That\u2019s good news for everyone, but especially for logistics companies, who happen to generate data in spades \u2014 from invoices, docking schedules, and maintenance reports to data on truck fleet mileage, inventory stockpiles, and weather forecasts.<\/p>\n
The ability to dissect this data and turn it into actionable business insights is one of the key benefits of predictive analytics in the supply chain. The predictive analysis enables logistics operators to reduce losses, minimize risks, and meet customer demand more efficiently. This is why many companies are turning to predictive analytics in the custom supply chain management software<\/a> and logistics to survive the coming recession.<\/p>\n At Django Stars, we\u2019re no strangers to the challenges of logistics management. It was a key feature we developed for a leading US-based corporate meal delivery company. We helped build a highly scalable and reliable system that orchestrates workplace meal delivery and keeps suppliers efficient regardless of the size of a customer\u2019s demand.<\/p>\n This article explores the market landscape of predictive analysis in transportation and logistics, the benefits of logistics analytics solutions, and the challenges a company can face when implementing them.<\/p>\n We\u2019ve decided to share this information because we often encounter similar matters while delivering logistics software development services. The recommendations provided might also prove beneficial to you.<\/p>\n Predictive analytics in logistics is the use of historical data, statistical models, machine learning, and a variety of external signals to forecast future events, demands, risks, and trends throughout the supply chain. That is what puts it apart from descriptive analytics (describing what<\/i> happened<\/i>) and diagnostic analytics (diagnosing why<\/i> something happened).<\/p>\n Instead, predictive analytics allows answering: \u201cWhat is likely to happen next?\u201d <\/i>and \u201cWhat potential challenges or demand spikes should we anticipate?\u201d<\/i><\/p>\n Using predictive analytics in logistics industry enables companies to discover actionable insights in their own data from past shipments, inventory records, and a variety of performance metrics accumulated over time. Perhaps that is why its global usage has grown dramatically.<\/p>\n Many factors have contributed to the stable growth of predictive analytics in supply chain market over the recent years. While economic feasibility has been key, technological availability and business future-proofing have also played a crucial part.<\/p>\n The COVID-19 pandemic highlighted the importance of contingency planning and company flexibility for coping with sudden supply chain limitations. According to a series of MHI and Deloitte reports<\/a> on the supply chain and logistics industry, in 2017, only 30% of supply chain managers were using software to support data-driven decision-making. By 2022, this percentage had grown to 54%.<\/p>\n These reports highlight five key applications of predictive analytics in the logistics industry in 2023 and beyond:<\/p>\n Elsewhere, 2022 research into the predictive analytics market<\/a> places the market size at $12.5 billion in 2022 and forecasts it will reach $38 billion by 2028.<\/p>\n Finally, recent advances in cloud-based supply chain management software<\/a> and the efficiency of Big Data analytics have made solutions for predictive analytics more affordable and accessible to small and medium businesses. This puts the power of supply chain predictive analytics in the hands of almost any SMB looking to increase its operational efficiency.<\/p>\n While predictive analytics has been around since the invention of computers<\/a>, for decades, the sector struggled with limited datasets and reliance on historical data that was often hidden in obscure formats.<\/p>\n Nowadays, predictive analytics for supply chains can combine unstructured data from a huge variety of disparate sources, analyze it in real-time (or near real-time), and provide actionable, easily visualized insights on the go. This makes analytics accessible to every business, even those with no data analyst expertise.<\/p>\n Here are just some of the benefits of predictive analytics in logistics and other industries, with examples of how they\u2019ve helped companies to thrive. One of the biggest value drivers of predictive analytics is its ability to identify bottlenecks in processes. In turn, this helps remove inefficiencies and optimize business processes.