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 | January 12, 2022
Real-time manufacturing analytics enables the manufacturing base to increase its efficiency and overall productivity in a variety of ways. Production data is an effective means of determining the factory's efficiency and identifying areas where it might be more productive.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
– Geoffrey Moore, an American Management Consultant and Author
Creating a product-specific data collection may assist you in determining and visualizing what needs to be improved and what is doing well. In this article, we'll look at why manufacturing data collection is vital for your organization and how it may help you improve your operations.
Why is Manufacturing Data Collection so Critical?
Visibility is the key benefit that every manufacturer gets from manufacturing data collection. By collecting real-time data, or what we refer to as "shop floor data," manufacturers better understand how to assess, comprehend, and improve their plant operations. Manufacturers can make informed decisions based on detailed shop floor data. This is why having precise, real-time production data is critical.
“According to Allied Market Research, the worldwide manufacturing analytics market was worth $5,950 million in 2018 and is expected to reach $28,443.7 million by 2026, rising at a 16.5% compound annual growth rate between 2019 and 2026.”
For modern manufacturers, the advantages of data collection in manufacturing are numerous. The manufacturing industry benefits from production data and data-driven strategy in the following ways.
Substantial reduction in downtime by identifying and addressing the root causes of downtime.
It increases manufacturing efficiency and productivity by minimizing production bottlenecks.
A more robust maintenance routine that is based on real-time alerts and machine circumstances.
Improvements in demand forecasting, supplier scoring, waste reduction, and warehouse optimization reduce supply chain costs.
Higher-quality goods that are more in line with customers' wishes and demands depending on how they are utilized in the current world.
So, after looking at some of the significant benefits of real-time manufacturing analytics, let’s see what type of data is collected from production data tracking.
What Sorts of Data May Be Collected for Production Tracking?
Downtime: Operators can record or track downtime for jams, cleaning, minor slowdowns, and stoppages, among other causes, with production tracking software. In the latter scenario, downtime accuracy is optimized by removing rounding, human error, and forgotten downtime occurrences. The software also lets you categorize different types of stops.
Changeovers: Changeovers can also be manually recorded. However, changeovers tracked by monitoring software provide valuable data points for analysis, considerably reducing the time required for new configurations.
Maintenance Failures: Similar to downtime classification, the program assists in tracking the types of maintenance breakdowns and service orders and their possible causes. This may result in cost savings and enable businesses to implement predictive or prescriptive maintenance strategies based on reliable real-time data.
Items of Good Quality: This is a fundamental component of production management. Companies can't fulfill requests for delivery on schedule unless they know what's created first quality. Real-time data collection guarantees that these numbers are accurate and orders are filled efficiently.
Scrap: For manufacturers, waste is a significant challenge. However, conventional techniques are prone to overlooking scrap parts or documenting them wrong. The production tracking system can record the number and type of errors, allowing for analysis and improvement. Additionally, it can capture rework, rework time, and associated activities.
WIP Inventory: Accurate inventory management is critical in production, yet a significant quantity of material may become "invisible" once it is distributed to the floor. Collecting data on the movement and state of work in progress is critical for determining overall efficiency.
Production Schedule: Accurate data collection is essential to managing manufacturing orders and assessing operational progress. Customers' requests may not be fulfilled within the specified lead time if out of stock. Shop floor data gathering provides accurate production histories and helps managers fulfill delivery deadlines.
Which Real-time Data Collection Techniques Do Manufacturers Employ?
Manufacturers frequently employ a wide range of data collection techniques due to the abundance of data sources. Manual data collection and automated data collection are two of the most common data collection methods. Here are a few examples from both methods:
IoT: To provide the appropriate information to the right people at the right time with the correct shop floor insight, IoT (Internet of Things) sensor integration is employed.
PLC: The integration of PLC (Programmable Logic Controller) is used to measure and regulate manufacturing operations.
HMI: It can provide human context to data by integrating line HMI (Human Machine Interface) systems (such as individual shop terminals like touch screens located on factory floor equipment).
SCADA: Overarching management of activities with SCADA (Supervisory Control and Data Acquisition) systems.
CNC and Other Machines: Integrating CNC and other machines (both new and older types) to keep tabs on production efficiency and machine well-being is a must these days.
One of the most challenging aspects of shop floor management is determining what to measure and what to overlook. The National Institute of Standards and Technology recently conducted a study on assisting manufacturing operations in determining which data to collect from the shop floor.Additionally, you may utilize the manufacturing data set described above to obtain information from your manufacturing facility and use it strategically to improve operations, productivity, efficiency, and total business revenue in the long term.
What is manufacturing analytics?
