Improving value chain data lifecycle management utilising design for Lean Six Sigma methods.

Purpose: This project aims to optimise a secondary agricultural company's reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users. Design/methodology/approach: A design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle. Findings: The study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team. Practical implications: This study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings. Social implications: Improving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts. Originality/value: This paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry's entire data and business intelligence lifecycle. [ABSTRACT FROM AUTHOR]