Article | December 23, 2021
Every manufacturing company aims to be as efficient as possible to maximize profit. However, it's difficult to determine where you stand and what targets to establish unless you can precisely measure your efficiency. Manufacturing KPIsplay an important role in this process.
Keeping track of many indicators without considering their commercial worth is a waste of time.
“Not everything that can be counted counts and not everything that counts can be counted”
- Albert Einstein
But connecting goals to measurements is a certain way to track progress and improve processes. So let's get started with how to choose the most appropriate key performance indicators(KPIs) for your business.
Manufacturing KPI 2022: How to Choose the Right One?
Why are KPIs called “Key” Performance Indicators? While any statistic can be used to assess performance, KPIs are the most critical. Hence they are called key performance indicators. Companies' priorities while selecting their company KPIs may differ substantially depending on the industry in which they operate.
“Strategic-operational KPIs alignment gives the organization a powerful tool to use when implementing change.”
― Pearl Zhu
A corporation should not track more than ten manufacturing KPIs to avoid overblowing processes. So, manufacturing efficiency, customer satisfaction, lead times, etc., should all be included.
Depending on your business nature, you must select your KPIs. However, each of those indicators must meet a set of criteria before being considered meaningful.
So, what is a decent KPI for manufacturing?
It gives objective and clear data on progress toward a certain goal
It measures efficiency, quality, punctuality, and performance
It allows for tracking performance over time
It helps in decision making
It should be the one that matches the company's long-term objectives
It has to be measurable and quantifiable
It must be realistic and actionable
Following that, let's have a look at the most important manufacturing KPIs for 2022, which will assist you in better understanding your manufacturing business and formulating a growth strategy in line with that understanding.
Most Critical Manufacturing KPIs in Order of Priority
Despite the fact that manufacturers should also monitor general key performance indicators (KPIs) such as sales revenue, net profit margin, and so on, the manufacturing business demands the tracking of specific manufacturing metrics. Below are some of the most important manufacturing key performance indicators (KPIs).
Using this manufacturing KPI metric, you can see how much value there is in products still in progress. It assists manufacturing organizations in determining how much of their working capital is locked up in incomplete products and can aid in identifying supply chain managementdifficulties.
You can compute the Work-In-Progress (WIP) by using the formula provided below.
Return on Assets
This manufacturing KPIseems to be more about financing than manufacturing. Yes, it does. However, financial measurements are just as vital as production ones. A firm cannot exist unless it generates revenue, and this indicator measures how efficiently your company uses its assets and generates revenues.
The Return on Assets (ROA) of your company can be calculated using the formula below.
Cost Per Unit
It is critical to understand the overall manufacturing cost per unit. You can't appropriately price a product without it. This manufacturing KPIdivides total manufacturing costs by the number of units produced. Materials, overhead, depreciation, and labor are standard costs.
Companies utilize this manufacturing statisticto forecast future raw material needs to satisfy client demand. Unfortunately, this statistic is more challenging to employ because it is mainly dependent on unpredictable external circumstances. The basic formula is:
The seasonal factors are distinct
Average demand is calculated as:
A company's lead time, also known as order cycle time, is an important KPI. It shows how swiftly your organization processes orders and meets client requests. It is time it takes to complete an order from confirmation to delivery.
Long lead times can imply process inefficiencies that produce bottlenecks and excessive expenses. Conversely, short lead times are important since they allow you to respond to consumer needs swiftly and efficiently.
The total lead time can be divided into smaller segments as follows:
The time it takes to manufacture a product from start to finish
The time it takes to deliver a product from stockto a client
The time it takes suppliers to deliver products to manufacturers
By segmenting the lead time, you may more precisely identify the areas where inefficiencies in the process occur.
Toyota’s Four Key Performance Metrics
As a company, Toyota places a high focus on environmental protection. Toyota's vehicles are designed to use less fuel and produce less waste.
