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How COVID-19 Will Modernize Supply Chain Analytics

Supply Chain Analytics

The COVID-19 shock turned the supply chain from an abstract concept to a household problem overnight. Grocery shelves went bare and e-commerce orders vanished as companies throughout the supply chain struggled with binge purchases, stalled shipments, and quarantined workers. By breaking the weak links, COVID-19 exposed what aspects of supply chain analytics need fixing.

As the world slowly dusts itself off, business leaders in manufacturing, shipping, consumer packaged goods, and other supply chain-dependent industries must answer the question: how can we take smarter action to mitigate the next disruption?

They will start with significant operational changes. Organizations will incorporate virus safety measures such as extra rotational shifts and social distancing into their business continuity plans. They will diversify factory output, suppliers, shippers, geographies and shipment routes to minimize single points of failure. They will create target inventory levels based on risk, rather than focusing exclusively on lean principles. Operations teams will consolidate stakeholder interactions on shared supply chain management platforms.

CIOs and CDOs, meanwhile, will fix issues with supply chain analytics and data modernization. They will establish end-to-end supply chain visibility, analyzing and prioritizing components, and run impact assessments. They will democratize data consumption by empowering more decision makers with data. And their data teams will modernize data architectures by doubling down on fundamentals such as integration, automation, scalability, and governance.

Case in Point. Dayton, Ohio-based Winsupply, provider of construction and industrial materials to 30,000 customers, underscores the value of supply chain analytics and data modernization. Winsupply broke down silos to integrate data across 500 distribution centers, 5,000 suppliers and 6 million products to give employees, suppliers, and customers a searchable view of their supply chain. They improved data quality and governance controls with the help of Informatica software. Their new supply chain analytics and management platform increased online sales by 10% and reduced data steward workload by 50%. Winsupply now can absorb acquired units’ data systems within days.

Supply Chain Analytics after COVID-19

Let’s explore how analytics will drive smarter supply chains in the aftermath of COVID-19. By understanding more components, from more angles, business leaders and operations managers will be more prepared to navigate the next disruption. 

  • Establish end to end visibility. Operations managers must work with BI managers to extend their dashboards and monitor more KPIs about inventory, shipments, plants, warehouses, fleets, workers, and end sales. They need to study reports on KPI trends over time and map the relationships of supply chain components with tools such as graph databases. They will deepen and extend their use of data visualization software, and start to brainstorm where to request new IoT sensors – possibly using RFID tags, Bluetooth networks or GPS systems – to track assets and fleets more closely.
  • Model new scenarios. Operational managers and BI analysts also must assess the impacts of future outbreaks (COVID-19 or others) on end sales, worker availability, and the operational capabilities of factories, warehouses, and fleets. They need to predict the results of potential government measures on operations, sales, and shipments. With those findings in hand, business leaders can sign off on supply chain contingency plans with a better sense of their likely outcomes.
  • Prioritize customers, products, and suppliers. Business leaders will ask BI and financial analysts to better prioritize customers, suppliers, and partners. By measuring each party’s revenue, profit, and strategic contribution, they can refine contingency plans to change links in the chain with minimal financial disruption.
  • Democratize data consumption. Business leaders and managers at all levels need to build more quantified evidence into their supply chain discussions, arguments, and decisions. This requires the CDO to democratize data usage by encouraging business managers at all levels to make more data-driven decisions. This requires training business managers to use data visualization software. It also requires frequent collaboration between operational managers, especially those involved in IoT initiatives, and data analysts, and IT.

Data Modernization

The supply chain analytics initiatives outlined above require CIOs and CDOs to continue to modernize their underlying data architecture after the COVID-19 shock. Let’s explore how four familiar aspects of data modernization – integration, automation, scale-out, and governance – contribute to smarter supply chain management.

  • Integrate data silos. Data teams will work closely with operational technicians to integrate IoT sensor data into streaming, data lake or other analytics platforms. They must transform data to reconcile different source formats, and rely on the operational technicians to ensure IoT devices perform in a range of environments. Data teams often must integrate this data with traditional supply chain records from SAP or other systems for more comprehensive supply chain management.
  • Automate. Data and operational teams alike benefit from the automation of manual tasks, whether on a keyboard or shipping container. Data teams should embrace graphical interfaces that reduce or eliminate the manual scripting required to manage data pipelines from source to platform to visualization interface. Many data preparation and data pipeline tools deliver clear ROI via automation and embedded machine learning capabilities.
  • Scale. As data teams continue to support new data sources, data types, and users, they should leverage the resource elasticity and usage-based payment models of cloud Infrastructure as a Service (IaaS). When applying IoT to localized tasks, they should consider processing data at the “edge” – i.e., close to the IoT device – to reduce latency, cost, and complexity.
  • Govern. As organizations seek to reduce uncertainty with data, they need to ensure datasets are accurate and align with consistent formats. CDOs will redouble their push for master data management and data quality as a result. They also need to carefully secure and monitor data access, to ensure that IoT gateways or other physical devices do not introduce unexpected security vulnerabilities or compliance risks to their supply chain management and analytics systems.

COVID-19 teaches us that company survival – not just success – depends on smart, modern supply chains. CIOs and CDOs will focus on supply chain analytics and data modernization to realize this vision.

Kevin Petrie

Kevin is the VP of Research at BARC US, where he writes and speaks about the intersection of AI, analytics, and data management. For nearly three decades Kevin has deciphered...

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