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5 Ways Big Data Can Optimize Your Supply Chain

Learn more benefits of Big Data supply chain management

By Nicole GarrisonPublished 4 years ago 5 min read
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44 zettabytes. That’s how much data is currently available in the world. This translates to 5,200 GB for every person on the planet. All of this data, of course, is unstructured, and it is a matter of collecting the right datapoints through the right channels, if businesses are to use this “ocean” for their benefit.

Big Data Disruption Crosses All Industries

Data must be gathered and then organized, relative to specific questions that companies have about their industries, their customers, etc. Already, this collection and collation is occurring in most all sectors of the economy – banking and finance, insurance, healthcare, e-Commerce, education, manufacturing, travel and leisure, and even governments at all levels. It allows organizations to make much better business decisions – decisions based upon science rather than mere hunches and opinions. Thus, a bank can predict what types of loan products consumers will find most attractive, even down to what times of the year such products will be most in demand. They then can make decisions about remodeling or developing new loan products, retiring others, and the best times to promote which loan products and to whom.

Being able to predict consumer behavior is a huge benefit for lots or companies. Using big data to identify potential fraud in the insurance industry is also a huge benefit.

One aspect of business operations in which big data can result in much greater efficiency, cost savings, and improvement of market competitiveness, is supply chain management. There is actually a lot of data out there that impacts SCM, and, if put together correctly, can achieve these operational goals.

Big Data and Supply Chain Management

Companies that are just beginning to explore the uses of big data for supply chain management, should consider these specific ways in which big data can be of benefit.

Five Ways in Which Big Data Can Transform SCM

Remember, supply chain management applies to a host of activities that must be accomplished in order for a product to ultimately reach a purchaser. This includes ordering and receiving raw materials, production, inventory/storage, and then distribution to the customer.

Obviously, the goal of any organization is to do all of this as efficiently as possible and in the most cost-effective way. Big data has a huge role to play in the following areas:

1. Transportation

Logistics technology can now gather and track data related to the flow of products through an entire supply chain. When data can provide statistics about frequency of shipments and endpoint destinations, patterns will emerge. These patterns will help companies plan for future shipping patterns and then make sure they have the capacity to move goods more efficiently. There are also implications for inventory management within this data. "Think about Amazon, for example. It now has fulfillment centers all across the U.S. When it can see how frequently specific goods flow from a fulfillment center and where those goods go, they can predict the inventory requirements for each of those centers, as well as how many vehicles will need to be available for movement of those goods", explain Diana Adjadj, a marketer at Studicus.

Big data also allows for visibility from the dispatch point to the customer. A transportation management system that uses big data can track the movement of goods through the entire transport process. And customers can track delivery as well. It can provide for alerts and notices, should there be disruptions in transport routes (e.g., closed roads, weather events), and then automatically craft alternative routes to minimize delivery delays. The other benefit of this end-to-end management is that it can prevent late penalties or drivers running out of time and being forced to pull off the road.

2. Inventory Management

This has traditionally one of the most frustrating aspects of logistics and supply chain management. Consider all of the issues that can present themselves:

● Having to notify customers that their order has been placed on “back order” because a company is currently out of the item. This upsets customers who want their items now.

● Having plenty of an item at one distribution center but not in the center that services specific customer orders. Companies then must arrange for transport of those items to another distribution center, where the demand is greater.

● Having surpluses of inventory that become a financial liability and may have to be put “on sale” in order to reduce those inventories. This impacts company ROI.

Big data can be used for predictive analysis. Using the information that provides purchasing patterns, even by geographic locations and seasons, can assist companies in acquiring/producing the right amounts of inventory and distributing that inventory to fulfillment centers in a more “scientific” way. This will reduce all of the issues stated above.

3. Customer Service

Big data can analyze the most common questions and issues that customers may have. Businesses can then develop methods for customer self-service, including chatbots, to handle these most common issues. This helps to prevent long waits on hold, and it also allows companies to reduce costs of customer service departments.

“Understanding the most common customer complaints about products can also help businesses make even small key changes to them that will make them more attractive to customers”, says Dorian Martin, a customer service representative at writing companies such as GrabMyEssay and BestEssayEducation.

4. Forecasting and Advance Planning

Businesses should constantly be asking themselves what might be happening a year or more down the road. In the area of supply chain, this can be significant. For example, how does Amazon determine where to place its next fulfillment center? How does a business determine the details of a new contract with its transport contractor? Big data can help to make these decisions based on predictive analysis of current patterns of changes in customer behavior, especially from a geographic standpoint.

5. Cost-Effective Ordering

Companies have suppliers – these are either in the form of raw materials for product production, or, in the case of retailers, for example, actual product ordering for re-sale. Traditionally, ordering decisions were based upon the “best guesses” of inventory control managers, based upon sales during the last few quarters, with adjustments for holidays in some cases. Gathering and analyzing data from one’s own sales history for the past several years and demonstrate the sales growth in specific product lines; gathering data that includes sales of competitors as well can point marketers into directions that may capture those customers as well. When actual scientific data is available, purchasing decisions are more accurate, reduce costs, and help to prevent inventory overload.

Time to Let Data Do Its Work

The technology is here. Large enterprises employ their own data scientists to answer their business questions. But small to mid-sized companies have access to data services companies that can perform the same gathering and analyses. Today, there is no reason for businesses not to incorporate the use of big data as they make key decisions that an impact their futures.The companies that use big data for their supply chain operations will find themselves “ahead of the game” – making decisions that ultimately save time and money, as well as serve their customers far better.

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