Manufacturing and the data conundrum

Size: px
Start display at page:

Download "Manufacturing and the data conundrum"

Transcription

1 Manufacturing and the data conundrum Too much? Too little? Or just right? A report by The Economist Intelligence Unit Commissioned by

2 Contents I. Executive summary 2 II. Ready or not, here it comes 4 III. Quality first 7 Case study: Meritor: Towards data-driven production perfection 9 IV. Where theory meets reality 10 Case study: ABB/Sandvik: Reducing deviations, eliminating imperfections 12 V. From monitoring to alerting, predicting and solving 13 VI. The plentiful returns of data success 15 Case study: GE s factory built to produce data 17 VII. Conclusion 18 Appendix: Survey results 20 The Economist Intelligence Unit Limited

3 I Executive summary About the research This Economist Intelligence Unit study, commissioned by Wipro, examines how manufacturers now collect, analyse and use the complex, real-time data generated in production processes. By far the most important finding is the increased understanding of how to use process data to improve product quality, but manufacturers are also realising gains in reliability, throughput and maintenance practices by tuning into what their production processes are telling them. This report, a follow-up to our 2013 omnibus report on data usage, The data directive: How data is driving corporate strategy and what still lies ahead, is based in part on a survey of 50 C-suite and senior factory executives from North America (50%) and Europe (50%) from companies that produce a broad range of industrial goods. These include electronics (12%), machinery (12%), chemicals and gases (12%), vehicle parts (10%), rubber or plastics (10%) and more. Respondents are from intermediate to very large organisations; 32% have global revenues in excess of US$5bn, 32% have revenues of between US$1bn and US$5bn and 36% have revenues of US$500m-$1bn. To complement the survey, the EIU conducted in-depth interviews with senior manufacturing executives and academics, as well as related additional research. The report was written by Steven Weiner and edited by David Line. Our thanks are due to all survey participants and interviewees for their time and insights. Interviewees (listed alphabetically by organisation) included: Peter Zornio, chief strategic officer, Emerson Process Management Stephan Biller, chief manufacturing scientist, GE Global Research Joe ElBehairy, vice president, engineering, quality and product strategy, Meritor Kent Potts, manager of industrialisation, Meritor Daniel W Apley, professor, industrial engineering and management sciences, Northwestern University Shiyu Zhou, professor, Department of Industrial Engineering, University of Wisconsin-Madison Respondents by job title Other C-suite role Head of plant/ factory Chief strategy officer Chief, supply chain management 10% 6% Chief safety officer 10% 2% 14% Chief operating officer 4% 16% 34% Chief financial officer 4% Chief technology officer Chief quality officer Respondent companies by revenue US$20bn or more US$5bn to US$20bn 20% 12% 32% US$1bn to US$5bn US$500m to US$1bn 36% 2 The Economist Intelligence Unit Limited 2014

4 Key findings from the survey include: Manufacturers have significantly ramped up their shop floor data collection. Some 86% of survey respondents report major increases in the amount of production and quality-control data stored for analysis over the past two years. But it hasn t been easy only 14% of those surveyed report no problems managing the data glut from real-time production sensors and associated reporting and analytical models. A minority of manufacturers has an advanced data-management strategy. Fewer than half of respondents (42%) have what they consider to be a well-defined data-management strategy. A further 44% say they understand why shop floor data is valuable and, consequently, are putting in place resources to realise that value. There is no doubting its importance, though: every single manufacturer surveyed reports that data collection is a priority concern for their business. Manufacturers find it difficult to integrate data from diverse sources and to find the skilled personnel to analyse it. Difficulty integrating data from multiple sources and formats is the most commonly cited problem in managing greater volumes of data, picked by 35% of respondents no surprise, given the age of most manufacturing plants and that technology is transitory while infrastructure is durable. Companies also find that because of the speed of data-technology advancement they often lack the internal expertise necessary to maximize the benefits of collected information (cited by 33%). While data collection from monitoring is common, data analysis to predict issues or solve problems is less so. While almost all manufacturers find it normal to monitor production processes for example, 90% or more say their companies have mature data analysis capabilities for such essentials as asset and facility management, safety, process design and supply chain management less than half have in place predictive data analytics, and less than 40% use data analytics to find solutions to production problems. Data is delivering stellar quality and production-efficiency gains Using insights gathered from productiondata analysis, two-thirds of companies report annual savings of 10% or more in terms of the cost of quality (that is, net losses incurred due to defects) and production efficiencies, and about one-third say their savings on both measures have been in the range of 11% to 25%. This may explain why more than threequarters of respondents identify aggressive data programmes as an important way to boost efficiency and lower costs. but collecting data doesn t automatically yield benefits. Despite many manufacturers reporting impressive savings from data analysis, 62% are not sure they have been able to keep up with the large volumes of data they collect, and just 50% are sure they can generate useful insights from it, as it comes from too many sources and in a variety of formats and speeds. The Economist Intelligence Unit Limited

5 II Ready or not, here it comes Today s integrated operations go above and beyond what has been the traditional realm of process control. Peter Zornio, chief strategic officer, Emerson Process Management Manufacturers have used data to measure production since at least 3000 BC, when the oldest discovered cuneiform tablets were marked with pictographic words and numbers. All it took was a reed or stick to mark damp clay, and the number of sheep, bags of grain or output of spears was readable, but only to the literate overseer. Similarly, today s industrial data, displayed on computer screens, is understandable and useful only to the trained overseer. But there is far more of it, and it is available instantly, so that as issues arise process adjustments can be made quickly. In today s ideal digitally networked production environment, complex data can be used far more easily than ever to improve product quality, boost throughput, improve shop floor reliability, enhance safety and predict maintenance requirements, eliminating unscheduled downtime. That is the ideal, at any rate. In the past decade, as more manufacturers have implemented a broader array of digital controls in the process linking together production machinery that used to operate independently it has become an appealing vision of what making things might actually become everywhere. Today s integrated operations go above and beyond what has been the traditional realm of process control, says Peter Zornio, chief strategic officer of Emerson Process Management, a unit of St Louis, Illinois-based Emerson Electric Company. We think there are three big ideas at the heart of it. The first is pervasive sensing. You can get more and more data points than ever before. Second, integrated operations means multiple disciplines can analyse and discuss data from the plant together, not just one discipline at a time. And third is the realm of big data and equally big analytics. Stephan Biller, chief manufacturing scientist for GE Global Research a group responsible, among other things, for finding ways to make General Electric s 400 factories as efficient as possible says the latest iteration of thinking there is called the brilliant factory. The brilliant factory idea works together with the industrial internet and software development that GE calls Predictivity, mirroring what theorists believe can be a manufacturing world so all-knowing that it routinely predicts production and product problems and solves them, too. It s the entire digital thread from engineering and design, to manufacturing engineering, the factory and our suppliers, says Dr Biller of the GE factory. What s new is envisioning the feedback loop from the factory in real time, through factory engineering and from the service shops. The amount of data is quite astounding. In fact, at GE s new battery production plant in Schenectady, New York, 10,000 variables of data are collected, in some cases every 250 milliseconds. We now have an infrastructure in the plant, data highways, that match what we have in the public Internet, says Dr Biller. 4 The Economist Intelligence Unit Limited 2014

