The supply chain and logistics industry has long relied on cold chain technology to transport perishable goods. Logistics firms face plenty of issues when it comes to monitoring refrigerated shipments, due to the complex nature of the global supply chain.
However, cold chain monitoring has come a long way over the past decade, thanks to advancements in the tech that powers data logger devices, the proliferation of analytics and increased data gathering.
Here are some of the key ways that data analytics is helping supply chain firms overcome issues and deliver high quality products to consumers.
Better Compliance with Local Regulations
Supply chains these days are global, and consumers have become accustomed to buying goods from all corners of the globe. Ensuring the quality of these goods while complying with local regulations is no small feat.
There is no uniform global distribution standard or code that countries follow. Each government prioritizes different objectives, and supply chain firms are expected to comply with them or face resistance in obtaining clearance to move packages past customs.
For example, the EU has recently enacted laws to eliminate the illicit trade of tobacco products. These laws require the establishment of global tracking and tracing systems that will help create a global sharing point. In the United States, the Food and Drug Administration has tightened legislation to ensure food safety through the Sanitary Transport Rule. Firms cannot comply with either of these laws without maintaining thorough and verifiable data logs.
Enterprises and governments are also using data analytics to reduce the inefficiencies in the customs declaration and inspection process. Firms use real-time monitoring software to process alerts from official government sources. Today’s supply chain monitoring software solutions can connect to data warehouses that store updated logic based on regulations of every country that the goods transit through.
For example, processing VAT on incoming shipments can be a tricky issue in the European Union, thanks to each member state applying different tax rates. The Netherlands government uses analytics to track the value of ecommerce goods in online marketplaces and reconciles this data with the declared value of each shipment.
Environmental sustainability is likewise an extremely important issue affecting the supply chain industry. Refrigerant gases (Hydrofluorocarbons) used in the cold chain are responsible for high greenhouse gas emissions. Supply chain companies track up-to-date information when shipping products across diverse geographies to keep track of compliance requirements.
Real-time analytics helps firms stay on top of regulations and map the degree to which each government enforces environmental guidelines ahead of time. This reduces delays and helps them create deeply audited reports that satisfy government regulations.
Better Supplier Relationships
Food is transported all around the world these days, and producers, distributors, wholesalers and retailers alike face tight shipping deadlines. Before the advancement of cold chain monitoring analytics, it was tough to determine whether damages were caused by improper handling during shipping or whether the quality of food was poor from the start.
Even if you could determine that damages occurred in transit, there were too many subcontractors involved to truly zero in on exactly whose quality assurance protocols were failing.
For an industry that depends heavily on quality suppliers, data analytics improvements have been heaven-sent. Supply chain firms can now provide all kinds of data monitoring capabilities to their clients, enhancing a sense of transparency and accountability. Everything from temperature, to shock, and even light is tracked closely. As a result, clear boundaries are established right from the beginning, and the cause of any damage can be determined with high accuracy.
For example, logistics managers can track and view condition reports of goods as they leave the producer’s warehouse. They can exercise their authority to either stop the transportation of goods if they’re damaged beyond acceptable thresholds. This eliminates any expenses incurred due to damaged goods being shipped and helps identify the source of product failure right from the start.
Some logistics providers even incorporate light sensors into their packages. Abnormal increases of light indicate that packages or storage boxes have been opened for too long during transit, or during the packing process. Armed with this insight, stakeholders can analyze who delivers the best quality goods repeatedly, and which logistics provider transports them most safely.
The healthcare industry also relies heavily on analytics. Drug shipments have to be tracked from the source to prevent contamination and theft. Sensors monitor the state of packaging from the beginning and alert users to temperature excursions. These happen when the package is stored in less than ideal conditions during the packing process and analytics help alert firms to them.
Track Causes of Failure
Supply chains are complex things and consist of many workflows overseen by many parties with many potential points of failure. For example, a logistics provider might choose a less than optimal route to deliver perishable goods, which can lead to spoilage in transit. One especially frustrating headache is the lack of uniform technology that supports cold chain transport at every location through which goods are transported.
When viewed in the context of the entire supply chain, these issues seem trivial, but they can wreck entire shipments if unaccounted for. While real-time tracking alerts customers to the condition of their goods, what’s far more important is having the ability to avoid repeating previous mistakes.