Regardless of the company's size, Toyota is committed to protecting the environment. Toyota's 'Earth Charter' was created in 1992 as part of the company's Global Policy initiative. It was Toyota's first overseas facility and the UK's first ISO14001-certified car manufacturer. Waterborne paints were utilized for the first time and zero waste was sent to landfills. In 2009, Toyota Manufacturing UK did not use any incinerators.
Toyota has developed a set of key performance indicators (KPIs) for each of its major production areas. There are four key performance metrics: energy, water, waste, and volatile organic compounds (VOCs).
Since its start in 1992, Toyota Manufacturing UK has attempted to mitigate its environmental impact. The figure below illustrates the environmental KPIsfor the Burnaston plant. Each year, Toyota sets new goals to improve its results.
79% reduction in vehicle energy consumption
62% reduction in waste per car
76% reduction in VOC emissions per car
79% reduction in water consumption per vehicle
You can use the aforementioned manufacturing KPIsto construct your manufacturing KPI template, but keep in mind that the manufacturing metricsyou need to track may differ from those listed here. The first prudent move any business can make while examining its operation is to identify and track the relevant KPIs. Also, in manufacturing, there are several different KPIs, phrases, and abbreviations need to be understood and used where it makes the most sense.
What is manufacturing KPI?
A manufacturing Key Performance Indicator (KPI) or metric is a well-defined and measurable indicator that the manufacturing sector uses to evaluate its performance over time and compare it to that of other industries.
What are the key KPIs for manufacturers?
On-Time Delivery, Production Schedule Attainment, Total Cycle Time, Throughput, Capacity Utilization, and Changeover Time are some of the key manufacturing KPIs.
Article | December 13, 2021
Lean manufacturing is a growing trend that aims to reduce waste while increasing productivity in manufacturing systems. But, unfortunately, waste doesn't add value to the product, and buyers don't want to pay for it.
This unusual method pushed Toyota Motor Corporation's industry to become a leading Toyota Production System (TPS). As a result, they are now efficiently producing some of the world's top cars with the least waste and the quickest turnaround.
The majority of manufacturers are now using lean management. According to the 2010 Compensation Data Manufacturing report, 69.7% of manufacturing businesses use Lean Manufacturing Practices.
Lean tools are the ones that help you in implementing lean practice in your organization. These lean tools assist in managing people and change while solving problems and monitoring performance. Lean Manufacturing technologies are designed to reduce waste, improve flow, improve quality control, and maximize manufacturing resources.
What Are the Five Best Lean Manufacturing Tools and How Do They Work?
There are roughly 50 Lean Manufacturing tools available in the market. This post will describe 5 of them and their value to your business and its developments.
The 5S system promotes efficiency by organizing and cleaning the workplace. To help increase workplace productivity, the system has five basic guidelines (five S's). The five Ss are Sort, Set, Shine, Standardize, and Sustain.
5S improves workplace efficiency and effectiveness by:
Sort: Removing unnecessary material from each work area
Set: Set the goal of creating efficient work areas for each individual
Shine: Maintaining a clean work area after each shift helps identify and resolve minor concerns
Standardize: Documenting changes to make other work areas' applications more accessible
Sustain: Repeat each stage for continuous improvement
5S is a lean tool used in manufacturing, software, and healthcare. Kaizen and Kanban can be utilized to produce the most efficient workplace possible.
Just-In-Time (JIT) manufacturing
Just-in-time manufacturing allows manufacturers to produce products only after a customer requests them. This reduces the risk of overstocking or damaging components or products during storage.
Consider JIT if your company can operate on-demand and limit the risk of only carrying inventory as needed. JIT can help manage inventory, but it can also hinder meeting customer demand if the supply chain breaks.
With Kaizen, you may enhance seven separate areas at once: business culture, leadership, procedures, quality, and safety. Kaizen is a Japanese word, means "improvement for the better" or "constant improvement."
“Many companies are not willing to change or think they are done once they make a change. But the truth is technology; consumer demands, the way we work, human needs and much more are constantly changing.”
