As of the inception of the fourth industrial revolution, data has become a major key player in a sheer array of fields. Big data, when it comes to the realm of supply chain management, is providing huge supplier networks with a much greater degree of clarity and data accuracy, thusly enabling other types of intelligence to be shared across the vast extension of the whole supply chain.
Avant-garde manufacturers are carrying out almost 75% of the activities commonly inherent to their supply chain network outside their facilities, using data and other cloud based technologies to get past the common limitations and constraints of traditional Enterprise Resource Planning (ERP) and Supply Chain Management software and systems. For all those manufacturers whose operational and business model, in general, are based on speed—rapid product turnover—, traditional ERPs are, in reality, a huge limitation. These systems, originally designed to allow the delivery, shipment, and transaction of information, are not capable of meeting today’s supply chain challenges for they cannot be scaled.
Today, companies that compete on speed and the quality of other forces such as accuracy, struggle to keep up the pace due to the impossibility of using traditional ERPs and supply chain management software. And even though many companies are reluctant to actively implement big data into their daily supply chain activities, the following factors, as David Kiger has found, will serve as the perfect opportunity to overcome limitations and keep moving forward:
1. The size—scope and depth—of information supply chains generate in today’s juncture is rapidly evolving, providing generous data chunks and sets to propel other types of intelligence (contextual intelligence). Today, after taking a look at how companies get their data, the fact that information is generated in supply chains while organizing the sources by volume, nature, variety, and speed, it is possible to assert that information is in fact generated outside facilities and enterprises. Thus, avant-garde supply chain specialists, are now interpreting data and information as the perfect driving force for achieving a much richer collaboration.
2. Allowing complex networks to focus rather on knowledge and collaborative activities is arguably one of the best ways to add value to every transaction. It is not a secret that big data has entirely revolutionized the way supplier networks function and venture into new markets. Thus, transactions should not be the only goal: creating a collaborative knowledge-sharing environment and network should be. Sharing information, simply put, is the best way for supply chains to progress from a simple network to a whole web. It is, unquestionably, a top priority.
3. Different metrics and analytics are now being implemented and integrated into other tools such as optimization tools, demand forecasting tools, planning tools and, obviously, supplier collaboration tools—since the latter have been growing at a staggering pace. These embody the roadmap and the general outline of supply chain collaborative teams.
4. More than 50% of today’s supply chain executives consider big data analytics and metrics a somewhat disruptive, game-changer and important technology with the capability of becoming the foundation for long-term management within their companies and organizations. In light of the quickening proliferation of high-end technologies, prioritizing the most disruptive ones over other types of technologies is vital.
5. Optimizing delivery networks is something easily achievable, to some extent, just by using geo-analytics based on big data and reliable information. Most of the today’s delivery networks are merged and optimized using geo-analytics and big data sets, thusly reducing idle times and improving speed and accuracy—which, in reality, fixes one of the today’s most daunting supply chain management challenges.
6. Big data has become key and is having a tremendous effect on how organizations react towards supply chain issues: increasing efficiency and enabling integration across different platforms. Using big data analytics can certainly improve performance and gain contextual intelligence as well.
7. Including big data analytics in routinary operations and activities leads to an improvement in delivery times and supply chain efficiency.
8. Supply chain visibility oftentimes means to the capability to see multiple layers within the same supply chain network. Greater contextual intelligence allows supply chains to track possible financial outcomes with the integration to other financial systems. Data, especially big data, is also a heavy influence of every financial objective within a supply chain.
9. Being able to track and trace operations heavily relies on the quality of information, thus, big data serves as a very pragmatic contribution. It has the potential to provide a much better traceability performance, thusly reducing hundreds of hours and idle times—which are commonly lost trying to access and integrate other databases. Recalls, likewise, rely on that same potential: depending on how well data and information can be traced, supply chains will still struggle to get back what got lost.
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