6 The allure of this vision is pervasive. In a survey of manufacturers conducted by The Economist Intelligence Unit for this paper, 86% say that during the last two years they have significantly increased the amount of production and qualitycontrol data stored for analysis. Nearly twothirds say they use sensor-generated data from networked machines an essential element of the integrated factory and 20% say they plan to use data from networked production machinery (Figure 1). Equally telling, two-thirds of those surveyed say they also use sensor-generated data from external sources, off their shop floor, for comparison purposes a move into the more complex and analytically difficult world now generally called big data. But not everything is settled when it comes to collection and use of digitised data. Most factories are decades old and predate in their design any consideration of this type of technology. The most recently completed complex greenfield oil refinery in the US began operations in 1977, for example. Despite the decades of post-world War II quality improvement programs such as the teachings of statistical process control guru W Edwards Deming, the scholarship of Joseph Juran, Japanese kaizen process improvement teams, Six Sigma programmes, the Toyota Way and Lean Figure 1 Manifold data sources What sources of data are used by your company to lower the cost of quality and improve manufacturing efficiency? Select all that apply. Customer feedback system Compliance/incidents management data Use now Plan to use 96 4 Manufacturing execution system (MES) process historian Enterprise data (ERP) Accounting/finance data Supply chain management system/supplier data After sales failure data Supplier-provided test data Demand forecasts Sensor-generated data from external sources for comparative purposes Sensor-generated data from networked machines Operator logs Sensor-generated data from individual machines RFID The Economist Intelligence Unit Limited

7 Manufacturing tens of thousands of factories in North America and Europe are light years removed from advanced, cutting-edge digital processes. Most of these plants installed control systems along the way, many of them proprietary systems that have been locally customised and continue to operate producing, perhaps, batch reports on operations at the end of the day. Migrations from systems of this nature are not for the faint of heart, notes a senior executive in the control systems industry. He tells the story of one large factory, with annual revenue of US$500m, where production is controlled by orders written on coloured pieces of paper, one colour for each day of the week. If every workstation in the plant is using the same colour, the process is in sync. It is therefore no surprise, in this environment, that only 14% of surveyed companies say they have experienced no problems as they manage increasing volumes of machine-generated process and quality data. Companies wrestle with efficiency and quality-improvement data from so many sources that confusion and applesto-oranges comparisons are easily made. The number-one source of data, used by 96% of surveyed companies, is old-fashioned customer feedback, followed by process historian systems (90%), existing enterprise resource planning systems (88%), accounting and financial data (88%), pre-existing supply chain management systems (86%) and after-sales failure data (82%; Figure 1). There is an enormous amount of data, and it s a challenge to figure out how to integrate it, says Daniel W Apley, professor of industrial engineering and management sciences at Northwestern University in Evanston, Illinois. What you would like to use it for is to identify root causes of quality problems and product variation. People have been talking about this for decades. But the truth is, there are still many open research challenges and no real established methodology that can be used to trace quality problems back to the root causes when there are thousands of upstream process variables that are potential root causes. When there are thousands of variables, you typically need data for hundreds of thousands, or millions of parts in order to find meaningful statistical associations between problems and root causes. As GE s Dr Biller says, When you think about all the tasks that people have to do the maintenance system, scheduling, material handling, incoming material, the machines themselves and their error codes, how much material is in each of the buffers, does the part pass or fail and each plant has 10 to 15 individual systems. This is what makes the task somewhat difficult. 6 The Economist Intelligence Unit Limited 2014

8 III Quality first Respondents to the EIU survey conducted for this report see product quality management as the area in which greater volumes of data are most likely to make the biggest difference. Nearly three-quarters (72%) pick this in their top three business areas likely to see gains from more data, a much larger proportion than for any of the other areas and 28 percentage points more than the proportion picking process controls, the number-two area of potential gains (Figure 2). Shiyu Zhou, a professor in the Department of Industrial Engineering at the University of Wisconsin-Madison, says that discussions about the need for better data analytics are typically reactive to customer queries or complaints which often link back to quality issues. In fact, he says, it has become easier than ever to hear the voice of the customer because of datadriven product designs that report performance issues automatically to manufacturer service departments. Examples, he says, are medical equipment, such as magnetic resonance imagers or CT scanners, or jet aircraft engines that are linked to the Internet and communicate on their own when service is needed. Emerging problems, in turn, lead to an enhanced need to boost analytic capacity linked directly to shop floor production processes. The machines themselves, in other words, feed the need for process data, leading to installation of more linked machines, and more actionable data in the factory. In this view, products that ask for service are like the razor blade, which by steadily growing duller creates the need for more razor blades, and a strategy for making them. At Meritor, a maker of drivetrains, axles brakes and other commercial vehicle components, customers tend to focus on one metric the number of rejected parts per million (PPM) to evaluate suppliers. When you take into account high-level manufacturing processes we do casting, forgings, stampings, machining, heat treating and assembly and every truck buyer wants to have the truck the way they want it with specific transmission, axles, and brakes the variations are in the thousands, says Joe ElBehairy, Meritor s vice president for engineering, quality and product strategy. What s more, truck demand can swing wildly in volume, which stresses manufacturing systems, where long and stable production runs most often reduce product variation. To respond, Meritor has as much as quintupled the amount of data it collects at its 28 manufacturing plants. Meritor began to track defect rates not just by part, but also by individual production operations. It also decided to differentiate between reject PPM of products shipped to customers and supplier PPM, which takes into account quality levels from component suppliers. In 2013, Meritor s reject rate was 139 PPM. During the first quarter of 2014, with more plants working to improve the traceability of production issues, the rate fell to 67. One plant, producing an entirely new type of air brake, achieved perfection zero PPM (see case study on page 9). The Economist Intelligence Unit Limited

