Supply chain management is an increasingly data-driven field. This trend is being accelerated by a confluence of factors. First the increasing complexity of global supply chains and the growing risk of more frequent disruption. Secondly, more responsibility is being placed on functions like supply chain and procurement. Once more tactical and functional, these functions are now expected to deliver strategic wins and cost reductions for the business.
As a result, supply chain leaders are investing heavily into data collection and analysis tools. Their hope is that, with the application of machine learning and artificial intelligence (AI) analytics, vast quantities of data can be leveraged into full organisational visibility and strategic insights.
“Capturing, protecting and then leveraging an organisation’s data through the use of AI and Machine Learning is an example of how organisations are increasingly turning towards intangible assets to extract new sources of value,” noted Ken Chadwick, VP Analyst at Gartner’s Supply Chain Practice, in a report from October. Spurred by the need to generate more valuable insights, supply chain organisations are collecting as much data as possible, from ERP platforms, advances tracking systems and, increasingly, from the Internet of Things (IoT).
However, making effective use of data is another matter entirely. Experts at KPMG argue that, despite massive investment, data remains “one of the core challenges facing supply chain management.”
The data management challenge
Every day, “millions and millions of date records are generated across the supply chain from multiple systems,” notes KPMG’s 2024 Supply Chain trends report. The problem is that supply chain operators are failing to successfully manage this growing wealth of data. The resulting deluge has “given rise to greater silos of data within the organisation.” In turn, this has led to disconnected data sets, among other issues.
They add: “Duplication and misinterpretation will become increasingly problematic, too. Critically, the fragmentation of data impedes the creation of a holistic view of the organisation’s supply chain.”
Addressing the data problem
Supply chain operators must abandon the “more is more” approach to data analytics that is currently creating pain points throughout the sector. If they intend to make strategic, informed decisions, these data management complexities need to be addressed.
Focusing on data availability, quality, reliability, cadence, and consistency enables supply chain managers to get better control over their data. By doing this, they will be significantly better positioned to eliminate pain points. By focusing on specific data use cases, organisations can take a more intentional and proactive approach to applying their data. In time, establishing data management standards will improve the overall quality of the data that is kept, handled, and used for decision making.
- Digital Supply Chain
- Risk & Resilience
