Article | January 3, 2022
Production planning and control are critical components of any manufacturing organization. It helps organizations with the regular and timely delivery of their goods. Furthermore, it allows manufacturing businesses to increase their plant’s efficiency and reduce production costs.
Numerous software and tools for production scheduling and planning are available on the market, including Visual Planning, MaxScheduler, and MRPeasy, which assist manufacturing organizations in planning, scheduling, and controlling their production.
According to KBV Research, the manufacturing operations management software market is anticipated to reach $14.6 billion by 2025 globally, expanding at a market growth of 10.2 percent CAGR during the forecast period.
So, what exactly is production planning and control?
Production planning is an administrative process within a manufacturing business. It ensures that sufficient raw materials, personnel, and other necessary items are procured and prepared to produce finished products according to the specified schedule.
Scheduling, dispatch, inspection, quality control, inventory management, supply chain management, and equipment management require production planning. Production control makes sure that the production team meets the required production targets, maximizes resource utilization, manages quality, and saves money.
“Manufacturing is more than just putting parts together. It’s coming up with ideas, testing principles and perfecting the engineering, as well as final assembly.”
– James Dyson
In oversize factories, production planning and control are frequently managed by a production planning department, which comprises production controllers and a production control manager. More significant operations are commonly monitored and controlled from a central location, such as a control room, operations room, or operations control center.
Why Should You Consider Production Planning?
An efficient production process that meets the needs of both customers and the organization can only be achieved through careful planning in the early stages of production. In addition, it streamlines both customer-dependent and customer-independent processes, such as on-time delivery and production cycle time.
A well-designed production plan minimizes lead time, the period between placing an order and its completion and delivery. The definition of lead time varies slightly according to the company and the type of production planning required. For example, in supply chain management, lead time refers to the time required for parts to be shipped from a supplier.
Steps in Production Planning and Control
The first stage of production planning determines the path that raw materials will take from their source to the finished product. You will use this section to determine the equipment, resources, materials, and sequencing used.
It is necessary to determine when operations will occur during the second stage of production planning. In this case, the objectives may be to increase throughput, reduce lead time, or increase profits, among other things. Numerous strategies can be employed to create the most efficient schedule.
The third and final production control stage begins when the manufacturing process is initiated. When the scheduling plan is implemented, materials and work orders are released, and work is flowing down the production line, the production line is considered to be running smoothly.
The fourth stage of manufacturing control ascertains whether the process has any bottlenecks or inefficiencies. You can use this stage to compare the predicted run hours and quantities with the actual values reported to see if any improvements can be made to the processes.
Production Planning Example
Though production planning is classified into several categories, including flow, mass production, process, job, and batch, we will look at a batch production planning example here.
Manufacturing products in batches is known as "batch production planning." This method allows for close monitoring at each stage of the process, and quick correction since an error discovered in one batch can be corrected in the next batch. However, batch manufacturing can lead to bottlenecks or delays if some equipment can handle more than others, so it's critical to consider capacity at every stage.
Consider the following example of batch production planning:
Jackson's Baked Goods is in the process of developing a production plan for their new cinnamon bread. To begin with, the head baker determines the batch production time required by the recipe.
He then adjusts the bakery's weekly ingredient orders to include the necessary supplies and schedules the weekly cinnamon bread bake during staff downtime.
Finally, he creates a list of standards for the bakery staff to check at each production stage, allowing them to quickly identify any substandard materials or other batch errors without wasting processing time on subpar cinnamon bread.
Running a smooth and problem-free manufacturing operation relies heavily on a precise production planner. Many large manufacturing companies already have a strong focus on streamlining their processes and making the most of every manufacturing operation, but small manufacturing companies still have work to do in this area. As a result, plan, schedule, and control a production that will enable you to run your business in order to meet its objectives.
What is the difference between planning and scheduling in production?
Production planning and scheduling are remarkably similar. But, it is critical to note that planning determines what operations need to be done and scheduling determines when and who will do the operations.
What is a production plan?
A product or service's production planning is the process of creating a guide for the design and manufacture of a given product or service. Production planning aims to help organizations make their manufacturing processes as productive as possible.
Article | November 20, 2021
Advanced manufacturing enables the concept of industry 4.0 and represents a significant milestone in the manufacturing industry. Additive manufacturing is a critical component of the industry 4.0 concept, propelling the industry to new heights of innovation.
In various fields that are not immediately related to industry 4.0 or manufacturing, additive manufacturing has alternatively been referred to as 3D printing. The numerous advantages of additive manufacturing, such as reduced cost and time, are boosting its popularity and use in manufacturing and other industries.
“Digital technology is so empowering on so many fronts, but for it to be empowering, it must be for everyone.”
– Michael Walton, Director, Industry Executive (Manufacturing) at Microsoft.
