The legendary trade route known as the Silk Road was actually a collection of piecemeal segments that offered no single, unified view of how things moved from China to the Mediterranean Sea. At the time, there was no need for one. But in a highly connected and digital world, fragmented supply chains simply don’t work. Organizations today require end-to-end visibility, to compete and navigate the challenges that have recently plagued global businesses.

Modern global supply chains suffer from demand volatility, a lack of visibility and sub-optimal processes across forecasting, manufacturing, supplier and inventory management, and logistics. Their plight is being further exacerbated by the fragmented nature of technology applications.

Global businesses need supply chain ecosystems powered by connected data, in order to make better and faster decisions. Following are three use cases that offer insights into effective demand, supplier and logistics management.  

Demand Forecasting

At the start of the pandemic, consumers raced to purchase exercise equipment, hand sanitizer, loungewear and home-office supplies, causing major shortages and fulfillment backlogs. Two years later, retailers are still trying to regain their footing. Companies must understand these variables with the critical insights that only data can provide. Macy’s, for one, saw its inventory rise only modestly, which it attributed to the use of data analytics.

To forecast and satisfy demand, a company must anticipate breakdowns both in its own systems and elsewhere along the supply chain. Shortages of labor, containers and packaging materials, as well as a jump in transportation costs, have resulted in uncertainty for retailers. Why would they invest in marketing a product if they can’t meet the demand they expect the campaign to generate? 

The traditional way of forecasting demand is to rely purely on historical data. The thesis states: If you understand what consumers wanted and needed yesterday, you can predict what they’ll buy tomorrow. This is an increasingly flawed approach, and fails to take into account the myriad of data sources available today, including those from social media and search-engine optimization reflecting consumer behavior. Companies should also look at elements such as competitors’ product pricing and reviews, mobility data and consumer search trends. 

Supplier Management

Supplier management ensures that an organization receives maximum value for the money it pays to suppliers within a specified time frame. Access to the right products or raw materials is critical. According to a McKinsey article, for companies in most sectors, a single prolonged shock to production could wipe out 30% to 50% of a full year’s earnings. So how can leaders use data to manage suppliers more effectively?

Assent Compliance used data from the World Health Organization to identify hotspots early in the pandemic. That data was combined with supplier location data, enabling 1,000 customers to generate their own maps showing those parts of the world where their supply chains were most likely to be disrupted, and identifying the level of risk based on individualized data.

Supplier management should also include clear communications with suppliers and adherence to a comprehensive supplier-management policy. It should encompass details about how, when and what data the supplier will provide to further trust between the parties. Armed with such data, a company will have better insights into factors that can impact the supply chain.

Beyond this level of visibility, an organization should also have data-driven contingency plans for supply chain interruptions. These can include having backup suppliers to contact if primary suppliers fall through, and additional marketing, sales and merchandising strategies to fulfill shifting customer demand. Sometimes point solutions may work; other times, a custom solution that starts with data engineering might be required.

Logistics Management

Logistics management is the administration of transportation, warehousing, packaging and related activities to move and position inventory. Again, data provides the most visibility into a company's end-to-end logistics. Data from tracking sensors linked to the internet of things can reveal where products are in the supply chain, while transportation data can help companies optimize routes.

DHL is among the logistics companies using data to optimize last-mile delivery. With full visibility into potential issues, such as availability and location of warehouse space and significant transportation delays, a company knows early on if it needs to reduce purchases of related products, cut back on marketing or even convert some retail outlets into dark stores.

Dark stores are former retail outlets that are no longer used for walk-in or curbside pickup orders. Instead, they now serve as distribution outlets that offer more space for store inventory and enable retailers to quickly fulfill orders. With multiple dark stores strategically located across a relevant marketing territory, retailers can fulfill orders more efficiently than through fewer larger warehouses. The use of dark stores can be a strategic advantage in this time of scarce warehouse space, with the average nationwide rental rate rising from $7.13 per square foot in the third quarter of 2021 to $8.70 per square foot last year, as The Wall Street Journal reported.

While each of these core supply chain functions relies on data individually, organizations must unify it all to garner the full supply chain picture. They need to be able to organize, store and access the data associated with each activity, and have a single-pane-of-glass view into all of it —have visibility over how everything works together.

Insights from demand forecasting, supplier management and logistics management can serve as the connective tissue between data hubs. This unification establishes relationships between different events that happen in the supply chain with data at the core, bringing resilience to the system as a whole. In the coming years, organizations that have a more connected view of the entire supply chain will be best positioned to mitigate risk and travel the modern-day “Silk Road” to long-term prosperity.

Sunder Balakrishnan is supply chain analytics leader with LatentView Analytics.