The combination of Big Data, analytical tools, and the lean management concept brings multi-million savings to manufacturing companies. What are the reasons for the double-digit productivity gains achieved with the use of these tools?
Developed in the 1960s and 1970s, the production thinking by Japanese engineer Taiichi Ohno initially functioned in English-language literature under the term TPS. In 1988, the concept of “lean production” was used for the first time in relation to this model, which resulted from the significant reduction of inventory levels characteristic of the traditional mass production system thanks to this method. Demand-oriented and improvement-focused, the model became the basis for the success of Japanese automotive groups in the last century’s second half. Its potential has grown even further with the development of concepts such as the Internet of Things (IoT) and Industry 4.0. According to McKinsey’s analysis, combining a lean approach with Big Data and advanced analytical tools can increase manufacturing companies’ profits by tens of billions of dollars.
Main assumptions of the lean manufacturing concept
The first pillar of Lean Manufacturing is the transition to suction manufacturing. It consists in making production dependent on the demand for a specific product. As a result, semi-finished products and raw materials are used when there is an actual recipient of the product. This allows not only to rationalise the number of component stocks but also to minimise the space and storage costs of finished products.
The second pillar of lean manufacturing is an orientation toward error detection and elimination, constantly striving to increase processes’ efficiency and involvement of employees at all levels in tracking down and eliminating all manifestations of waste. This happens, among other things, by standardising work and building quality into the entire process.
How lean manufacturing benefits from Big Data
The Internet of Things, Big Data, 5G and Industry 4.0 – complementary technological trends, increasingly marking their presence at the beginning of the 3rd decade of the 21st century and enabling the connection of almost every device operating in a factory with a multi-directional network of information exchange – make production “leaner” than ever before. Provided that appropriate sensors, transmitters, receivers and software capable of collecting and pre-processing information are used, it is possible to track the performance of individual machines and production lines in real-time, react immediately to downtime, production rate drops, reaching the inventory limit value, failures and irregularities. Tools of this type increase the utilisation rate of production resources. They also guarantee that the improvements implemented in the Deming cycle (Plan-Do-Check-Act) are based on reliable information and facilitate the inclusion of employees in the process improvement culture. According to the review of implementations of digital data collection and processing tools in manufacturing companies developed by McKinsey, the effect of their use may be a 30% increase in profits (example of a pharmaceutical company) or an increase in throughput by 20% and a reduction in the cost of raw materials by about 5% (steel ).
Kanri Soft tools – a combination of lean, big data and visual management
Kanri Soft software is an example of a system that allows you to integrate within one platform, among others, data from machines and production lines, messages from operators and schedules of service activities, as well as information from other IT systems. The application aggregates data and processes it automatically. They are processed and visualised to meet the needs of employees of various levels and divisions. The panels, which contain accessible data visualisations, are individualised in terms of the needs of operators, foremen, mechanic engineers, production and maintenance managers and top management. According to the company’s estimates, by implementing Kanri Soft software, it is possible to reduce the amount of time wasted by production employees on activities that do not create value for the customer by more than 20%.