The global market of additive manufacturing is anticipated to increase at a 14.42 percent compound annual growth rate from USD 9.52 billion in 2020 to USD 27.91 billion in 2025. According to this market research, the future of 3D printing or additive manufacturing is quite bright in the coming years, and we will see widespread application across industries.
First, let us understand the idea of additive manufacturing and its benefits to various industries.
Concept of Additive Manufacturing
Additive manufacturing is building a real thing from a three-dimensional computer model, often by successively layering a material. This technique utilizes computer-aided design (CAD) software or 3D object scanners to command devices to deposit material in exact geometric shapes layer by layer. As the name implies, additive manufacturing involves the addition of material to produce an object.
Additive Manufacturing Benefits
Produces Fewer Scraps and Trash
When we compare additive manufacturing to traditional manufacturing techniques such as milling or turning, additive manufacturing adds only the amount of material required to build a product. As a result, it generates less waste and conserves scarce resources.
Reduces the Time and Cost of Prototyping
Making a product prototype is now faster, easier, and cheaper. Other production processes, like milling, have high setup and material costs. Prototyping is less expensive and takes less time, so you can quickly produce, test, and modify. It also shows practically instant verification of progress done.
It Encourages the Digitalization of Businesses
Continuous and effective communication between devices, machines, and robots is required for additive manufacturing. However, this is only achievable with effective digitization of production processes. As a result, businesses invest more in digital and IoT, a prerequisite for Industry 4.0.
It Simplifies the Assembling Process by Condensing it into a Single Component
Additive manufacturing in Industry 4.0 also simplifies the production process, especially product assembly. A traditional component requires numerous manufacturing procedures. This increases material and labor expenses as well as production time. However, additive manufacturing allows you to print the group in one piece.
The Top Three Industries That Make the Most Use of Additive Manufacturing
Additive manufacturing is presently used in a variety of industries. However, specific sectors make the best use of it. Thus, we will examine the industries embracing additive manufacturing technology and emerging with new life easing solutions.
In the healthcare industry, dentistry is the critical application of additive manufacturing. Technology helps it create bridges, crowns, braces, and dentures, always in high demand.
Additive manufacturing has also been used to create tissues and organs, surgical tools, patient-specific surgical models, and personalized prosthetics. For example, many medical equipment companies employ 3D printing to build patient-specific organ replicas that surgeons can practice before completing complex surgeries.
Additive manufacturing is utilized to fabricate metal brackets that serve as structural components within airplanes. Prototypes are increasingly being printed in three dimensions, allowing designers to fine-tune the shape and fit of finished parts. In addition, interior airplane components such as cockpit dashboards and door handles are manufactured using 3D printing services.
3D printing can manufacture molds and thermoforming tools, grips, jigs, and fixtures for the automotive industry. Automakers utilize additive printing to customize parts for specific vehicles or drivers (e.g., seats for racing cars).
An appealing colored dashboard, efficient fuel systems, and complicated braking mechanisms are all possible with 3D printing in the automotive industry. Therefore, it is best suited for pre-production, manufacture, and modification of automotive parts.
How Does NASA use additive manufacturing in its space projects?
The space environment has always been unpredictable, and scientists must be adequately prepared before embarking on any space mission. They must consider the durability and weight of all the objects they propose to transport into space. To land any object on a planet that does not have a flat surface or similar weather conditions to earth, scientists must design each object with these considerations in mind.
“You always want it to be as light as possible, but you also want it to be strong enough.”
-Chris Chapman, NASA Test Engineer
It is not conceivable to make items capable of dealing with all the changes on other planets and achieving these project objectives using conventional materials and production processes. However, scientists do require a technique that will enable them to manufacture lighter and stronger objects for their space missions.
3D printing has played a significant part in meeting this demand and has provided space projects to manufacture objects that would withstand any unexpected events during space missions. For example, NASA employed 3D-printed metal components in their Mars project.
NASA's specialized engineers are utilizing additive manufacturing to create rocket engines and possible Moon and Mars outposts. NASA used the 11 3D printed metal components on its Mars mission as well. It employed 3D printed components for the first time in the Curiosity rover, which landed on Mars in 2012. It was a successful project, and NASA has since begun employing 3D printed parts in its space missions to make machines lighter while remaining robust and functional.
Additive manufacturing technology is making a real difference in the manufacturing process, and it is becoming the trending technology in the manufacturing industry. The benefits of additive manufacturing make the manufacturing process more advanced, easy, and customer-oriented. Additive manufacturing is the major transformation in the manufacturing industry and will take it to new heights of precision.
Why is additive manufacturing critical?
Additive manufacturing reduces the time and cost of prototyping and reduces the scraps amount during the manufacturing process of any object. In addition, it simplifies multiple processes from various industries.
Are additive manufacturing and 3D printing the same?