Manufacturing analytics uses operations and event data and technology in the manufacturing business to assure quality, improve performance and yield, lower costs, and optimize supply chains.
How is data collected in manufacturing?
Data collection from a manufacturing process can be done through manual methods, paperwork, or a production/process management software system.
Article | December 16, 2021
Computer-aided manufacturing (CAM) is a technology that revolutionized the manufacturing business. Pierre Bézier, a Renault engineer, produced the world's first real 3D CAD/CAM application, UNISURF CAD. His game-changing program redefined the product design process and profoundly altered the design and manufacturing industries.
So, what is CAM in its most basic definition?
Computer-aided manufacturing (CAM) is the application of computer systems to the planning, control, and administration of manufacturing operations. This is accomplished by using either direct or indirect links between the computer and the manufacturing processes. In a nutshell, CAM provides greater manufacturing efficiency, accuracy, and consistency.
As technology takes over and enhances many of the processes we used to handle with manual labor, we are freed up to use our minds creatively, which leads to bigger and better leaps in innovation and productivity.”
– Matt Mong, VP Market Innovation and Project Business Evangelist at Adeaca
In light of the numerous advantages and uses of computer-aided manufacturing, manufacturers have opted to use it extensively. The future of computer-aided manufacturing is brightening due to the rapid and rising adoption of CAM.
According to Allied Market Research, the global computer-aided manufacturing market was worth $2,689 million in 2020 and is expected to reach $5,477 million by 2028, rising at an 8.4% compound annual growth rate between 2021 and 2028.
Despite all this, each new development has benefits and challenges of its own. In this article, we'll discuss the benefits of CAM, the challenges that come with it, and how to deal with them. Let's start with the advantages of computer-aided manufacturing.
Benefits of Computer Aided Manufacturing (CAM)
There are significant benefits of using computer-aided manufacturing (CAM). CAM typically provides the following benefits:
Increased component production speed
Maximizes the utilization of a wide variety of manufacturing equipment
Allows for the rapid and waste-free creation of prototypes
Assists in optimizing NC programs for maximum productivity during machining
Creates performance reports automatically
As part of the manufacturing process, it integrates multiple systems and procedures.
The advancement of CAD and CAM software provides visual representation and integration of modeling and testing applications.
Greater precision and consistency, with similar components and products
Less downtime due to computer-controlled devices
High superiority in following intricate patterns like circuit board tracks
Three Challenges in CAM and Their Solutions
We have focused on the three primary challenges and their solutions that we have observed.
Receiving Incomplete CAD Updates
Receiving insufficient CAD updates is one of the challenges. If, for example, the part update from a CAD engineer does not include the pockets that are required in the assembly, to the CAM engineer.
SOLUTION: A modeler that enables developers of a CAM programs to create intuitive processes for features such as feature extraction and duplication across CAD version updates. A modeler is capable of recognizing and extracting the pocket's architecture and the parameters that define it. Additionally, the CAM application can enable the engineer to reproduce the pocket in a few simple steps by exploiting the modeler's editing features such as scaling, filling, extruding, symmetrical patterning, and removing.
Last Minute Design Updates
The second major challenge is last-minute design changes may impact manufacturers as a result of simulation.
SOLUTION: With 3D software components, you may create applications in which many simulation engineers can work together to make design modifications to the CAD at the same time, with the changes being automatically merged at the end.
Challenging Human-driven CAM Manufacturing
The third major challenge we have included is that CAM engineers must perform manual steps in human-driven CAM programming, which takes time and requires expert CAM software developers. Furthermore, when the structure of the target components grows more complicated, the associated costs and possibility of human failure rise.
SOLUTION: Self-driving CAM is the best solution for this challenge. Machine-driven CAM programming, also known as self-driving CAM, provides an opportunity to improve this approach with a more automated solution. Preparing for CAM is simple with the self-driving CAM approach, and it can be done by untrained operators regardless of part complexity. The technology handles all of the necessary decisions for CAM programming operations automatically. In conclusion, self-driving CAM allows for efficient fabrication of bespoke parts, which can provide substantial value and potential for job shops and machine tool builders.
Computer Aided Manufacturing Examples
CAM is widely utilized in various sectors and has emerged as a dominant technology in the manufacturing and design industries. Here are two examples of sectors where CAM is employed efficiently and drives solutions to many challenges in the specific business.
Virtual 3D prototype systems, such as Modaris 3D fit and Marvellous Designer, are already used by designers and manufacturers to visualize 2D blueprints into 3D virtual prototyping. Many other programs, such as Accumark V-stitcher and Optitex 3D runway, show the user a 3D simulation to show how a garment fits and how the cloth drapes to educate the customer better.