<\/p>\n New Jersey healthcare provider Atlantic Health saved $70 million<\/a> in labor and premium costs in three years by implementing a predictive analytics system. They were able to analyze labor costs and predict hospital productivity, which resulted in better patient outcomes, earlier discharges, and shorter hospital stays.<\/p>\n Data is only valuable if a business or customer can act on it while it\u2019s relevant. Enabling the timely application of corrective action is what makes analytics software useful.<\/p>\n Harvard University\u2019s Project Abbie<\/a> sensor technology analyzes patient vitals in real-time to detect early signs of anaphylactic shock caused by an allergic reaction. The sensor then sends a signal to a wearable device to administer a dose of epinephrine. It\u2019s an example of early detection and fast data processing being used to literally save lives.<\/p>\n Excess inventory means money wasted on storage, while stock-outs mean lost revenue. Using predictive analytics in supply chain management allows companies to accurately predict spikes in demand and address them fast.<\/p>\n PepsiCo\u2019s Sales Intelligence Platform<\/a> keeps track of stock at every one of its vendors and lets retailers know in time that they need to replenish, ensuring maximum efficiency of the company\u2019s supply chain.<\/p>\n Then again, simply having the needed resources somewhere is not enough. They also have to be instantly accessible when a customer needs them.<\/p>\n As far back as 2014, Amazon was already patenting its famous anticipatory shipping technology, which relies on logistics predictive analytics to anticipate the demand in any Amazon hub area based on historical data and real-time factors. This helps the company optimally distribute stock and enable one-day customer delivery.<\/p>\n Amazon\u2019s solution also calculates the number of drivers needed daily, the routes they should take, and even the way the delivery vehicles are loaded, so the driver spends less time searching for the next parcel, improving last-mile delivery efficiency.<\/p>\n While predictive analytics shines in fighting cybercrime and supply chain fraud, it can be used in any risk mitigation process. Currency exchanges use prediction tools to mitigate volatility caused by political events. Logistics companies, in turn, can enhance their plans and schedules with risk alerts based on weather forecasts, equipment maintenance status, and other factors.<\/p>\n Back in 2015, one in three shipping containers<\/a> was transported empty, costing Danish shipping giant Maersk and Germany\u2019s DHL billions of dollars annually. In 2018, DHL invested $350 million<\/a> to build MySupplyChain \u2014 a predictive analytics platform aimed at optimizing and digitalizing their operations.<\/p>\n The empty container problem is nothing new. When export-oriented countries ship goods or raw materials to import-oriented countries, empty containers must be shipped back for the next haul. This implies costs of ownership, costs of transportation, and costs of repositioning, as empty containers still require ships to deliver them while carrying no goods. These costs amount to over $20 billion yearly<\/a> for the top 100 logistics companies worldwide.<\/p>\n Maersk Line, a shipping giant responsible for transporting nearly 15% of annual global GDP, developed a proprietary solution for predictive analysis in transportation to ensure they use their fleet with utmost efficiency and can relocate empty containers in time to meet demand.<\/p>\n![\"Role](\"https:\/\/djangostars.com\/blog\/wp-content\/uploads\/2023\/07\/The-Role-of-Predictive-Analytics-in-the-Supply-Chain.png\")
<\/p>\nWhat is predictive analytics in logistics?<\/h2>\n
The growth of the transportation predictive analytics market<\/h2>\n
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![\"Using](\"https:\/\/djangostars.com\/blog\/wp-content\/uploads\/2023\/07\/Key-Role-of-APIs-is-Enabling-Better-Customer-Service.png\")
<\/p>\nBenefits of predictive analytics in logistics and supply chain<\/h2>\n
\n![\"Benefits](\"https:\/\/djangostars.com\/blog\/wp-content\/uploads\/2023\/07\/Benefits-of-Predictive-Analytics-in-Supply-Chain-Logistics-Software.png\")
<\/p>\nBoosting productivity<\/h3>\n
Faster data processing<\/h3>\n
Increased logistics strategy effectiveness<\/h3>\n
Improved resource management<\/h3>\n
Effective risk prediction<\/h3>\n
Reduced costs<\/h3>\n