9 Figure 2 Where data can make the difference In which of the following areas do you see greater volumes of data yielding the biggest gains? Select the top three. Product quality management 72 Process controls Operations management Process design and improvements 36 Predictive maintenance/asset management Supply chain management/sourcing Safety & facility management 20 Throughput improvement Targeted capital spending First you gather data and network it. Then you give the people in the plant the ability to operate the system using the data. You need to go through the steps rather slowly so that people in the plant understand what we re trying to do, and so that we can work with them as a collaborator. Stephan Biller, chief manufacturing scientist for GE Global Research A key element is that we realise, as a company, that quality is valuable to our customers, says Mr ElBehairy. Some of the principles we applied are not new or earth-shattering, but we ve been able to apply them to the complexity that we provide in our products. With the proper analysis of complex production data potentially yielding such dramatic gains in quality and efficiency, it is perhaps no surprise that the rush to collect it still outpaces planning to use it. Indeed, just 42% of companies responding to the EIU survey say they have a well-defined data management strategy, although a slightly larger proportion (44%) say they understand the value of shop floor data and are working to capture that value. This is despite the fact that all realise the paramount importance of data: every single company surveyed places a priority on data collection. GE s Dr Biller emphasises that an important part of any change in data strategy, and consequent alterations to production processes, is careful and considered planning. It s a step process, he says. First you gather data and network it. Then you give the people in the plant the ability to operate the system using the data. You need to go through the steps rather slowly so that people in the plant understand what we re trying to do, and so that we can work with them as a collaborator. Most of the time, the people in the plant know far more about it than you do. Equally important to realising value from this kind of initiative, says the senior executive from the control systems industry, is absolute commitment from senior management, especially the CEO, to building an integrated data process. You can put in all the components to make it work, the computers and software, but if you don t have leadership skills and trust, it can lead to failure no matter what system you have, he says. Having commitment from the CEO is an absolute prerequisite. 8 The Economist Intelligence Unit Limited 2014

10 Case study: Meritor: Towards data-driven production perfection Like most manufacturers, Meritor, of Michigan, has been on a long and determined drive to improve processes and products at its 28 factories in a dozen countries. The company makes drivetrain, braking and other components for trucks, trailers, off-highway, defence and speciality vehicles. The latest iteration of company strategy, dubbed M2016, made operational excellence a renewed priority. We adopted as our top metric reject parts per million [PPM], says Joe ElBehairy, vice president for engineering, quality and product strategy. In 2013, companywide this figure was 139 reject PPM. With a goal of lowering that to 75 PPM by 2016, Meritor has turned, in part, to carefully heeded, real-time shop floor data. Our data collection is an order of magnitude larger than it was several years ago, says Mr ElBehairy. Some of it is related to safety, but a lot of our data gathering is related to traceability. If something goes wrong, Meritor wants to know where and why it happened. And it s not just collecting data, but realtime acting on that data, says Kent Potts, industrialisation manager and leader of a quality improvement push at Meritor s factory in York, South Carolina. At York, three workstations assemble calipers for Meritor s EX+ air disc brake from start to finish. More than 40 steps are required for the basic brake, but that s only the beginning of the product s complexity. Originally launched with 14 different specifications of weight, stopping power, pads, packaging and the like, EX+ assembly ballooned to 169 specifications after sales volume rose sharply following a contract award two years ago. A major customer wanted the brakes, but insisted that rejects had to be 10 PPM or less. To comply, the York plant added sensors, monitoring gear, a programmable controller system and its own custom programming. Employees were trained on an error-proofing system that verifies that the correct parts and processes are applied for each brake. Bar codes are used to keep track of parts, and Meritor devised a system called fit to light, in which a computer keeps track of the assembly steps for each brake and turns on lights over the correct bin for the next component. Reach for the wrong component, and a red light flashes. Meritor used tools that communicate with the programmable controllers and socket trays so that the tools could be used for multiple assembly operations and brake specifications. The programmable controllers verify that the correct socket and torque gun recipe is used for each assembly process; each piece receives the correct customised treatment. Additionally, process data are stored in our manufacturing genealogy database for each air disc brake that s assembled. The data includes the brake serial number, who assembled it, the component parts installed and process data such as fastener torques, says Mr Potts. The result of this application of real-time networked data to improve shop floor processes has been better than any manufacturer usually expects. During the year from March 2013 to March 2014, the York factory had a zero defect rate. No product rejects. Error-free production also permitted improvement of the on-time delivery rate to 98% the best of any Meritor plant. Techniques like these and tighter attention to quality lowered the company s overall reject PPM in the first quarter of 2014 to 67, below the 2016 goal. Now, the goal is to sustain the progress. The Economist Intelligence Unit Limited

11 IV Where theory meets reality One of the guys at an oil refinery told me, We re staffed to run; we re not staffed to change. Peter Zornio, chief strategic officer, Emerson Process Management For every brilliant idea in building real-time, system-wide data collection and analysis, and for every example of skillfully fine-tuning a complicated production process using the industrial Internet and sensor data, there s a real-life story that brings you back to actual shop floors. The control systems industry executive describes a steelmaker where, after sophisticated, automated process controls were installed, operators replaced intricate plant-wide readouts temperatures, process adjustments to compensate for feedstock variations and so forth with data of greater personal interest. (In the background, the integrated digital system continued to monitor and collect vast amounts of production information). There are just two numbers [on display], he says. One is variable income being produced right now, and the other is how much bonus money will be made at the end of the month. Precisely calibrated automated readouts, displaying thousands of variables every second, were turned off because machine operators from each shift preferred to compete manually with operators from other shifts rather than with an automated system. One of the guys at an oil refinery told me, We re staffed to run; we re not staffed to change, says Emerson s Mr Zornio. The capital-spending priority, and the manpower priority, is to just keep the place going. There is no manpower or capital to put in place the next generation of stuff that gets the plant to the next level of improvement. Figure 3 Common problems What problems have you experienced in managing greater volumes of machine-generated process and quality data? Select all that apply. Difficulty in integrating data from multiple sources/formats Lack of personnel/expertise necessary to analyse data from diverse sources Internal siloes make it difficult to employ data effectively We find it difficult to formulate the right questions to use the data appropriately Analysis of data frequently does not produce actionable information 9 We have experienced no problems The Economist Intelligence Unit Limited 2014

