Your supply chain is a collection of complex processes that cover everything from acquiring raw materials to last-mile delivery of your manufactured goods. Professionals working in supply chain roles face many challenges when ensuring products arrive on time and in the best condition possible.
Reliability is especially important when goods such as food, medicines, and precision electronics are shipped. The slightest disruption to supply chains can render them unusable and create millions in damages. Big data and analytics offer a way forward for companies looking to design reliable supply chains.
One survey of supply chain professionals revealed that 41% of them consider data analysis a top priority. It's a view that's echoed by Niko Polvinen, the CEO of Finnish logistics data solutions provider Logmore.
"Data allows you to react quickly to problems and avoid losses due to kinks in the supply chain," he says. "For larger companies, this can run into millions of dollars of losses."
Here are three ways in which data is helping companies design more reliable supply chains.
1. Overcoming Unpredictable Local Conditions
When talking of supply chains, the average observer visualizes vast fleets of trucks loading goods from warehouses. However, one of the most critical pieces of the supply chain is last-mile delivery. This stage is where goods are delivered to the consumer. It's also where the greatest inefficiencies lie.
These problems arise due to the unpredictable nature of the local environment. Road closures, the lack of parking space at certain locations, traffic, and special events make last-mile delivery a tough task to execute efficiently.
Real-time data feeds of conditions on the ground are increasingly helping companies mitigate these risks. In addition to this, package condition-monitoring data also helps companies unearth flaws in their delivery processes.
Logmore's Guardians, electronic data loggers that encode data via QR codes attached to shipments, provide some great examples of how companies can leverage these data.
"The Guardians upload data via the Logmore Cloud where they can be collated and analyzed at all levels," explains Polvinen. "Whether it's individual shipments, the flow of the supply chain, or spotting choke points, you can address inefficiencies."
Chokepoint analysis is useful when it comes to designing efficient last-mile delivery routes. Repeated issues with final package conditions along certain routes indicate areas that require deeper analysis.
For example, upon analyzing data, companies might find that the issue is a lack of truck parking spaces. In response, adopting an electric bike last-mile delivery program, such as the one UPS is trialing, might mitigate the issue.
2. Predicting Demand and Planning Supply
Manufacturers are challenged with shifting demand cycles for their products. Demand forecasting is a tricky process that needs to consider emerging consumer trends, historic buying patterns, and logistics provider capabilities.
Once demand schedules have been created, supply and production schedules need figuring out. A big part of this process is taking vendor capabilities into account.
Logistics providers once again play an important role in delivering raw material. Monitoring historical condition-related data of the goods they deliver helps manufacturers measure vendor performance. For instance, a vendor that routinely delivers goods outside acceptable condition thresholds is unlikely to deliver under pressure.
Condition-related data analysis can also uncover seasonal patterns that might be hampering reliability. A vendor might struggle to deliver goods at certain times of the year due to weather conditions or other factors along their delivery routes. Switching vendors during these times helps manufacturers avoid delays.
Polvinen emphasizes the importance of analytics coupled with big data collection. "More data in the hands of decision-makers is always good,” he says. “In addition to data, we also believe in providing them with all the tools and analytical abilities they need to make sense of that data."
Thanks to IIoT devices and condition monitoring sensors at every step of the supply chain, companies collect more data than ever before. However, these data have to be backed up with analytics. Analytics lies at the core of successful demand and supply forecasting.
3. Mitigating Weak Links in Lengthy Delivery Chains
Supply chains ensure that goods are delivered all around the world these days. A consumer in the Middle East expects high-quality Dover Sole, even if the fish is found a continent away from their location. Lengthy supply chains have more potential failure points, and data helps logistics providers mitigate these risks.
The biggest risk in a long supply chain is the delivery route. Before the widespread adoption of analytics, companies would default to the shortest route between two places. However, the shortest route on a map isn't always the best choice.
Customs regulations, geopolitical events, and seasonal weather might ensure the shortest route turns into the longest one. For instance, a supply of temperature-sensitive medicines will be unusable if they sit in an unrefrigerated customs shed while waiting for shipping clearance.
Data analytics solutions help companies take multiple variables into account before creating a delivery route that is reliable and flexible. A troublesome route can be unearthed by monitoring adverse trends in condition-related data.
"Our cloud platform, Logmore Cloud, puts data into two different categories," says Polvinen, "individual shipment and operational macro. It's easy to identify where changes need to be made and make sure everything runs smoothly."
Condition-related data also helps companies pinpoint areas of failure by creating comprehensive audit trails. These trails are useful when it comes to detecting possible threshold violations and mitigating product damage immediately.
A Wealth of Data
Data collection backed by analytics is helping supply chain companies ensure product integrity remains intact until delivery. Whether it's creating optimal shipping routes, overcoming last-minute obstacles, or planning complex production schedules, smarter use of data holds the key to success.