Analytics reports and ad-hoc dashboards allow customers and logistics firms to determine points of failure within the supply chain. These days, AI algorithms can detect emerging patterns of failure quickly and alert customers to them before it’s too late. Supply chain software provide stakeholders with end-to-end visibility of product conditions, from the supplier all the way to retail shelves.
End-to-end visibility is of particular importance in healthcare. EU government regulations stipulate that QR codes must accompany medicine packaging since they allow consumers to trace the journey of the product right from inception. Healthcare manufacturers, pharmacies and shipping providers need to store medicines in specific conditions to ensure they aren’t rendered ineffective.
In case goods are damaged, it’s critical to narrow down the point of failure to a granular level. The ability to do this also helps process insurance claims quickly since responsibility can be assigned instantly.
Mitigating Human Handling Risk
The logistics industry is one that depends heavily on proper human handling of goods. More often than not, goods are manually transported from one storage location to another, and this introduces weaknesses into the process. With accurate condition monitoring, cargo managers can instantly spot SLA violations and alert their employees.
Real-time monitoring also allows stakeholders to quickly decide whether the shipment needs to move onto the next leg or if it needs to be written off. While losses are never desirable, real-time analytics help companies reduce their commonality and severity considerably. By analyzing the weaknesses in human handling procedures, companies can adopt better training programs and educate employees better.
Analytics can also help override routing decisions that appear optimal on paper. For example, a certain port or location might have a higher than acceptable failure rate thanks to improper employee training.
This scenario occurs with surprising frequency, because cold chain shipments routinely experience multiple transfers. Cargo moves from the warehouse to a truck, to a container, back onto a truck, and finally to a warehouse at the destination. All of these storage facilities are operated by human beings, and tiny variations or deviations in temperature can destroy shipments.
Analytics can also reveal employee ignorance of handling procedures. For example, a truck operator might switch the air conditioning off when they’re traveling through a frigid environment. Some operators do this to save fuel, but it can lead to unacceptable product damage. In some cases, intermediary logistics providers might use contract staff to handle goods, and analytics can help companies instantly assess whether this is a good policy or not. A high failure rate in that region can point to deal-breaking human handling issues.
Better Workflows
Predictive and prescriptive analytics is a rapidly growing market. Research firm Gartner predicts that the market for these types of analytics will reach $1.88 billion in 2022.
Logistics firms use prescriptive analytics to develop condition profiles of all route options on hand. Previously, companies would choose the shortest route without taking other factors that might damage goods into account. A route profile is developed by logging relevant statistics and condition-related events throughout. As a result, companies can instantly evaluate optimal routes for their shipments based on first-party empirical data.
These days, many end-to-end supply chain visibility software products come equipped with predictive analytics. ML algorithms scan not just existing conditions but also analyze routes for possible disruptions due to geopolitical events, adverse weather, and proposed regulation changes. Supply chain firms use these algorithms to achieve efficient profit margins ,and customers can use ML to project shipments that cover future order cycles.
What’s more, ML is increasingly being used by retailers to track product returns and defects reported by customers. Manually linking these events back to the supply chain to analyze causes is a tough task. After all, it’s a challenge to narrow down damage to a single cause unless it’s all been documented. AI-driven analytics can quickly analyze potential causes of failure and narrow down further potential events that might damage products.
Most products face seasonal demand variations, and AI is being used to manage inventories and monitor fulfillment lag times. Supply times can vary due to various conditions, and this causes retailers to juggle multiple processes, each drought by many unknowns.
Inventory and storage costs are high, and to minimize them, retailers and other supply chain stakeholders use predictive analytics to determine order workflows. For example, a healthcare provider might experience increased demand for flu shots at certain times of the year. Predictive analytics can help them anticipate demand and plan inventory ahead of time.
Predictive analytics for demand cycles is a growing market and its use is only expected to increase over the next few years. However, it’s proving extremely useful even in its current nascent stage.
Better Visibility Through Analytics
Transparency across the supply chain has always been a Holy Grail, and analytics has brought supply chain stakeholders closer than ever to this ideal state. There’s a lot of room for analytics to grow, and the future holds a ton of promise for supply chain applications.
