Lean Manufacturing
Article | December 21, 2021
Consumer demand has shifted dramatically in recent years, and manufacturers are trying to adapt to this shift. To maintain high product quality, minimize costs, and optimize supply chains, manufacturing analyticshas become essential for manufacturers.
Manufacturing analyticsis the process of gathering and analyzing data from various systems, equipment, and IoT devices in real-time to get essential insights.
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
Manufacturing analyticscan assist in maintaining production quality, boost performance with high-profit returns, decrease costs, and optimize supply networks.
This article will outline manufacturing analyticsand present a list of possible application cases. It will also highlight the benefits of manufacturing analyticsfor any shop floor or factory.
Manufacturing analytics: An Overview
With manufacturing analytics, we can streamline and speed up the entire process. Data interchange and automation helps in speeding up the production process. Manufacturing analyticsuses predictive manufacturing, big data, Industrial IoT, network virtualization, and machine learningto produce better scalable production solutions.
Manufacturing analyticscollects and analyses data from many sources via sensors embedded in machinery to identify areas for improvement. Data is collected and presented in an easy-to-understand structure to illustrate where difficulties emerge throughout the process.
In short, manufacturing analyticscollects and analyses large volumes of data to reveal insights that might improve performance. Users can also obtain automated business reports to reply in real-time.
Why Manufacturing analytics is Vital for Leading Businesses
There are numerous benefits of manufacturing analyticsthat drive any company’s production and overall manufacturing business growth. The benefits of manufacturing analyticsfall into three distinct categories as below.
It reduces the overall cost: Analytics may save a significant amount of money if used more efficiently. Labor costs are also reduced due to automation and semi-autonomous machinery. Similarly, preventive and prescriptive maintenance programs may save money while enhancing productivity.
It boosts profits for businesses: Manufacturers can respond swiftly to changes in demand using real-time insights in production, inventory management, and demand and supply forecasting. For example, assume the data indicates that they are approaching their maximum capacity. In such instances, they can increase over time, increase capacity, modify procedures, or tweak other production areas to adapt and maintain delivery times.
Other unforeseen benefits: There are several advantages to the increased capabilities enabled by manufacturing analytics. These benefits include lower energy use, safer environmental practices, fewer compliance failures, and more customer satisfaction.
Five Real-world Applications of Manufacturing Analytics
Predictive Maintenance
A machine's analytics uses aggregate data from real-time detectors to anticipate when it needs to be replaced or functioning irregularly. This process helps predict machine failure or equipment defects.
Analytics can assist in determining a plant's capacity and how many products are produced by the unit in every production cycle, which is helpful in capacity planning. In addition, analytics may help determine the ideal number of units to create over time by considering capacity, sales predictions, and parallel schedules.
Predictive analytics solutions can automate maintenance requests and readings that shortens the procedure and reduce maintenance expenses.
Product Development
Product development is an expensive process in manufacturing. As a result, businesses must invest in R&D to develop new product lines, improve existing models, and generate new value-added services.
Earlier, this approach was in place by repeated modeling to get the finest outcome. This approach can now be modeled to a large extent, with the help of data science and technologically superior analytics. Real-world circumstances can be replicated electronically using "digital twins" and other modeling approaches to anticipate performance and decrease R&D expenses.
Demand Forecasting
Many factors that might help in the plan significant capital expenditures or brief breakdowns can be explained using historical data and a few high-impact variable strategies. For example, consider the seasonality of products like ice cream. As a result, historical market data and a few high-impact factors can help explain numerous variables and plan major capital expenditures or short-term shutdowns.
In addition to demand forecasting, predictive analytics incorporates advanced statistical techniques. With predictive analytics, a wide range of parameters, including customer buying behavior, raw material availability, and trade war implications, may be taken into consideration.
Warranty Analysis
Warranty support may be a load for many manufacturers. Warranties are frequently based on a "one-size-fits-all" approach that is broader. This approach introduces uncertainty and unanticipated complications into the equation.
Products may be modified or updated to decrease failure and hence expense by using data science and obtaining information from active warranties in the field. It can also lead to better-informed iterations for new product lines to minimize field complaints.
Managing Supply Chain Risks
Data may be recorded from commodities in transit and sent straight from vendor equipment to the software platform, helping to enable end-to-end visibility in the supply chain.
Manufacturing analyticsallows organizations to manage their supply chains like a "control tower," directing resources to speed up or slow down. They may also order backup supplies and activate secondary suppliers when demand changes.
