Ghani Rizky Febrian 02411840000125 Manufacturing System Q Class SHORT REPORT on How IoT Works for Supporting the Latest Version of Automatic Identification and Data Capture (AIDC) In today’s era of Industrial Revolution 4.0, the maturation of digital technology finally has been realized and ready to be implemented in the midst of highly competitive manufacturing sector. As of today, there are many state-of-the-art technologies that are being embedded into each production systems, manufacturing systems, or even in the corporate scale. One of which that a recent study from consultants of McKinsey proved to be impactful to companies is the use of Internet of Things (IoT) in optimizing the performance of its identification and data processing system. Automatic Identification and Data Capture (AIDC) is one of the most widely used identification system in terms of inspection, quality control, and performance management. Consultants Vineet Gupta and Rainer Ulrich of McKinsey, stated that IoT can be utilized to enhance data capturing and processing performance which in turn gives companies competitive advantage that can drive continuous improvement. They also stated that companies nowadays are looking to achieve the ability to improve production operations continually at pace their competitors struggle to match. According to the study, there are 4 main benefits of implementing IoT in a manufacturing system: i. Connectivity In many traditional production systems, collection of separate tools is bound together loosely by the governing rules. Implementing IoT could improve such links to be much tighter and more automated, therefore enabling the whole system to operate as a seamless, cohesive entity. Such integration will subsequently enhance performance measurement and management to be based on precise data, with sensors monitoring the entire production process from inspection and shipping. This allows the data captured from AIDC systems such as Radio-frequency Identification (RFID) tags, barcodes, etc. to be stored in a single, central, “data lake” system which gives workers access to performance statuses and support better fact-based decision making. ii. Speed By introducing real-time data collection analysis, the captured data from AIDC systems can be instantly processed and analysed, flagging the slightest deviation from measurement standards and possibly identify potential countermeasures. This allows the entire improvement cycle to be accelerated. iii. Accessibility To ensure continuous improvement, every level of the corporate system has to be able to get the tools and data they need to maintain performance quality and standards. IoT allows them to obtain it through a single application or portal that will simplify and accelerate AIDC systems. For example, if it identifies a deviation from standards on a production line, it will be able to alert the team leader/manager, show current and historical data on the production line, and offer appropriate problem-solving tools. iv. Anchoring One of the most powerful benefits of IoT in AIDC systems that Gupta and Ulrich foresee is to anchor the production system into the organization’s psyche. The anchoring effect will overcome most critical challenges today: sustaining change, to improve continually. This will be achieved in several ways, first by unifying data, interface, and tools, the adoption of standards will be enhanced, while also ensuring that the right way of solving problems or optimizing performance is a button click away. Second, collaboration of production systems will be more transparent, and decisions are made holistically. Finally, supervision will be far more visible than ever, when the whole level of the organization can see the direct link between performance and profitability through the use of computers, mobile devices, and even smartwatches. All in all, data integration is the key to success in the raging competition of manufacturing Industry 4.0 era. Companies are enhancing and improving every detail of its operational processes, before moving on and extending them to the entire enterprise, and ultimately, the entire supply chain. “For companies that succeed, the reward will be greater efficiency, rich new insights, and dramatic, continuous improvement in performance” said Gupta and Ulrich. References Rouse, M., 2010. What Is Automatic Identification And Data Capture (AIDC)? - Definition From Whatis.Com. [online] SearchERP. Available at: <https://searcherp.techtarget.com/definition/Automatic-Identification-and-Data-CaptureAIDC> [Accessed 14 March 2020]. Scdigest.com. 2018. RFID, AIDC And Iot News: The Impact Of The Internet Of Things On Manufacturing Operations. [online] Available at: <http://www.scdigest.com/ontarget/18-0612-1.php?cid=14317> [Accessed 14 March 2020]. Uzialko, A., 2017. How Industry 4.0 Is Revolutionizing Manufacturing. [online] Business News Daily. Available at: <https://www.businessnewsdaily.com/10156-industry-manufacturingiot.html> [Accessed 14 March 2020].