It’s that time of the year when business leaders turn an eye to the future. What’s in store for the months ahead? What’s going to shape the challenges and opportunities the organisation will face? Where should we invest and where should we play it safe?
Data is going to be a key driver of change in 2025. The quality and agility of businesses’ data strategy will make the difference as they navigate the ups and downs of the market. Manufacturers and logistics operators are facing sustained pressure, juggling the need to comply with evolving supply chain regulation, handle the impact of climate change, remain adaptable in the face of economic challenges, and work to meet their corporate social responsibility requirements.
To achieve this formidable combination of tasks while also driving commercial growth, companies need to prioritise good data processes. They need a complete, end-to-end view of their supply chain, not only to ensure they can appease regulators, but also to enable business success, giving them the insight they need to adapt to change at each level.
As tough as it can be to secure growth in a volatile market, the real challenge will be felt by companies that don’t develop systems that ensure their data is relevant, responsible, and robust. Organisations that fall back on incomplete or scattered data that delivers inaccurate results will soon feel the pinch of poor decision-making.
So here are three key data trends for supply chain professionals to keep on top of: data quality, data visibility, and AI for data management.
Data quality has to come first
When it comes to data strategy, quality underpins everything. If data is incomplete, out of date, or unusable, it might as well not exist at all. Organisations need to detect and migigate data quality issues early, before they proliferate across systems and processes. Observability tools and enterprise data quality tools play a crucial part here.
As companies seek to improve their data quality, there are some critical factors to consider. The first is to identify and, if possible, remediate data quality issues at sourc. Addressing these errors and inconsistencies where they originate prevents them cascading into the rest of your supply chain processes and applications.
Another key area to work on is data observability. This essentially means ensuring that everyone who needs to know about possible data quality issues is kept informed in real time. Businesses should aim for proactive monitoring, with notifications rapidly going to the right stakeholders if patterns of poor data quality appear. Additionally, as data ecosystems and infrastructures become increasingly complex, robust data integration and interoperability is crucial. Integrating systems effectively ensures that data flows efficiently and accurately, allowing businesses to unlock the full potential of their data, while avoiding hold-ups caused by siloed or incompatible systems.
Visibility growing clearer
Once data quality is assured, organisations need to put that data to use. For supply chain success, it’s crucial to have real-time insight into key information streams like supplier movements, product availability, and shipping routes. That means making the right data available to the right people, in the right format, at the right time.
As a result, supply chain visibility systems need to include end-to-end monitoring and enable data-driven decision making. This means integrating both internal and external data in a trusted and timely manner. The best way to do this is to take a cloud-native data management approach, ensuring all your data is accessible and secure at all times.
Consider a product recall: a manufacturer must quickly trace affected items across a complex supply chain.
With high-quality, visible data, businesses can pinpoint the exact locations and quantities of defective products and rapidly communicate with retailers and consumers. And let’s not forget the importance of data visibility for ESG reporting. Without visibility into ESG-related data across an organisation and its supply chain, it becomes challenging to get an integrated picture of ESG performance. By implementing metadata management and data lineage, and maintaining a unified 360-degree view of all products and suppliers, businesses can make informed decisions based on trusted data.
How AI and analytics are changing the industry
Good data processes don’t just form the foundation for good decision-making and supply chain management: they also drive the development of next-gen AI applications that can transform how companies operate.
For example, AI can extract actionable insights from supply chain data much faster and to a far greater level of depth than was previously possible. It can identify trends and predict outcomes that would have been tough to spot in the past.
This capability will be a necessity for identifying alternative suppliers, creating more effective partnerships and getting the right products to the right places fast. However, the success of AI depends on orchestrating AI models with the right data at the right time, supported by real-time visibility and trusted data integration.
AI can also help automate data management processes, connecting the dots between technical and businesses’ meta data. Beyond ensuring data quality at scale, these technologies can help automatically classify data in line with sensitive and organisational policies. Metadata-driven AI models can help automate and improve the data management experience. Plus, as natural language models à la Chat GPT are merged into these systems, operators will be able to use intuitive prompts to discover and understand supply chain data visibility, and generate data management artifacts to support scaling of these insights.
In short, AI can connect the manufacturing chain to the supply chain, from start to finish.
- Digital Supply Chain
