Why we need manufacturing IoT in Indonesia
When I visited customers in industrial parks in Indonesia as part of my system sales activities, one of the lines I often heard from people in charge was, "Our president (the chairman) invests positively in machinery, but is reluctant to invest in systems.
This is a story we often hear in small and medium-sized owner companies that have survived in Indonesia by adopting a business strategy of only increasing production capacity in the days when they could get orders from certain customer clusters, such as only exporting to Japan or only Japanese companies in Indonesia.
However, it is not always possible to guarantee a stable supply of large-lot orders from large customers, as is the case today, and in an era where it is impossible to maintain business performance without accumulating small-lot orders from multiple customers, it is necessary to make efforts to increase competitiveness by reducing costs through productivity improvement, which is a top priority for the manufacturing industry.
This can be said of the competitiveness of Indonesia's domestic industry in general in exporting to foreign countries, and the biggest reason is the lack of export competitiveness, which is why production remains stagnant at just under one million units, even though Indonesia's domestic automobile production capacity is more than three million units.
In order to increase the country's wealth, it is essential to increase export competitiveness, and to this end, the Indonesian government has proposed Making Indonesia 4.0, the Indonesian version of Industry 4.0, with the aim of increasing productivity through information technology, but no specific guidelines have been provided.
The purpose of introducing IoT in the manufacturing industry is to collect information on capital goods, such as machines and people, for the efficient flow of production goods such as materials, work in process, and products.
Content of the Manufacturing IoT
In the case of IoT in the manufacturing industry where "goods and the Internet are connected", goods are "industry goods" and "capital goods", and the breakdown of production goods is materials, work-in-progress, products, and the breakdown of capital goods is machines and people.
The information collected on production goods is "results" such as the number of inputs and production, the number of yields, and the number of NGs. The information collected on capital goods is "conditions" such as direct time (operating time and working time) and indirect time (down time), temperature, and number of rotations.
- Results: Actual information on production goods ⇒ Number of inputs, number of production, number of yields, number of NG
- Outcome: Quality control information on production goods ⇒ NG reason/NG image information
- Status: Machine maintenance information ⇒ Direct time (operating time), indirect time (stop time), temperature, number of revolutions, number of strokes
- Status: human conservation information ⇒ direct time (work time), indirect time (rest time)
These are the information collected from production and capital goods in the manufacturing IoT, and while 1 and 2 were managed by traditional ERP systems, 3 and 4 are managed by manufacturing execution systems (MES), the purpose of which is to make decisions to improve production efficiency.
Machine Maintenance Information
The work of the maintenance department (maintenance) is divided into "preventive maintenance," in which maintenance such as the regular replacement of parts is performed based on the number of shots and operating hours of the press, and "predictive maintenance," in which the signs of problems occurring in the machine are predicted.
We propose an IoT system that realizes operation management and trend management by converting existing patrol lights and analog meters directly into numerical data, analyzing the data for predictive maintenance, and "visualizing" changes in machine operation rates and performance.
- Operation management ⇒ Information on lighting is acquired from the light sensor installed on the patrol light and operating hours are collected.
- Trend management⇒Analog meters such as thermometers and ammeters are analyzed and converted into numerical data.
The collected data can be used to prevent future machine stoppages, analyze the causes of performance degradation, and analyze causal relationships, which directly leads to improved productivity.
The figure on the left shows an IoT system that uses an optical sensor to detect the color of the patrol light signal and convert it into numerical data on machine operating hours and stopping times, while the figure on the right shows an IoT system that uses a surveillance camera to acquire analog meter images and convert them into numerical data.
The point is that you can easily start your IoT installation without having to replace existing analog devices with IoT-capable devices, and it is possible to introduce inexpensive IoT as an intermediary until it is time to replace full-fledged devices.