Big data utilization is the wave of the future for all aspects of business including supply chain management. There is simply too much information available in today’s world to ignore all but what can be comprehended by a single person? The problem is that not every company has the resources to begin the process of big data management and have yet to see the extent of benefits it can provide to analyze and use such data management. However, as the importance of big data analytics is realized companies are beginning to realize that it is not only worth the investment but vital toward remaining competitive.
There are several direct and indirect benefits to using big data analytics for your supply chain. As you review the specifics, you’ll see that often an easily recognized benefit leads to lesser benefits that help the company in the long run.
The main benefit of big data analytics regarding supply chain management is customer service. By expediting the source of supplies in tandem with customer orders, faster service is provided with less expense. Even the best manager can’t keep up with all sources of supplies, much less which supplies currently have the best prices along with their locations and shipping speeds while staying abreast of standard needs and special orders. Big data management provides that information in an easy to use format.
Another benefit is real time tracking of orders and shipments. Knowing the location of a package, whether incoming supplies or outgoing orders, is crucial to scheduling and in turn, service. Since today’s technology allows you to know the exact location of a package, customers expect you to be able to provide that information when asked. Sometimes just as important is the peace of mind that comes with the knowledge of knowing when to expect a shipment.
Price is often the first consideration toward purchasing supplies but is rarely the only consideration. As mentioned already, the location and shipping speed are important, especially when working with rush orders or custom products. The quality of supplies is something to consider as well. With just a little input, big data analytics allow computer programming to make the final determination as to what supplies are available that fit your immediate needs.
The knowledge of available supplies and their costs is the key to determining price for your final product. The labor and shipping costs are consistent as you know the capabilities of your employees, but the cost of supplies often fluctuate 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.
Although not every business offers customized product, most successful ones offer it at least on some level. The problem is that customized product requires customized supplies. Your supply manager likely doesn’t have connections developed for supplies that aren’t typical to the services you provide. Computerized data management can be useful for providing leads to those resources and simplify the ability to find supplies outside of what you typically use.
Big data analytics allows for evaluation of trends than people can conduct on their own. When programming can incorporate data on sales trends along with technology advances and equipment upgrades, the future usage of your product can be estimated in advance of receiving the actual orders. Then you can preemptively work on a proactive basis to fill those orders. By upping or slowing down production of particular items you can increase the speed of filling orders. Especially if you can even predict regional market fluctuations and store product in locations in anticipation of expected ordering.
The downsides of incorporating a big data supply chain into your company’s process may sound intimidating. It often requires purchase of new software along with training in that programming and implementation. There are ways to mitigate these problems.
Most notably, to introduce the system one department at a time rather than implementing the system companywide can result in other benefits outside of the actual new system. It can promote inter-department communications as they rely on each other for help and advice in the new supply system. Departments that begin using it first will be called on to help train the departments that receive it later, while the later users may be called on to help run things the old way while the new is being built up. Such cross-training is nearly always a good business solution and practice.
How are you incorporating big data analytics to supply chain management? Let us know in the comments below!
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