12 Companies that responded to the EIU survey raise a series of impediments to the enhanced use of complex, machine-generated data to improve their processes. Number one on the list, cited by 35% of manufacturers, is difficulty integrating data from multiple sources and formats (Figure 3). The bottleneck is not in sensing; we have incredible sensing technology, says Northwestern s Professor Apley. It gets back to the thousands of variables and identifying which is the root cause of the problem. Older, proprietary systems, including some enormously popular ERP systems, produce only summary, batch reports; newer ones may crank out data, in different formats, four times each second. Most of these older factories are not networked, says Dr Biller of GE. The data stays within the production machinery. If you want to improve a system s performance, you have to get the data out of the machine, then integrate it into an IT system some kind of intelligent platform. But simply installing that platform isn t the whole answer, either. Thirty-three percent of surveyed companies say an important issue is finding highly trained people to use it. A related problem asking the right questions of your systems to generate the right answers, was cited as an issue by 21% of surveyed companies. Also problematic: companies organised into feuding siloes that don t share essential information (also cited by 21%). Talent, says Northwestern s Professor Apley, is thin on the ground. Relative to 20 years ago, it is more difficult now to find young people who are highly trained in analytics and data sciences and who want to go into manufacturing, he says. They are often more drawn to financial companies, or companies like Google and Facebook. Even so, Northwestern is among the universities that have recently launched an engineering-oriented masters of science in analytics program. The student body is roughly one-third international and two-thirds domestic students, and so far, all have received multiple job offers. There are just so many companies looking for people who have the skills to analyse large amounts of data, Professor Apley says. Mr Zornio has found siloing can be a significant issue because when every facility makes their own decision about which efficiency controls to put in place, interplant uniformity becomes impossible. Nonetheless, centrally controlled manufacturers may make decisions about best practices that individual facilities resist because of inevitable local variability. In this big-data world, you may know that you don t have the people who can look at all the data and figure out what needs to be done, he says. But suppose you do have the people. The next tripwire, says the senior executive from the control systems industry, is information overload. There s not enough intelligence in software to sort out this overload. For example, let s say there s a machine that sends out an alarm to the operator. It needs grease or whatever. But what if three or four machines do this? Then suddenly you have alarm overload, and then you have to have alarm management. It is easy to find 500 things to do in a plant. But it s damn tough to find the 497 things we are not going to do. That s the real challenge. It is more difficult now to find young people who are highly trained in analytics and data sciences and who want to go into manufacturing. They are often more drawn to financial companies, or companies like Google and Facebook. Daniel Apley, professor of industrial engineering and management sciences, Northwestern University The Economist Intelligence Unit Limited

13 Case study: ABB/Sandvik: Reducing deviations, eliminating imperfections ABB, based in Zurich, Switzerland, makes power, automation and electrical products and provides a range of industrial control services. One of its recent successes came in helping Sandvik Materials Technology of Sandviken, Sweden (about 190 km north of Stockholm), which makes specialty stainless steel, titanium and alloys for equally specialised uses. Sandvik faced the problem that as the uses of steel grew more intricate, with greater precision required from each delivery, production equipment had to keep pace. In 2013, as part of a long-term process improvement effort, Sandvik s attention turned to an important component of the production system a bidirectional rolling mill, or Steckel mill, used to make metal strips thinner and thinner with each pass through the rollers. Sandvik had no plan to replace the equipment. Instead, it opted to improve how it used the mill with a few new sensors feeding digital controls. Based on a tightly defined model of perfection, these would continually adjust rolling speeds, pressures and the number of passes through the mill to compensate for variation. First, Sandvik installed additional sensors to measure precisely the width of the rolled metal and its temperature, which changes during rolling as the metal interacts with the machinery. In some factories, dozens or even hundreds of sensors might be required, but Sandvik made do with just nine. To control the process, the company needed more data, but not a flood of it. Every rolling job begins with a detailed model of what the exactly right outcome should be. This means the system provided by ABB must take multiple factors into account, including the material being rolled; its thickness, width and grade; the target thickness; the number of passes through the mill that should be required; and the adaptations to rolling pressure, temperatures, rolling speed, torque and flatness that must be made. Increasingly, customers want thinner steel, but thinner steel strips can easily be brittle and prone to deformation and in-process separation. We run all kinds of special steels, the entire range from stainless to high-alloy, says Patrick Högström, hot rolling mills production manager at Sandvik. The variety of steel grades and sheet dimensions make production very complex and knowledge intensive. The model makes it possible to optimise rolling in a completely different way from what a human being is capable of. It becomes smoother, and with noticeably less scrap, which means increased yield. Thanks to the new sensors and related process controls, Sandvik can now roll specialty metal to thinner tolerances while maintaining the metallic properties, such as strength and formability, required for the final use. Compared with its old process, the new controls have reduced the degree of deviation from perfection by 35%, and the average volume of imperfections has dropped by 80%. 12 The Economist Intelligence Unit Limited 2014

14 V From monitoring to alerting, predicting and solving Even with the many complications between shop floor data theory and practice, companies surveyed by the EIU have found a number of comfort zones where the benefits of real-time machine-generated information are accessible. More than 80% of companies report mature data analysis capabilities when it comes to everyday issues of safety, facilities management, supply chain management, formulation of capital spending plans, process design, the use of process controls, asset maintenance and generalised product quality management. In other words, when production processes are normal, their comfort level with digital technology is at high levels. But outside of monitoring normal operations, confidence levels drop precipitously. For example, when it comes to analysing responses to alerts about problems and their causes, half or more of surveyed companies lack mature capabilities. Two-thirds of companies report analytical weakness when it comes to dealing with asset maintenance and throughput alerts, and 76% lack mature capabilities to analyse potential process design issues (Figure 4). Figure 4 Reporting yes; alerting/predicting/solving not yet For which of the following functions and areas does your company have mature data analysis capabilities? Safety and facilities management Reporting normal operations Alerting about problems 60% 40% Predicting future problems Prescribing solutions to problems Asset maintenance/management 100% Product quality management 80% Supply chain management 20% 0% Process controls Capital spending Throughput Process design Operations management The Economist Intelligence Unit Limited

