The global supply chain problems that retailers across many markets experienced during and in the aftermath of the COVID pandemic have been well documented. Lockdowns, travel restrictions, lack of certain raw materials, and labor shortages affected manufacturing, transportation, and logistics. Kaycee Lai of Promethium discusses how data fabric can change the supply chain game.
Demand fluctuations and panic buying resulted in severe shortages of many products. At an organizational level, an incumbent lack of connectivity, siloed order, logistics, and inventory operations was exposed. Many retailers realized that a lack of visibility across supply chain ecosystems meant they were unable to view both inventory and order information for all products in all distribution centers and could not reallocate inventory dynamically, leading to potential revenue shortfall, compounding problems further.
The pandemic also saw consumers move towards online commerce via web, mobile, and applications. This resulted in retailers expanding their delivery service to include same-day and expedited options and enhanced in-store pick-up options. As a result of the fundamental challenges from the pandemic allied to the evolution of channels to market, many retail organizations have expedited their digital transformation efforts.
Retail ecosystems are complex by nature, so to ensure supply chain optimization and solve operational challenges, retailers need complete visibility of their supply chains in order to identify specific issues and formulate a resolution strategy.
Key to success in retail is making sure that stores have the right inventory at the right time – this is the crux of demand planning. The capability to access data from multiple sources scattered both inside and outside of the organization, often including hundreds or thousands of outlets, plus warehouses, distribution hubs and networks, suppliers, and logistics providers, in real-time, is critical. With data often dispersed across many sources and locations, retailers do not want to have to disrupt operations by having to “reinvent the wheel” and move vast swatches of data. It’s here that virtualizing data by deploying a data fabric model across an entire IT ecosystem can solve legacy supply chain challenges for retailers, resulting in multiple operational benefits.
What Is Data Fabric?
A data fabric is an enterprise data management architecture that helps businesses handle massive volumes and varieties of data at high velocity by providing a unified and scalable approach to data management. Building a virtualized data access layer using a data fabric connects the entirety of an organization’s data, regardless of source and location, along with all of the processes and platforms that might be connected to it.
A data fabric provides a single source solution to find, verify and share data for all users. It can replace multiple solutions, meaning a reduction in costs for customers, and enables easy, rapid data virtualization that does not require data movement.
See More: How Better CX Can Solve Supply Chain Issues in Manufacturing
Gartner reported that “data fabric reduces the time for integration design by 30%, deployment by 30%, and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles. Plus, data fabrics can leverage existing skills and technologies from data hubs, data lakes, and data warehouses while also introducing new approaches and tools for the future.”
For retailers, data virtualization using a data fabric allows the creation of a virtual bridge, seamlessly connecting all internal and external data sources and systems scattered across the supply chain and making them accessible through one portal in a single view.
Supply chains generate vast amounts of data from various sources, such as sensors, IoT devices, transactional systems, and external partners. Through the ability to access and seamlessly integrate data from multiple sources, with data added live and consolidated into a single, unified data store, and without the need to physically replicate or move data, data analytics capabilities can be exponentially improved. Having a real-time end-to-end view of the flow of goods across the supply chain and insights into inventory levels, demand patterns, production status, and logistics information, supply chain managers can make faster and better-informed decisions. Bottlenecks, inefficiencies, and exceptions in the supply chain can be identified, and corrective action can be taken to minimize disruption and optimize operations, all in real-time.
Demand planning and supply reallocation are also optimized using a data fabric to identify the data it requires, then create data sets from all of its existing data sources and data warehouses. This can significantly improve operational intelligence, agility, and customer service. Retailers can generate new revenue and reduce costs on excess inventory, uncovering inventory and unfulfilled orders across distribution centers and highlighting which centers can fulfill orders for others should that be necessary. Importantly, no technical knowledge is required to use the technology, meaning that any user can access and modify data as they need, a key factor for dispersed retail organizations.
Supply chains involve multiple stakeholders, including suppliers, manufacturers, distributors, and retailers. Data fabric facilitates collaboration by providing secure and controlled access to relevant data for different partners. It enables real-time data sharing, collaboration on analytics, and joint decision-making, fostering better coordination and alignment across the supply chain network. Data fabric also ensures data quality and governance by implementing consistent data standards, data cleansing, and validation processes. It enables businesses to establish data policies, monitor data lineage, and maintain data integrity across the supply chain. This ensures that decision-makers have access to reliable and trustworthy data for making informed choices.
As data volumes and velocities increase, traditional data management approaches may struggle to handle the scale. Data fabric offers scalability and agility, allowing businesses to adapt and grow their data infrastructure as needed. It supports distributed computing, parallel processing, and elastic scalability, ensuring that the system can handle the growing demands of supply chain operations.
Harnessing the Power of Big Data in Real-time
The data fabric approach empowers businesses in the supply chain domain to harness the power of big data, enable real-time visibility, optimize operations, and drive strategic decision-making. By providing a unified and scalable data management approach, data fabric enhances efficiency, agility, and competitiveness in an increasingly complex and data-driven supply chain landscape.
Using data fabric, retailers are able to generate supply chain faster insights and answer data queries in minutes, reducing wait times across the business. Additionally, productivity is boosted through the automation of repetitive tasks, reduced operational complexity, and the ability for teams to collaborate in real-time. The solution is also future-proof as the agnostic nature of data fabric enables customers to change to new data sources and tools easily.
What are your thoughts on the data fabric revolution? How can it solve supply chain obstacles? Share with us on Facebook, Twitter, and LinkedIn. We’d love to hear from you!
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