No one really doubts the competitive advantage that data can bring, especially when it comes to its use in front-end customer-facing applications, such as targeted marketing or sentiment analysis. However, when it comes to supply chain use cases, things get blurred and are often relegated to prioritizing aspects that are perceived as having a higher potential to create actionable insights.
It is these untapped areas I would like to focus on here.
The use of data in the supply chain encompasses the full end-to-end scope of processes and activities of the ecosystem – from planning and procurement, to consumer fulfillment, including warehousing and transportation. Within these elements, we find specific vertical use cases of data analytics such as real-time re-routing, demand/supply planning, and sensing and horizontal use cases that are more connected to the rest of the ecosystem, such as master data management (MDM) or intelligent automation. In addition to the benefits already covered, a touchless and autonomous supply chain can only be achieved with consistent and fully integrated supply chain data that drives intelligence and machine learning.
Data quality is the cornerstone of supply chain excellence. Inaccurate, outdated, and inflexible (difficult and tedious to update) data adversely affects operations by making insights irrelevant and/or outdated, and requires considerable manual effort to simply run standard supply chain operations. Conversely, high data quality, accuracy, and flexibility not only saves a tremendous amount of time and manual effort, as well as sustained and standardized operations, but it also enables companies to generate more insights and gain a significant competitive advantage – including time to market and rapid adjustment to changing regulations.
It also enables organizations to unleash the full potential of their supply chain platforms. All too often, companies get bogged down after making significant investments in costly best-in-class platforms without troubleshooting their data issues, leaving them unable to reduce their cost-to-serve and gain the insights they were hoping for.
But supply chain data structures can be incredibly complex – especially when years of accumulated data in various formats has clogged up non-integrated legacy systems and left companies unable to reap the efficiency benefits from their data strategy they were hoping for. Not only do supply chain data structures affect the overall quality of data, but they also directly influence other processes and functions down the line, making data all the more important to companies’ overall supply chain strategies.
Once this complexity is understood and managed, supply chain data needs to be automated to reduce and ideally eliminate human interaction. As error-prone manual tasks are removed from the day-to-day operations, the supply chain becomes near touchless, and far more efficient and effective. Automation in supply chain data management is not only important for obvious productivity reasons, but also for quality, consistency, and integration aspects.
Whether or not companies leverage the available tools on the market or proprietary solutions and scripts to handle pre and post validation supply chain data, the automation of data creation and cleansing, as well as regular data quality checks and maintenance, is paramount in improving a company’s entire supply chain ecosystem.
Data management is often seen as a tech issue and, as such, does not get the attention it deserves. Indeed, supply chain data evolves dynamically in a broad ecosystem of processes, governance, people, and intelligent or platform automation. It is only by looking at supply chain data performance holistically that companies can hope to yield tangible sustainable benefits.