15 Although state-of-the-art digital systems can predict problems and suggest solutions in advance of actual need, more than half of manufacturers aren t confident that their analytical skills are up to the task. Just 22% of surveyed companies have predictive analytical capabilities for production throughput, for example; just 16% have mature analytical capacity to generate potential solutions. In terms of functions, the highest levels of predictive capability are found in supply chain management (44%) and safety and facility management (48%). Mature analytical capabilities to prescribe solutions are most prevalent for asset maintenance (38%) and product quality management (38%). Of course, when evaluating the current state of manufacturing s digital readiness, two additional issues must be considered. First, not all factories actually need the most advanced possible integrated, real-time data. If you operate a smaller factory, and you have a supervisor who can see where everything is, you don t need all that much data to run it efficiently, says Dr Biller. If I have a plant that can already run at optimal efficiency, I don t want to implement sophisticated technology because there is no return on investment from it. Once you lean out a plant and make every process as simple as possible you don t want to automate it. Second, even the most sophisticated, integrated digital system is of limited use if companies don t ask the right questions about their processes or quality issues. Data collection and analysis return rewards only when root causes of issues are addressed, rather than just symptoms of those issues. One practice used in Six Sigma quality-improvement systems is to repeatedly use the question Why? until the root cause is identified. For example: A door hinge isn t working as it should. Why? Because it is slightly too large for the fitting. Why? Is it because the wrong materials have been used to make it, leading to slight deformations of the part? Are processes slightly maladjusted, leading to hinges of the wrong size? Is the part designed the way it should have been? Why questions apply equally to the use of shop-floor data analytics. You must ask the right question to arrive at the right answer, the old adage goes, and nothing about the modern digital world has changed the truth of this. 14 The Economist Intelligence Unit Limited 2014

16 VI The plentiful returns of data success After all is said and done, it turns out that coordinated digital control systems can and do produce insights that are extremely valuable. Two-thirds of companies say data from the shop floor have realised savings or gains of more than 10% annually in both new production efficiencies and the cost of quality. Some 34% of those surveyed have generated annual savings of more than 25% annually in the cost of quality; the same proportion report 25% or better efficiency improvements annually (Figure 5). Although the results should be interpreted with caution given the sample size and the fact that correlation does not prove causation the survey results nonetheless indicate that the most dataadept companies are also the most profitable. In the survey, manufacturing companies with average earnings growth of 10% or more over the past three years are more likely to have a welldefined data management strategy than those who experienced slower or no earnings growth (59% vs 10%). They are more likely to find ways to improve efficiency and lower the cost of quality (45% vs 25%). They are also less likely to lack staff with sufficient data-analysis capabilities to meet their needs (21% vs 50%; Figure 6). Although the potential for improvement varies widely by facility, Dr Biller says that Figure 5 Savings from data To what extent have insights from shop floor/machine-generated data delivered gains in the following areas? 40 Cost of quality Production efficiency No impact 1-10% 11-25% 26-50% >50% The Economist Intelligence Unit Limited

17 Figure 6 Profiting from data High growth = average EBITDA growth of 10% or more for each of past three years Low growth = average EBITDA growth of under 10% for each of past three years 70 High growth Low growth Well-defined data-management strategy 25 Routinely analyse data to find ways to improve efficiency and lower cost of quality 21 Lack key staff with skills to analyse data At GE, where we carry US$15bn in inventory, a 10% saving can be huge. How about optimising scheduling to improve throughput? Ten percent is a pretty conservative number for that. Stephan Biller, chief manufacturing scientist, GE Global Research optimising factory systems provides substantial opportunities. If you look at the supply chain and driving out excess inventory, we talk about savings of perhaps 6% to 20%; we think 10% is actually a conservative number for those implementations, he says. At GE, where we carry US$15bn in inventory, a 10% saving can be huge. How about optimising scheduling to improve throughput? Ten percent is a pretty conservative number for that, and that means you can save in plant and equipment investment because we don t have to buy additional machines. Machine optimisation can produce gains of 20%. The benefits can be huge. Current gains also involve more than money or ROI that justifies the necessary investments in sensors, RFID and bar code equipment, computers, software, training, process realignment, improved production machinery and continual product improvements. Evangelists for widely integrated production systems believe a new industrial revolution is in process. Fully 50% of surveyed companies agree with the statement: A rigorous and advanced data analysis capability can be a differentiator for our company. This marks an important attitudinal step for manufacturing companies into realtime controls and rapid responses to production variations. Ultimately, the goals of decades of work on inspection and quality control boil down to understanding variation, says Professor Apley. Perfect manufacturing hypothetically produces the same part, identically, with no variations. What we see now is that huge amounts of data are collected, but largely underutilised. But data analytics is a new and rapidly expanding area that has great potential for helping to understand variation. Think of the manufacturing revolution that s happening, says Dr Biller. Yes, we want to make all of our factories more agile, but the great part of this is that we re building an ecosystem that allows people all over the world to innovate. We re all working on how we can turn that into traditional manufacturing, and then traditional manufacturing into smart manufacturing. We have every science and engineering discipline working for GE in research. Of course, 16 The Economist Intelligence Unit Limited 2014

18 smaller manufacturers can t do that. But remember, 80% of our parts are designed and made by suppliers. We want them and all the links to be part of this digital thread. Case study: GE s factory built to produce data The General Electric factory in Schenectady, New York, was built, on one level, to make the company s new-technology Durathon battery. But for the GE Global Research arm, the factory s most important product is data. GE has packed more than 10,000 sensors into the US$170m plant. All of them are linked into the most tightly integrated digital control and information systems of any of the company s 400 global factories. The sensors monitor performance indicators for every manufacturing process, building information such as energy use, temperatures and humidity, and even extend to the rooftop weather station. If something goes wrong, the sensors, using wi-fi links to staff members wielding hand-held computers, send alerts, and can, if necessary, send text messages to plant employees at home. The plant, which began battery production in July 2012, is the test bed and most pronounced expression of GE s research into ways to create and use the emerging industrial Internet called by some the Internet of things to build what the company calls the Brilliant Factory. This plant has one of the most advanced implementations of our process suite from GE Intelligent Platforms, GE s process controls business, says Stephan Biller, chief manufacturing scientist for GE Global Research of nearby Niskayuna, New York. Initially we used the data to improve our own processes. What input parameters do we have to change? If a battery fails, was it the humidity of that particular process, or the temperature, or operators who we didn t train well enough? All of this was useful in improving the process. With precise manufacturing parameters set for the Durathon which stores and produces electric power on demand using a sodiumnickel chemical process GE has begun to use the geyser of data to improve efficiency and throughput. The questions now are how do we improve costs and get more out of that factory? says Dr Biller. The key is to think of the entire system, not just parts of it, and by doing this, you can reach an optimal state. I don t want to optimise each individual machine by itself, but as part of a whole system. This permits me to find the bottlenecks. That s not a trivial task, and it s systems thinking that gives you the gains. The Economist Intelligence Unit Limited

