2017 Industrial Metal-Cutting Outlook
April 1, 2017 / benchmarking, best practices, continuous improvement, Cost Management, industry news, LIT, maintaining talent, operations metrics, optimization, performance metrics, productivity, skills gap, supplier relationships
While no one would likely call it a “boom,” recent months have provided good news for industrial manufacturing. Reports have been positive, and business confidence among metal-cutting companies and other industrial manufacturers is up. Experts admit that some challenges and risks remain, but most believe that growth will continue in 2017 and well into 2018.
There is no question that uncertainty has plagued the manufacturing sector for the last several years. Hints of recovery followed by sluggish growth have made it hard for many companies to trust that business was fully rebounding. Last year, terms like “cautiously optimistic” were being thrown around, but many were still wondering — “Are we there yet?’”
Reports and forecasts indicate that we are at least heading in the right direction—both globally and within the U.S. The JP Morgan Global Purchasing Managers’ Index (PMI) has remained above the neutral 50.0 mark throughout the past 13 months and registered 53.0 in February and March—its highest level in 69 months. According to the bank, the expansion in March “remained broad-based by product type, with PMI readings for the consumer, intermediate, and investment goods sectors all signaling further solid growth.”
Forecasts from Manufacturers Alliance for Productivity and Innovation (MAPI) also point to growth, although slower than some would like. According to the latest outlook, manufacturing growth is expected to be 1.2% in 2017 but then accelerate to 2.6% in 2018. Average annual manufacturing output growth is expected to be 1.5% between 2017 and 2020.
Recent data show U.S. manufacturing expanded in March, following a very strong February. The Institute for Supply Management Purchasing Managers’ Index (PMI) hit 57.2% in March, a 0.5 percentage point reduction from a record-setting 57.8% in February 2017. Of the 18 manufacturing industries, 17 reported growth in March, including Fabricated Metal Products and the Primary Metals industries. According to one survey respondent from the Fabricated Metals segment: “Regional business is strong. Hiring qualified team members has improved.”
Cliff Waldman of MAPI says that March data adds to mounting evidence that U.S. manufacturing output performance is on track for moderate improvement, relieving the factory sector from the sluggish growth that has plagued it since 2013. “Data on actual manufacturing output from the Federal Reserve are basically in sync with the recent ISM data as they show an acceleration of growth in U.S. manufacturing since the beginning of 2017,” Waldman said in a blog post. “However, the year-over-year improvement thus far is moderate. Nonetheless, the reasonably broad-based nature of factory sector growth in both January and February suggests growth stability.”
Business confidence among industrial metal-cutting companies and other manufacturers is also up. The first-quarter Manufacturers’ Outlook Survey from The National Association of Manufacturers (NAM) revealed that manufacturers’ optimism rose to a new all-time high in the survey’s nearly 20-year history.
According to NAM, the rising confidence stems from the hope that the new administration in Washington, D.C. will bring much-needed regulatory relief, as well as tax code reforms and a significant infrastructure package. “Indeed, business leaders are cautiously hopeful that pro-growth policies from Washington will allow the country to emerge from the sluggish expansion seen in the years since the Great Recession,” the association said in the report.
Metal companies are confident as well. According to industry leader ArcelorMittal, global apparent steel consumption is estimated to have expanded by 1% in 2016. Based on the current economic outlook, ArcelorMittal expects global apparent steel consumption to grow further in 2017 by between 0.5% and 1.5%.
In the U.S., Mittal says that apparent steel consumption (ASC) declined in 2016 by approximately 1.0% to 1.5%, driven in large part by a significant destock in the second half of 2016. “However, underlying demand continues to expand and the expected absence of a further destock in 2017 should support ASC growth in the U.S. of approximately 3.0% to 4.0% in 2017,” the company said in its 2016 Annual Report.
Sentiment about customer markets is also positive. Mark Millett, president and CEO of Steel Dynamics Inc., told Modern Metals that he expects growth in the energy sector and continued growth in construction spending, “especially for larger public sector infrastructure projects.”
And although there have been reports that automotive manufacturing peaked in 2016 and will decline in 2017, metals companies don’t seem too worried. AK Steel told MM that a richer product mix, including the premium pricing that can be obtained on newer, more specialized or custom grades, should help offset declines. “Our volumes are going to be fairly stable, and fairly steady compared to what they were last year,” Kirk Reich, AK Steel president and COO, said in the MM article.
Trends to Watch
That’s not to say that companies don’t still have some concerns. In late January, M. Robert Weidner III, president and CEO of the Metals Service Center Institute (MSCI), urged the new Trump administration to take serious and immediate action to restore growth and to help the industrial metals supply chain fully recover from the lingering effects of the Great Recession and government policy.