Final Words
Businesses should adapt to changing times. Using analytics in manufacturinghas altered the business industry and spared it from possible hazards while boosting production lines. Industry 4.0's route has been carved. Manufacturing analyticsis the key to true Industry 4.0, and without it, the data produced by clever IoT devices is meaningless. The future is data-driven, and success will go to those who are ready to adopt it. The faster adoption, the sooner firms go ahead of the competition.
FAQ
How can data analytics help manufacturers?
Data analytics tools can help manufacturers analyze machine conditions and efficiency in real-time. It enables manufacturers to do predictive maintenance, something they were previously unable to accomplish.
Why is data so crucial in manufacturing?
Data helps enhance manufacturing quality control. Manufacturers can better understand their company's performance and make changes by collecting data. Data-driven manufacturing helps management to track production and labor time, improve maintenance and quality, and reduce business and safety concerns.
What is Predictive Manufacturing?
Predictive manufacturing uses descriptive analytics and data visualization to offer a real-time perspective of asset health and dependability performance. In addition, it helps factories spot quality issues and takes remedial action quicker by eliminating the waste and the cost associated with it.
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Procurement & Supply Chain
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
Routing
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.
Scheduling
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.
Dispatching
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.
Follow-Up
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.
Example
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.
Final Words
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.
FAQ
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.
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Smart Factory, Industrial 4.0
Article | July 8, 2022
Industry 4.0 is a new phase of the Industrial Revolution that emphasizes interconnectivity, automation, machine learning, and real-time data. Industry 4.0 and IIoT combine physical production with smart digital technology to create a holistic and better connected ecosystem for manufacturing and supply chain management.
Industry 4.0 is about revolutionizing the way your entire business operates and grows by investing in new technology and tools to improve manufacturing efficiency. In most cases, businesses are deploying iIoT devices and machinery linked by communication technologies to help industries collect, monitor, analyze, and deliver valuable insights. The specifics of how this technology is used vary from company to company, but the goal is always the same: to improve operational efficiency through analytics, automation, and connectivity.
For example,
The manufacturing sector is already undergoing a revolution. According to an IDC report, the sector spent $178 billion on the iIoT in 2016. Also, as per MarketWatch, iIoT investment in the manufacturing sector will grow at a CAGR of 29.68% until 2020.
Amazon paid $775 million for the robotics company Kiva in 2012. It crosses our mind: why such a big investment? It made more sense for them to use robots to locate products and deliver them to their workers than for their workers to find the products themselves.
Smart Manufacturing Use Cases:
Industry 4.0 technology is being implemented by a North American tool manufacturer in order to improve efficiencies and productivity in its global manufacturing operations.
Caterpillar recently collaborated with industrial analytics firm Uptake to develop technologies that will assist their customers in monitoring the health of their equipment. They used a variety of IoT and augmented reality technologies to provide machine operators with an instant view of vitals such as fuel levels and when the air filter needs to be replaced. When an air filter needs to be replaced, the technology sends detailed instructions to the machine operator.
Siemens' factory in Amberg, Germany, personifies the company's passion for technological innovation. The plant was created as part of a collaboration with the German government to create a series of fully automated smart factories. The plant itself manufactures parts for BMW and is said to be approximately 75% automated.
The purpose of going over these use cases is to help you imagine and consider how smart manufacturing could be integrated into your own organization.
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Industrial 4.0
Article | February 11, 2022
Industry 4.0 technologies, ranging from simulation to big data, have advanced significantly during the past few years. It is critical to gaining access to real-time outcomes and data that will propel the sector to new heights of lean success. Growing industry expertise and technological applications are making all cutting-edge technologies commercially available.
However, the notion of Industry 4.0 is not straightforward. It comprises a wide range of technologies and is applied across a variety of circumstances. This article will explore some of the key components of Industry 4.0 and their application scenarios. All of them are critical components for industry to work smoothly, accurately, and effortlessly. Each individual component plays a unique role in the overall efficacy of Industry 4.0 technologies.
Industry 4.0 Components
Big Data and Analytics & Use Case
Big data analytics is one of the core components of Industry 4.0. With big data analytics, businesses may identify important correlations, patterns, trends, and preferences to help them make better decisions. In Industry 4.0, big data analytics is used in smart factories to forecast when maintenance and repair procedures are required. Manufacturers benefit from increased production efficiency, real-time data analysis, predictive maintenance optimization, and production management automation.
