An example of big data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on).
Today’s companies face overwhelming reams of endless data flooding in from a broad range of channels.
In spite of this often-daunting aspect of business today, with appropriate use of this data, you can realize a powerful advantage over the competition by boosting efficiencies in your organization’s supply chain.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. … Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Big data is more useful than many people fully realize. That being said, there are a few different ways that big data can be used to help optimize supply chains for a wide range of companies. These seven solutions can help any business visualize how big data can help make their supply chains more efficient and better optimized to increase their bottom line.
The key to Big Data is real-time analytics. This complements the end-to-end visibility of the supply chain and enables your organization to act fast enough to avoid revenue and profit loss that can take place at several points in the supply chain. Below are 7 ways your organization’s supply chain can take advantage of Big Data analytics.
1. Customer Service
The key benefit of big data analytics regarding supply chain management is customer service. By accelerating the source of supplies in conjunction with customer orders, faster service is delivered with less expense. Even the best department supervisor is unable to keep up with all sources of supplies, much less which supplies presently have the best prices. Big data management affords that information in an intuitive layout.
2. Shipping and Delivery
Another benefit of big data analytics is the real-time tracking of orders and shipments. Knowing the exact location of packages, whether incoming supplies or outgoing orders, is crucial to scheduling and in turn, service. Since today’s technology lets consumers know the precise location of a package, customers expect a business to be able to provide that information when prompted.
3. Forecasting
If your business is overproducing, or producing at the wrong time (when consumer demand is not there) chances are you are losing revenue. Data on sales trends, along with technology advances and equipment upgrades can assist an organization to determine the future usage of any product well in advance of receiving the actual orders. This permits an organization to work on a proactive basis to fill those orders. By increasing or slowing down the production of particular items one can increase the speed of filling orders.
4. Optimizing vendor management
Before a product makes it to the customer, it moves along a line of suppliers that specialize in transportation, third-party logistics, packaging, etc. With so many stops along the way, there is ample opportunity for errors to occur such as delays, wrong deliveries, and other interruptions. Big data analytics solutions empower real-time management by assessing vendor performance against a set of key performance indicators (KPIs). These KPIs include vendor profitability, on-time service, and customer reviews and complaints. Policies can be produced to create alerts if the KPIs do not stay within the defined range.
5. Automating product sourcing
If your organization is turning away potential customers due to products being out of stock, data analytics can help. Big data solutions offer a real-time view of the product demand, product sales, and sourcing process. Moreover, once the big data solutions are implemented, retailers can stop marking certain products as ‘backorder’ as they always know the precise lead times for sourcing them.
6. Personalizing service
In the time of the catalog, retailers would send out one mailing and expect customers to go through it and pick out the items they desired. In today’s economy, however, consumers demand far more – and the responsibility is on organizations to deliver this elevated level of service. Personalization is key, and irrelevant product offerings can result in irritated customers. But how are retailers expected to keep everyone’s preferences in order?
With big data, retailers can evaluate customer interactions amongst all channels – social, mobile and web – to establish how the customer is utilizing the products they purchased or will purchase. For example, retailers can segment their supply chain to offer some shoppers configurable products, where they can select features like color or size.
7. Pricing Management
The knowledge of available supplies and their costs is the key to determining the price for your final product. While labor and shipping costs may be fairly consistent, the cost of supplies often fluctuates with market resources and supply chain flow. By using big data analytics you can often rely on sensible average pricing for your supplies, as well as their value on the wider market. This allows you to keep pricing at levels that are fair both to your company’s profit expectations and your customers’ needs.
The drawbacks of integrating big data in your organization’s supply chain process can seem daunting. It often requires enhancing the capabilities of current business systems as well as procuring and implementing new software tools. There are ways to avoid these concerns.
For example, it may be easier to implement the solution one department at a time rather than implementing the system enterprise-wide. The initial implementations will provide quick-wins for the organization while the learnings will benefit subsequent implementations.
Source: scm.ncsu.edu, cerasis.com, www.mordorintelligence.com, www.litcom.ca, www.forbes.com.
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