Automation alone can be inflexible and expensive since robots are typically designed to carry out only one specific task. The companies are realizing that the best option is to collaborate robots and human workers thus combining the speed and strength of robots with the skill and creativity of humans.

Some companies are now deploying robots to perform simple and repetitive tasks and allowing their human workforce to focus on tasks that require deeper strategizing and thinking.

This new way of collaboration between humans and robots is termed as “cobot” which means that each type of worker is allowed to focus on the task that they can perform best.

The cobot market is expected to be worth $18 billion by 2030 as per a report from Root Analysis.

The need for transformation and challenges

The need for transformation goes beyond this pandemic. With the ever-changing market needs, the manufacturing industry is under pressure to meet the modern demands of customization, quality expectations, high product variability, and faster product cycles.

Today, customers expect delivery guarantees and when they don’t get what they want, they can find another source. Therefore, good inventory management accompanied by the ability to drive goods through the supply chain at a high speed has become a must-have.

To tackle these challenges pairing robots with human skills has become the need of the hour. However, there are certain challenges with automation. Automation is time-consuming and expensive to design. The precision robots require specific coding at each step and rely on predictable and uniform tools and parts. With a slight fluctuation in any part, the machine doesn’t function properly and the production line may come to a halt as a result, long production schedules.

Similarly, relying only on the human workforce to handle huge, bulky parts can make the work strenuous, dangerous, and slow-paced. Moreover, repetitive motions may cause stress injuries. Furthermore, uncomfortable and exhausted workers are more prone to making mistakes, reducing productivity and quality.

Supply chain adoption of the human and robot collaboration

Now, deploying cobots has become simpler and faster nowadays. Unlike conventional robotics, the industrial robots are now loaded with sensors to detect the presence of humans around them and are enabled with force and speed limits that minimize any impacts.

With new technologies like artificial intelligence, IoT and cloud, robots are now able to autonomously navigate the facility as they have the in-built intelligence to identify any barrier and make a decision about how to respond to such situations. IoT enables the robots to capture data in real-time about a task while cloud is helping to drive improvements in robotics. Cloud coupled with IoT allows organizations to monitor, operate, and manage robots from anywhere which has drastically enhanced the usage of robots in the workplace.

Additionally, the cost of ‘cobots’ is becoming increasingly affordable- from complete purchase to paying rent per month/week/day/hour options is making it easier for even small businesses to justify their expense. Research done by the Massachusetts Institute of Technology further boosts the ROI, according to this report human-robot collaborations are 85 percent more productive than either of them working alone.

Working in collaboration

The idea of only machine programing and maintenance may not work successfully as there is always a need for human skills and adaptability. With products becoming more customized and varied to the customer needs and local markets, the economics of complete automation does not fit in full. The time required and the vast engineering costs and the time to bring up a completely automated line for a complex product can never be paid back before the product needs to be changed.

The best option is to combine the strength, speed, and precision of industrial robots with the skill, dexterity, and judgment of human workers. With such collaboration, human workers can perform tasks that require flexibility while the robots can manage tasks that make the best use of their speed and strength.

The future of robots in supply chain management

The future of robots in the supply chain seems to be bright. The growing market demand will encourage robotic vendors to innovate further and bring advanced functionality to the supply chain. This will prompt more companies to invest in robotics and make their supply chains more efficient.

The collaboration between humans and robots will help supply chains to deliver remarkable customer service and cost to serve while refining business performance. Therefore, the businesses that embrace this change will quickly disrupt the competitor that sits idle and believes it is at the top.

With increasing cost pressures and a focus on customer centricity, the adoption of robotics will only increase.

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For most of the businesses, supply chains are the lifeblood. Collaboration across  various suppliers, processes, software has long led to creation of huge data off the back of even small-scale supply chains. Unfortunately, the data generated is useless in its raw form, however, when businesses leverage the power of analytics to create insights from them can help them in making informed decisions and run supply chain networks seamlessly.

Today, analytics is advancing at a breakneck pace, from developing innovations around the cloud, democratizing technologies, new AI capabilities and much more; various Analytics trends are gaining traction in the market.

With this horizon and data analytics technology on the upward path, it is critical to stay informed of what’s new and upcoming. Here we have compiled key supply chain analytics trends for 2022.

Supply Chain Analytics Trends for 2022

The COVID-19 pandemic has taught us that agility is essential to avoid significant supply chain disruption during these unprecedented times. Over the last two years, competitive advantage within supply chains has relied on the ability of all businesses to adapt quickly, embrace new technologies, and find innovative solutions.

Analytics that highlights the trepidations as they arise can help in achieving this much needed agility. However, a customized approach is required for businesses to focus on getting better insights from their software and align it with business outcomes.

AI and ML will become more intelligent

In recent years, Artificial Intelligence (AI) and Machine Learning (ML) space has been complex, with more companies entering the space than before. However, as we begin to enter a more mature space in 2022, more companies will invest in AI-driven automated insights. Low-code and no-code will democratize artificial intelligence. While data scientists will continue to focus on high-value issues, the participation will increase in advanced analytics utilizing automation, natural language processing, computer vision, and machine learning. More responsible AI will reduce the gap from design to innovation.

More businesses will move to the Cloud

While the migration to the cloud provides various opportunities and advantage to the businesses, such as scaling analytics processes, it also means they are bound to governance around data ownership, data access, and data control.

In 2022, analytics will finally cross the gap to the cloud. Adoption of Cloud technology is steadily growing as businesses seek to leverage the big data already available in the cloud repositories. These businesses are geared up to take the benefit of cloud native computing and reap the benefits of easier analytics access.

Data Fabric will become the base for the distributed enterprise

With online sales channels and digital businesses proliferate and remote working becoming a norm, a complex and diverse ecosystem of applications, devices, and data infrastructure is created. Especially, data infrastructure can extent on-premises, hybrid-cloud, multi-cloud, single-cloud, or a combination of these, extended across regional boundaries with no single solution to combine all this data together.

This year, organizations will create Data Fabric to drive organization-wide data and analytics to automate many of the data exploration, preparation, and integration. Data fabric will help in unifying the data assets spread across different formats, locations, and latency using physical, logical, or hybrid approaches. These data fabrics will enable organizations to choose their preferred approach that will lead to reduced time-to-delivery and make it a preferred Data Management approach in 2022.

Data Mesh architectures will become more appealing

As companies grow in size, central data teams have to deal with wide range of functional units and associated data consumers that makes it difficult to understand data requirements across cross-functional teams and provide right set of data products to consumers. Data Mesh offers a new decentralized data architecture approach for analytics that intends to remove the hurdles and take data decisions nearer to those who understand the data.

Beginning this year, big enterprises with distributed data environments will implement a data mesh architecture. With different domains or functional units within large organizations have a better understanding of how the data should be used, enabling the domains implement and define their own data infrastructure results in lesser iterations until business needs are met and are of high quality.

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