– Michael Walton, Director, Industry Executive at Microsoft
The idea behind Kaizen is that everyone in the organization can contribute suggestions for process improvement. Accepting everyone's viewpoints may not result in significant organizational changes, but minor improvements here and there will add up over time to substantial reductions in wasted resources.
Kanban is a visual production method that delivers parts to the production line as needed. This lean tool works by ensuring workers get what they need when they need it.
Previously, employees used Kanban cards to request new components, and new parts were not provided until the card asked them to. In recent years, sophisticated software has replaced Kanban cards to signal demand electronically. Using scanned barcodes to signify when new components are needed, the system may automatically request new parts.
Kanban allows businesses to manage inventory better, decrease unnecessary stock, and focus on the products that must be stored. To reduce waste and improve efficiency, facilities can react to current needs rather than predict the future.
Kanban encourages teams and individuals to improve Kanban solutions and overall production processes like Kaizen. Kanban as a lean tool can be used with Kaizen and 5S.
PDCA (Plan, Do, Check, Act)
Plan-Do-Check-Act (PDCA) is a scientific strategy for managing change. Dr. W. Edwards Deming invented it in the 1950s; hence, it is called the ‘Deming Cycle.’
The PDCA cycle has four steps:
Problem or Opportunity: Determine whether a problem or an opportunity exists
Do: Make a small test
Examine: Look over the test results
Act: Take action depending on results
How Nestlé Used the Kaizen Lean Manufacturing Tool
Nestlé is the largest food corporation in the world, yet it is also a company that practices Lean principles, particularly the Kaizen method. Nestlé Waters used a technique known as value stream mapping, which is frequently associated with Kaizen. They designed a new bottling factory from scratch to guarantee that operations were as efficient as possible. Nestlé has been aiming to make ongoing changes to their processes to reduce waste and the amount of time and materials that can be wasted during their operations.
Lean manufacturing techniques enable many businesses to solve their manufacturing difficulties and become more productive and customer-centric. In addition, useful lean manufacturing tools assist companies in obtaining the anticipated outcomes and arranging their operations in many excellent ways to meet buyer expectations. Hence, gather a list of the top lean manufacturing tools and choose the best fit for your organization to maximize your ROI and address the performance issue that is causing your outcomes to lag.
What are the standard tools in lean manufacturing?
Among the more than 50 lean manufacturing tools, Kaizen, 5S, Kanban, Value Stream Mapping, and PDCA are the most commonly used lean manufacturing tools.
How to Select the Best Lean Manufacturing Tools for Your Business?
Choosing a lean manufacturing tool begins with identifying the issue or lag in your organization that affects overall productivity and work quality. To select the lean device that best meets your company's needs, you must first grasp each one's benefits and implementation techniques.
What is included in a Lean 5S toolkit?
The lean 5S toolbox contains some essential items for achieving the goal. It comes with a notepad or tablet, a camera, a high-quality flashlight, a tape measure, and a stopwatch.
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Article | June 28, 2021
Manufacturing journalist Thomas R. Cutler visited the remarkable and magnificent country of Uganda.
Foreign investment is coming into the country and that is a good thing; it is not however, enough. To tap into this workforce corporate citizenship and contribution is essential. Just as I underestimated the stamina needed to climb the mountain to experience the gorillas, the role of transforming Uganda requires a careful, well-thought approach.
Article | December 7, 2021
Machine learning in manufacturing is becoming more widespread, with businesses like GE, Siemens, Intel, Bosch, NVIDIA, and Microsoft all investing heavily in machine learning-based ways to enhance manufacturing.
Machine learning is predicted to expand from $1 billion in 2016 to USD 9 billion by 2022at a compound annual growth rate (CAGR) of 44% throughout the forecast period, according to Markets & Markets.
The technology is being utilized to cut labor costs, achieve better transition times, and increase manufacturing speed.
“I advocate business leaders get to know more about what AI can do and then leverage AI in proofs of concept.”