Aerospace and Astronomy
The James Webb Space Telescope's 18 hexagonal beryllium segments require the utmost level of precision, and CAM is providing it. Its primary mirror is 1.3 meters wide and 250 kilograms heavy, but machining and etching will reduce the weight by 92% to just 21 kilograms.
What is the best software for CAM?
Mastercam has been the most extensively utilized CAM software for 26 years in a row, according to CIMdata, an independent NC research business.
How CAD-CAM helps manufacturers?
Customers can send CAD files to manufacturers via CAD-CAM software. They can then build up the machining tool path and run simulations to calculate the machining cycle times.
What is the difference between CAD and CAM?
Computer-aided design (CAD) is the process of developing a design (drafting). CAM is the use of computers and software to guide machines to build something, usually a mass-produced part.
Article | December 8, 2021
A digital twin is a virtual model of an object or system that comprises its lifecycle. It is updated with real-time data and aids decision-making through simulation, machine learning, and reasoning for the production system.
IoT sensor data from the original object is used to create a digital twin of the system. This cloud-connected data allows engineers to monitor systems and model system dynamics in real-time.
Modifications can be tested on the digital twin before making changes to the original system.
Considering that digital twins are supposed to replicate a product's complete lifecycle and are used throughout the production process, it's not unexpected that digital twins have become prevalent in all stages of manufacturing.
“More than a blueprint or schematic, a digital twin combines a real-time simulation of system dynamics with a set of executive controls,”
– Dr. Daniel Araya, consultant and advisor with a special interest in artificial intelligence, technology policy, and governance
Companies will increasingly embrace digital twins to boost productivity and decrease expenses. As per recent research by Research and Markets, nearly 36% of executives across industries recognize the benefits of digital twinning, with half planning to implement it by 2028.So how does this digital twin technology benefit modern manufacturing? Let's have a look.
How the Digital Twin Drives Smart Manufacturing
Digital twins in manufacturing are used to replicate production systems. Manufacturers can develop virtual representations of real-world products, equipment, processes, or systems using data from sensors connected to machines, tools, and other devices.
In manufacturing, such simulations assist in monitoring and adapting equipment performance in real-time. With machine learning techniques, digital twins can predict future events and anticipate potential difficulties.
For maintenance, digital twins allow for quick detection of any problems. They collect real-time system data, prior failure data, and relevant maintenance data. The technique employs machine learning and artificial intelligence to predict maintenance requirements. Using this data, companies can avoid production downtime.
Digital Twin and Artificial Intelligence (AI) in manufacturing
Using digital twins and AI in production can enhance uptime by predicting potential failures and keeping equipment working smoothly. In addition, there are significant cost savings in the planning and design process as digital twins and AI can be used to replicate a specific scenario.
Maintenance is another area that has seen significant progress with the use of digital twin manufacturing. A Digital Twin powered by AI can predict when a piece of equipment will fail, allowing you to arrange predictive maintenance that is not simply taking information from OEM manuals but can significantly cut maintenance expenses along with reducing downtime.
Using the digital twin, it is feasible to train virtual workers in high-risk functions, similar to how pilots are trained using flight simulators. It also frees up highly skilled workers to upgrade the plant and streamline operations.
General Electric Created the Most Advanced Digital Twin
General Electric Company (GE) is a multinational business based in Boston that was founded in 1892. It has developed the world's most advanced digital twin, which blends analytic models for power plant components that monitor asset health, wear, and performance with KPIs (Key Performance Indicators) determined by the customer and the organization's objectives. The Digital Twin is powered by PredixTM, an industrial platform built to manage huge amounts of data and run analytic algorithms. General Electric Company provides extra "control knobs" or "dimensionality" that can be utilized to improve the operation of the system or asset modeled with GE Digital Twin.
Given the numerous advantages of digital twin manufacturing, the potential for digital twins to be used in manufacturing is virtually endless in the near future. There will be a slew of new advancements in the field of digital twin manufacturing. As a result, digital twins are continually acquiring new skills and capabilities. The ultimate goal of all of these enhancements is to create the insights necessary to improve products and streamline processes in the future.
What is a digital twin in manufacturing?
The digital twins could be used to monitor and enhance a production line or perhaps the whole manufacturing process, from product design to production.
How digital twin benefit manufacturers?
Using digital twins to represent products and manufacturing processes, manufacturers can save assembly, installation, and validation time and costs.
What is a digital thread?
A digital twin is a realistic version of a product or system that replicates a company's equipment, controls, workflows, and systems. The digital thread, on the other hand, records a product's life cycle from creation to dissolution.
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