Smart manufacturing can optimize performance across a network, adapt to and learn from new conditions in real time, and autonomously run production processes. This feature originally appeared in the ebook Automation 2022: IIoT and Industry 4.0 (Volume 3).
Manufacturers are on a digital transformation journey toward the smart factory. The smart factory represents a leap forward from more traditional automation to a fully connected and flexible system—one that can use a constant stream of data from connected operations and production systems to learn and adapt to new demands. To fully realize the digital supply network, manufacturers likely need to unlock several capabilities: horizontal integration through myriad operational systems that power the organization; vertical integration through connected manufacturing systems; and end-to- end, holistic integration through the entire value chain. SICK’s consulting and digital solutions team collaborates with customers in their digital transformation journey. Solutions include smart sensor applications that bridge the gap between the shop floor and the data floor. There are numerous benefits to implementing digital transformation solutions. Figure 1: Intelligent camera technologies capture fill level of a supply box whether it is stored on the shelf or has been moved to the production line.
Addressing labor gaps: Since labor is an important cost driver in most industries, improving labor productivity can drive significant value. This value can be captured via levers that reduce waiting time (e.g., completion of previous process step in manufacturing, delayed delivery of a good in manufacturing, or a prototype in R&D) or increase the speed of workers’ operations by reducing the strain or complexity of their tasks. Human-robot collaboration allows humans and machines to work near each other without risking worker injury. Inventory challenges: Too much inventory ties up capital, leading to high capital costs. Reducing excessive supply in stock can lower these. Digital transformation levers target the various drivers of excess inventory, such as inaccurate stock numbers that increase sludge, unreliable demand planning necessitating safety stock, or overproduction. Intelligent camera technologies (Figure 1) capture the actual fill level of a supply box whether it is stored on the shelf or has been moved to the production line. Improved quality: Improving quality is a value driver since scrap and products requiring rework often lead to extra costs (for machine time, material and labor). These quality inefficiencies are caused by unstable processes in manufacturing, deficient packaging in the supply chain or distribution, and unskilled installation. Statistical process control (SPC), advanced process control (APC), and digital performance management can create value. Supply/demand match: Only a perfect understanding of the customer demand—regarding both the quantity and product features customers are willing to pay for—maximizes the value captured from the market. Optimizing the match of supply to the actual demand with digital transformation solutions can seize value potential. Improved resources and processes: A process can be improved in terms of material consumption, speed, or yield-driven value: in the case of material consumption, via decreased material costs; in the cases of speed or yield-driven value, via increased revenues through more output. Reducing time to market: Reaching the market with a new product earlier creates additional value through increased revenues and potential early mover advantages. Every digital transformation solution that speeds up the development process, such as concurrent engineering or rapid experimentation/prototyping (e.g., through 3-D printing), will help drive this value. Figure 2: Remote monitoring and predictive maintenance play an important role in capturing value. Decrease service costs: Since the costs of operation are driven by service costs (e.g., maintenance or repair) and machine downtimes (e.g., due to unexpected incidents), offering customer solutions that decrease these can open further value potential. Better asset utilization: In asset- heavy manufacturing businesses, such as those in the automotive industry, asset utilization is a big value driver. Remote monitoring and predictive maintenance (Figure 2) play an important role in capturing value. Both are levers to improve asset utilization by decreasing unscheduled downtime.