“The industrial metals sector needs action now,” Weidner said, noting that service center aluminum shipments are registering 20 percent below their pre-Great Recession peak, and carbon steel shipments from service centers are still down 30 percent. “The erosion in the U.S. industrial metals supply chain hurts our communities; erodes local, state, and federal tax revenue; and reduces the pool of well-paying U.S. jobs,” Weidner continued.
The strong dollar and reduced capital spending are also concerns. “Signs of wide, yet modest, improvement in global growth are the key drivers of better performance in U.S. manufacturing,” Waldman of MAPI says. “Unfortunately, the problems of a high dollar, a long-term capital spending malaise, and significant policy uncertainty remain to challenge the magnitude of the U.S. manufacturing improvement, even as the world finally provides much-needed support for U.S factories.”
Many industrial manufacturers also remain risk averse. In a recent PwC survey, only 30 percent of U.S.-based industrial manufacturing senior executives said that their companies were planning to increase spending on information technology in the subsequent 12 months. “There is a remarkable opportunity here,” PwC says in a blog post. “Yet the industrial manufacturing sector remains risk averse, unwilling to spend on new machinery, software, and talent during a period of protracted slow growth and limited proven solution.”
According to PwC, there are six actions industrial manufacturers can take to be more profitable in 2017. You can read the full list here, but the following four strategies are the most applicable to industrial metal-cutting companies:
- Leverage data and analytics in a new business model. By upgrading their technical capabilities, industrial manufacturers can bundle a variety of services enabled by connectivity and data, replacing the increasingly outmoded model of selling one big complex machine under warranty and a service agreement for maintenance and repair.
- Develop strategic partnerships. Industrial manufacturers must become more active players in the technology ecosystem, seeking expertise outside the industry in order to develop equipment connectivity, data analysis, and software that are beyond their current abilities.
- Mine operational data. If connected machines—the primary components of the Internet of Things (IoT)—are to be the backbone of industry in the near future, industrial manufacturers will have to figure out how to manage the data coming from an avalanche of sensors, integrated equipment and platforms, and faster information processing systems. There is a critical need to hire people who can mine these bits and bytes of information and work more closely with customers to use the data to improve equipment performance and open new revenue streams.
- Create strategies for talent development and retention. industrial manufacturers must purposefully map out an exciting technology strategy with specific benchmarks and achievements anticipated for the next 18 to 36 months—and then communicate this story clearly to job candidates. Even companies that have not yet felt the shortage of technology-savvy staffers need to take steps to prepare for it as the number of job openings in this field will continue to outpace the number of available hires for the foreseeable future.
Of course, a major technology overhaul may not be possible for every shop, but there are always improvements that can be made. As stated in the eBook, Five Performance-Boosting Best Practices for your Industrial Metal-Cutting Organization, thriving in today’s market requires companies to embrace change and focus on continuous improvement in all areas of their business.
“Whether implementing a lean manufacturing tool to improve processes or investing in training to develop people, proactive leaders are focused on making positive changes in their operations so they can quickly respond to today’s changing customer demands,” the eBook states.
Yes, the sentiment among industry players and experts is positive, but that doesn’t mean companies should put improvement activities on the backburner. Industrial metal-cutting organizations that keep a close eye on mega trends while continuing to optimize their internal operations may not only do well in 2017, but exceed expectations.
Using Predictive Analytics in Ball and Roller Bearing Manufacturing
October 30, 2016 / agility, benchmarking, best practices, bottlenecks, continuous improvement, Cost Management, industry, LIT, predictive management, preventative maintenance, quality, strategic planning, workf
In today’s competitive and quickly changing market, manufacturers are finding that it pays to be proactive—not reactive—in their strategic approaches. That’s why a growing number of industrial manufacturers are starting to take a serious look at advanced technologies like predictive analytics, which allows them to not only measure performance, but to also predict and prevent future challenges.
According to Deloitte’s 2016 Global Manufacturing Competitiveness Index, more than 500 senior manufacturing executives from around the world ranked predictive analytics as the number one technology vital to their companies’ future competitiveness. As reported here, another report from Aberdeen Group shows that 86 percent of top-performing manufacturers are already using predictive analytics to reduce risk and improve operations, compared to 38 percent of those companies with an average performance and 26 percent of those with less than stellar results.
The trend has found its way into industrial metal cutting as well. According to the LENOX Institute of Technology’s benchmark study of more than 100 industrial metal-cutting organizations, companies can gain additional productivity and efficiency on the shop floor by “investing in smarter, more predictive and more agile operations management approaches.”
What is Predictive Analytics?
