How Data Can Help the Supply Chain for Real-Time Disaster Preparedness

The pandemic helped businesses across every industry identify shortcomings in their processes and challenged them to work differently. With the help of data insights and a fresh perspective, businesses are making virtual environments work and are preparing for disaster scenarios to mitigate risk and maximize productivity in any situation, explains Lewis Carr, senior director, product marketing at Actian.

The most tangible lesson the pandemic has taught businesses is how to manage virtual work environments. But it’s also prepared companies in other ways, like helping them do a better job running their supply chains. 

COVID-19 has reinforced the notion that just-in-time order strategies expose companies to far more risk than they bargained for. Price, fit, and availability will always be the three most important considerations, but the pandemic has shown that these factors can be quickly and involuntarily reprioritized by one ill-timed supply disruption.

The quickest way to reduce risk is to apply more of a data-driven approach to their supply chains. Companies can prepare themselves by making more strategic use of existing and prospective supplier data and ensuring the systems they’ve set up to react to disruptions are solid and on point.

Breaks in the Chain

The pandemic, of course, caused massive disruptions in the supply chain. Factories shut down and forced companies to send workers home. Quarantines in certain regions idled key suppliers. Ports, distribution centers and transportation routes oscillated from being severely underutilized to being wildly oversubscribed. 

But there have been other contributors to supply chain volatility in recent years. Political instability has increased the level of violence in northern Mexico and Burma. Countries formerly known as low-cost manufacturing locations like regions in China have lost business because they’ve grown more affluent and more expensive to build in. Conversely, China rebounded more strongly than other manufacturing offshore hubs in 2020 because it moved more quickly to control its COVID-19 outbreak.

Companies can react to these volatile dynamics by using data more efficiently, implementing technologies, and creating processes to minimize risk. The key is finding the most optimal way to phase out just-in-time supply chain strategies while retaining most of the cost benefits of the approach.

The problem is companies are making these assessments based on limited sets of data that historically have focused solely on the quantities of actual components supplied, consumed and expected to be consumed. Businesses already make widespread use of data for many functions, including supply chain planning. Most of the data is generated by the ERP packages that support the supply chain management process. Historical data from prior orders, including delivery dates, quantities ordered against delivered by expected date and other pertinent information, can be used along with other external data to assess risk and avoid unexpected disruptions.

See More: Understanding the Role of Edge Computing in Post-COVID Restaurant Recovery

Bringing in Outside Data

Companies need to look at data from outside the supply chain and associated business processes, integrate it and gather insights to inform their decisions to shift strategies. Joining data on price and availability by a company is no longer a sufficient baseline. Businesses need to add additional data covering a range of factors that could lead to supply chain disruption.

For example, although China has seen a faster rebound in contracts, recent U.S. government action targeting the province of Xinjiang has significantly impacted imports of solar energy and batteries. In this particular case, the ban targets a specific province and specific companies, causing localized disruptions in customers’ order patterns.

Other factors may be seasonal and even impacted by climate change. For example, companies need to do detailed evaluations of the likelihood that tropical storms will disrupt shipments to southern U.S. retail destinations through ports in Florida or the Gulf of Mexico. Long-term weather forecasting service data will be critical to their risk assessments.  

The key part of this comes from an architectural standpoint: the acts of demand planning and forecasting. It’s also the way companies assess their supply chains how they set up secondary sourcing. In the example above regarding Chinese solar-energy components and the new U.S. ban, it’s not just a matter of watching changing policy from U.S. government administration to administration. It’s also a matter of looking at suppliers’ labor situations – suppliers’ labor-management relationships and the relationship between the company and the overseeing government. In other words, price and availability are often downstream dependent variables, riding on several other signals. 

Companies need to look across all of their data to make an informed risk assessment. Data analytics are important because companies have to factor in issues relating to suppliers’ logistics and, in many cases, suppliers operating upstream to them. A car manufacturer, for example, needs to buy upwards of 2,000 different components to build a modern combustion engine-based drivetrain. Electric cars have under 50 components. Manufacturers need to be concerned not only about the supplier of the engine but also about where subcontractors get their components from.

The point is that the risk isn’t just from the number of components. In the electric car, the key risk increasingly is the sourcing of the batteries and, in turn, for the battery manufacturer, the sourcing of rare metals necessary to make the batteries. It’s about the levels of planning and looking into data sets a manufacturer doesn’t own and hasn’t bothered to investigate, let alone join together in the past. In order to handle these disparate and diverse data sets, a manufacturer needs to lean on new, powerful technologies. A cloud data warehouse with the ability to access disparate and diverse data sets both internal and external to the organization can embed large stores of data, unify it for detailed analysis, and deliver rapid insights that help to avoid supply chain disruptions.

See More: Why You Need a Data Fabric for Effective AI

Integrating Data

Being able to pull together large sets of data gives manufacturers the ability to pivot more nimbly in case issues arise in their supply chains. Based on the risk of one supplier, a manufacturer may need to swap it out and order from the backup. Can the company run its demand forecasting and planning with Plan B and Plan C suppliers to assess how much its risk may be decreased and the trade-off in delay and cost that may entail?

If a company has to route an order through the Suez Canal, it has backup route scenarios to airship key components. How much would these shipments hurt margins? And how long can the company ship through backup means before it starts losing money? 

Supply chain decisions can’t be made solely on the highest quality for the cheapest price. Volatility is increasing. Companies need to assess the risk with particular suppliers, use the data it has available and model solutions in case the risk crosses an unacceptable level. 

Here are two actionable tips for dealing with supply chain disruption:

• • Double-click on your suppliers’ supply chain management. Make them provide you not just with price and availability but also risk and associated factors used to estimate that risk. Make this part of the contractual negotiations and ongoing data provided in the course of doing business.
  • Be proactive. Have an ongoing process to find second and third option suppliers at all times. 

Conclusion

Just-in-time strategies will always be part of sound supply chain management. But volatility is increasing across the board. Climate change, political instability and a pandemic that stubbornly refuses to loosen its grip will impact your supply lines. The point is, what data and analysis will you need to quickly and accurately create insights to maintain business agility?  What velocity is needed to build out contingency plans and the ability to execute them?  Who needs to be involved in defining, collecting, aggregating and analyzing this expanded set of data?  Going forward, you will find that a unified view of the data associated with your SCM is necessary but insufficient to meet your business needs.  Instead, in an ever-changing environment, you must include the SCM data for your suppliers and external data mapped to assess risk to your SCM and theirs.

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