How Process Mining, AI And Machine Learning Can Transform Manufacturing Operations
Artificial Intelligence (AI) and machine learning have gained significant momentum over the past few years, and with businesses from all industries continuing to embrace the technology at rapid rates, adoption stands to increase even further. In fact, Gartner predicts that by 2020, 85 percent of all customer interactions will be managed without a human. Furthermore, Narrative Science found that 80 percent of executives believe AI improves worker performance and creates jobs.
For manufacturers, AI and machine learning are particularly important technologies, because they have the power to disrupt the very way products are made, moved and sold. Rather than guessing about which materials are most appropriate for various products, mistakenly selecting a subpar logistics provider or spending months researching which markets are most applicable for certain items, AI and machine learning-powered solutions have the ability to continually and accurately recognize trends and make data-driven decisions, all without any human intervention.
By incorporating the automation and efficiencies of AI and machine learning into their operations, manufacturers have an opportunity to disrupt their entire supply chain. They can produce products at lower costs and avoid processing issues such as delayed deliveries. Additionally, they can reduce their inventory levels by having access to more intelligent planning systems that dynamically update according to warehouse status and/or current market needs.
Data analytics has allowed some manufacturers to make more informed decisions through utilizing massive amounts of data collected to uncover hidden patterns, correlations and customer preferences. However, it has limitations. While analytics software can shed some insight on business operations, it has always required a hypothesis on where to shine the light in order to see where things work well or work poorly. Users need to pose the questions first to the analytics software. Without those questions, analytics cannot uncover anything. Also, global supply chains and manufacturing operations tend to get so complex that it is hard to understand what is really going on in detail.
And that is where process mining, fueled by AI and machine learning, comes in. A new type of Big Data Analytics, process mining leverages the digital footprint organizations leave behind in their IT systems to automatically reengineer any organization’s supply chain processes. This provides manufacturers with complete transparency into how processes are working in real life, enabling them to pinpoint business process inefficiencies. This way you see the “as-is” state, including all the analytics. Process mining can explain why processes are broken and how to fix them by giving corporate data a full body scan and unbiased analytics to solve problems manufacturers did not even know they had.
Given the various departments, individuals and moving parts that make up a manufacturing organization, business process oversights can occur daily, and sometimes those oversights remain undetected for months on end. These cross-departmental inefficiencies can seriously harm manufacturing operations by slowing organizational throughput, impacting working capital, extending customer response time and creating team performance bottlenecks.