Predictive analytics utilizes a variety of statistical and analytical techniques to develop mathematical models that “predict” future events or behaviors based on past data. As the Deloitte study explains, this allows companies to uncover hidden patterns, relationships, and greater insights by analyzing both structured and unstructured data.
In a manufacturing environment, companies can use predictive analytics to measure the health of production equipment and detect potential failures. However, the possibilities are virtually limitless. According to one analyst’s blog, manufacturers could potentially use software and predictive analytics to forecast potential staffing or supply-chain interruptions, such as a flu outbreak that could cause a temporary personnel shortage or even a blizzard that could disrupt deliveries.
Bearing manufacturing leader Timken has taken a different approach and is using predictive analytics to improve inventory optimization and supply chain performance in the automotive aftermarket sector. As reported by SearchAutoParts.com, Timken is leveraging sales history, registration data, and other information, along with complex analytics, to improve sales and reduce costs.
“Timken’s catalog team matches parts and vehicles, and combines that information with vehicle registration and replacement/failure rates, along with internal sales data,” the article explains. “Crunching that data using proprietary algorithms helps them predict how many parts will be needed in a given geography, and how those parts sales will fall within the premium aftermarket, economy aftermarket and OEMs.”
Common Use Cases
Because predictive analytics is an emerging technology, applications are typically specific to each manufacturer’s products and processes—as in the Timken example. However, an article from Toolbox.com describes four common use cases for predictive analytics that are applicable in most manufacturing environments:
- Quality Improvement. Improvements in databases and data storage and easier-to-use analytical software are the big changes for quality improvement. Standard quality improvement analysis is being pushed toward less technical analysts using new software that automates much of the analytical process. Storing more information about products and the manufacturing process also leads to analysis of more factors that influence quality.
- Demand Forecast. Predictive analytics takes historical sales data and applies forms of regression to predict future sales based upon past sales. Good predictive analytics modelers find additional factors that influenced sales in the past and apply those factors into forecasted sales models.
- Preventative Maintenance. Predictive analytics increases production equipment uptime. Knowing that a machine is likely to break down in the near future means a manufacturer can perform the needed maintenance in non-emergency conditions without shutting down production.
- Machine Utilization. Predictive analytics applications for machine scheduling combines forecast for demand with product mix to optimize machine utilization. Using new predictive analytics techniques improves accuracy.
While there is no question that predictive analytics is still new to many ball and roller bearing manufacturers, industry leaders know that proactive strategies are key in today’s uncertain market. Finding ways to anticipate future events and reduce unplanned downtime can not only help your operation gain efficiency but, more importantly, help you stay competitive.
Benefits of Using Predictive Maintenance Analytics in Your Forging Operation
September 25, 2016 / benchmarking, best practices, continuous improvement, Cost Management, LIT, operations metrics, predictive management, preventative maintenance, strategic planning
With changing customer requirements and an increasingly competitive marketplace, leading manufacturers are finding it pays to be proactive—not reactive—in their strategic approaches. Instead of simply measuring performance, many companies are taking the next step and using measurement to anticipate and prevent future challenges—a concept known as predictive operations management.
This trend has found its way into industrial metal cutting. According to the LENOX Institute of Technology’s benchmark study of more than 100 forges and other industrial metal-cutting organizations, companies can gain additional productivity and efficiency on the shop floor by “investing in smarter, more predictive and more agile operations management approaches.”
One such approach is predictive maintenance. Not to be confused with preventative maintenance, which uses planned maintenance activities to prevent possible failures, predictive maintenance (also known as condition-based maintenance) uses data-driven analytics to optimize capital equipment upkeep.
Reliable Plant defines predictive maintenance as “the application of condition-based monitoring technologies, statistical process control, or equipment performance for the purpose of early detection and elimination of equipment defects that could lead to unplanned downtime or unnecessary expenditures.” By using tools to predict and then correct possible failures, operators can keep machines running while eliminating unnecessary preventative maintenance downtime and reducing reactive maintenance downtime.
In fact, predictive maintenance was identified in a McKinsey Global Institute report as one of the most valuable applications of the Internet of Things (IoT) on the factory floor. The report, The Internet of Things: Mapping the Value Beyond the Hype, says that predictive maintenance using IoT has the potential to reduce equipment downtime by up to 50 percent and reduce equipment capital investment by 3 to 5 percent by extending the useful life of machinery. “In manufacturing, these savings have a potential economic impact of nearly $630 billion per year in 2025,” the report states.
According to an article from Manufacturing Business Technology, the potential benefits of predictive maintenance analytics go beyond predicting machine failure. The magazine lists several wide-ranging implications the technology has for the manufacturing industry, including the following:
- Part harmonization. Predictive models are able to show which parts will be the first in line to fail, what will need replacing in the next six months, for example. This then allows teams to better manage inventories, stockpile the right parts, and even bulk order replacements before they are needed.