19 VII Conclusion What do these findings mean for manufacturers large and small? Several conclusions seem apparent. Firstly, as the science-fiction novelist William Gibson noted, the future has already arrived it s just not evenly distributed. This is as true of manufacturing as any other field. At the cutting edge are companies that operate with complete transparency from end to end of their design, supply, production, shipping and quality control processes. They typically have a good grip, through sensors, RFID tags, bar codes and other devices, on how things are going in their factories. They can electronically send precise adjustments to production gear and workstations. They can measure quickly whether a part isn t performing as it should. They can schedule supplies and shipments accurately and respond to problems effectively. Small variations at one part of their data chain don t produce major surprises elsewhere because their systems, integrated and networked, see the potential impact and fix it. GE and others imagine a perfect factory that will never stop producing, ever, because every potential variable is monitored and adjusted and every product is exactly what it should be. However, the reality is that very few of today s aggressive, heads-up manufacturers are close to this vision yet. This is partly because the process of integrating data systems and analysis into manufacturing is far from simple. In this arena, one size definitely does not fit all. In fact there really is no standard approach other than to match a company s appetite for change. In addition, the transition is rarely straightforward because most factories are not new and many may not be ideal environments for complex data collection and analysis. Older machinery, computer systems that don t provide real-time data or that are incompatible with others in a production process, and time-honoured production habits clash with the promise of efficiency and quality improvements from the digital world. Secondly, the research shows that once manufacturers have decided to implement analytical models they need to think carefully about how data needs to be stored, processed and used. Volumes of data have become so large that some companies aren t certain where to store it all. Many still don t know how to use the information, or even how to formulate production questions that fit the capacity of sensors and software to provide answers. Many lack the resources, and probably the trained personnel, to look at much more than monitoring of simply networked shop-floor data. Companies that now use multiple separate types of monitoring with each machine or portion of the production process isolated digitally from the others won t find it easy to create such networks. But it is worth the try, and in this technological environment, with relatively inexpensive wireless data collection commonplace, the financial commitment is manageable. 18 The Economist Intelligence Unit Limited 2014

20 Thirdly, customers will increasingly see the digitised processing, collection and analysis of complex data by manufacturers as a guarantor of quality over and above traditional accreditations. This will become another means of differentiation among peers; companies that lag in this area may find themselves compelled by competition to ramp up their complex data analysis capabilities. Finally, the research has shown that the embrace of shop-floor data even on a relatively small scale can be beneficial for even the smallest of producers. By monitoring production machinery and processes, efficiencies can be gained and product quality is likely to improve. Therefore the question for manufacturing is not whether advanced, integrated, systemic data capabilities are beneficial or the pathway to greater productivity and operations excellence certainly, they are, and in many cases, the gains from adoption of the latest digital control techniques can be substantial. The real issue is how manufacturers can make this vision of the future a reality today. The Economist Intelligence Unit Limited

SMART FACTORY IN THE AGE OF BIG DATA AND IoT

SMART FACTORY IN THE AGE OF BIG DATA AND IoT WWW.WIPRO.COM SMART FACTORY IN THE AGE OF BIG DATA AND IoT THE SHAPE OF THINGS TO COME Narendra Ghate Senior Manager at Wipro Analytics Table of contents 01 Industrial Revolution 4.0 is here. Where are

More information

Lean manufacturing in the age of the Industrial Internet

Lean manufacturing in the age of the Industrial Internet Lean manufacturing in the age of the Industrial Internet From Henry Ford s moving assembly line to Taiichi Ohno s Toyota production system, now known as lean production, manufacturers globally have constantly

More information

Operational Business Intelligence in Manufacturing

Operational Business Intelligence in Manufacturing Operational Business Intelligence in Manufacturing Copyright 2007 KeyTone Technologies Inc. Page 1 SmartWIP - Intelligent Manufacturing with RFID Manufacturers are under competitive pressure to fulfill

More information

What sets breakthrough innovators apart PwC s Global Innovation Survey 2013: US Summary

What sets breakthrough innovators apart PwC s Global Innovation Survey 2013: US Summary What sets breakthrough innovators apart PwC s Global Innovation Survey 2013: US Summary www.pwc.com/innovationsurvey 60% $250b The top innovators in our study plan to grow by more than 60 percent over

More information

Ten Critical Questions to Ask a Manufacturing ERP Vendor

Ten Critical Questions to Ask a Manufacturing ERP Vendor Ten Critical Questions to Ask a Manufacturing ERP Vendor At a Glance: The ERP industry has earned such a poor reputation for delivery in the last 20 years that users have learned to live within a very

More information

VMware vcenter Log Insight Delivers Immediate Value to IT Operations. The Value of VMware vcenter Log Insight : The Customer Perspective

VMware vcenter Log Insight Delivers Immediate Value to IT Operations. The Value of VMware vcenter Log Insight : The Customer Perspective VMware vcenter Log Insight Delivers Immediate Value to IT Operations VMware vcenter Log Insight VMware vcenter Log Insight delivers a powerful real-time log management for VMware environments, with machine

More information

Sage X3. Enterprise Business Management Solutions in the 21st Century: Key Buying Considerations

Sage X3. Enterprise Business Management Solutions in the 21st Century: Key Buying Considerations Sage X3 Enterprise Business Management Solutions in the 21st Century: Table of Contents 3 Legacy Systems are Good to a Point 3 On-Premise or Cloud: Which Option makes Sense 4 The Modern Business Management

More information

Predictive Analytics. Going from reactive to proactive. Mats Stellwall - Nordic Predictive Analytics Enterprise Architect 2012-06-14

Predictive Analytics. Going from reactive to proactive. Mats Stellwall - Nordic Predictive Analytics Enterprise Architect 2012-06-14 Mats Stellwall - Nordic Predictive Analytics Enterprise Architect 2012-06-14 Predictive Analytics Going from reactive to proactive 2011 IBM Corporation Nothing exists until it is measured Niels Bohr the

More information

Traceability Data Integrity: Challenges and Solutions By Mitch DeCaire, Cogiscan, Inc.

Traceability Data Integrity: Challenges and Solutions By Mitch DeCaire, Cogiscan, Inc. Traceability Data Integrity: Challenges and Solutions By Mitch DeCaire, Cogiscan, Inc. The electronics manufacturing industry is experiencing increased demands for material traceability. Competitive pressures

More information

The Connected CFO a company s secret silver bullet?

