PROBLEM #1: Workers struggle to use upgraded machines

A common problem in the age of Industry 4.0 is that workers have difficulty handling the new equipment that manufacturers need to keep up with the competition. This leads to a decrease in productivity.

Traditional approach: Implement costly retraining programs. Such programs usually require writing new training manuals, creating courses and finding employees or instructors to teach those courses. Once an operator becomes skilled at the new processes, she is usually tasked with training new recruits, decreasing the time she spends creating value on the line. Investing time and funds in retraining programs leads to productivity loss, especially if retention rates are low.  

Agile approach: Turn to technology to reskill workers without increasing costs. Industry 4.0  technologies have revolutionary applications for training. These devices bolster autonomy and improvement. For example, employees can follow instructions with images, videos and integrated feedback to self-guide their way through new processes.

A 2016 McKinsey report revealed that 62% of executives believe they will need to retrain or replace more than a quarter of their workforce in the next five years because of automation. Some manufacturers might opt for a traditional retraining program. Agile manufacturers, on the other hand, use technology to augment their workers. 

Augmentation can be usefully contrasted with automation. Rather than solely using technology to automate the tasks, agile manufacturers use technology to enhance workers’ capabilities. 

Employees can follow interactive, media-rich instructions to learn how to use new machines on their own.

Employees can follow interactive, media-rich instructions to learn how to use new machines on their own.

 

PROBLEM #2: Machine breakdown

Lean factory floors have to make and deliver what is needed, when it is needed, and in the amount it is needed. Disruptions, such as machine breakdowns, can wreck the production flow, making it impossible to meet deadlines and customer demands.

Traditional approach: Call in operators or technicians to take a look at the machine. They pull and analyze machine data to isolate the cause of the problem if it isn’t immediately apparent. It might take a couple hours for them to find the defect and repair it. If the defect is impossible to find or repair, the machine is replaced.

Agile approach: Connect and monitor all assets in a single place, and integrate them with human data.

Most machine problems are caused by improper usage. To help workers use equipment correctly, machine monitoring on its own is not sufficient. Collecting data on machine usage enables agile manufacturers to track how operators are using the machines. With such visibility into operator and machine performance, engineers can better isolate root causes.

Identifying the underlying cause of the breakdown allows operators to remedy it and to prevent it from happening again in the future. Using new and historical machine data to understand and anticipate performance problems before they happen is called Predictive Maintenance, and it can lead to huge bumps in overall process effectiveness (OPE). 

 

PROBLEM #3: More demand for customization equals more manufacturing complexity

The automotive industry is strongly impacted by mass customization. Car manufacturers need to offer multiple models, each with innumerable customization options.Demand for customization is a recurrent problem across industries. Customers want customization, but they do not want to pay more for it or wait longer for it. Traditional production lines struggle to deliver the thousands of combinations of built-to-order products.

Traditional approach: Train workers to know every step needed to make any possible end product or give them paper instructions for each specific customized product. Such training is time-consuming and demanding, for both workers and manufacturers. It makes process optimization nearly impossible, leads to more mistakes, and lowers efficiency.

Agile approach: Use apps to guide workers through each step of the production process. Using a drag-and-drop interface, process engineers can build apps without writing a single line of code. 

Apps can save a lot of time. Rather than writing paper instructions, process engineers can use an app to create interactive work instructions. The apps themselves can be made from templates and once built, they can be adapted to multiple processes. Engineers are not the only ones to benefit from apps: workers no longer have to memorize and they make fewer mistakes.

With the right technology, highly customized products can be delivered quickly, with high productivity and quality. Customer demands are met quickly, as the agile approach commands.

 

PROBLEM #4: Sharing and accessing data

When all departments store their data in different places, it can be slow and arduous to retrieve. The problem is worsened when some of the data is not stored on dedicated servers, but rather recorded on clipboards or Excel.  

Data and information silos reduce collaboration and autonomy among teams.

Data and information silos reduce collaboration and autonomy among teams.

Traditional approach: Work with IT to try to custom-integrate legacy systems. This is no small task: each department might have a separate system collecting data for their activities, data entry and data processing might be done manually, and data silos might be frequent. Connecting multiple outdated systems would require a lot of work.

Agile approach: Use a holistic platform to connect systems across the company. Thus, data can be pulled from the shop floor and shared to other systems. Similarly, data from other systems can be pushed into shop floor workflows as needed. This gives manufacturers visibility on the entirety of their operations, in real-time. With such visibility, manufacturers can observe trends and make improvements quickly and efficiently, thus increasing their agility.

Moreover, having information shared across the company increases flexibility, visibility and accountability – all essential to agile. Teams can be more autonomous since they have access to all of the information they need. Management also gains visibility into performance on the shop floor in real time.

 

PROBLEM #5: Compliance slowing down operations

The regulations that come with compliance make it hard for manufacturers to monitor and increase the efficiency of their operations. In the pharmaceutical sector especially, the strict regulations limit the potential to improve productivity and visibility.

Traditional approach: Follow the FDA’s 21 CFR Part 11 outline strict rules for digital documentation, leading many manufacturers to continue with  paper-based processes. Room-based production schedules makes communication difficult in clean facilities, leading to visual communication tools like magnets and paper production logs

Agile approach: Use a no-code database to track the status of all the rooms around the production space and automatically collect data. Thus, engineers can track bottlenecks and see which operations are slowing down production. Automatic data collection from operators and machines can also ensure product traceability and respect of compliance policies. By sharing this data with workers, manufacturers can involve them in the compliance process and increase accountability.

 

PROBLEM #6: End products have defects

When quality defects are only caught at the end of the production line, it comes at a cost. The further a defect moves downstream, the greater the cost of scrap and rework. 

Traditional approach: Test each product for potential defects before letting it out of the factory. Therefore, defects are only caught downstream. Then, a traditional approach would be to perform a root cause analysis over an extended period of time. The lack of data and the time lag between the event and the analysis would make it very hard to get to the bottom of the problem. 

Agile approach: Test for defects at every single step of the production process, rather than only once at the end. Checking quality inline means defects are caught downstream rather than at the end, so they can be corrected then and there. 

To enforce quality in-line, agile manufacturers can use computer vision to identify and localize defects that operators might miss. They can also leverage IoT connected cameras to take pictures of a product at various stages of the production process, which can be used as references in the future.

If agile manufacturers are to iterate faster and respond to customer demands quickly, quality needs to keep up. With Quality 4.0, digital technologies are used to deliver quality products faster. 

Inline quality checks prevent defects from moving downstream.

Inline quality checks prevent defects from moving downstream.

 

Enabling technologies, rapid iterations and automatic data collection enable agile manufacturers to find the root of problems, and give operators the tools they need to solve them. With the agile methodology in mind, manufacturers can face day-to-day or make-or-break problems.

 

Tulip’s flexible and intuitive manufacturing app platform is designed to help manufacturers become agile. Using Tulip, engineers can create applications that collect data from the operators, machines, and processes involved in production. With real-time visibility into their production, manufacturers can efficiently understand and solve problems. Start transforming your shop floor with a 30-day free trial!