Businesses in all industries depend on the smooth operation of global supply chains. Yet as these vital systems and processes become more complex, they can also become more fragile – needing careful management to keep them running effectively.
Data is an important enabler of modern supply chains, as long as it’s of the highest possible quality. When data is accurate and reliable, organisations can optimise their supply chain by streamlining operations, improving decision-making and reducing risk. However, poor quality data can have the opposite effect by adding pain points, reducing output, hindering inventory management and stopping companies’ ability to measure and assess risk factors. Improving both data quality and data traceability should therefore be a priority at every stage of the supply chain.
The data puzzle
With some companies managing upwards of 75,000 suppliers, tracking, reporting and analysing supply chain data is an arduous task. This is particularly true when it comes to fragmented data sets stored in multiple siloed systems distributed across geographies, business units and suppliers.
And when data is not reliable, accessible and up to date, it can impact many parts of the supply chain. For instance, successful inventory management requires companies to deliver just the right quantity of the right product to the right place at the right time. And the whole process heavily relies on accurate data from multiple places – customer service, suppliers, warehouses and shipping providers.
If this information is fragmented, incomplete or difficult to interpret, organisations will find it difficult to deliver products or services in line with customer expectations. But imagine if that data could be pulled into a single view giving users the ability to see – in one place – not only all the data they needed, but information on the quality of that data and the processes associated with it.
Yet gathering data is also just one part of the puzzle. This challenge will grow exponentially in the short to medium term with the proliferation of Internet of Things (IoT) devices, the increasing use of both public and private cloud services, and generative AI. Supple chains must account for the sheer volume and diversity of data. There is a heightened need to automate processes to ensure that data is well managed and catalogued throughout the supply chain.
Mastering supply chain data
To overcome data challenges, organisations need to focus on introducing the tools and processes to share data and collaborate with partners. In addition, trusted, relevant data on everything from bills of materials and supplier challenges, to shipping routes and customer demand, needs to be available on-demand and in near-real-time.
Achieving this approach relies on having a clear, end-to-end view of the entire supply chain, ensuring supply chain managers use data optimally. For example, a cloud-based platform approach to managing data can seamlessly integrate internal and external data, bringing together trusted, governed and relevant supplier information from across the entire business into a centrally managed system.
Ensuring data quality is crucial. Organisations must guarantee the accuracy of their own data as well as that of their partners or suppliers. To prevent issues from spreading through complex supply chains, it’s important to monitor data directly at its source. Implementing data observability practices enables proactive monitoring and early detection of data quality patterns, allowing for quick remediation and smooth operations.
Finally, AI and machine learning (ML) can significantly enhance supply chain management by automating many aspects of data management. These technologies analyse vast amounts of information to provide useable insights. For instance, AI and ML can help detect and maintain data quality across large datasets, automatically classifying data to meet organisational standards.
Delivering on demands
With a holistic, trusted, single view of suppliers, ML and AI can extract valuable insights from supply chain data. By connecting technical data with business metadata, these technologies enable organisations to gain a deeper understanding of their supply chain operations and make more informed decisions. This improved data comprehension can lead to more efficient inventory management, better demand forecasting, and enhanced supplier relationships.
For example, we’ve recently seen supermarkets contending with supply chain disruptions – cold weather, high energy costs and transport disruption. A 360 view of all supplier profiles helps supermarkets navigate turbulence. The ability to visualise and understand strategic supplier relationships is crucial to identifying alternative suppliers and getting the right products to the right places fast.
Ultimately, data is critical to maintaining a supply chain. As such, supply chain managers must organise and manage their data effectively. To do this, it’s fundamentally important to ensure data is of the highest possible quality and is traceable at every stage. With an accurate, holistic view of suppliers feeding applications, AI and analytics, companies can quickly understand macro demand trends, gain visibility of suppliers, improve collaboration and optimise supply chains to deliver their product or service more quickly.
- Digital Supply Chain