The Connected CFO a company s secret silver bullet? a company s secret silver bullet? Imagine if the Chief Financial Officer (CFO) had a real-time dashboard of the business that automatically alerted him or her to specific triggers about the financial performance

More information

Ten Critical Questions to Ask a Manufacturing ERP Vendor

Ten Critical Questions to Ask a Manufacturing ERP Vendor Ten Critical Questions to Ask a Manufacturing ERP Vendor Plex Online White Paper At a Glance: The ERP industry has earned such a poor reputation for delivery in the last 20 years that users have learned

More information

Achieving Basic Stability By Art Smalley

Achieving Basic Stability By Art Smalley Achieving Basic Stability By Art Smalley Introduction Lean production has dramatically lifted the competitiveness of many manufacturing companies and the value they deliver to customers. What s more, encouraging

More information

BIG DATA ANALYTICS: THE TRANSFORMATIVE POWERHOUSE FOR BIOTECH INDUSTRY ADVANCEMENT. David Wiggin October 8, 2013

BIG DATA ANALYTICS: THE TRANSFORMATIVE POWERHOUSE FOR BIOTECH INDUSTRY ADVANCEMENT. David Wiggin October 8, 2013 BIG DATA ANALYTICS: THE TRANSFORMATIVE POWERHOUSE FOR BIOTECH INDUSTRY ADVANCEMENT David Wiggin October 8, 2013 AGENDA Big Data Analytics Four Examples Global Supply Chain Visibility Demand Signal Repository

More information

SEYMOUR SLOAN IDEAS THAT MATTER

SEYMOUR SLOAN IDEAS THAT MATTER SEYMOUR SLOAN IDEAS THAT MATTER The value of Big Data: How analytics differentiate winners A DATA DRIVEN FUTURE Big data is fast becoming the term keeping senior executives up at night. The promise of

More information

Understanding Manufacturing Execution Systems (MES)

Understanding Manufacturing Execution Systems (MES) Understanding Manufacturing Execution Systems (MES) Presented by: Shirley Schmidt Freedom Technologies 10370 Citation Dr., Suite 200 Brighton, MI 48116 Phone: 810-227-3737 www.freedomcorp.com What is a

More information

Lean manufacturing in the age of the Industrial Internet

Lean manufacturing in the age of the Industrial Internet Lean manufacturing in the age of the Industrial Internet From Henry Ford s moving assembly line to Taiichi Ohno s Toyota production system, now known as lean production, manufacturers globally have constantly

More information

AXIS ENTERPRISE RESOURSE PLANNING

AXIS ENTERPRISE RESOURSE PLANNING AXIS ENTERPRISE RESOURSE PLANNING ERP FOR METALS, WIRE AND CABLE Streamline operations, increase productivity and reduce costs: Axis helps you compete in today s global markets with superior tools and

More information

Prescriptive Analytics. A business guide

Prescriptive Analytics. A business guide Prescriptive Analytics A business guide May 2014 Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications

More information

MARKETING AUTOMATION: HOW TO UNLOCK THE VALUE OF YOUR CRM DATA

MARKETING AUTOMATION: HOW TO UNLOCK THE VALUE OF YOUR CRM DATA : HOW TO UNLOCK THE VALUE OF YOUR CRM DATA Kynetix Technology Group Introduction People who remember using a Rolodex to keep track of their clients consigned this little piece of history to the back of

More information

Louis Gudema: Founder and President of Revenue + Associates

Louis Gudema: Founder and President of Revenue + Associates The Interview Series - Presented by SmartFunnel Interviews of Sales + Marketing Industry Leaders Louis Gudema: Founder and President of Revenue + Associates PETER: Hello folks this is Peter Fillmore speaking.

More information

FORGE A PERSONAL CONNECTION

FORGE A PERSONAL CONNECTION ONLINE REPORT SPONSORED BY: SNAPSHOT: FORGE A PERSONAL CONNECTION EMPLOY CRM IN HIGHER EDUCATION TO STREAMLINE AND SOLIDIFY STUDENT RECRUITING AND RETENTION. INSIDE P2 DEPLOY AN INTEGRATED CRM SYSTEM P3

More information

Industrial Automation. A Manufacturing Revolution in Automotive and Industrial Equipment

Industrial Automation. A Manufacturing Revolution in Automotive and Industrial Equipment Industrial Automation A Manufacturing Revolution in Automotive and Industrial Equipment 1 Automated production, integrated end-to-end: transparent, reliable, predictable and efficient. That s the promise

More information

T r a n s f o r m i ng Manufacturing w ith the I n t e r n e t o f Things

T r a n s f o r m i ng Manufacturing w ith the I n t e r n e t o f Things M A R K E T S P O T L I G H T T r a n s f o r m i ng Manufacturing w ith the I n t e r n e t o f Things May 2015 Adapted from Perspective: The Internet of Things Gains Momentum in Manufacturing in 2015,

More information

Rockwell Automation s Business Intelligence Solutions for Manufacturers

Rockwell Automation s Business Intelligence Solutions for Manufacturers ARC VIEW DECEMBER 3, 2009 Rockwell Automation s Business Intelligence Solutions for Manufacturers By Craig Resnick Summary Rockwell Automation recently briefed ARC regarding its latest business intelligence

More information

Improve the Agility of Demand-Driven Supply Networks

Improve the Agility of Demand-Driven Supply Networks GE Intelligent Platforms Improve the Agility of Demand-Driven Supply Networks Leverage real-time production data to optimize the supply chain for a sustainable competitive advantage Improve the Agility

More information

penguins penguins event apps: the current statistics

penguins penguins event apps: the current statistics penguins penguins event apps: the current statistics event apps: the current statistics In the information age, the roles played by technology and data have transformed industries of every kind. Not only

More information

The Distribution Evolution: Survival of the Fastest. Technology and the Evolving Distribution Business Model

The Distribution Evolution: Survival of the Fastest. Technology and the Evolving Distribution Business Model The Distribution Evolution: Survival of the Fastest Technology and the Evolving Distribution Business Model An Industry Viewpoint based on an Infor Distribution Webcast June 26, 2012 TABLE OF CONTENTS

More information

Agile Manufacturing for ALUMINIUM SMELTERS

Agile Manufacturing for ALUMINIUM SMELTERS Agile Manufacturing for ALUMINIUM SMELTERS White Paper This White Paper describes how Advanced Information Management and Planning & Scheduling solutions for Aluminium Smelters can transform production

More information

Chevron Drives Process Standardization and Efficiency with Mobile Decision Support

Chevron Drives Process Standardization and Efficiency with Mobile Decision Support Microsoft Customer Solution Manufacturing Industry Case Study Chevron Drives Process Standardization and Efficiency with Mobile Decision Support Overview Country or Region: United States Industry: Manufacturing

More information

PRECISION METAL STAMPINGS & ASSEMBLIES FOR THE MEDICAL DEVICE INDUSTRY

PRECISION METAL STAMPINGS & ASSEMBLIES FOR THE MEDICAL DEVICE INDUSTRY PRECISION METAL STAMPINGS & ASSEMBLIES FOR THE MEDICAL DEVICE INDUSTRY COMPONENTS & ASSEMBLIES FOR SURGICAL INSTRUMENTS & CLASS-CRITICAL IMPLANTABLE DEVICES COMPONENTS AND ASSEMBLIES FOR INDUSTRIES, INC.

