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.
"name": "How is machine learning used in manufacturing?",
"text": "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."
"name": "Which two forms of machine learning are there?",
"text": "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."
"name": "What is a machine learning model?",
"text": "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."
Article | August 20, 2020
In a world where up to 800 million people are chronically undernourished roughly one-third of food produced for human consumption is lost or wasted across the entire supply chain every year. In a report by the World Resources Institute (WRI), “The most immediate reasons food leaves the human food supply chain tie back to concern about a food’s safety or suitability for consumption, or there being no perceived use or market for it.” These causes are further exasperated by “deterioration or suboptimal quality, or issues such as the food’s appearance, excess supply, and seasonal production fluctuations.”
Article | December 8, 2021
The manufacturing production schedule is a critical aspect that enables the manufacturing business to complete each production activity precisely and on time. Allocating different raw materials, resources, or processes to distinct project phases is called a production schedule. Its goal is to make your manufacturing process as efficient and cost-effective as possible in terms of resources and labor — all while delivering products on schedule.
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
So, how is the overall production schedule managed?
According to businesswire, the global APS (Advanced Production Planning and Scheduling) software market was valued at $1,491.22 million in 2020 and is anticipated to raise $2,941.27 million by 2028 expanding at an 8.86 percent CAGR from 2020 to 2028.
Some software and tools are available to assist manufacturing organizations in properly scheduling production planning, including MaxScheduler, TACTIC, MRPeasy, and Gantt charts. Though there are numerous software programs available on the market for production scheduling, the most crucial aspect is determining which elements to consider when planning production.
This blog will look at the five most important factors to consider while planning the production schedule.
Five Elements to Consider When Scheduling Production
As we saw in the introduction, production scheduling is used in the manufacturing process to assign plant and machinery resources, schedule human resources, plan production processes, and purchase materials.
So, what are the primary components or stages of this production scheduling process? Let's take a quick look at each of them.
Planning to Make the Best Use of the Company's Resources
The role of planning in production scheduling is to use the company's resources to maintain a regular production flow. As a result, downtime is decreased, and bottlenecks are minimized, allowing production to be optimized. For production scheduling, two forms of planning can be used:
Dynamic Planning: Dynamic planning is carried out under the idea that process stages will alter. So, materials must be ready, but production cannot begin until demand is decided.
Static Planning: Static planning is done keeping in mind that all process steps will be completed on schedule and without adjustments.
Routing to Determine the Order of Actions
A “bill of materials” is used in discrete manufacturing to specify what things are needed and in what quantities.
Routing determines the path and sequence of required phases of the process. It may involve in-house operations, but it may also comprise sub-contracted components that must be returned to the production flow for final assembly.
Scheduling to Make Use of Predetermined Planning Levels
To manufacture products from components or raw materials, scheduling makes use of the previously set planning level. As a result, it is time-dependent and must meet the demand outlined at the planning level.
Each department, product, and procedure can have their own unique set of timetables. Sub-schedules for sub-assemblies or mixes and blends may be defined by department-specific master production schedules, utilized at the highest level to define product timeframes.
Dispatching to Decide on Immediate Actions
Dispatching assigns the following jobs to be done from a subset of the production queue. Dispatching is utilized to make quick decisions. This is in contrast to planning, which involves the planning of future actions. Dispatching is utilized in both pull and push production systems.
Execution to Ensure that all Processes are Carried out Correctly
Production scheduling must rely on proper execution to ensure that all processes are completed appropriately and in the sequence planned.
It requires everyone to know what they are expected to do and when they are expected to do it. Execution requires knowledgeable management decisions, well-trained employees, correct data in the manufacturing plan and schedule, and consistent sales statistics and forecast numbers. All must be present for the organization to carry out its production plan and fulfill orders.
How MRPeasy – A Production Scheduling Software Assist Manufacturing Companies in Scheduling Their Production?
MRPeasy is a cloud-based material requirements planning (MRP) application explicitly designed for small manufacturing units. Its primary functions are purchase order management, forecasting, and inventory management.
This software simplifies the process of scheduling production. It enables you to evaluate all of your anticipated manufacturing orders (MO). The bill of materials (BOM), purchasing, and stocking are all maintained in one location, allowing you to quickly book inventory and increase purchase orders (PO) for acquired parts.
MRPeasy enables you to:
Obtain all of the detailed information on all of your MOs
Consider MOs as a single block or as distinct operations.
Drag-and-drop operations and operations to reschedule
Calendar or Gantt chart views are available for monitoring scheduled orders.
Additionally, you can manage MOs smoothly. With the production planning component, you may create, amend, and update MOs. This app compiles an exhaustive list of all your MOs. You can track their progress based on the status of an order or a part's availability. Additionally, you can search for, filter, and export your MOs.
How to schedule production for your organization requires extensive research, planning, and analysis of overall product demand as well as a grasp of the time required to meet the demand. Production scheduling techniques such as job-based planning, batch method, flow method, and others help develop a productive manufacturing production schedule. Include the elements mentioned above in your manufacturing scheduling to get the best possible benefits, such as better production efficiency, lower production costs, and on-time product delivery for your manufacturing in 2022.
How production planning differ from production scheduler?
Production planning and scheduling are often mixed. But there is a difference. Planning decides what and how much work must be done, whereas scheduling specifies who and when the work will be done.