“Data is the new science. Big data holds the answers.”
– Pat Gelsinger, CEO at VMware
The IoT and current production systems create a lot of data that must be acted upon. That's why big data organizes data and develops insights that help businesses enhance their operations.
Big Data Use Cases
Optimizing Warehouse Operations: Businesses may increase operational efficiency by identifying human mistakes, running quality checks, and displaying ideal production or assembly routes using sensors and portable devices.
Eliminating Bottlenecks: Big data helps identify variables that may slow the operation’s performance and diagnose the issue at an early stage and eliminate bottlenecks.
Predicting Demand: More accurate and relevant forecasts are made possible by visualizing activities beyond historical data through internal analysis (consumer preferences) and external analysis (trends and external events). This enables the business to predict demand, adjust and optimize its product portfolio.
Proactive Upkeep: By recognizing breakdowns in patterns, data-fed sensors indicate potential problems in the operation of machinery before they become breakdowns. The system notifies the equipment in order for it to react appropriately. These are only a few of the applications of big data analysis in manufacturing systems; there are several others, including enhanced security, load optimization, supply chain meanagemnt, and non-conformity analysis.
Industrial Internet of Things (IIoT) & Use Case
The next component in the industry 4.0 components list is IIoT. By virtue of its unique characteristics, the Industrial Internet of Things (IIoT) is creating massive changes in industrial applications. It greatly improves the operational efficiency and workflow of factories by monitoring assets and processes in real time. The IIoT presents several opportunities for entrepreneurs to improve their industry exponentially.
“The Internet of Things is the game-changer for an overall business ecosystem transformation.”
– Joerg Grafe, Senior Market Analyst, IBM
IIOT Use Cases
Predictive Maintenance: Maintenance schedules are established for machines and assets that run continually. Unplanned maintenance and failures often cost over $88 million a year. Predictive maintenance can help control these overhead costs.
Sensor and device data allows predictive analytics systems to swiftly analyze current conditions, identify danger indications, send alerts, and initiate maintenance activities. For example, a pumping station motor in an ideal IoT facility may schedule maintenance if it detects irregularities in sensor data. This method saves money on routine and frequent maintenance.
Asset Tracking: Asset tracking is designed to find and track valuable assets. Industries can track assets to improve logistics, maintain inventory, and identify inefficiencies or theft.
Real-time asset tracking is vital in manufacturing. It may be used in warehouse and inventory management to keep track of the goods. This helps in finding the lost or misplaced goods in the warehouse. Industries with scattered assets may use IoT to track, monitor, and control them.
Workplace analytics: More IIoT devices mean more workflow data for organizations. Data scientists can use analytics engines to find inefficiencies and offer improved operations. Location data analysis might also reveal warehouse inefficiencies.
Remote quality monitoring: Sensors give faster and more cost-effective information about products or processes, leading to faster and more effective actions. Industry 4.0-enabled quality monitoring systems can also be obtained from the IIoT.
Manufacturing factories can utilize IoT devices to remotely check material or product quality. It increases efficiency by allowing staff to verify many processes quickly. Similarly, real-time alarms make it easier for people to respond quickly, which lowers the risk of a failed product if left unchecked.
Because remote quality monitoring is a novel concept, there aren't any ready-made solutions or services. Developing customized IoT technology to measure certain metrics can be costly and difficult.
Cyber security & Use Case
Industrial manufacturing has one of the highest data breach costs of any sector. The Ponemon Institute's 2019 Cost of a Data Breach Report estimates the average industrial breach at $5.2 million. In May 2017, the WannaCry ransomware assault crippled several manufacturing companies, forcing some to shut down plants for days. Overall losses were in the billions.
“Cyber-Security is much more than a matter of IT.”
― Stephane Nappo
Cyber security is vital for a safer digital zone on your factory floor or in your manufacturing business. It is one of the crucial 4.0 industry components. It's essential to be mindful of the weaknesses while modernizing manufacturing. The largest risk in an open factory environment with widely distributed partners and operations is an incident that disrupts operations. No manufacturing company, or any organization, for that matter, should pursue digital transformation without including cyber security in every step and decision.