- Cost-benefit analyses. Teams are better equipped to do cost benefit analyses and further understand the risks of not performing maintenance at any given time. Presenting this data to the C-suite, and outlining future risk weighed against a smaller outlay at the present time, is a far more compelling argument than suggesting a piston might eventually need replacing.
- Warranty Claims. Defining the optimal cost and duration for any given warranty is a great challenge for many manufacturers. Analytics can help better define these boundaries by modeling usage patterns.
Of course, all of these benefits come with a cost. One of the major drawbacks of predictive maintenance analytics is that it requires a high upfront investment for condition monitoring equipment and software, as well as a high skill level and experience to accurately interpret condition-monitoring data. There are also privacy and security issues that need to be addressed. For smaller forges, this could be a huge stumbling block, although some may discover that the long-term benefits outweigh the short-term costs.
In the end, predictive maintenance may not be an option for every shop or every piece of equipment, but in today’s competitive market, it might be worth the research. Many companies are finding that the potential benefits of the technology are opening up new opportunities for improvement and growth that were once not possible.
How Forges Can Use Cloud-Based Monitoring and other Advanced Technologies to Increase Efficiency
August 25, 2016 / benchmarking, best practices, blade failure, bottlenecks, continuous improvement, LIT, operator training, Output, performance metrics, preventative maintenance, productivity, quality
Any manufacturing executive tracking industry trends will no doubt run across terms like “big data,” “cloud computing,” and the “Internet of Things.” In fact, according to the results of a survey from Deloitte and the Council on Competitiveness, these types of advanced technologies have the power to put the U.S. back on the map as the most competitive manufacturing nation.
“CEOs say advanced manufacturing technologies are key to unlocking future competitiveness,” the report summary states. “As the digital and physical worlds converge within manufacturing, executives indicate the path to manufacturing competitiveness is through advanced technologies, ranking predictive analytics, Internet-of-Things (IoT), both smart products and smart factories via Industry 4.0, as well as advanced materials as critical to future competitiveness.”
As a forging executive, however, the question becomes: How does this technology apply to my operation? Or to put it another way: How do these “buzz words” play out on the shop floor?
One technology application, featured here in Forging magazine, gives a good indication of what cloud-computing and connectivity could look like in a metal-cutting operation. Specifically, the article features a cloud-based bandsaw monitoring system that offers three key features:
- Blade Life Assessment. Monitoring and alert notification of a saw blade’s remaining useful life. The technology will provide advance notice of required saw blade replacement.
- Increased Machine Efficiency & Machine Life. The technology provides real-time analysis of individual components and overall machine health status. It can send notifications of abnormal conditions from motors and bearings. It also alerts on frequent consumable items like hydraulic and cutting fluid.
- Increased Operational Efficiency. The technology can provide production reports to aid in identifying best practices and training needs. An advanced monitoring and notification system alerts the operation when machine maintenance is needed which aids efficiency in the scheduling of planned events.
These are no small benefits. In fact, they fall right in line with two of the strategies listed in the Benchmark Study of Industrial Metal-Cutting Organizations from the LENOX Institute of Technology. According to findings from the study, forges and other industrial metal-cutting organizations can gain additional productivity on the shop floor by investing in smarter, more predictive operations management approaches and by taking a more proactive approach to equipment and blade maintenance. By using cloud-based monitoring to track blade life and machine health status, managers can do just that by anticipating downtime, which, as the study states, “translates into more jobs completed on time.”
Of course, bandsaw monitoring is just one possible application. As we reported here in our annual forging industry forecast, controls and sensors are also being developed and implemented to monitor the forging process in a bid to automatically sense and compensate for any variation in the process. This type of consistency not only boosts efficiency, but could have some major quality benefits as well.
An article from IndustryWeek provides a few more application examples. The article describes how three leading companies are using advanced technologies to connect just about everything and anything—video cameras to monitor workflow process, safety helmets to track employees, and end products to predict reliability—all of which shows that the potential applications are only as limited as a manufacturer’s creativity.
What possible applications could cloud-based monitoring and other advanced technologies have in your forging operation?
How to Effectively Utilize OEE in Your Industrial Metal-Cutting Organization
August 15, 2016 / benchmarking, best practices, bottlenecks, continuous improvement, KPI, lean manufacturing, performance metrics, productivity, quality, supplier relationships, value-added services, workflow process
As part of the push toward continuous improvement, more and more industrial metal-cutting companies are measuring overall equipment effectiveness (OEE). This is definitely a good trend, as measurement is the first step in making quantifiable change. However, some companies have jumped on the OEE bandwagon without being fully informed, which can cause a lot of misunderstanding and misuse of this important metric.