What is real-time manufacturing scheduling?
Real-Time Scheduling is a production planning, scheduling, and tracking tool that enables manufacturing organizations to improve customer satisfaction and achieve optimal operational performance cost-effectively.
How can scheduling be improved?
Communication with staff is a great way to improve scheduling. This is true for all businesses, software or otherwise. However, management should not burden employees with ambiguous or unclear communication, and vice versa.
"name": "How production planning differ from production scheduler?",
"text": "Production planning and scheduling are often mixed. But there is a difference. Planning decides what and how much work must be done, whereas scheduling specifies who and when the work will be done."
"name": "What is real-time manufacturing scheduling?",
"text": "Real-Time Scheduling is a production planning, scheduling, and tracking tool that enables manufacturing organizations to improve customer satisfaction and achieve optimal operational performance cost-effectively."
"name": "How can scheduling be improved?",
"text": "Communication with staff is a great way to improve scheduling. This is true for all businesses, software or otherwise. However, management should not burden employees with ambiguous or unclear communication, and vice versa."
Article | March 22, 2022
Manufacturing analytics, or real-time manufacturing analytics, is the process of collecting, cleansing, and analyzing data from machines to forecast their future use, prevent failures, forecast maintenance requirements, and identify areas for improvement.
“The goal is to turn the data into information and information into insight.”
- Carly Fiorina, ex CEO of Hewlett-Packard
Manufacturing data incorporates all structured and unstructured information collected manually or through software from machines and humans throughout the manufacturing process, up to the point at which a product is launched to the market.
In this article, we will look at the use cases of data analysis in manufacturing and some of the start-ups from the U.S. that are helping manufacturers gather their real-time manufacturing analytics.
Data Analysis in Manufacturing: Use Case Analytics
Forecasting demand is highly dependent on historical data on supply levels, material costs, purchase trends, and customer behavior. Manufacturers can use analytics to accomplish the following:
Define the products to be manufactured in a time frame
Define products that are no longer in stock
Determine the quantity of products to be manufactured
Forecast sales prospects
Forecasting demand enables manufacturers to manage inventory, purchase materials, and optimize storage capacity based on data. Additionally, manufacturing industry data analysis provides insight into:
The sales-to-inventory ratio indicates the average inventory value over net sales.
Days in inventory refers to the time a manufacturer retains before selling a product.
Gross margin return on inventory (GMROI) is a term that refers to the amount of gross margin a manufacturer receives for each dollar invested in inventory.
Data collected from various manufacturing machines, tools, and devices, as well as information about operations and the gears required for the machines, can be analyzed to:
Predict when a machine will require maintenance based on the amount of time and the operations in which it has been used.
Identify and resolve operational anomalies caused by or will result in machine failure.
Prevent downtime by scheduling machine breakdowns, repairs, and replacements in advance.
Utilizing analytics can assist manufacturers in determining the actual cost of a product based on the costs of materials, labor, machines, and tools used or purchased during the manufacturing process. Additionally, manufacturers can optimize prices based on data about competitors, market trends, consumer behavior, and purchase history. Additionally, analytics can assist in setting dynamic prices that are determined by demand, supply, competition, and subsidiary product prices.
Analytics for Manufacturing as a Service: Three U.S.-based Startups
Uptake offers predictive analytics solutions powered by artificial intelligence for various industries. It provides a compass, which allows organizations to optimize work orders and scout. This allows users to analyze data and custom alerts and radar to get failure and anomaly detection solutions. Failure prediction, noise filtering, situational analytics, and detecting changes in operational behavior are just a few of the features that these systems offer to their customers today. The product, by Uptake, is intended for use in various industries like mining, construction, fleet management, manufacturing, aviation, government, and oil and gas.
Seeq is a leading provider of industrial data analytics solutions. Its big data analysis solutions help in the analysis and comprehension of industrial process data (IPD) more effectively and quickly than typical alternatives. Reduced analysis time, quicker relationship discovery, ERP and other system connectivity, support for business intelligence (BI) tools such as Excel, Tableau, SAS, and MATLAB, and collaboration support are some of the features.
Sight Machine provides a platform for manufacturing applications that utilize digital twins. It provides solutions for continually analyzing images captured by industrial cameras, sensor data, and data from manufacturing systems to improve product quality and operations. It provides real-time visibility and actionable data for every part, machine, line, and plant manufacturing process. Its clientele includes Nike, Sony, Nissan, and Google, to name a few.
Big data analytics in manufacturing assists businesses in identifying the parameters that have a direct effect on production. Additionally, modifying the target process helped businesses increase productivity by 50%.
McKinsey estimates that when analytics are used in design-to-value workflows and projects, manufacturers' gross margins can increase by as much as 40%. Manufacturing analytics can help with design-to-value, supply chain management, and after-sales support. Real-time manufacturing analytics enables manufacturers to optimize their overall production.
Why is data critical in manufacturing?
Big data helps manufacturers understand their customers' needs and wants better. To launch a new product or upgrade an old one, data is required to make it appealing to customers and assess the risks of competition.
What is production analysis?
Production analysis visualizes production output and helps assess production losses and associated costs.
What is predictive manufacturing?
Predictive manufacturing uses descriptive analytics and data visualization to provide a real-time perspective of asset health and reliability performance.