– Michael Walton, Director and Industry Executive, Microsoft speaking with Media 7
Machine learning can help enhance manufacturing processes at the industrial level. This can be achieved by assessing current manufacturing models and identifying flaws and pain factors. Businesses can rapidly address any difficulties to keep the manufacturing pipeline running smoothly.
Let us explore how machine learning is transforming manufacturing operations.
How Machine Learning Is Transforming Manufacturing Operations
“The greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.”
- Pedro Domingos
Machine learning in manufacturing is revolutionizing manufacturing operations and making them more advanced and result-oriented, so let's have a look at how this is unfolding.
Allows for Predictive Maintenance
Machine learning provides predictive maintenance by forecasting equipment breakdowns and eliminating wasteful downtime. Manufacturers spend far too much time correcting problems instead of planning upkeep. In addition to enhancing asset dependability and product quality, machine learning systems can forecast equipment breakdown with 92% accuracy. Machine learning and predictive analytics increased overall equipment efficiency from 65% to 85%.
Increases Product Inspection and Quality Control
Machine learning is also utilized for product inspection. Automated inspection and supervision using ML-based computer vision algorithms can discriminate between excellent and bad products. These algorithms simply need excellent samples to train; therefore a fault library is not required. However, an algorithm that compares samples to the most common errors can be built. Machine learning reduces visual quality control costs in manufacturing. Forbe's says AI-powered quality testing can boost detection rates by up to 80%.
Logistics-related Tasks Are Automated
To run a production line, industrial companies need considerable logistics skills. The use of machine learning-based solutions can improve logistics efficiency and save expenses. Manual, time-consuming operations like logistics and production-related documentation cost the average US business $171,340 annually. It saves thousands of manual working hours every year to automate these everyday procedures. Using Deep Mind AI, Google was able to lower its data center cooling bill by 40%.
Creates More Business Opportunities
Machine learning is frequently used in the production process. Substantial data analysis is required to create new items or improve existing products. Collection and analysis of huge amounts of product data can help find hidden defects and new business opportunities. This can help improve existing product designs and provide new revenue streams for the company. With machine learning, companies can reduce product development risks by making smarter decisions with better insights.
Protects Company’s Digital Assets
On-premise and cloud-based machine learning systems require networks, data, and technological platforms to function. Machine learning can help secure these systems and data by restricting access to vital digital platforms and information. Humans’ access sensitive data, choose applications, and connect to it using machine learning. This can help secure digital assets by immediately recognizing irregularities and taking appropriate action.
Harley Davidson's Sales Climbed by 40% Using Albert – The ML & AI-Powered Robot
Today, traditional marketing is harder to break through. It's easy to see why Albert (an AI-powered robot) would be a good fit for Harley Davidson NYC. Thanks to machine learning and artificial intelligence, robots are producing news stories, working in hotels, controlling traffic, and even running McDonald's.
Albert works well with social media and email marketing. It analyzed which customers are more likely to convert and modifies the personal creative copies on its own for the next process.
Harley-Davidson is the only company to employ Albert in its business. The company evaluated customer data to find prior consumers who made purchases and spent more time browsing the website than normal. Albert used this data to categorize customers and scale up test campaigns.
Using Albert, Harley-Davidson's sales climbed by 40% and leads increased 2,930%, with half coming from high-converting ‘lookalikes' detected by AI and machine learning.
The groundbreaking benefits of machine learning are the pillars of machine learning applications in manufacturing. Machine learning in manufacturing helps enhance productivity without compromising quality. According to Forbes, Amazon has automated warehouse logistics picking and packing using a machine learning system. With Kiva's help, Amazon's typical ‘click to ship' time dropped from 60-75 minutes to 15 minutes. So, industry leaders are seeing fantastic outcomes, and machine learning in manufacturing is the future.
How is machine learning used in manufacturing?
Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process.
Which two forms of machine learning are there?
Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data.
What is a machine learning model?
A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm.
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