Knowing what OEE is—and what it isn’t—is the only way to make sure you are using it effectively. The following is a quick primer.
What is OEE?
According to leanproduction.com, OEE is a best practices metric that measures the percentage of production time that is truly productive. It takes into account all six types of loss, resulting in a measure of productive manufacturing time.
In simple terms, OEE can be described as the ratio of fully productive time to planned production time. According to leanproduction.com, it can be measured in one of two ways:
(Good Pieces x Ideal Cycle Time) / Planned Production Time
Availability x Performance x Quality
(You can find a more detailed description of the calculation here, as well as a sample calculation.)
A plant with an OEE score of 100 percent has achieved perfect production—high quality parts as fast as possible, with zero down time. While that’s ideal, it’s not quite possible in the real world. According to oee.com, studies show that the average OEE rate among manufacturing plants is 60 percent, which leaves substantial room for improvement. Most experts agree that an OEE rate of 85 percent or better is considered “world class,” and many companies use that number as a long-term goal for their operations.
Managers can use OEE as both a benchmark and baseline. Specifically, leanproduction.com says it can be used to “compare the performance of a given production asset to industry standards, to similar in-house assets, or to results for different shifts working on the same asset.” It can also be used as a baseline “to track progress over time in eliminating waste from a given production asset.”
How to Use—and not Use—OEE
It’s important to note that OEE is not necessarily a useful metric for every manufacturing operation. “Measuring OEE only makes sense if you are trying to meet a certain demand on a daily basis,” explains Paul Bryant, senior OPEX manager, LENOX Tools. “If you have a problem with yield, then I would definitely suggest OEE.
“If you have a problem with inconsistent production output and/or downtime on a piece of manufacturing equipment, OEE is a great way to measure and identify how to where to improve your operations,” Bryant continues. However, for smaller metal-cutting operations that are more custom and low volume, Bryant says OEE probably isn’t worth measuring.
Bryant also says that a lot of shops use OEE incorrectly. Specifically, he says there are two common ways metal-cutting operations misuse the metric:
- Too Focused on the Benchmark. “Everyone knows that world-class OEE is 85%, but too many people get hung up on that number and how their shop compares to it. When I look at OEE, the number doesn’t mean much to me. I look at three components—availability, performance, and quality—and then break them apart and look for opportunities. That is the true essence of OEE: To find opportunities that help keep your machine and production system optimal.”
- Too Focused on the Operator. “Another misuse is that people use OEE to measure the operator. OEE is used to measure equipment. If you run into an issue with the metric, look at the machine first. There are so many variables, don’t always assume it is the operator. Once you’ve evaluated the machine, look at the material and then the operator last.”
An article from IndustryWeek (IW) adds that OEE should be used as an improvement measure, not a Key Performance Indicator (KPI). It also states that it is best used on a single piece of equipment or synchronized line.
Finally, if your shop is ready to start measuring OEE but doesn’t know where to start, enlist the help of some key suppliers. As stated in the eBook, Five Performance-Boosting Best Practices for Your Industrial Metal-Cutting Company, many companies don’t possess all of the knowledge, resources, or infrastructure necessary to do in-depth measurement. This is where a willing supply partner can help. In today’s competitive market, there are plenty of equipment and tooling suppliers that are willing to share their knowledge and experience as a free, value-added service.
A Helpful Tool
There is no question that OEE can be misused and misunderstood, but as the IW article reiterates, it is not a “bad metric.” When calculated and applied correctly, OEE can be a very useful tool to help industrial metal-cutting companies quantify and uncover new improvement opportunities.
For more information on OEE, check out the article, “The ‘Quick & Dirty’ About OEE,” or you can find a more in-depth overview here.
Predictive Maintenance Helps Metal Service Centers Reduce Downtime
June 5, 2016 / benchmarking, best practices, bottlenecks, continuous improvement, Cost Management, lean manufacturing, LIT, operations metrics, Output, performance metrics, predictive management, preventative maintenance, productivity, quality, strategic planning, workflow process
Manufacturers know that downtime results in lost productivity and profits. However, thanks to technological advancements in predictive maintenance, service centers and other industrial metal-cutting companies can nearly eliminate downtime altogether.
Unlike preventative maintenance, which uses anticipated and planned downtime to prevent unplanned breakdowns and minimize cost impacts, predictive maintenance aims to predict breakdowns before they even occur. Software and sensors collect data, and algorithms identify not only the anticipated failure, but also calculate the probable time that failure will occur. This enables companies to repair or replace parts before failure and helps eliminate both planned and unplanned downtime.
