2009: ZAR 140 billion
Extremely competitive, searching for better management strategies to improve profitability and market share Recommendations Demand forecast inaccuracy and the dependent variable costs Flow of goods and data through the supply chain
Transport
Data management
Keeping track of performance - KPIs used
Methodology applied Techniques applied Data sorting
Data analysis
Trend line analysis
Multi-criteria classification and sorting process
Pareto chart analysis (Company X provided data on express condition) Trend lines showed noteworthy relationship
Positive gradients - no reason to reject hypothesis
Mismanaged data proved to be a problem
Improving average over-forecast error by 1% over ten months - R 0.6 million saved
Improving average under-forecast error by 1% over 10 months - R 3.3 million saved
Specific sub-departments were responsible for most costly errors
Results indicated that methodology has value Analysis of greater scope of FMCG retail companies
More extensive and complete data required
Improved data management, more transparent communication
Emphasis on upstream process - employ feedback response
Information system that incorporates these calculations Over-forecast error Under-forecast error Results: under-forecast product analysis Problem statement What I have learned Importance of communication
Data management's role
Garbage in, garbage out
Establishment of relationship of trust
There is always room for improvement
Importance of consulting and teamwork
Keeping the "bigger picture" in mind Product 4 > Product 1 > Product 5 > Product 2 > Product 3 Product 4 > Product 1 > Product 5 > Product 3 > Product 2
Full transcript