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The Financial Express

Supply Chain Management will be big with Big Data!


Supply Chain Management will be big with Big Data!

Big Data is used to describe datasets that are complex, large, and unable to be handled by traditional applications. So it is a term that describes large volumes of high velocity, complex, and variable data that requires advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.

Data, by itself, is completely useless. But when it is processed, it can become an asset for any organisation among its various functions. The overall goal of Big Data analysis is to support better decision-making.

Analytics of Big Data is playing an instrumental part in improving Supply Chain Management. From improving delivery time to recognising approaches to decrease the communication gap among manufacturers and suppliers, it has an impact everywhere. Analytics reports empower decision-makers to accomplish operational efficiency and oversee aiming to improve productivity. Supply chain analytics expand data-driven decisions to lessen costs and improve administration levels.

Data is the driver of corporate dynamic on tactical, strategic, and operational levels. Organisations in the supply chain must look forward to the latest, unambiguous, precise and significant data. That is the reason why taking advantage of the potential Big Data is crucial for Supply Chain Management. Hence, we need to know precisely what the solid opportunities are that Big Data provides to Supply Chain Management.

Efficient and productive inventory management

Crucial information turns out to be more transparent and accessible at a big number as parties collaborate and share Big Data insights across the supply chain network. This permits abbreviating of planning cycles and the activity of planning with more significant levels of granularity, prompting more proficient inventory management practices, eventually bringing about optimised inventory stocks.

For example, Amazon uses Big Data analytics in its inventory management. It chooses warehouses on the basis of the nearness of its vendors and customers to lessen distribution costs. Amazon uses Big Data analytics to disperse stock according to customer preferences in specific regions. Big Data permits automated systems to work through intelligently routing a wide range of different data sets and data streams. For instance, Amazon already has automated their fulfillment centers,  which use little orange KIVA robots to grab things from shelves.

Expanded operations efficiency and maintenance

Upgraded modeling has been empowered by Big Data analytics taking into account more precise choices, regular productivity enhancements through mechanisation, leaner operations, and advanced servicing through predictive analytics.

For example, the last mile of a supply chain is famously inefficient, costing up to 28 per cent of the total delivery cost of a package. There are numerous reasons that lead to this, for instance, it is challenging for large delivery trucks to park close to their destination in urban areas. Drivers often need to park quite a while away, and afterward walk the package to its final location. Also, if a customer isn't home, the product can't be delivered and the process requires repetition which increases cost.

Big Data can address many of these difficulties. In an interview with the Wall Street Journal, Matthias Winkenbach, director of MIT's Megacity Logistics Lab, explained how last-mile analytics are yielding helpful information. Due to the low cost, ease, and universality of fast mobile internet and GPS enabled smartphones, shippers can see how the delivery process goes from beginning to end, even during the last mile. 

To make things easier, imagine, an UPS delivery truck with a GPS sensor on it makes a delivery in a big city. After parking nearby, the delivery man's smartphone GPS keeps on streaming information to the UPS center, giving updates of how long the delivery is taking. This permits logistics companies to see patterns at play that can be utilised to optimise their delivery strategies.

Added integration and collaboration

Big Data can allow better mix and collaboration inside supply chains. Implementing cross functional integration and collaboration approaches with key accomplices can construct a culture of trust, prompting more significant levels of information sharing, and assisting with improvements across the entire supply chain ecosystem.

For instance, keeping perishables fresh has been a constant test for logistics companies. However, Big Data and the Internet of Things (IoT) could give delivery drivers and managers a vastly improved idea of how they can prevent costs because of perished goods. Let’s say a delivery truck of Igloo is transporting a shipment of ice cream and desserts. They could install a temperature sensor inside the truck to monitor the condition of the products inside, and provide this data alongside traffic and road work data to a central routing computer. This computer could then alert the driver if the initially picked route would bring about the ice cream melting, and recommend backup or alternate ways to go instead.

Improved information management

Big Data empowers upgraded discovery, accessibility, utilisation, and provisioning of data inside organisations and the supply chain. It enhances information which is an important driver of supply chain management. Here, information in the supply chain is basically the data and analysis concerning facilities, inventory, transportation, and customers.

For example, Flipkart depends on Big Data to ensure top-notch supply chain management. Flipkart improves its algorithms to precisely foresee delivery dates, increase warehouse automation, and optimise routes through advanced mobile technology

Intensified logistics

Product traceability, with the help of data, prompts a decrease of lead-time, for instance by in-transit processing of products. Real-time rescheduling, route planning, re-routing, etc. can be enabled by it.

Big Data and prescient analytics give logistics companies the additional edge they need to overcome different obstacles. Sensors on delivery trucks, weather data, street upkeep data, fleet maintenance plans, real-time fleet status pointers, and staff schedules can all be coordinated into a framework that looks at the past historical trends and offers guidance appropriately. 

United Parcel Service (UPS) is a real-world example of Big Data logistics prompting big savings. After examining their data, UPS found that trucks turning left were setting them back a great deal of cash. In other words, UPS found that transforming into oncoming traffic was causing a lot of delays, squandered fuel,  and danger. Since then, as UPS claims, it utilises 10 million gallons less fuel, emits 20,000 tonnes less carbon dioxide and delivers 350,000 additional bundles each year.

Now UPS drivers just turn left about 10 per cent of the time, opting to go straight or turn right instead. Because of this "left turns only when absolutely necessary" strategy, UPS has additionally diminished the number of trucks it uses by 1,110 and decreased the company fleet’s total distance traveled by 28.5 million miles

Bangladesh need to adapt

We're on the cusp of Big Data changing the idea of supply chain. Big Data in the supply chain can be utilised to diminish various inefficiencies like last mile delivery, provide transparency, advance and optimise deliveries, protect perishable goods, and automate the entire supply chain. 

Nowadays companies are getting more and more aware of these possibilities, and are endeavouring to make more data-driven decisions moving forward. Using sensors and IoT, combined with business intelligence software, forward thinking companies are already diminishing costs and expanding consumer loyalty and satisfaction.

But in the perspective of Bangladesh, there are still many barriers for implementing this. Poor data quality and lack of trust in data, time-consuming activity, data scalability, behavioural issues, etc. are the main barriers for  implementing Big Data analytics in the supply chain of various Bangladeshi companies.

Still  there are lots of positives and potentials. If the companies can overcome the lack of sufficient resources, security and privacy, financial support, top management support, skills, data integration and management, lastly and most importantly, techniques or procedures, then there is a huge opportunity waiting ahead to be utilised.

The writer is a Management Information Systems student at University of Dhaka.

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