Several industries are adopting predictive maintenance as part of their operations. An article from the Harvard Business Review provides a few examples:
- Airlines can now predict mechanical failures in advance and can reduce flight delays or cancellations based on data sources such as maintenance history and flight route information.
- The oil and gas industry can use real-time data to predict the failure of electric submersible pumps used to extract crude oil.
- Banks can use sensor data to predict the failure of an ATM cash withdrawal transaction.
The manufacturing industry is also adopting predictive maintenance, but research shows it is doing so at a slower rate compared to others. For example, a recent survey by the Manufacturing Enterprise Solutions Association and LNS Research concluded that manufacturers have some work to do to catch up to current capabilities—only 14 percent of survey respondents said they used manufacturing data in their analytic program.
Of course, building a predictive maintenance program requires both time and money, but many manufacturers are finding that the benefits outweigh the cost. An article from American Metals Market lists just a few of the many potential benefits of using predictive maintenance:
- Reassurance of safe, continued plant operation
- Improved operating efficiencies
- Reduced lost production
- Reduced cost of maintenance
- Less likelihood of secondary damage to equipment
- Reduced inventory of spare parts
- Extension of the life of plant and mill equipment
- Improved product quality
According to the AMM article, several metals leaders are reaping the rewards of predictive maintenance, including:
- U.S. Steel Corp. uses machinery diagnostic services for oil analysis, vibration analysis, electrical thermographic analysis and more to keep its operations up and running.
- ArcelorMittal is using thermal imaging cameras to ensure proper operation of its production plants, saying it improves efficiency, safety, and helps avoid breakdowns and minimizes downtime.
The trend is also starting to gain traction in industrial metal cutting. The LENOX Institute of Technology’s benchmark study of more than 100 metal service centers and other industrial metal-cutting organizations found that companies are gaining additional productivity and efficiency on the shop floor by “investing in smarter, more predictive and more agile operations management approaches.”
While there is no question that predictive maintenance is proving beneficial in the metals industry and beyond, some companies may be hesitant to adopt the technology due to the investment and the training required for implementation. However, if your goal is to reduce downtime and increase the chances of future success, this may be one technology worth considering.
For more information on predictive maintenance, check out this overview article, which lists common tools and techniques, as well as a video.
Should Your Industrial Metal-Cutting Organization Rethink Operations Management?
March 1, 2016 / benchmarking, best practices, continuous improvement, Cost Management, industry news, LIT, operations metrics, strategic planning
Today’s industrial metal-cutting executives face enormous challenges. While the goal has always been for manufacturers to do more with less, that expectation has intensified in recent years. Increased pricing pressures, global competition, and customer expectations are just a few of the current issues that are putting a strain on even the most efficient metal-cutting operations.
This has pushed many companies to look for new ways to manage their operations. In fact, a Global Operations Survey from PwC found that industry leaders are “reimagining operations” by aligning it with business strategy.
“These leaders don’t think of operations as mere utilities,” the PwC report states. “Instead, they see new opportunities to drive their company’s destiny like never before. They are cultivating a coordinated set of operational strengths based on what customers want and what fits with company strategy.”
After surveying more than 1,200 operations leaders across various industries, PwC uncovered several trends that are shaping the way companies are running their operations. Below are the report’s four key insights:
- Knowing what customers value is a real and persistent challenge for operations executives. This can make it difficult to set priorities, manage costs in a strategic way, and choose the right tradeoffs when necessary.
- Companies plan to do more than just improve existing processes. Today’s leaders are looking for ways to transform their businesses without letting day-to-day performance slip.
- Operations itself is being reimagined. Leading companies realize they need a model that aligns operations with business strategy and helps them stay resilient in the face of significant change.
- Strategically aligned companies are more confident and more likely to focus on a few differentiating capabilities. When taking this path, there are two dimensions to consider: what customers value in your chosen markets and your company’s existing operational strengths.
One of the studies most important suggestions was that companies need to design their operations around their customer. “Without this understanding, we often see operations stretched too thin,” Mark Strom, PwC’s Principal and Global Operations Leader, told Supply Chain Management Review. “When one team tries to innovate, they come in direct conflict with an operational assignment to cut costs – or they create some new complexity that’s harder to manage.”
Another takeaway from the survey was the importance of collaboration within an organization. Hyper-efficient silos, the report says, should no longer be the goal. In fact, PwC found that 61% of operations leaders believe cross-functional collaboration has the greatest potential for helping the company reach its strategic goals.
While the PwC survey was done across geographies and industries, research specific to industrial metal-cutting confirms that new approaches to operations management can offer tangible benefits. For example, data from a benchmark survey of leading fabricators, metal service centers, and other metal-cutting organizations suggests that companies with high machine uptime can gain efficiency by investing in smarter, more predictive and more agile operations management approaches.