When users are ready to tackle a digital transformation project in their facilities, what’s the best way to start? These five steps can help users reliably increase productivity by unlocking data via digital transformation. Step 1: Infrastructure and operational assessment First, start with an assessment of operations and existing infrastructure. This helps determine the steps to move forward with creating a solution and concept that best meet the needs of the company. This starts with discussing the business strategy:
Understanding these strategic objectives is vital to ensure that subsequent discussions of how to achieve these goals stay focused on remaining competitive. Step 2: Solution concept Once an assessment is completed, the next step is to create a solution concept. To achieve these previously identified business goals, it is a must to identify digitalization projects that align with the business objectives. Examples include reducing risk and addressing compliance requirements, which align with operational projects that address track- and-trace solutions. To do this, secure automation system connectivity and strategic data movement are critical. Step 3: Solution design The enablers to make a digital transformation solution design feasible include connectivity technology, affordable Internet of Things (IoT) hardware, and standardized communication protocols. Collecting meaningful data and analyzing for implications are still the biggest challenges to driving the impact from digital transformation. There are different directions in which a digital solution can be implemented:
Step 4: Installation and commissioning Next is design implementation. “The team at SICK is agile and agnostic to consult with customers on their challenges and potential needs. This helps determine the ideal infrastructure to develop the most suitable enterprise solutions that can adapt to the disruptive industry needs,” said Salim Dabbous, Director of Sensor and Safety Integration at SICK. An example of an Industry 4.0 enterprise offering is the implementation of a data concentrator methodology into a pre-existing controls platform to connect current machines and push non-process- related data seamlessly upstream to the cloud or an enterprise resource planning (ERP) system. The reliable data pushed upstream might include machine status, part count, or temperature and pressure data. This feeds into dashboards and key performance indicators (KPIs), providing transparency and predictive maintenance measures that optimize processes and increase throughputs. Step 5: Verification and validation Lastly, SICK is a partner from beginning to end on digital transformation projects. Timely and comprehensive services can be provided to ensure that everything remains running in top condition. As a global provider of digital solutions, SICK takes a holistic approach to working with customers to find creative solutions to their problems. This is supported by a multidisciplinary team that spans the expertise required for a digital transformation, including key enabling technologies. Methodology includes a repeatable, scalable engineering process that focuses on data gathering and reporting to customers in multiple steps to achieve the granularity required to unlock greater production efficiencies (Figure 3). Figure 3: Methodology includes an engineering process that focuses on data gathering and reporting to customers to achieve the granularity required to unlock greater production efficiencies.
A supplier increased throughput by more than 300% with an automated picking solution and audit system to gain better access to process and sensor data. An estimated 5 percent of total shipping costs is lost due to shipping and picking errors every year. For businesses with tight margins, this can have a huge impact on the bottom line. One of the most common errors is sending the incorrect items or number of items. These errors most often occur during the picking stage. It could be as simple as two items looking very similar, so the wrong item is picked. Another common error is delivering items to the incorrect address, which is often the result of simply misreading the documentation. Many shipping and picking errors can be resolved by automating picking processes and implementing automated audit systems to ensure that everything on an order is correct before it is sent out (Figure 4). A global manufacturing supply company was looking for a way to automate these processes to ensure that correct quantities of its products are shipped to the correct retail store. Figure 4: Shipping and picking errors can be resolved by automating picking processes and implementing automated audit systems to ensure that an order is correct before it is sent out. The challenge This supplier was having issues with stock being delivered to incorrect retail stores or delivering incorrect quantities. Prior to involving SICK, this process was manual, with no automation, which led to many errors and added costs to resolve the issues. The retailer that was receiving incorrect orders recommended that the supplier contact SICK for a solution, as the retailer had successfully been using SICK’s scanning tunnels in its own operations. The solution A track-and-trace end-to-end solution called the Pallet Audit System was installed to improve these processes. Using SICK’s ICR camera tunnel system, packages can be validated against the manifest to ensure that the correct quantity is shipped and sent to the correct location. The ICR tunnel produces high-resolution image quality for highly accurate read rates for identification applications on sorting processes. It can help increase throughput of more than 18,000 objects per hour at conveyor speeds of up to 4 meters per second. The image quality from the integrated cameras also enables it to be used in optical character recognition (OCR), video coding, and vision applications. The process starts (Figure 5) when the manifest data is received on the local server, the operator scans an LPN using a handheld scanner, and the API populates the pallet results on its display using SICK’s SIM2000, a sensor integration machine that uses IO-Link technology to enable sensor integration and data transparency. It is a low-cost combination of edge gateway functions and sensor data processing that provides greater access to data from sensors to improve processes. The operator then loads packages onto the conveyor through the camera tunnel system to validate that the correct SKUs and number of packages are goingonto the pallet when compared to the manifest in the system. Once it is verified as accurate, the pallet is complete and ready to ship to the store. Figure 5: Manifest data is received on the local server, the operator scans an LPN using a handheld scanner, and the API populates the pallet results on its display using a sensor integration machine that uses IO-Link technology to enable sensor integration and data transparency. This feature originally appeared in the ebook Automation 2022: IIoT and Industry 4.0 (Volume 3).
Divya Prakash, director of business consulting and Industry4.0 at SICK. With more than 30 years of experience in the industrial automation industry, Prakash has expertise in engineering and business consulting with an emphasis on Industry 4.0 solutions. He has extensive knowledgein digital transformation, supply chain management solutions, and manufacturing operations management solutions.
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