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Industry 4.0 Glossary

What Is Lean Manufacturing?

Lean manufacturing is a set of continuous improvement methodologies that help eliminate waste and maximize efficiency, effectiveness and productivity. The core value at the heart of every lean manufacturing strategy is to maximize value addition and minimize or remove non-value activities.

The lean manufacturing principles that are widely practiced today evolved from a management philosophy instituted by the Toyota Production System (TPS). Its basic tenets focus on ways to eliminate possible waste within a manufacturing system in the automotive space but its agnostic relevance earned acceptance in a wide variety of industries.

Lean manufacturing’s business methodology is driven by the principle of continuously eliminating the seven deadly wastes in any manufacturing process.

The Seven Deadly Wastes In Manufacturing

Eliminating waste is the core goal of lean manufacturing. Waste reduces the intrinsic value of any process and eventually renders the product being produced less competitive for the manufacturer and less valuable for the consumer.

Overproduction

Overproduction is considered to be one of the most detrimental types of wastes. Not only is it a waste itself, but overproduction results in many other types of waste. It leads to waste of inventory and can lead to the depletion of scarce resources needed for manufacturing. When the manufacturing component involves hazardous material, overproduction results in an increased effort and cost for waste disposal as well as more harm to the environment.

Waiting

Waste of time on hand can also be referred to as waste by waiting. Waiting is the waste that arises from time lost due to slow or halted production at a certain point in the production chain. In other words, a slow down at one point in the production chain creates a ripple effect that brings the rest of the processes to a standstill. A classic example would be the amount of time wasted by an employee waiting for another employee with excessive turnaround time. Other consequences of waiting include wasted energy and overhead costs consumed during the waiting period. In certain cases, the delay can also negatively impact the quality of material or the production recipe due to inefficient workflow.

Transportation

Transportation denotes the action of moving materials from one place to another. In manufacturing, transportation by itself adds almost no value to the product; hence, minimizing these costs is essential. Manufacturers can reduce transportation waste by ensuring close proximity of plants in the production chain. Cost effective transportation systems can also be used to alleviate waste. Excessive transportation can also trigger waste of time if a production chain has to stop production processes while waiting for material to arrive. Gas emissions, packing costs, possible damage in transit are some of the other possible offshoots of transportation wastes.

Over-processing

Over-processing refers to any part of the manufacturing process that is unnecessary. For instance, adding aesthetic elements to areas that will never be seen or including features that are very unlikely to be used – like the redial option in today’s mobile phones. The time, energy and emissions involved in the over-production of such a component also fall under this category of waste.

Inventory

Inventory waste is the unprocessed inventory that lies dormant taking up space and capital. Elements of inventory waste include wasted storage space, capital and transportation. Inventory waste can also be a consequence of unnecessary variance in attributes (non-standard) resulting in increased SKUs. This not only reduces the volume benefits of standard units, but also increases cost of holding.

Defects

Any deviation from prescribed standards or client’s expectation can be termed a defect. Defective products can prove to be the most expensive form of waste. Its repercussions could range from replacement of defective product to potential loss of client. Depending on limitation of liability, the consequence of defects can greatly impact and impede manufacturing success. Resources utilized when manufacturing a defective product are also a form of waste as the final products are generally discarded. Like other waste, defects also consume its share of space, effort, capital and energy.

Movements

Waste of movement refers to unnecessary actions by personnel or machines that could be minimized. If excess movement is used when the same value could be added with lesser movement, the extraneous movement can be avoided. Examples include the act of factory personnel bending over to pick something up more times than necessary or excessive wear and tear on machines that could lead to capital depreciation.

Advantages Of Lean Manufacturing

Lean manufacturing seeks to consistently make small incremental changes in processes to improve speed, precision, efficiency and quality. There are several benefits that can improve both productivity and your bottom line.

Improved Quality

Lean manufacturing is equipped with problem-solving tools and methods. Techniques like ‘5 Whys’, ‘8Ds’ have proved to be very effective in performing root cause analysis. Error proofing (Poka-Yoke) can further help prevent the recurrence of common problems. Lean optimizes processes to avoid errors, save time and money that would be lost in remaking the product. Thereby, improving quality and reducing costs at the same time.

