3 Examples of Hyperautomation in Supply Chain That Drastically Boosted Performance

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Global research firm Gartner defines hyperautomation as an organization’s business-driven and people-centric approach to rapidly identify, vet, and automate as many business and IT processes as possible. According to them, hyperautomation deals with the orchestrated use of multiple technologies, tools, or platforms.

Gartner also predicts that by 2024, the drive towards hyperautomation will lead organizations to adopt at least three of the many process-agonistic software enabling hyperautomation.

What is Hyperautomation in Supply Chain?

The fear of large-scale supply chain disruptions, as seen during the pandemic, has prompted companies to turn towards technology to tide over unforeseen events.

Some adopt a solution stack that may help operational needs but not percolate to supply chain service levels. Others may integrate supply chain processes but fail to link data sets to gain insights.

Buying and installing new software in bits and pieces amounts to a band-aid approach to digital transformation.

Instead, firstly, articulate a supply chain vision; secondly, interpret it in terms of business and technical capabilities; and finally, assess the strategic business technologies that can help you improve the productivity and efficiency of business processes across the supply chain.

You will discover umpteen opportunities to bring hyperautomation to your supply chain. Automate every task a bot can handle to empower humans to deliver high-value production performance improvements. You are already on the brink of hyperautomation.

Look around, and you will find combinations of the following technologies, tools, and platforms ruling the digital transformation landscape:

• Artificial intelligence (AI): simulation of intelligence in machines
• Machine learning (ML): AI that “learns” to predict outcomes without being programmed
• Event-driven software architecture (EDA): A system design that notifies an event to trigger real-time independent actions by microservices
• Robotic process automation (RPA): Software robots that learn, mimic, and execute rules-based business processes, just as humans do, but repetitively, round-the-clock, and with precision
• Intelligent business process management (iBPM): BPM that leverages AI, ML, and RPA to provide real-time and predictive insights to improve processes
• Integration platform as a service (iPaaS): Connects on-premises and cloud-native applications, data, message queues, and EDA.
• Low-code –No-code platforms: Visual and interactive, component-based software development
• Packaged and other software tools: Off-the-shelf solutions that support decision, process, and task automation

How Hyperautomation Has Evolved in Supply Chain

In the beginning, there was just the procure-produce-provide approach to the supply chain. Traditional supply chains were concerned with procuring raw materials, manufacturing, distribution, shipping, sale, and consumption.

When the trend of digital supply chain emerged, information technology integrated with operational technology to modernize processes. Then came the connected supply chain with cloud computing, software as a service, business intelligence, and stakeholder collaboration.

As new technologies like data analytics enhanced performance management, there was scope for introducing AI and ML. Predictive supply chains with the power to forecast demand came up.

As the Internet of Things surfaced and sensors became pervasive, the supply chain turned intelligent, with large chunks getting automated and giving rise to supply chain hyperautomation.

We are already seeing digital twins, computer vision, autonomous robots, drones, and vehicles automate many tasks across the supply chain.

In coming years, supply chains will witness the use of hyperautomation to improve production performance, warehouse optimization, logistics and transport augmentation, intelligent fulfillment networks, and yard management.

3 Examples of Hyperautomation in Supply Chain from Real-time Projects

At Gramener, we have overseen automation projects while deploying predictive analytics-based performance improvement solutions across sectors. When automation is carried out intelligently, it sparks more automation along the line.

Here are 3 compelling examples of how task allocation, appointment scheduling, and capacity planning benefitted from hyperautomation in the supply chain. These examples are derived from a real-time project we did with a leading cold chain company in North America.

Optimal task allocation using ML algorithms

A cold chain logistics provider suffered from low staff productivity and high overtime costs.

Using data analytics, Gramener analyzed the historical data of all staff members and their task performance. An ML -application was developed to map the tasks based on the location of the staff in the warehouse and their performance levels.

The algorithm was able to monitor and assess the time taken by each person to complete a task. Based on this learning, the solution could autonomously allocate work to the staff who completed the task well in the least possible time.

The solution can reallocate tasks dynamically throughout the day based on updated truck arrival times. Thus, the picking task, central to smooth and timely warehouse operations, was completely automated, reducing the burden on the supervisor who was earlier in charge of manual task allocation.

Intelligent Appointment Scheduling using Predictive Analytics

United States Cold Storage (USCS) wanted to solve the problem of the long turn times of trucks and the resultant detention charges.

Gramener first analyzed the turn time of outbound trucks. A solution was developed based on machine learning to gather further insights on warehouse load, estimated picking effort, and complexity of orders.

The algorithm learned to analyze the impact of a scheduled appointment on other appointments of the day. Based on these learnings, the solution was refined to predict the turn time of the outbound trucks intelligently.

Now, the solution has been integrated with the warehouse management system. It automatically generates optimal schedules based on different parameters, including expected delays from the carrier end. The warehouse staff can carry out their work without worrying about prolonged truck dwell time.

Warehouse Capacity Planning using analytics

A North American logistics provider needed accurate demand estimates to match its warehouse capacity.

Gramener stepped in to analyze the gap between weekly forecasts and actual levels of both demand and capacity. Using various staffing levers, an analytics solution was developed based on time series forecasting and capacity simulation to optimize forecasts.

The insights provided helped to pinpoint risks much before a major gap developed. The operations tool was finetuned with other critical parameters influencing the warehouse capacity, such as staff availability and efficiency, working hours, time spent on picking, and the bulk ratio of orders.

The supervisor no longer needs to guess the likely demand and plan capacity based on experience. An automated alert is generated to highlight the gap between simulated capacity and projected demand, and remedial measures can be taken.

A Low-Code Platform that Powers Automated Intelligence

In all three cases above, Gramener used its in-house low-code platform Gramex to come up with intuitive data analytics solutions.

These solutions automate little bits of processes, tasks, and decision-making to accelerate production performance improvements.

Not only does a low-code platform helps to accelerate the digital automation of an enterprise, but it also brings together business processes through hyperautomation technologies.

Businesses prefer low code as it supports application innovation and easy integration. In fact, the economic effects of COVID-19 have highlighted the value proposition of low-code, which can deliver remote work functions such as digital forms and automated workflows.

The supply chain is no longer a backend operation for businesses. It has turned smart, with information streaming in from machines.

Stakeholders such as suppliers, manufacturers, warehouses, and retailers are connecting and interacting. Advanced analytics and artificial intelligence are taking over decision-making. Treading the path of automation and intelligently “augmenting” humans, the AI-powered supply chain is on its way to becoming cognitive.