Manufacturers are constantly balancing a complex web of functions that span product innovation, engineering, planning, production and logistics. These highly interconnected processes span across the organization and its partners, creating gaps in data, information latency and barriers that create complex associations across operations and constituents.

The past year of disruptions brought many global supply chains to the brink. Bad data strategies were the culprit, as they treated the supply chain as a rigid system when, in reality, it is a complex network of actors that need to be in sync to quickly adapt to change. As consumer demand reached an all-time high for countless products during the pandemic, data latency from source systems delayed responses that prohibited many manufacturers and suppliers from being able to react to the changing market environment.

Traditional data management systems worked well when supply chain professionals had more time to adapt, and the enterprise data landscape was more uniform, structured and simple. But the world is different now. Supply chain data needs to be reusable, which is something traditional approaches can’t do given they require non-repeatable data extractions for problem identification and for solving equations. Worse, the emergence of the internet of things (IoT), the rise in unstructured data volume, increasing relevance of external data sources, and trend towards hybrid multi-cloud environments are obstacles to satisfying each new data request.

The old data strategy that centered around relational data systems is fundamentally broken, but how can manufacturers shift from a reactive to a responsive data strategy? To overcome information delays, manufacturers are adopting new technology approaches like data fabrics to create a digitized supply network that accurately represents data as it moves along the supply chain and the relationships that define how work gets done. Enterprise data fabrics weave together data from internal silos and external sources and create a network of information to power business applications, artificial intelligence and analytics. 

Supporting the full breadth of today 's complex and connected enterprise, this digital representation includes all processes, products, people, partners, policies and third-party data sources represented in the supply network to provide a clear view across the value chain. With this visibility, manufacturers can create impact and root cause analysis, manage distributed hierarchies and perform just-in-time decision making leveraging real time and loT data.

Data fabrics provide data professionals with the ability to generate a composable, machine-understandable representation of the key entities and relationships, as well as business logic and rules, that govern the business. Unlike older data integration techniques, data fabrics are expressive, enabling manufacturers to ask questions and describe the real-world effects, consequences and properties of certain actions. The fabric is extensible and reusable across all use cases/functions, and it’s easy to maintain and extend to partners as needed.

Driving Supply Chain Outcomes 

To create business value within the enterprise, manufacturers must be able to connect all the data that matters. Data fabrics change the status quo by delivering meaning, not just data, across the enterprise. This meaning is woven together from many sources: data and metadata, internal and external sources, and cloud and on-premise systems. Meaning is captured within the data model, with all context on each data asset fully present and available, in machine-understandable form. With a data fabric, people and algorithms can make better decisions while also reducing the likelihood and risk of data misuse or misinterpretation. More specifically, data fabrics are helping manufacturers:

• Improve demand sensing. Closing the time lag to meet emergent demands is critical for manufacturers; however, demand sensing is difficult due to data latency and an inability to find connections across everything from social media to POS data. Data fabrics remove these gaps without having to rewire existing ERP or demand forecasting solutions.

Insights can be delivered to demand planning leaders who can leverage it to improve business planning.

• Connect operations. The high level of demand volatility has a ripple effect on manufacturers that need to quickly understand operational performance, product availability and trends that impact yield. Unfortunately, multiple MES or shop floor operating systems are unable to identify and support real time scenario trade-offs around supply availability.

Using data fabrics, manufacturers can identify any changes to supply and make the necessary adjustments without assuming additional data lake silos or operational support/procurement analyst costs.

• Provide root cause analysis of customer complaints. Customer complaints due to product defects can set off a slew of follow-up assessments. What was the source of the defect? Which other customers were impacted? Does this require a recall? Data fabrics enable manufacturers to trace a customer complaint regarding a defective product all the way back to the raw materials, easily cross-referencing the relationships between finished goods and raw materials.

Further, because these raw materials are produced by multiple manufacturing sites and may be called different names by different suppliers, data fabrics enable traceability across customer, manufacturing, field support, product and other data other domains. This allows manufacturers to see the full extent of the situation so they can act appropriately and cost-effectively manage the root cause analysis of customer complaints.

• Create a digital supply chain twin. The digital supply chain twin requires predictive analytics, a model to connect source data and, of course, the source data itself (e.g., ERP, CRM, MES, loT, customer networks) to improve decision making, especially around supply chain planning. A digital twin must be able to represent hundreds of millions of relationships. A data fabric, with the complex business logic capabilities of semantic graphs and the ability to represent varied data, provide manufacturers with automated control that allow them to manage business rules capable of handling complex logic and situational decision-making.

Data fabrics continue to gain attention for their ability to stitch together existing data management systems, enriching all connected applications and users in the process. They are considered the next step forward in the data management space — supporting the full breadth of today’s increasingly complex, connected enterprise.

Rob Harris is vice president of solutions at Stardog, an enterprise knowledge graph (EKG) platform provider.