They say, “Take no half measures.” Half measures are actions that only achieve part of what they are intended to achieve and are the reason many home improvement projects, famous portraits of presidents, and great novels remain unfinished. And let’s be honest — how does your spouse feel about projects around the house gone by the wayside?
Half measures occur in the supply chain too. They lead to a patchwork of solutions that are not integrated, optimized, or fully capable of delivering the promise of smart manufacturing. This patchwork is made up of legacy business processes, sensors, and philosophies that often stand in stark opposition to one another, or at the very least lead to extensive workarounds that don’t quite feel like real solutions — doubly so when a disaster strikes.
Consider that 83 percent of enterprises are more aware of supply chain risks — such as raw material shortages, manufacturing shutdowns, or transport blockage — than they were a year ago. Nearly 80 percent have accelerated their digital supply chain strategies, while almost 50 percent are considering overhauling risk procurement and risk management strategies within the next two years.
And Nearly 42% Of Enterprises Are Using automation To Manage Risk and That Number Could Double Over The Next 2 Years
The fact is, greater automation leads to greater efficiency, fewer errors, and reduced risk. So what could possibly be better than greater automation? Try hyperautomation.
Hyperautomation uses advanced automation — such as robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) — to take far more tasks out of human hands. It’s next-level end-to-end process automation designed to mitigate challenges in the supply chain. Hyperautomation takes advantage of the highly repetitive, routine-based, predictable tasks often found in supply chain processes to create an environment where they occur around the clock, at greater velocity, and with fewer errors. Indeed, by the mid-2030s, up to 30 percent of all jobs could be automated.
Across the supply chain, the hyperautomation of high-volume business processes can wean us off from a just-in-time model in favor of something far closer to real-time adaptation to shifts in consumer demand and external market factors. Just imagine how much better our response could have been to the Covid-19 pandemic had hyperautomation been more widely adapted.
The Upside Of Hyper-Automation
Currently, businesses lose about 6,500 hours per year processing paperwork (which takes up about 55 hours per week), adjusting purchase orders (39 hours per week), and responding to supplier inquiries (23 hours per week). Retail behemoths Amazon and Alibaba use automation in the form of machines and robots to box customer orders at their warehouses for a tidy 30 percent gain stock times.
AI and ML benefit supply chain planning by processing multiple petabytes of data. This data hyperautomation delivers deep analysis on thousands of SKUs and unmatched forecasting of inventory, supply, and demand. The net gain is more balanced supply and demand as well as faster product delivery with minimal human interaction.
Chatbots are capable of taking on as much as 80 percent of all customer engagements, further decreasing the amount of human intervention necessary for customer service. Meanwhile, in the face of a mounting shortfall of truck drivers across the United States — to the tune of 80,000 fewer available drivers this year than last — the prospect of driverless trucks unrestrained by rigid driving hours regulations offer a tremendous opportunity to double the output of U.S. transportation at 25 percent of the cost.
Hyper-Automation In Manufacturing
In terms of manufacturing, hyperautomation can take the form of advanced material-centric operations. Utilizing AI, ML, and IoT, a platform such as that offered by ThinkIQ can identify previously unseen correlations and root causes in the supply chain through internal manufacturing processes. We utilize a next-gen historian, semantic modelling, TIQQL (ThinkIQ’s query language), and our material ledger to deliver a transformative view of manufacturing data that can put organizations well on their way to hyperautomation. This path provides optics into material flow and dwell times, expressions and valuation scripts, material flow diagram (MFD), material process analyzer (MPA) frequency and scatter plots, and more.
To subvert any half measures in your supply chain and learn more about the incredible potential of hyperautomation, get in touch with a ThinkIQ expert today, or download our ebook “Advanced Material Traceability Revolutionizes Digital Transformation.”