Increased Productivity

Tasks like gathering unused data, incorporating needless features, idle time due to slow systems are examples of no-value activities. Elimination of such tasks will have a direct impact on productivity. Techniques like Value Stream Mapping (VSM) ensure that the workforce participates more on activities that add value.

Elimination Of Defects

Defects mean wastage and rework, and result in additional time, more money, and vulnerability in promised turnaround time. Techniques such as Jidoka (quality at source) are aligned towards eliminating defects and avoiding subsequent wastage. Lean aspires for making things right at first time, every time.

Reduced Lead Time

Mass customization is increasingly becoming today’s norm. The demand for high volume and highly customized products with aggressive turnaround time has become the differentiation strategy. Lean techniques eliminate non-value activities in the work content thereby reducing lead time and Dock-to-Dock (DTD) time.

Safer Working Environment

A safe working environment is a fundamental requisite for any manufacturing activity. Organization of tools and equipment by implementing 5S (Sort, Set, Shine, Standardize, and Sustain), and ‘Visual Factory’ minimize possibilities for errors. Total Productive Maintenance (TPM) can also be implemented for ensuring safety and limiting the risk of accidents due to faulty equipment.

Improved Bottom Line

Improved productivity and seamless operations enable increased production for the same overheads, if not lesser. The flexibility and responsiveness achieved through lean practice eliminate the need for excess inventory and promote optimal inventory through EOQ (Economic Order Quantity). Improvement in quality translates to fewer defects that directly contribute to profit margins. Better quality products appropriately meeting demands also promise future transactions.

Challenges Of Lean Manufacturing

Lean manufacturing methodologies heavily rely on integrated technology, workforce size, and the skill of employees. This may even ask for a radical shift in the company culture. A true lean manufacturing system can only be achieved by understanding the implications and overcoming challenges.

Issues Related To Supply Chain

Lean methodologies work towards eliminating storage time for incoming materials and outgoing products. In other words, materials and products must consistently be on the go. A Just-in-Time (JIT) ordering system is mandatory to ensure a consistent rate of flow of materials from both receiving and distribution. This will require close coordination with suppliers and distribution channels. It can also be applicable to the outflow of finished products as well. A JIT ordering system will also require vendors to adopt JIT.

Upskilling Workforce

Incorporating lean manufacturing techniques into existing business models create a talent gap vacuum that must be filled. The technological infrastructure required to maintain and manage supply-chain issues would require well trained, educated, experienced and agile employees in comparison to traditional assembly line setups. Technical employees, in most cases, will require safety certifications and special licenses to operate and maintain such systems.

Technology Challenges

Lean manufacturing methodologies generally require a significant upfront investment in technology. Picking the right systems will be critical for success. Lean practices also tend to shift towards automation as much as possible.

Machine Learning For Lean Manufacturing

The implications of lean manufacturing are synonymous with minimizing waste and maximizing productivity. Leveraging technology components like machine learning and applied analytics can further empower these manufacturing methodologies.These tools can help effectively demonstrate the ROI of lean practices and offer detailed insights into the implementation and production process.

Diagnostic Analytics:

Diagnostic analytics are normally used to assess the pre-post scenario after lean implementation. They offer insights for improved process performance by helping identify the areas susceptible to failure. For instance, this could be as simple as identifying the scope for increased material utilization through a kaizen event. The insights derived from data analytics can also help identify areas for long term support, along with process modification for minimized manufacturing cost.

Predictive Analytics:

Predictive analytics are employed to prevent and resolve quality failures by quickly identifying problems in advance and taking corrective measures to minimize the impact. This can help reduce the likelihood of unchecked producing defective products over an extended period of time. It can also be used to predict inline quality along with product and material scrap rates to minimize waste and maximize overall product traceability.

Prescriptive Analytics:

Prescriptive analytics differ from predictive analytics in that it can offer insights into prescribing the optimal process parameters for machine settings. It constantly and continuously monitors for improving the quality at source, reducing process failure along with elimination of process variations. This eliminates the need to use guesswork to pinpoint areas of possible failure.

