Continuous improvement is the foundation of operational excellence. Businesses that have the processes, structures, and tools in place to best use their existing resources show operational excellence. However, it is essential to remember that operational excellence is a moving target. Meeting today's goals is great, but all companies should strive to improve on targets. That is the underlying principle of operational excellence. Artificial intelligence and machine learning offer new ways to transform business operations.
The Benefits of Operational Excellence
With continuous improvement comes a cumulative advantage over the competition. That advantage lasts provided you improve faster than the rest of the industry. When every company is showing regular, incremental gains, you need a sudden surge to pull ahead — and that's where technology can help. AI and machine learning offer the ability to automate tasks and reduce the time invested in operational efficiencies. In much the same way the assembly line changed auto manufacturing in 1913, AI and machine learning stand poised to do the same for virtually every industry a century later.
AI and Machine Learning In Use
Along with every new technology comes the hype cycle, but AI and machine learning moved past the early phases and into adoption in many industries. Here are a few examples of how companies are already deploying these smart solutions.
Mashreq Bank introduced a digital workforce platform. As one of the largest financial institutions in the United Arab Emirates, it faces strict regulations. Their new digital workforce offers the ability to automate customer service. It improves efficiency by reducing the amount of employee time spent answering routine questions. The software interfaces with other technology platforms and answers questions without human intervention. The Mashreq Neo branch is digital. It expands the company's delivery-on-demand services without increasing their related costs. It is in areas of automation that AI and machine learning shine.
The German retailer Otto found another way to leverage AI and machine learning in the retail space. Amazon, the world leader in e-commerce, has focused on automating customer interactions. In contrast, Otto has focused on predictive analytics and reducing return rates. Using a program developed at CERN, Otto analyzed its customer behavior. Ultimately, the company found that customers dislike receiving multiple packages. Extended shipping times drive up return rates. If a package takes longer than two days, customers are more likely to make a return. To offset the issue, Otto implemented a software program that tracks consumer behavior. It uses that data to make predictions about buying habits. The software then orders items for in-stock purchases that are likely to sell within 30 days. The software has a success rate of 90%. By automating this purchasing, Otto is available to deliver on customer orders more rapidly. It also reduces the decision-making time on what to order and when.
Oil and Gas Industry
The entire oil and gas industry faced a significant challenge with the sudden drop in prices starting in 2014. For the first time in more than a century, this industry met negative spending for two years in a row. That's a lot of capital to eat with little to show for it. To make up ground and turn profits around, both down and upstream operators faced the challenge of operating in a digital world. A wealth of data was the deciding factor in the industry's turn around.
The oil and gas industry has led the charge on operational efficiencies for decades. Faced with this new challenge, it immediately started leveraging intelligent technologies. By pulling actionable insights out of the terabytes of data available, the industry found an area of focus. Profitability management was the key to future success. Improving performance and compliance at the wellhead level and all along the supply chain was a significant component. McKinsey estimates that the effective use of intelligent technologies could reduce capital expenditures by 20%. Much of that savings comes from automating maintenance and disaster response when unplanned downtime does occur.
The Uses Behind the Technology
Just these three companies have isolated three areas where AI and machine learning can add to operational excellence:
- Robotic process management for round the clock customer service
- Automated ordering based on historical buying trends reducing decision-making time to near zero
- Reduced capital expenditure through improved supply chain management
The technology to enhance operational excellence is already here. The difficulty lies in recognizing internal areas that can benefit and applying the right solution. Chatbots are a well-established option for customer service that is growing in popularity. Customers overwhelmingly prefer chatbots, so this an area where businesses can quickly adopt intelligent technology to improve efficiency.
Supply chain management is getting more granular with every passing day. Some sectors (agricultural and food are big ones) already face the need to be more transparent with their supply chain. Supply chains are another area where AI and machine learning can be particularly useful. AI can rapidly analyze massive amounts of data to identify potential problems. Businesses already have the data. The next step is in using the data to make operational decisions. Smart technology can be most helpful by bringing decision-making into real time.