Optimizing big data using supply chain management software

     

Big data empowers numerous organizations to make smarter decisions, breathing new life into management software and internal applications. Supply chain management is one area that stands to benefit most extensively from the influx of information into business tools. However, benefiting from big data isn't as simple as collecting and routing data into supply chain management software and other programs and expecting results to appear. The big secret of big data is that, amid all the buzz, it can be incredibly difficult to apply with real, long-lasting success. 
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The companies that have seemed to seamlessly glide into big data are those that are designed to leverage it. Organizations with data-based services or a digital business model are primed for big data by virtue of what they are. Supply chains still rely on a traditional model that far predates the presence of big data, and thus don't have an optimized design. It's just the reality of the situation. This doesn't mean that supply chains are barred from building extensive, high-functioning systems that turn data into insights, but it does mean they might have to work harder. Embracing big data presents new challenges, which can be hard to incorporate alongside current operational issues without putting the organization in jeopardy. 

This is where supply chain management software comes into play. Businesses need a launching pad for their integration of big data into the supply chain that can be built up, tested and improved without compromising regular day-to-day operations or crippling any supply chain stakeholder's ability to carry out its duties. By leveraging supply chain management software, companies can take the incremental, highly structured approach to big data integration that the information management force demands.

A curious gap between belief and implementation
The idea that big data can be beneficial to the supply chain is itself not news - according to one study, 84 percent of supply chain and logistics executives believe that big data will have a definitive impact on their organization's performance. Two-thirds of companies have implemented or are mulling deployment of big data-driven projects. The potential business value is widely acknowledged.

Although it is interesting to note the nearly 20 percent gap between supply chain executives who believe in the disruptive power of big data and those who are actually making an effort to wield it. This may be characteristic of a belief that big data will eventually come to them, a trickle-down mentality that believes that such a massive, widespread force cannot help but eventually influence them for the better. Or, maybe they just want their peers to experiment with big data projects before taking on any themselves. Whatever the reason, there are companies that are not aligning their actions with their beliefs.

One reason for this discrepancy could be that supply chain executives want to wait until the best possible applications for big data have announced themselves. Lagging behind first wave adopters can sometimes be beneficial, but big data may simply be too game-changing to allow dawdlers the same competitive advantage early users may find. A company doesn't have to recalibrate its entire supply chain model in order to open the floodgates to big data, but a targeted application can help give it a push. Supply chain management software can help in this regard.


Predictive analytics: Making the most of supply chain management software
Supply chains thrive on predictability. Every successful operation is predicated on each supply chain stakeholder being where it's needed at the right time, doing what it was supposed to do and delivering the results expected of it. Supply chain executives already rely on a rudimentary form of predictive modeling, using what they expect to happen to inform their decision-making processes. This is reflected in supply chain management practices.

This "predictive" quality, however, is based purely on empirical evidence and traditional models. It's the "if it ain't broke, don't fix it" mentality. Known inefficiencies may be allowed to exist - while they cause the supply chain to function sub-optimally, there's no real way to measure it. Now, supply chains have the tools available to evaluate segments of their operations they could not previously assess, receiving granular, real-time information about production and inventory through an automated network of embedded sensors. These can be used for predictive analytics, which not only can increase supply chain functionality but allow management to address common problems before they occur, supply chain analyst Rahul Mistry told EBN​ contributor Susan Fourtané.

"Tighter integration and analysis of these databases using big data can be helpful to improve efficiencies of inventory management, sales and distribution process and continuous monitoring of devices," Mistry said. "Predictive maintenance of equipment is an immediate segment in this sector ripe for growth."

Supply chain management software can incorporate these sources of big data, creating a central repository of information on which future decisions can be made. If data shows that a machine in a manufacturing facility might need maintenance ahead of its schedule, supply chain operations could be adjusted to work around it for the duration it would be unavailable. This decision could then be communicated to all relevant users through the supply chain management software. It's much easier to make smarter decisions about handling irregularities ahead of schedule, rather than in the throes of an unplanned incident.

Big data builds visibility
Visibility is an important part of todays supply chains. The more each stakeholder knows exactly what all other organizations are doing and why they are doing it, the better the whole chain will function. A central supply chain management platform provides value in transparency. A data-based approach can provide fairly unimpeachable evidence backing up business decisions and informing company priorities. Interoperability and collaboration depend on this visibility, wrote Forbes contributor Lora Cecere. Through supply chain management software, leaders can effectively communicate with data from warehouses, production facilities and even in-transit vehicles. It allows companies to expand their communications portfolios and collaborate in new ways.

This use of big data effectively improves an area of business that may have been outside of the purview of traditional supply chain oversight. Making informed predictions that provide more insight than traditional models is a low-risk proposition with a high upside. It's a way businesses can shepherd big data into supply chains without putting themselves at risk during a time of transition.

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