Yes, additive manufacturing and 3D printing are the same processes with different names as per the choice of the different industries. For example, in some industries such as space missions, It is also referred to as Fused Deposition Modelling (FDM).
Which is the most applied sector for additive manufacturing?
Healthcare is the industry that utilizes additive manufacturing technology the most. It also helps medical practitioners practice surgery on any critical body part with its 3D printed model from human tissues.
"name": "Why is additive manufacturing critical?",
"text": "Additive manufacturing reduces the time and cost of prototyping and reduces the scraps amount during the manufacturing process of any object. In addition, it simplifies multiple processes from various industries."
"name": "Are additive manufacturing and 3D printing the same?",
"text": "Yes, additive manufacturing and 3D printing are the same processes with different names as per the choice of the different industries. For example, in some industries such as space missions, It is also referred to as Fused Deposition Modelling (FDM)."
"name": "Which is the most applied sector for additive manufacturing?",
"text": "Healthcare is the industry that utilizes additive manufacturing technology the most. It also helps medical practitioners practice surgery on any critical body part with its 3D printed model from human tissues."
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 | December 10, 2021
A new form of robot is entering manufacturing plants all around the globe. Instead of being locked away in their own work cell, collaborative robots work side by side with their human counterparts. Together, they form the manufacturing crew of the future.
Collaborative robots, or cobots, are more flexible, easy to use, and safer than industrial robots. Instead of ending up abandoned in a corner, they are proving to be serious expansions of production capacity leading to better ways of creating superior quality products.
1.1 A New Breed of Bot
Cobots are a new type of automation product with their own ISO standards for safety and usability. For a robot to qualify as a cobot, it has to be used for tasks of a collaborative nature while sharing all or part of its reach space with human operators. So it is not the product alone that classifies it as a cobot.
Industrial robots must be expertly programmed for one specific job along the production line. This requires hard line coding and endless tweaking and testing, which together with other factors make for a sizable upfront investment. Not so with collaborative robots.
Cobots may look similar to traditional robots in some ways, but they are much easier to install and program. This foregoes the need to cooperate with a robotic integration service. Their lightweight and friendly form factor lets manufacturers conveniently relocate them on the shopfloor from one project to another.
This renders the robotics technology perfect for a data-driven, Industry 4.0 work environment. Cobots can side with traditional machinery and additive manufacturing equipment, aided by artificial intelligence and cloud connectivity while embedded in a networked environment rich with smart sensors and mixed reality interfaces.
1.2 A Unique Blend of Benefits
Because it is fairly straightforward to reprogram a cobot to various tasks, they are perfect for high-mix, low-volume work to meet the rising demand for ultra-customized products.
They can also do multiple tasks in unison, such as alternatingly loading a machine and finishing parts from the previous cycle. Here are some other advantages in addition to flexibility:
• Low investment. Cobots typically cost a fraction of the price of an industrial robot, but they offer much lower payload and reach. ROI is typically one to two years.
• Safety. With rounded surfaces, force-limited joints, and advanced vision systems, cobots are exceptionally safe. This reduces the risk of injury due to impact, crushing, and pinching. Driverless transport systems are wheeled mobile robots that immediately halt when their lasers detect the presence of a nearby human being.
• Accuracy. Cobots score well on accuracy with 0.1mm precision or well below that. While they do typically sacrifice speed, dual-mode cobots can be converted to fully-fledged tools of mass production that run at full speed in their own safeguarded space.
• Easy to program. Many brands offer user-friendly programming interfaces from beginner to expert level. This reduces the need for continuous availability of expensive and scarce expertise while giving current employees an incentive to upskill. And because they can be deployed within hours, cobots can be leased for temporary projects.
• Research. Small processing plants, agile start-ups, and schools can invest in cobots to experiment with ways to automate processes before committing to full automation.
1.3 Cobot Activity Repertoire
Cobots are perfect candidates for taking over strenuous, dirty, difficult, or dull jobs previously handled by human workers. This relieves their human co-workers from risk of repetitive strain injury, muscle fatigue, and back problems. They can also increase job satisfaction and ultimately a better retirement.
The cobot’s program of responsibilities includes:
• Production tasks such as lathing, wire EDM, and sheet stamping.
• Welding, brazing, and soldering.
• Precision mounting of components and fasteners, and applying adhesive in various stages of general assembly.
• Part post-finishing such as hole drilling, deburring, edge trimming, deflashing, sanding, and polishing.
• Loading and unloading traditional equipment such as CNC and injection molding machines, and operating it using a control panel to drastically reduce cycle times.
• Post-inspection such as damage detection, electronic circuit board testing, and checking for circularity or planarity tolerances.
• Box-packing, wrapping, and palletizing.
• Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) assist with internal transport and inventory management.