Specifically, the survey found that 67% of industrial metal-cutting operations that follow all scheduled and planned maintenance on their machines also report that their job completion rate is trending upward year over year—a meaningful correlation. “The implication is that less disruptive, unplanned downtime and more anticipated, planned downtime translates into more jobs being completed on time,” states a summary of the survey findings.
Is it time for your organization to rethink or “reimagine” its approach to operations management? Based on the findings of the aforementioned studies, there are a few questions today’ managers should consider:
- Could you encourage better collaboration among departments?
- Could your customer play a larger role in your management decisions?
- Could you use more predictive strategies to minimize downtime?
If the answer to any of these questions is yes, then perhaps it is time for a change.
Predictive Technology Helps Industrial Metal-Cutting Companies Stay Ahead of the Curve
January 15, 2016 / benchmarking, best practices, blade failure, bottlenecks, continuous improvement, LIT, predictive management, preventative maintenance, productivity, strategic planning
The U.S. manufacturing landscape is changing, and industrial metal-cutting companies are no exception. Technology has created an increasingly connected industry, and manufacturers are realizing that while traditional lean practices have proved successful in the past, when it comes to operational efficiency, data and other advanced “smart” technologies are the wave of the future.
One area that is quickly gaining popularity is the use of predictive technologies. As reported in a recent Manufacturing.net article, nearly three dozen manufacturing company executives and national research facility directors identified predictive data analytics as the number one advanced manufacturing technology critical to growth, as part of a study conducted by Deloitte Global and the U.S. Council on Competitiveness.
Predictive analytics utilizes a variety of statistical and analytical techniques to develop mathematical models that “predict” future events or behaviors based on past data. As the study explains, this allows companies to uncover hidden patterns, relationships, and greater insights by analyzing both structured and unstructured data.
Several industries are already benefiting from the use of predictive technologies. Healthcare, for example, is using predictive analytics to improve the effectiveness of new procedures, medical tests, and medications. Manufacturing companies are using the technology to identify quality and production issues, as well as optimize delivery and distribution. Other industries, such as aerospace, automotive, and consumer products, are also finding interesting applications.
Thyssen Krupp, for example, recently used predictive analysis to improve the reliability of more than 1.1. million elevators it maintains worldwide. With the help of Microsoft cloud technology, the company gathered data from thousands of sensors and systems in its elevators to measure motor temperature, shaft alignment, cab speed and door functioning. After being sent to the cloud, the data is then displayed on a single dashboard in real-time. The data is also used in predictive model formulas, helping technicians know when and where a failure may occur.
The trend is also finding its way into industrial metal cutting. Data from the LENOX Institute of Technology’s Benchmark Survey of Industrial Metal-Cutting Organizations suggests that investing in smarter, more predictive operations strategies can help companies gain additional productivity and efficiency on the shop floor.
Although not through the use of analytics, the benchmark survey found that industry leaders are using strategies such as planned maintenance and blade care to prevent downtime and predict blade failure. Specifically, the benchmark study found that:
- 67% of industrial metal cutting operations that follow all scheduled and planned maintenance on their machines also report that their job completion rate is trending upward year over year—a meaningful correlation. The implication is that less disruptive, unplanned downtime and more anticipated, planned downtime translates into more jobs being completed on time.
- 51% of organizations that “always” follow scheduled and preventative maintenance plans say that blade failure is predicted “always” or “mostly.”
While there is no question that predictive analytics is still an emerging area, it is clear that proactive strategies are key in today’s uncertain market. Whether you invest in advanced predictive analytics software or simply stick to your preventative maintenance program, finding ways to anticipate future events and reduce unplanned downtime can help your operation gain efficiency and, more importantly, stay competitive.
What predictive operational strategies are you using to make your operation more efficient?
Common Missteps Industrial Metal-Cutting Companies Should Avoid in Lean Manufacturing
October 15, 2015 / benchmarking, best practices, Cost Management, KPIs, lean manufacturing, LIT, operations metrics, performance metrics, preventative maintenance, strategic planning, workflow process
If there is one “go-to” answer for solving a company’s productivity issues, most experts point to lean manufacturing. The lean movement is, as one author put it here, “our current silver bullet.”
At this point, most manufacturers have jumped on the bandwagon and have incorporated at least some lean principles into their operation. Some companies like A.M. Castle, a metal service center featured in a recent case study, have undergone complete lean transformations, while others have adopted basic lean tools like 5S.
