4 Lean Six Sigma manufacturing trends in APAC

Lean Six Sigma methodology can be applied to any company, in any industry, around the globe. It is particularly well suited to manufacturing companies, however, which is why powerhouses in the APAC region are increasingly applying the principles to drive down costs and reduce waste.

Lean Six Sigma is a management methodology that combines two distinct but complementary approaches to process improvement: Lean and Six Sigma. Lean focuses on eliminating waste and maximizing customer value by streamlining processes and reducing cycle times, while Six Sigma focuses on reducing variability and defects in processes to improve quality and reduce costs.

With the goal of achieving process excellence by continuously improving processes, products and services to meet or exceed customer expectations, we look at the trends driving Lean Six Sigma adoption throughout the APAC region.

Contents:

• Toshiba: The deployment of data analytics in predictive maintenance
• Toyota: Using Lean management to improve equipment and engage employee
• Huawei: Using visual management boards to drive productivity
• MAS Holdings: Using robotics and automation to reduce waste

Toshiba: The deployment of data analytics in predictive maintenance

Data analytics is an integral part of Lean Six Sigma in manufacturing as it helps to identify inefficiencies and opportunities for improvement in the production process. By analyzing data, manufacturers can identify and eliminate waste, reduce defects and improve overall quality, which are all key objectives of both Lean and Six Sigma methodologies.

In one example, the Toshiba Corporation used data analytics to enable predictive maintenance, which is a Lean principle that aims to minimize downtime and reduce costs by identifying potential equipment failures before they occur. Considering downtime costs the average manufacturing plant more than US$172mn per annum, all reductions can be considered valuable.

Predictive maintenance involves collecting and analyzing data from sensors and other sources to identify patterns and anomalies that can indicate when equipment is likely to fail or require maintenance. By identifying issues before they become major problems, predictive maintenance can help to prevent unplanned downtime and reduce costs.

Toshiba collected data from various sensors and sources to predict when maintenance was required for its equipment. By using machine-learning algorithms and statistical analysis, Toshiba could identify patterns and anomalies in the data that could indicate potential equipment failures. By analyzing this data in real-time, Toshiba was able to proactively schedule maintenance before a failure occurs, minimizing downtime and reduce maintenance costs.

Toshiba's predictive maintenance system also helped to optimize equipment performance and extend its lifespan. By continuously monitoring equipment performance, the system could identify areas where performance could be improved or where maintenance was required to prevent degradation in performance. This helped to prevent costly equipment failures and ensures that the equipment operates at its maximum efficiency, reducing energy consumption and operating costs.

Toyota: Using Lean management to improve equipment and engage employee

Japan’s Toyota Motor Corporation is well known for its use of lean management principles in manufacturing, focusing on continuous improvement, waste reduction and respect for people.

One of the core principles of lean management at Toyota is standardization which makes it easier to pinpoint flaws by identifying how and where processes are failing, enabling employees to examine processes and making improvements. As Taiichi Ohno, the famous industrial engineer, put it, “Without a standard, there can’t be improvement.”

The Toyota Production System (TPS) relies on employees to keep an eye out for areas where waste can be removed, increasing efficiency. Each employee takes ownership of their work and looks for ways to make their work easier and improve processes, which leads to consistently improving the quality of their forklifts. By involving everyone in the improvement process, Toyota has been able to deliver top-of-the-line products consistently.

Employees feel more valued and appreciated when their suggestions are heard and acted upon, leading to higher employee morale and retention rates at the company’s manufacturing plants.

Huawei: Using visual management boards to drive productivity

Visual management boards can be used to help track and display process metrics, goals and progress in a clear and concise manner, in line with the principles of Six Sigma. By providing a visual representation of key performance indicators (KPIs) and improvement opportunities, visual management boards can help teams to identify areas for improvement and track progress over time.

They can help to promote communication, collaboration, and engagement among team members, which are important aspects of the Six Sigma approach.

The board typically consists of a physical or digital display that is updated regularly with metrics, charts, graphs, and other visual aids that help team members understand the status of the process or project at a glance.

By providing a real-time view of progress and performance, the board helps team members identify problems, prioritize tasks and make data-driven decisions that can lead to continuous improvement.

Huawei uses visual management boards in their production lines to track the status of each product being assembled and ensure that production targets are met. The boards display information such as the number of units produced, the number of defective products and the progress of each production line. This helps the workers identify any issues in the production process and take corrective actions in real-time to prevent delays and reduce waste.

The implementation of the visual management board resulted in a number of benefits for the plant, including increased productivity, improved quality, and better communication between employees and management. By providing employees with clear goals and performance metrics, the board helped to create a culture of continuous improvement, where employees were motivated to identify and eliminate waste in their processes.

MAS Holdings: Using robotics and automation to reduce waste

APAC's robotic process automation (RPA) market is expected to grow at a compound annual growth rate (CAGR) of 34.8 percent during the forecast period (2023–28) due to the increasing adoption of a digital workforce and functional benefits of RPA solutions for business process automation across various industries.

One company that has embraced this trend is MAS Holdings, a fast-moving apparel and textile manufacturer in Sri Lanka. The company operates more than 53 manufacturing facilities across 16 countries, with 99,000 employees. As one of the world’s largest suppliers of lingerie and sports apparel, and with an ambitious and aggressive growth plan, the company needed to make rapid improvements surrounding productivity, efficiency and cost savings. After a comprehensive research study, the company concluded that these improvements could not be made without automation, with RPA forming the base of their digital transformation agenda.

Working with UiPath, MAS began automating the labor order placement process before building up its internal knowledge base and taking the technology organization-wide.

In keeping with the Lean Six Sigma principles of improving quality and efficiency by identifying and eliminating waste, reducing variation, and streamlining processes, the company was able to create tangible business value in three critical areas right off the bat:

• PO creation: When POs were delayed or inaccurate due to human area, goods delivery became delayed, leading to idle backend capacity and productivity losses. By using robotics, the company was able to save around 2,500 labor days and speed up the entire PO process, while achieving greater accuracy.
• Advanced shipping notes (ASNs) generation: MAS was able to speed up the delivery of ASNs and improve lead times. The three employees involved in this documentation were freed up to work on more productive tasks and the new system was able to send 20 to 50 ASNs within an hour.
• Product development: By using robotics to fetch information and convert it to internal formats, the company was able to improve scheduling and productivity.

MAS automated seven processes in 2017, building more than 52 processes by 2022, ranging from product development to shipping and procurement. MAS saved more than 14,000 labor days per year, which not only freed employees to work on more valuable tasks but increased their motivation.