Getting started with 4.0 Technologies

However, companies that invest in technology upgrades like automation and robotics are increasing efficiency, boosting collaboration between departments, and enabling predictive and prescriptive analytics. These manufacturing sector advancements allow operators, managers, and executives to fully use real-time data and intelligence to make better decisions while managing day-to-day responsibilities.

Laying the foundation

There are many different flavors of technologies that fall under the 4.0 banner that can solve modern manufacturing challenges. Before getting started, leaders should therefore compare their activities against other industry leaders, both within and beyond their industry sector, to get a sense of what technologies others are considering for similar challenges and opportunities. They should also assess their current data landscape to understand gaps, accuracy, quality, and existing controls. Data management is at the heart of modern industrial transformation and is often the overlooked requirement to achieve success with Industry 4.0 initiatives.

Once those points are considered, manufacturers can then begin researching the technologies that possess the most potential in helping them achieve their objectives.

Common starting points

Among the multiple digital tools now available, leaders may wish to consider the following four key technologies as useful starting points.

Sensor technology

By embedding sensor technologies in strategic locations, organizations can monitor, collect, and report on information being generated by assets across their surrounding environment and respond swiftly and effectively to remote insights and updates. This allows leaders to make business changes in real time based on both efficiency gains and mitigating circumstances. During the height of the pandemic for example, Unilever used this technology to predict the spread of COVID-19 and better prepare facilities in impacted locations.

Artificial intelligence (AI)

AI enables businesses to make smarter decisions faster by considering more data than any one human can process. Organizations can take in unlimited data points from different sources and determine the best answer to their issues. This can reduce labor costs, increase margins, and maximize revenue. AI can also effectively create a new capability within business decision making that scales expertise by using deep learning AI to apply the experience of one expert decision maker to a vast data set. In the industrial space, AI is frequently used in demand forecasting, pricing, inventory consumption, visual quality control, chemical formulation, and process manufacturing control.

Robotic Process Automation (RPA)

RPA uses bots that operate in the background to automate different manual steps in a repetitive process, including many clerical tasks. In the manufacturing space RPA is used to run payroll, perform large data transfers, enter data from purchase orders, receive and audit invoices, process ecommerce orders, respond to simple customer queries, manage compliance reporting and more.

Automatic Guided Vehicles (AGV)

AGVs are autonomous vehicles used to transport materials and products across warehouses and other large spaces. Companies like Einride are using AGVs to move products without people directing activities, freeing up time for workers and increasing capacity for material handling tasks.

The data factor

Responding to the need for supply chain synchronization and integration, these kinds of new technologies are increasing the velocity of the physical flow of goods and services, while increasing the speed at which companies can make critical decisions on how to allocate resources, so enabling them to move at the pace of their customers and suppliers.

However, up to 73 percent of data generated isn’t used by businesses. It is important for leaders to understand form the outset the value of data in supporting business decisions in the digital world. Making sure that data they have available is insightful and easy to access and understand, is critical to delivering value from their digital investments.

Planning for the future

Human workers will continue to have a place alongside automation, of course. Skilled trade workers such as mechanics and operators, whose roles require dexterity, mobility, and problem-solving skills in highly unpredictable settings, are still in high demand. This type of work is beyond the current capability of any existing robot and these jobs will remain for the foreseeable future.

But by putting the right 4.0 technologies in place, companies can more easily identify key opportunities for improvements across their operations. By using 4.0 technologies to help them recognize key problems, businesses can see where the bottlenecks are and seek to implement appropriate solutions. Whether there are inefficiencies in the packaging line, someone within the line itself, a specific area of the manufacturing process, or something else, these can all be addressed and continually monitored to better cope with any future issues.

Industry 4.0 makes the process of identifying issues and solutions easier than ever. With the appropriate tools in place, manufacturers can then seek to maximize productivity while prioritizing worker safety and time for the future.

This article was first published in the Manufacturing Leadership Council’s ML Journal