The Benefits Of Machine Learning In Lean Implementations

Minimizing waste and maximizing production are two primary benefits of lean manufacturing. Optimization is often constrained by equipment effectiveness, maintenance that can create costly downtime and limited operational visibility across the factory floor. Machine learning uses the wealth of data that already exists within the manufacturing processes to create actionable visibility to not only mitigate the production of scrap, but also identify machine issues and failures before they happen, while identifying the metrics needed to sustainably replicate your most efficient runs.

Overall Equipment Effectiveness

Overall Equipment Effectiveness (OEE) is one aspect of manufacturing that has consistently challenged industries, especially in terms of utilization and efficiency. The quality (scrap) aspect of OEE is known to have more limitations when it comes to improvement. It has constraints like complex part design, machinery process capability, non-availability of advanced metrology tools, and availability of skilled labor. On the other hand, machine utilization and efficiency – the scope of improvement largely rests on internal and controllable factors. They are also considered the low hanging fruits of OEE. IoT powered devices can help gather information like machine downtime due to waiting for setup even after SMED implementation, non-availability of materials, and operators. Data from auxiliaries like collecting bin and scrap bin can also be easily collected continuously and monitored in real-time with the IoT sensors and data analytics. While SMED can handle Quicker setups, machines waiting for setup are purely data driven. Hence, these platforms play a vital role in lean implementation for sustained improvement.

Usually when it comes to lean manufacturing, process improvements are short lived because of the lack of continuous data for monitoring and sustaining the improvement. Capturing of big data allows the ML platform to run applied data analytics for various scenarios and thereby providing insights for lost production hours along with units. In other words, these applied analytics provides manufacturers with opportunity hours for more production (opening up existing / new additional capacity) along with increased efficiency for peak performance.

Preventive Maintenance

Unplanned downtime with condition-based monitoring and real-time alerts notify personnel of any vulnerable parts such as fans, motors, and pumps are operating outside of normal thresholds. Tracking the pressure, vibration, temperature, and energy usage of machines can help predict potential problems allowing factory personnel to address issues before they become incidents.

Andon Systems, Alarms & Alerts, And Operational Visibility

It can enable increase in contribution margins by optimizing production with automated data analytic insights that can improve machine throughput, first-pass yield, and more. Effective scheduling and sequencing possible for mixed batch size and product variants are not only made possible but also optimized because of the applied data analytics with ML platform. With enhanced operational visibility and data driven manufacturing process the lean implementation and sustenance have improved tremendously. This is because all the stakeholders in the process starting from shop floor employees all the way to top floor strategic personnel is constantly under vigilance. All of this could be easily achieved through ML and applied analytics, which creates the base platform for continuously evolving improved processes. A process dashboard with optimized machine parameters along with its control limits can help the line operator to pull the Andon or the system can alert the operation to be suspended until appropriate actions are taken.

Jidoka (Quality at Source)

It can help optimize production runs with an out-of-the-box recommendation engine that identifies the key settings that contribute to peak performance. It can also provide recommendations to replicate those runs more consistently. Real time alerts offer the ability to act quickly, avoid problems and continually realize cost-saving improvements. It can assist factory personnel to proactively address issues and resolve problems quickly with alerts that reduce quality failures.

Machine Learning can integrate and structure data from multiple sources including machines like ERP, Standalone Quality Systems, CMM tools, and MES systems. Recent AI Models based on applied analytics are trained and validated enabling teams to solve problems and make quicker informed and data driven decisions. This tremendously empowers the CI team and Operations to constantly evolve and improve the Lean Implementation methodologies. ML and Applied Analytics play a critical role in the implementation of effective lean manufacturing.

How To Implement Lean Manufacturing

Businesses of all sizes utilize lean manufacturing techniques today. While most large corporations house a few lean experts, smaller businesses do not necessarily have in-house lean experts. When it comes to the implementation of a lean strategy, nothing is more crucial than a roadmap. Here are few essential steps organizations are required to take while applying lean manufacturing to their business practices.

Step 1: Identification of areas for improvement
Step 2: Lean assessment
Step 3: Defining the scope
Step 4: Selection of lean tools or techniques
Step 5: Training and implementation
Step 6: Kaizen events
Step 7: Pre and post implementation analysis
Step 8: Best practices and lean metrics for sustenance
Step 9: Move to next project