Cyber Security Use Cases
Analyzing network traffic to detect patterns indicative of a possible attack
Detect harmful activities or insider risks
Response to incidents and forensics
Manage the risk associated with third- and fourth-party vendors
Identify data intrusions and compromised accounts
Risk management, governance, and compliance
Threat hunting is a technique for identifying signs of attack
Additive Manufacturing & Use Case
Additive manufacturing is a set of manufacturing processes that create a final product by layering material. Additive manufacturing reduces production and supply chain costs by enabling the rapid creation of large quantities of parts. It eliminates stock and the requirement for molds. Initially, 3D printing was utilized for prototyping and is still the rule. However, 3D printing technology has advanced; it is now more inventive than ever before.
“3D printing is going to be way bigger than what the 3D printing companies are saying.”
– Credit Suisse
Additive Manufacturing Use Cases
Parts for New Products: Porsche is 3D printing aluminum pistons for the Porsche 911 G2 RS engine. The improved product was made feasible using generative design software, aluminum powder, and 3D printer improvements. General Atomics Aeronautical Systems has teamed up with GE Additive to print a NACA inlet. The component is made via laser powder bed fusion.
Parts for the Aftermarket: Aftermarket components are defined as non-OEM (original equipment manufacturer) replacement parts. Thyssenkrupp and Wilhelmsen Marine Products have teamed up to offer 3D printed replacement components. With aged ships, the maritime sector frequently needs hard-to-find, costly, and time-consuming spare components. 3D printing spare parts near to the source reduces lead times and shipping costs.
Jigs, Fixtures, Molds and Tools: Jigs, fixtures, molds, and tools are essential in manufacturing. When one of these fails, a plant's downtime is prolonged. Jabil, a manufacturing services firm, has adopted 3D printing. They no longer have to wait weeks for tools or components. They can now produce tooling, fixtures, and manufacturing aids in-house in days, speeding up new product launches and increasing customer satisfaction.
Simulation and Virtualization & Use Case
Simulation in manufacturing systems is the process of using software to create computer models of production systems for the purpose of analyzing them and obtaining valuable information. According to syndicated research, it is the second-most popular management discipline among industrial managers.
“Simulation is the situation created by any system of signs when it becomes sophisticated enough, autonomous enough, to abolish its own referent and to replace it with itself.”
- Jean Baudrillard
Simulator software lets businesses try out new technologies and principles in a risk-free, virtual setting so they can make sure they're making the right investments.
Simulation Use Cases
Interoperability: The simulation showed how downstream work stations may use extra location data to more efficiently choose and organize work batches to satisfy client demand.
Information Transparency: Using sensor data, we may construct a virtual replica of the physical world, such as a manufacturing plant or contact center. This technology allows an operator to visually evaluate and certify products.
Technical Assistance: Simulating the use of Automated Guided Vehicles (AGVs) to accelerate traditional production and manufacturing processes. Additionally to substitute physically hard jobs such as stock moving is becoming increasingly popular.
Due to simulation's ability to capture the process time variation, it is an effective tool for validating critical design parameters. For example, the number of AGVs to purchase, the overall benefits to throughput, maintenance planning, and track layout.
Decentralized Decisions: In a high-mix, high-volume production plant, a simulation is performed to examine the feasibility of increasing a palletizer's storage capacity in order to 'rack-up' a series of basic tasks for overnight processing while reserving more complex processes for staff hours.
The simulation lets you try out a large number of test scenarios, including worst-case scenarios in which the machine becomes stuck near the start of its overnight operation.
Final Word
Industry 4.0 is a solution bundle for manufacturers to improve their manufacturing, inventory, and supply chain management. The key components mentioned above are only a few from an extensive list. There are more industry 4.0 technologies to include in the list, including digital twins, cloud, virtualization, robots, augmented reality, artificial intelligence, and more. Many of these technologies are now accessible to make future forward smart factories a reality today. Know about the uses of each component and learn how to integrate it into your digital manufacturing.
FAQ
What is industry 4.0 also called?
Industry 4.0 is also known as IIoT or smart manufacturing. It combines physical manufacturing and operations with smart digital technologies such as machine learning, and big data to create a more holistic and linked environment for manufacturing and supply chain businesses.
Why is Industry 4.0 needed?
Industry 4.0 technologies help you control and optimize your production and supply chain operations. It provides real-time data and insights to help you make better business decisions, eventually increasing the productivity and profitability of your company.
What are the four core components of industry 4.0?
In an attempt to define Industry 4.0 concept, German researchers developed a list of industry-defining components. They are: cyber-physical systems, IoT, Internet of Things, and smart factories.
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