Tons of consignments. Thousands of routes. Millions of transactions. The data landscape of transportation and supply chain is huge making it a perfect candidate for Big Data analytics. With the data-crunching capabilities of big data, logistic players will be able to turn their everyday data into actionable insights.
The positive impact of Big Data will reach not just one area of operations, but a whole web of the supply chain system. Right from sales and inventory planning to last mile delivery and even fraud detection, there is a whole web of supply chain functions that big data can transform.
Curious to know how each of these functions will be positively impacted? Read ahead.
In a typical supply chain set up, demand forecasting is carried out by combining data from one ERP system with another and enriching it with data from an external CRM system or supply chain management tools. In other words, a single unified view of the data is hard to come by.
Big Data creates the potential to have this unified view of demand by redefining how external data and internal data is used. Logistic players can now tie together market forces of demand and supply to supply chain route optimization. For instance, Big Data can help reveal which geography will have a spike in demand for a particular SKU based on variables like seasons, holidays, cultural practices and so on.
Amazon’s anticipatory shipping is a real-world example. With predictive analytics of customer data of previous orders and browsing patterns and supply chain routes, Amazon is literally able to ship goods even before customers have placed the order. This makes the supply super-agile and market ready.
Do you know which is the biggest cost that every logistics player has to incur? It is fuel. Shipping carriers, truck companies, courier agencies, last mile delivery services — all of them have to incur fuel expenses to go from pick up locations to destined customer locations.
Is there a way to keep fuel costs down? Can Big Data lend supply chain managers with insights to streamline their routes for fuel optimization?
UPS - one of the global logistics leaders has its own software system called ORION (On-Road Integrated Optimization and Navigation). The purpose of the software is simple - turn every package box into data that can be used for driving insights. And, they did drive insights with the help of Big Data. With Big Data’s power, UPS was able to reduce 100 million delivery miles -- and 100,000 metric tons of carbon emissions (Zdnet).
The transportation component of a typical supply chain is made up of air, road, and waterways. Relying on only one mode of transport can cause significant delay and inefficiencies in deliveries. Unfortunately, that is how most of the logistic players are functioning right now. There is a constant disconnect between the available networks and delivery requirements.
Big data can help create a strategic network of logistics that connects each mode of transport to one another. It can help narrow down on routes where each mode of transport can be connected to each other for faster deliveries at lower costs. Big data analytics, when combined with regular analytics of seasonal patterns, will also lead to identifying strategic routes that will ease peak time traffic.
If you look at it from a 30,000 feet height, the supply chain landscape is made up of at least a 100+ complex variables. From crude oil prices to canons of taxation, every major micro and macro-level variables form part of the supply chain machinery. Add to that other supply chain challenges like natural calamities, labor shortage, power outage, etc. which further disrupt the smooth flow of the supply chain machinery.
The end result? There is always the peril of uncertainty. When uncertainty is higher the amount of risk involved is also higher. Big data, being a data-driven approach to complex challenges can step in as a risk mitigator. It can help supply chain managers, financial advisors, and 3PLs to connect the dots between several supply chain variables. By connecting the dots they are able to look at the big picture and spot area where risk is high and how it can be managed.
A typical example would be connecting weather patterns to sea routes during specific seasons. Logistic players can take a step back and look at how many of their sea routes that pass through regions that are prone to high-risk of cyclones. Alternate routes can be planned for specific parts of the year to prevent jettisoning of precious cargo.
But, leveraging big data for upping supply chain performance is not an arrow-straight road. There are obstacles en route. In fact, there are two tangle obstacles that need to be resolved to size the big opportunities that Big Data offers.
First, supply chain managers who have a real-time pulse of operations are not data scientists. You cannot expect them to wield Big Data with the efficacy with which they wield their daily operations.
Second, despite being a massive data spewing domain, structured supply chain and transportation data is hard to come by. Structured data makes Big Data analytics seamless, while unstructured data delays the outcomes.
If these two obstacles are dealt with, all the possibilities of Big Data are for you to leverage.
In the end, a parcel without an address is just a box, and data without insight is just used petabytes in a cloud. Big data can help unravel the truth hidden inside your supply chain data.
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