However, even with the growing popularity of lean manufacturing and its countless success stories, the reality is that not every lean journey is smooth. In fact, according to research from management consulting firm Quality for Business Success, Inc. (QBS), many are actually quite bumpy. After conducting 200 interviews with managers and lean champions from 71 different companies engaged in lean implementations, QBS found that many managers experienced “false starts” and felt overwhelmed by the learning curve. “Many managers we spoke with find themselves ‘drowning in a sea of half-understood tools and techniques,’” the firm states in a white paper. “Others, unaware of their narrow interpretation of lean, boast successful implementations when they’ve actually barely scratched the surface.”
To help companies achieve successful lean implementations, QBS outlined the most common missteps companies make in the process in a white paper. Below are the top 15 pitfalls managers should avoid:
- Thinking of 5S as something you do to an area
- Imposing 5S top-down, with limited involvement bottom-up
- Equating waste reduction with cost cutting
- Remaining aloof to the larger global end-to-end value stream
- Assuming your Future State Value Stream Mapping (VSM) is nothing more than your Current State VSM with the identified improvement opportunities corrected or addressed.
- Equating visual workplace with top-down visual communication
- Viewing Total Productive Maintenance (TPM) as an improvement initiative that exclusively relates to engineering and maintenance personnel
- Using overall equipment effectiveness (OEE) to evaluate operations rather than as an improvement gauge
- Equating Standard Work with procedures
- Engaging in “industrial tourism” and thinking you are benchmarking
- Pursuing a one-size-fits-all solution to production planning and control
- Forgetting to reduce supermarket inventories once established
- Preconditioning continuous flow to waste elimination
- Believing you will achieve a lean transformation applying lean tools
- Betting your strategy on lean
To read more about these common missteps, you can download the full QBS white paper, The 15 Most Common Mistakes in Lean Implementations, here.
What has been your experience with lean manufacturing? Have you made one or more of these missteps?
Reducing Metal Scrap and Rework in Your Machine Shop
September 20, 2015 / benchmarking, best practices, blade failure, blade selection, continuous improvement, Cost Management, industry news, LIT, operations metrics, operator training, performance metrics, preventative maintenance, productivity, quality, root cause analysis, strategic planning
In any manufacturing operation, a small amount of scrap is inevitable. However, reducing material waste should still be a top goal for machine shops that cut and process metal. Like all other forms of waste, scrap can negatively affect profitability, especially if it is generated as a result of an error.
The truth is that any amount of scrap or rework you’re experiencing in your operations provides an opportunity for improvement. Taking the time to reduce scrap often leads to better productivity and higher quality cuts. As this manufacturing.net article points out, eliminating scrap and waste also contributes to your company’s environmental efforts, which may be important to some customers.
How can you keep your scrap and rework costs low? While there are several ways to accomplish scrap reduction, below are a few simple strategies any machine shop can implement:
- Measure and Compare. As with any continuous improvement activity, you need to start with measurement. If you aren’t measuring your scrap rate, this is your first step. You should also know your scrap and rework costs. Once you have some quantifiable data, you should compare your operation to others in your industry. For example, scrap and rework costs of Industry Week’s Best Plants winners and finalists for the last five years were a median (or middle) 0.5% of sales, while the mean — or average — was 1% of sales. How does your shop compare?
- Evaluate Operators. If you know your scrap and rework rates could be better, identifying the root cause of the issue is the only way to make any real, sustainable improvements. Often times, high inventory levels and scrap rates are indicators of “hidden” inefficiencies such as operator error. Are all of your operators properly trained on how to use equipment? Are they running saws at optimal levels, or are they just focused on getting the job done as fast as possible? Have you recently taken on a new job that may require a different saw setting or saw blade type? Poorly trained operators that misuse equipment often lead to low-quality cuts, higher instances of scrap due to error, and shortened blade life—all of which add up to elevated costs.
- Break in Blades. Proper use and maintenance of metal-cutting equipment and tooling can also play a role in keeping scrap and rework costs low. Based on the results of a benchmark study conducted by the LENOX Institute of Technology, this important, everyday practice can have a direct impact on the bottom line. Survey data showed that 70 percent of organizations that report their scrap and rework costs are less than five percent also say they “always” break in their band saw blades. This provides strong economic validation for the proactive care of saws and blades. By breaking in blades properly, organizations are able to reduce “soft” failure that leads to waste and scrap that eats into their bottom line.
- Pick for Clean. As more and more customers expect deliveries in half the time, many shops are doing whatever they can to speed up turnaround. However, companies need to be sure they are taking the time to reuse material whenever possible. Scrap can quickly get out of control if operators are reaching for a new piece of material every time they start a job. That’s why many shops are moving away from the “pick for speed” method of inventory selection and, instead, are embracing “pick for clean” methods. Picking for clean is the practice of picking high-quality leftover materials from a previous job to use up the inventory. In other words, you reach for remnants first. This keeps inventory and material costs low.