At $100 per barrel, crude oil is an extremely valuable resource, but even more prized to members of the supply chain these days is data -- or rather, big-data.
The term big-data does not describe how much information is resident in a database or IT system, but rather, it defines a category of data derived in real-time or near real-time from multiple sources in multiple forms. Unlike execution or transaction-focused platforms like ERP or CRM, big-data offers the potential to turn available information into actionable insights using pre-defined predictive analytics.
This is where the road diverges for those that successfully capture and utilize big-data, and those that are derailed by information overload. According to a 2011 report on big-data by McKinsey & Company, 15 out of 17 sectors in the US have more data stored per company than the US Library of Congress. To put that in perspective, McKinsey pegs the volume of data resident in the Library of Congress in April 2011 to be 235 terabytes or 235 trillion bytes.
Clearly, manually mining this mass of facts and figures is an impossible task, and current database tools do not have the capacity to manage and analyze the growing volume of data that is communicated and captured every day. But, there is a new breed of cloud-based tools and services designed to facilitate many to multi-enterprise communications. This allows members of an extended supply chain, including raw materials providers, sub-component suppliers, contract manufacturers, and logistics providers to efficiently aggregate and manage critical supply and demand data.
We have similar data-to-information conversion issues within the enterprise-wide supply chain. In many cases, the various players within the supply chain hold key data, that if used properly can help to inform the balance of the supply chain participants about potential risks before they occur, providing an opportunity for mitigation. This growing need has driven Avnet Velocity to introduce their "Control Tower" solution.
The Control Tower gives customers "predictive visibility," in which the systems can use pre-defined business metrics to forecast potential problems and generate alarms for upcoming events. Control Tower customers then receive push notifications alerting them to a change in conditions that might impact their supply chain, such as a shortage in supply, expansion in lead time, concerns over cost relative to currency conversion, or natural disaster affecting raw materials availability.
Access to this critical supply chain intelligence enables OEMs to proactively shift or reprioritize their supply chains based on changing supply or demand signals within multiple tiers of the supply chain -- both up and downstream. In this way, Control Tower moves beyond being just a tool for visibility, and becomes a tool for decision making.
The ability to refine data into greater business insights through sophisticated what-if analysis is swiftly becoming the next frontier for electronics supply chain innovation and productivity. Those that "know sooner" cannot only act faster, but with greater assurance that their efforts are in alignment with the operational and financial goals of their organizations.
Given the constant increase in complexity within the supply chain, with its accompanying sea of data transactions, I have no doubt that harnessing the power of data will increasingly become the defining characteristic of highly successful supply chains. Arguably, big-data is a bigger issue. However, it's a justifiable need for us to turn our supply chain data rapidly into information used to avoid disruption and provide assurance of supply.