By giving manufacturers unprecedented insight into not only process patterns, but also vital throughput factors such as dependency, complexity, and interlinkage, big data is changing manufacturing in big ways.
In a dramatic shift from years past, many manufacturers are putting themselves on the cutting edge by completing more than 80% of supplier network activity via cloud-based technologies, leaving spreadsheets and other physical, software-based methods behind. Why? Because traditional ERP and SCM systems are currently not capable of scaling to the demands of today’s continuously evolving supply chain networks.
Understanding how big data enhances the supply chain management process is key to not only increasing speed and accuracy in any supply chain operation, but also to optimizing the most vital component of any manufacturing business: throughput.
From supplier audit to final inspection, big data pulls back the curtain on supply chain analytics, providing essential insight into supplier process and performance.
Early-warning systems, such as IBM’s Quality Early Warning System, detect and define prioritization frameworks, helping companies discover quality problems faster than ever before.
These systems allow organizations to seek out additional vendors if need be, and handle potentially harmful situations on a foundational level to mitigate continued risk and ultimately, customer dissatisfaction.
From demand forecasting and optimization, to supplier collaboration and integrated business planning, big data is reshaping how companies analyze risk.
According to a Deloitte study, these supply chain capabilities are the primary focus of big data moving forward. Data sets provided by big data initiative — and their analyzation processes — allow key parties to refocus expenditures at the exact moment of need, while also sharing critical information across an organization much earlier than traditional systems.
As data sets grow in depth and scope, their contextual intelligence multiplies. As many large scale capital projects involve dozens, if not hundreds, of people, huge amounts of data is often changing hands. Corral that data under one roof to maximize analyzation – and optimize corporate sharing.
Through enhanced geoanalytics, two or more delivery networks can be optimized and merged drastically reducing lead times.
Because the analyzation of big data increases service accuracy, delivery networks become much more efficient. For organizations facing such a challenge, big data makes life simpler. With an optimized delivery network, fewer vehicles are necessary for deliveries and individuals on the ground can make more efficient use of their time – exponentially inflating capital savings over time.
In a recent report, SCM World said that 64% of supply chain executives view big data analytics as the most important disruptive technology for long-term management change. With big data analytics rated the most meaningful technology to supply chain processes over other emerging technologies such as digital supply chain (45%), and the Internet of Things (45%), that’s no surprise.
Integrating big data into supply chain networks amplifies data accuracy and transparency, enabling companies to address problems as they happen, not after the fact. It enables proactivity. Not reactivity.
Moving forward, the question isn’t if big data is a value-add technology for supply chain networks. The question is: Why don’t more manufacturers use it?