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].