Article | July 28, 2021
Rex Moore Group, Inc. is a Top50 electrical contractor delivering unmatched integrated electrical solutions. As an early adopter of Lean manufacturing principles, Rex Moore has created a company-wide culture of continuous improvement that drives significant value to their clients. The firm contracts and performs both design/build and bid work for all electrical, telecommunications, and integrated systems market segments.
Rex Moore has a full-service maintenance department to cover emergency and routine requirements for all facilities, whether an existing facility or one that has been recently completed by the company. The ability to negotiate and competitively bid various forms of contracts including lump-sum, fixed fee, hourly rate, and cost-plus work as a prime contractor, subcontractor, or joint venture is enhanced with Project Business Automation (PBA) from Adeaca. This solution permits the company to propose work only if they are in a position to be competitive in the marketplace and provide excellent service with fair compensation.
Rex Moore used Adeaca PBA as a construction management software for builders and contractors to integrate and facilitate its business processes in its ERP system. Together with Microsoft Dynamics, PBA integrated processes across the company on a single end-to-end platform. This allowed the company to replace 15 different applications with a single comprehensive system, eliminating the costs and inefficiencies associated with multiple systems and silos of information.
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 13, 2021
Lean manufacturing is a growing trend that aims to reduce waste while increasing productivity in manufacturing systems. But, unfortunately, waste doesn't add value to the product, and buyers don't want to pay for it.
This unusual method pushed Toyota Motor Corporation's industry to become a leading Toyota Production System (TPS). As a result, they are now efficiently producing some of the world's top cars with the least waste and the quickest turnaround.
The majority of manufacturers are now using lean management. According to the 2010 Compensation Data Manufacturing report, 69.7% of manufacturing businesses use Lean Manufacturing Practices.
Lean tools are the ones that help you in implementing lean practice in your organization. These lean tools assist in managing people and change while solving problems and monitoring performance. Lean Manufacturing technologies are designed to reduce waste, improve flow, improve quality control, and maximize manufacturing resources.
What Are the Five Best Lean Manufacturing Tools and How Do They Work?
There are roughly 50 Lean Manufacturing tools available in the market. This post will describe 5 of them and their value to your business and its developments.
The 5S system promotes efficiency by organizing and cleaning the workplace. To help increase workplace productivity, the system has five basic guidelines (five S's). The five Ss are Sort, Set, Shine, Standardize, and Sustain.
5S improves workplace efficiency and effectiveness by:
Sort: Removing unnecessary material from each work area
Set: Set the goal of creating efficient work areas for each individual
Shine: Maintaining a clean work area after each shift helps identify and resolve minor concerns
Standardize: Documenting changes to make other work areas' applications more accessible
Sustain: Repeat each stage for continuous improvement
5S is a lean tool used in manufacturing, software, and healthcare. Kaizen and Kanban can be utilized to produce the most efficient workplace possible.
Just-In-Time (JIT) manufacturing
Just-in-time manufacturing allows manufacturers to produce products only after a customer requests them. This reduces the risk of overstocking or damaging components or products during storage.
Consider JIT if your company can operate on-demand and limit the risk of only carrying inventory as needed. JIT can help manage inventory, but it can also hinder meeting customer demand if the supply chain breaks.
With Kaizen, you may enhance seven separate areas at once: business culture, leadership, procedures, quality, and safety. Kaizen is a Japanese word, means "improvement for the better" or "constant improvement."
“Many companies are not willing to change or think they are done once they make a change. But the truth is technology; consumer demands, the way we work, human needs and much more are constantly changing.”
– Michael Walton, Director, Industry Executive at Microsoft
The idea behind Kaizen is that everyone in the organization can contribute suggestions for process improvement. Accepting everyone's viewpoints may not result in significant organizational changes, but minor improvements here and there will add up over time to substantial reductions in wasted resources.
Kanban is a visual production method that delivers parts to the production line as needed. This lean tool works by ensuring workers get what they need when they need it.
Previously, employees used Kanban cards to request new components, and new parts were not provided until the card asked them to. In recent years, sophisticated software has replaced Kanban cards to signal demand electronically. Using scanned barcodes to signify when new components are needed, the system may automatically request new parts.
Kanban allows businesses to manage inventory better, decrease unnecessary stock, and focus on the products that must be stored. To reduce waste and improve efficiency, facilities can react to current needs rather than predict the future.
Kanban encourages teams and individuals to improve Kanban solutions and overall production processes like Kaizen. Kanban as a lean tool can be used with Kaizen and 5S.
PDCA (Plan, Do, Check, Act)
Plan-Do-Check-Act (PDCA) is a scientific strategy for managing change. Dr. W. Edwards Deming invented it in the 1950s; hence, it is called the ‘Deming Cycle.’
The PDCA cycle has four steps:
Problem or Opportunity: Determine whether a problem or an opportunity exists
Do: Make a small test
Examine: Look over the test results
Act: Take action depending on results
How Nestlé Used the Kaizen Lean Manufacturing Tool
Nestlé is the largest food corporation in the world, yet it is also a company that practices Lean principles, particularly the Kaizen method. Nestlé Waters used a technique known as value stream mapping, which is frequently associated with Kaizen. They designed a new bottling factory from scratch to guarantee that operations were as efficient as possible. Nestlé has been aiming to make ongoing changes to their processes to reduce waste and the amount of time and materials that can be wasted during their operations.
Lean manufacturing techniques enable many businesses to solve their manufacturing difficulties and become more productive and customer-centric. In addition, useful lean manufacturing tools assist companies in obtaining the anticipated outcomes and arranging their operations in many excellent ways to meet buyer expectations. Hence, gather a list of the top lean manufacturing tools and choose the best fit for your organization to maximize your ROI and address the performance issue that is causing your outcomes to lag.
What are the standard tools in lean manufacturing?
Among the more than 50 lean manufacturing tools, Kaizen, 5S, Kanban, Value Stream Mapping, and PDCA are the most commonly used lean manufacturing tools.
How to Select the Best Lean Manufacturing Tools for Your Business?
Choosing a lean manufacturing tool begins with identifying the issue or lag in your organization that affects overall productivity and work quality. To select the lean device that best meets your company's needs, you must first grasp each one's benefits and implementation techniques.
What is included in a Lean 5S toolkit?
The lean 5S toolbox contains some essential items for achieving the goal. It comes with a notepad or tablet, a camera, a high-quality flashlight, a tape measure, and a stopwatch.
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Article | 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.
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.
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.