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Big Data: Applicable use cases for Indonesian manufacturing industry

Photo by Penyeruputjahe on Unsplash

Before the global covid-19 pandemic, there was a huge optimism that Indonesia’s manufacturing industry could thrive in the near future. This optimism was justified by a report that Indonesia will see an average growth rate of 6.31 percent between 2020 and 2024 under a good scenario. However, the coronavirus phenomenon is surely not a good scenario to begin with. So, what’s next?

Prior to coronavirus phenomenon

Based on 2018’s BPS report, Indonesia’s manufacturing sector employs 14.7% percent of the workforce. It’s predicted that in 2024 — under a good scenario — manufacturing sector will increase its employment share gradually to 20% of the workforce by 2024.

But, what is the good scenario tho? The report mentioned above (developed by Asian Development Bank and BAPPENAS) analyzed 3 possible scenarios based on the role that manufacturing sector can play as an engine of growth:

  1. Moderate scenario: The structural composition of the economy in terms of sectoral employment shares remains the same, and thus the relative importance of the manufacturing sector also stays the same. In this scenario, the role played by manufacturing as an engine of growth does not change.
  2. Good scenario: The economy experiences manufacturing-biased structural change, such that the relative weight of the manufacturing sector increases. In this scenario, manufacturing enhances to some extent its role as an engine of growth.
  3. Bad scenario: The structural composition of the economy changes such that the manufacturing employment share decreases. In this scenario, the role that manufacturing plays as an engine of growth diminishes.

The scenarios consider three effects to arrive at potential growth rate: (i) working-age population growth; (ii) the direct impact of structural change on potential growth rate; and (iii) the impact on labor productivity growth of the following channels.

Table of Potential Growth Rate for Good Scenario, based on ADB-BAPPENAS’ joint report.

The prediction is looking good, isn’t it? But after the global pandemic occurs, can it be defined as a bad scenario? Let’s take a look at the table below.

Table of Potential Growth Rate for Bad Scenario, based on ADB-BAPPENAS’ joint report.

Up to this article is written, there’s still no report on how coronavirus affects Indonesia’s manufacturing industry. However, from the workforce’s perspective alone, some might predict that in 2020, there will be a major decrease in employment growth.

The role of technology

The primary jargon of the fourth industrial revolution is about the growing use of technology. We may overhear the jargon up to the point that we lose the essential meaning of it.

As per PWC’s report, 29% of its respondents say their companies have implemented networking technologies that connect employees, machines, production management, transportation vehicles, and even products.

The percentage might grow to 60% in 2022 as these companies are starting to adopt the technologies that enabled machines to connect each other and establish a central instance to process information, like a Manufacturing Execution System (MES).

The Use of Connectivity Technologies and Big Data Analytics, based on PWC’s report.

One one side, some connect the jargon to the powerfully innovative Artificial Intelligence or Big Data. On the other side, these fancy technologies may harm the growth of employment as it embrace the power of automation. We also know that as the employment rises, the production cost also would increase as well.

A technological context: Big Data use cases for manufacturing industry

1) Gaining predictive and preventive maintenance system

Case study: Sitech

One of the notable Big Data’s use cases is Sitech Services, a Netherlands-based company that provides manufacturing, park, and wastewater services. They develop a software that allows asset owners to connect their assets to a platform to search for a pattern.

Sitech’s software, TrendMiner, capturing the pattern.

How does it work? The plant managers employ sensors and models for all rotary pumps. Through this system, it stores information about thermal stress due to repetitive fast cooling/heating of a reactor, then create a predictive model to determine how many thermal cycles to result a failure.

This software has allowed Sitech engineers to make great strides in up-scaling predictive maintenance at the site. Further, they are working towards a complete Asset Performance Management solution that not only includes maintenance and reliability, but also safety, process performance and energy consumption.

2) Optimizing data-driven logistic services

Study case: Fujitsu Digital Campus

Fujitsu has been known as the digital transformation leader in the manufacturing industry. One of its ambitious digital-driven projects is a smart factory called Fujitsu Digital Campus in Augsburg, Germany. The factory combined the entire chain in a single factory: from product developments to training for end customers.

Fujitsu’s Smart Factory Journey

Fujitsu centralizes its data management, using a system that automatically collects data by attaching sensor to personnel, products, and on-site equipments. All kinds of data like product production progress, materials used, etc. can be understood in real time from remote location.

This system also reduces the burden of inputting data manually. Employees are no longer hassled by daily operations reports and work progress status, which facilitates more accurate and timely factory management decision making.

In its storage facility, components are prepared to be assembled and stored in a special transport cartons for individual orders. The facility even has a self-driving electric vehicle that would make sure every production unit get the components they need in a sequential order.

3) Improving risk management

Study case: Starbucks

Your favorite coffee franchise, Starbucks, is not only a mere coffee company. In the last 2 years, Starbucks has been known to use big data to provide personalization for customers and to make corporate decision to expand their business.

Atlas’ interface. Image by Integral.

Starbucks collaborated with Integral and Esri to developed a geographic information system (GIS) called Atlas. Atlas analyzes maps and retail locations by collecting data such as population density, average incomes, and traffic patterns to identify target areas for a new store. Utilizing this analytics, Starbucks will be able to estimate the profitability of a new store without having much fusses by paying a market research agency.

And Starbucks also build a system to predict the impact of a new store to its existing stores by connecting Atlas to external and internal APIs, then uses R to build cannibalization models that can determine the impact to existing stores if a new store enters the area.

Not only reducing the risk in building new stores, on one side Starbucks also uses the data about customer’s favorite menu to give personalized promo to re-engage them in the most efficient way. On the other side, Starbucks planted IoT devices to its coffee machines to create data points. The data from the individual machines has allowed Starbucks to ensure a level of standardization and quality in all of their products.

The unanswered question

Now we are at the point of understanding the manufacturing industry landscape and learn about its technological use cases, but still, leaving one unanswered question that is essential in this economy:

Thus, how does technology help to improve the employment rate especially in the time of pandemic?

The PWC report stated that 89% of digitization will drive the hiring of new employees with the necessary qualifications. But we have to ensure that enough employees have the right skills to support digital factories. And, is hiring new — and probably expensive — employees the right answer?

Digitization will have a strong impact on hiring, training, and factor cost of employees. As per PWC’s report.

To answer the big question, we’d like to offer you another perspective: what if we empower employees to optimize technology rather than reducing them to cut off to the production cost?

As per the same report, 60% of the respondents thought that education at all levels need to adjust with digital technology’s rapid pace. For example, classical engineering studies should be re-designed in a multidisciplinary way so that the engineer also has skills in data analytics, product management, as well as IT architecture and security.

This innovative mindset can be applied to our companies as well by forging the employees with the necessary digital skills such as mentioned above. By adding these skills alone, the cost of recruiting new (and expensive) employees can be suppressed and manufacturing companies would be able to innovate independently using fancy technologies like Big Data — that we often heard in the industrial revolution 4.0 jargon.

Thus, still thriving under a bad scenario.

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