1.4 No-Code Programming
While an industrial robot requires the attention of a high-paid robotics engineer, anyone with basic programming savviness can install and maintain a collaborative unit.
Brands are releasing more and more kits for quick installation and specific use cases. Instead of being all numbers and line-coding, current user interaction is exceptionally people-focused.
At the lowest skill level, lead-through programming lets operators physically guide the cobot’s end-of-arm-tool (EOAT) through the desired motion path, after which it will flawlessly replicate the instructed behaviour.
It is also possible to enter desired waypoints as coordinates. At the highest level, it is of course still possible to have full scripting control.
An intermediate step is visual programming interfaces. These let users create blocks of functionality that they can string together into more advanced action sequences, while entering the appropriate parameters for each function such as gripping strength, screwing tightness, or pressing force.
These UIs come in the form of in-browser or mobile apps.
Based on a 3D-CAD model of the machine and its industrial environment, a digital twin of the cobot can simulate and optimize its operations, for example to prevent collisions.
It also lets operators remotely monitor and adjust the machine while it’s running. All the while, back-end artificial intelligence can do its analyses to find further efficiency improvements.
3D models of the to-be-manufactured product can be imported for edge extraction of complex surfaces. These will then be converted into the cobot’s desired movement trajectories instead of tedious manual programming.
This makes them feasible to implement for highly dexterous tasks like welding curved hydroformed metal parts or sanding and polishing the most intricate of 3D printed geometries.
Interfacing directly with the robot is becoming increasingly human-centered as well. Future cobots will respond to voice interaction as well as touch input, eradicating the screens-and-buttons paradigm of current devices.
Some brands are giving the cobot a face with emotional expressions, hoping to lower the barrier to adoption. The upcoming generation of cobots can even respond to body language, as well as show its intentions by projecting light to where they are about to reach or move next.
1.5 A Human World
Ultimately, the objective of any company is to create value for people. It is not an option to completely remove humans from the shop floor in an attempt to stay at the forefront of innovation.
Attempting to leap to full automation and the utopian “lights-out factory” does not work anyway, as automotive giants such as Ford, Chrysler, GM, and Tesla can testify. A significant portion of human employees will indeed need to give up their roles. On the other hand, improved productivity levels open up space to retain personnel and uplift them to more creative, managerial, analytical, social, or overall more enjoyable jobs.
For certain tasks, humans still need to be kept inside the manufacturing loop. For example:
• Complex assembly routines and handling of flexible components.
• Large vehicle subassemblies contain many variable components and require more hand-eye coordination than one cobot can handle. Humans are needed to make sure everything lands in the right position while the cobot provides assistive muscle power.
• Fashion, footwear, jewellery, art pieces, and other products where creation borders on artistry rather than mechanical assembly require the aesthetic eye of humans. People are also needed to spot aesthetic deficiencies in custom one-offs in order to correspond with customers before finishing the production batch.
• While intelligent automation software can spot bottlenecks in efficiency, humans are required for creative problem solving and context-awareness to make decisions. A spirit of flexibility and innovation is just as important as the accuracy of perfect repetitions.
1.6 Mission: Install a Cobot
Cobots have numerous advantages over industrial solutions or people-only workspaces. They enable faster, more precise, and more sophisticated operations while reducing downtime and maintaining employee satisfaction.
Low-voltage operation and reduced material waste fits with sustainable innovation and corporate social responsibility programs.
Many companies are reporting surges in production capacity and staff generally experience the presence of cobots as favorable. For example, industry leviathans like BMW and Mercedes-Benz are reaching the conclusion that in many parts of the production process implementing a cobot has been the right decision.
Connecting all parts of the production line with full automation solutions is a pipedream. It works only when all steps are perfectly attuned, and in reality this never happens and one misstep can be catastrophic.
Whether to hire a human, a robot, or a co-robot is a complex and ever-more pressing decision. Statistical process control is paramount for large organizations to make unbiased data-driven decisions.
Determine the key performance indicators, then find the most critical bottlenecks and major opportunities for leaps in production efficiency, product quality, or staff unburdening.
Talk to employees for their insights and probe their level of skill and enthusiasm needed for working with their new artificial assistants. Digital transformation should be an exciting shift in the organization and its people, so apply new technological advancements only where it makes sense.
Despite common beliefs about robotization, the cobot is an entirely separate product category that can be a surprisingly plug-and-play solution for simple tasks, with programming apps becoming increasingly intuitive.
A cobot’s flexibility makes it perfect to run early experiments to help companies find its best spot on the factory floor. Its unbelievable precision, consistency, and level of control generally can make a strong first impression on customers.
Not only can cobots increase production capacity while reducing idle time and cycle time to accelerate manufacturing across many vertical markets, but they also enrich the work environment resulting in happier and more involved employees.
For many companies, a cobot can be the next logical step in their digital transformation.