This article will discuss:
- Use cases for digital transformation in the manufacturing industry
- Examples of five manufacturers who have executed smart factory transformations
- The benefits manufacturers say smart factory transformations bring to the table
Although the smart transformation of brownfield facilities is on the rise, it’s not moving anywhere near the pace that was first envisaged. The integration of digital tech to develop a smart factory comes with multiple benefits such as data-driven optimization, predictive maintenance, machine as a service, and many others - but implementation comes with many challenges. That said, there are a selection of companies, highlighted in the use cases below, who have been able to implement successfully and are now reaping the benefits.
1. Optimizing machine utilization in a med-tech device manufacturing facility
A maker of medical devices and equipment with four manufacturing facilities across the United States struggled with optimizing the use of its lathes and grinding machines. Thus, production cycles were beleaguered by scrap, delays, and scheduling inefficiencies. To optimize the utilization rate of its production machines, the manufacturer turned to digitally transforming its machine monitoring, data collection, and utilization processes.
In this case, Industrial IoT solutions tied to an industrial cloud platform were used to drive the transformation to a smart factory that could capture and process machine data. Modern machines were easily plugged to wireless networks to capture the data they produced; while digital I/Os were used to wire legacy equipment to the IoT platform, thus creating an interconnected smart factory with cyber-physical connections and implementing a data-driven optimization model within the shop floor.
The data collected were processed and aggregated on the industrial cloud platform and it provided insight into the facilities operations. The digital transformation enabled the manufacturer of medical devices to realize that its production schedules were out of sync and it had too few operators to properly utilize available machines, which affected their utilization rate. With the insight obtained from the smart factory transformation of its machine-monitoring process, the manufacturer was able to optimize machine use while enhancing its scheduling and plugging staffing holes.
According to the manufacturer, the smart process saved 14,000 operational hours within a year of implementation. The ability to monitor machine performance in real time led to a reduction in downtime and the scheduling team having accurate data to develop optimized master production plans.
2. Reducing downtime at an automotive diesel system factory
The effects of downtime on the manufacturing industry have been well documented. Statistics put the financial cost of an hour of downtime at up to $260,000. This particular automotive systems manufacturer struggled with unscheduled downtime caused by unexpected machine breakdowns. To reduce its downtime and develop a predictive system for maintaining its machines, the manufacturer turned to digital technology.
In this case, temperature, vibration, and monitoring sensors were embedded into the factory’s machines to collect specific data that highlighted the condition of the machines. The captured data sets were transferred to an industrial cloud platform with the data analytics tools to aggregate and process these data sets in real time. This created a smart factory in which connected machines could transfer and receive data in real time.
The ability to track machine performance metrics enabled the collection of data about equipment failure times. With the collected historical data, a predictive maintenance system to forestall downtime was developed using the accurate analytics now capable of predicting machine failure. According to the manufacturer, the smart monitoring of its equipment drastically reduced downtime and increased operational efficiency. This highlights the interconnected benefits smart factory transformations bring to the table. While reducing operational downtime, the trickle-down effect of optimized machine use improved overall operational efficiency levels.
3. Robots assist warehouse in achieving its smart factory implementation goals
Solving the different challenges manufacturers and warehouses face with material handling has become an important consideration for many organizations. This is because a streamlined material handling system reduces shop-floor accidents and traffic, simplifies material transportation, and improves operational efficiency. Today, multiple smart systems exist that automate the conventional manual processes of moving materials across the shop floor. One such example is the use of autonomous mobile robots in warehouse facilities.
To reduce clutter and shop-floor traffic the delivery service provider integrated the use of Fetch autonomous mobile robots within its factory. The robots are programmed to understand the shop-floor layout and are connected to an industrial cloud platform to help with navigation and data processing. The robot receives data such as navigation schedules over WiFi networks to perform its duties.
The autonomous robots are equipped with 2D and 3D camera sensors which help with object detection and support the collision prevention technology system within the robots. The integration of an autonomous material handling system transformed its shop floor into a smart factory where human operators assisted with sorting orders but left transportation to robots. The implementation of this smart system led to a 50% reduction in the time it took to fulfill customer orders. It also increased the efficiency in its order-picking process and the service levels its customers received.
4. The digital twin improves layout planning in the food and beverage industry
The digital twin involves the development of digital representations of physical objects and systems which share data between one another in real time. This gives it multiple use cases such as monitoring and improving facility operations, developing schedules, and simulating events to receive accurate business insight. To restructure its facilities to improve efficiency levels and productivity, this restaurant company, turned to the digital twin and augmented reality.
With digital twin technology, the enterprise was able to optimize its shop-floor layout by developing designs that ensured its employees could easily reach ingredients and navigate through the kitchen easily. Augmented reality was also used to train its employees to understand the new layout. The result was a quicker drive-through service which led to increased customer satisfaction.
5. A waste company augments its revenues with Machine as a Service (MaaS) offerings
The purchase of incinerators has always been a bottleneck for private enterprises offering industrial waste discarding services. This is due to the initial cost of industrial incinerators and the life-long maintenance cost associated with their use. An OEM that manufactures incinerators, developed a MaaS initiative for small and medium enterprises looking to rent its equipment.
The MaaS model the OEM initiated involved renting out their incinerators over five years with servicing and additional support to the end-user. In return, it collected the utilization and maintenance data from the used incinerators alongside a subscription fee. According to the manufacturer, its MaaS initiative provided the needed data for developing optimized equipment. The initiative also accounts for 20% of its yearly revenue.
Conclusion
Smart factories are here to stay. While implementation can be challenging, the use cases above illustrate the many benefits that manufacturers are already started to see across a range of industries. The rise of newer, smarter, and faster ways to automate, optimize, use, and connect shop-floor assets will continue to enable business growth as more manufacturers embrace Industrie 4.0.