More information

RACK AND CONTAINER TRACKING SOLUTION

RACK AND CONTAINER TRACKING SOLUTION RACK AND CONTAINER TRACKING SOLUTION Rack and Container Tracking Solution Overview Zebra Technologies is the market leader in providing solutions for tracking and managing assets, people and events through

More information

The Guide to: Email Marketing Analytics"

The Guide to: Email Marketing Analytics The Guide to: Email Marketing Analytics" This guide has been lovingly created by Sign-Up.to we provide email marketing, mobile marketing and social media tools and services to organisations of all shapes

More information

PRINTING AND MAILING, INC. UPDATED January 23, 2014. Let s Be Friends!

PRINTING AND MAILING, INC. UPDATED January 23, 2014. Let s Be Friends! PRINTING AND MAILING, INC. Understanding Mailing UPDATED January 23, 2014 www.successprint.com www.successprint.com Let s Be Friends! 10 Pearl Street Norwalk, CT 06850 tel 203-847-1112 fax 203-846-2770

More information

BYD Builds a Smart Factory with the Innovation Control Center and SAP MaxAttention

BYD Builds a Smart Factory with the Innovation Control Center and SAP MaxAttention 2014 SAP AG or an SAP affiliate company. All rights reserved. BYD Builds a Smart Factory with the Innovation Control Center and SAP MaxAttention BYD Limited Industry Automotive and high tech IT and renewable

More information

Continuous delivery Release software on-demand, not on Red Alert

Continuous delivery Release software on-demand, not on Red Alert Continuous delivery Release software on-demand, not on Red Alert Have it all. Ahead of the competition Value In a world where customers expect a mobile and connected 24x7 experience, businesses must adapt

More information

The Continuously Current Enterprise: Trends in lifecycle management of automation assets

The Continuously Current Enterprise: Trends in lifecycle management of automation assets : Trends in lifecycle management of automation assets 1. Introduction 2. Managing the Automation Lifecycle 3. Return on Upgrade Investment 4. Ramping Up to the Enterprise 5. Customer FIRST Regardless of

More information

Tapping the benefits of business analytics and optimization

Tapping the benefits of business analytics and optimization IBM Sales and Distribution Chemicals and Petroleum White Paper Tapping the benefits of business analytics and optimization A rich source of intelligence for the chemicals and petroleum industries 2 Tapping

More information

The Analysis of Quality Escapes in the Aerospace & Defense Industry

The Analysis of Quality Escapes in the Aerospace & Defense Industry The Analysis of Quality Escapes in the Aerospace & Defense Industry White Paper November 1, 2012 1825 Commerce Center Blvd Fairborn, Ohio 45324 937-322-3227 www.ren-rervices.com The Analysis of Quality

More information

Oracle Manufacturing Operations Center Provides Real-time Benefits for Leading Gear Manufacturer in Asia

Oracle Manufacturing Operations Center Provides Real-time Benefits for Leading Gear Manufacturer in Asia JANUARY 3, 2013 Provides Real-time Benefits for Leading Gear Manufacturer in Asia By Janice Abel Keywords Enterprise Manufacturing Intelligence (EMI), Shop Floor Data, Real-Time Data, Big Data, Integration,

More information

Survey of more than 1,500 Auditors Concludes that Audit Professionals are Not Maximizing Use of Available Audit Technology

Survey of more than 1,500 Auditors Concludes that Audit Professionals are Not Maximizing Use of Available Audit Technology Survey of more than 1,500 Auditors Concludes that Audit Professionals are Not Maximizing Use of Available Audit Technology Key findings from the survey include: while audit software tools have been available

More information

MULTICHANNEL MARKETING

MULTICHANNEL MARKETING REPORT Report Multichannel Marketing MULTICHANNEL MARKETING A Study Highlighting Current Approaches and Investment, Opportunities and Key Challenges 1 2 Introduction 4 Key findings 6 Conclusion 19 3 INTRODUCTION

More information

Oracle Manufacturing Operations Center

Oracle Manufacturing Operations Center Oracle Manufacturing Operations Center Today's leading manufacturers demand insight into real-time shop floor performance. Rapid analysis of equipment performance and the impact on production is critical

More information

Synergy THE O NEAL FAMILY OF COMPANIES

Synergy THE O NEAL FAMILY OF COMPANIES Synergy Family is synonymous with synergy at O Neal Industries (ONI). As part of the nation s largest family-owned group of metals service centers, each ONI company and each individual operation have the

More information

5 Keys Driving Successful Manufacturing CIOs and IT Leaders

5 Keys Driving Successful Manufacturing CIOs and IT Leaders THE PLEX MANUFACTURING CLOUD 5 Keys Driving Successful Manufacturing CIOs and IT Leaders Delivering business success through IT leadership requires discipline and focus on several key tenets. As a technology

More information

Digital Strategy. How to create a successful business strategy for the digital world.

Digital Strategy. How to create a successful business strategy for the digital world. Digital Strategy How to create a successful business strategy for the digital world. Digital Strategy Overview Every business today needs a digital strategy. Products and services need to be digitally

More information

Business Improvement Project Management Training

Business Improvement Project Management Training Business Improvement Project Management Introduction This article describes the approach to project management as it relates to business improvement projects. Obviously there are many forms of project

More information

Sales Compensation Management. Research Report Executive Summary. Improving the Impact of Pay and Incentives to Maximize Revenue.

Sales Compensation Management. Research Report Executive Summary. Improving the Impact of Pay and Incentives to Maximize Revenue. Sales Compensation Management Improving the Impact of Pay and Incentives to Maximize Revenue Research Report Executive Summary Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without Permission

More information

A RE YOU SUFFERING FROM A DATA PROBLEM?

A RE YOU SUFFERING FROM A DATA PROBLEM? June 2012 A RE YOU SUFFERING FROM A DATA PROBLEM? DO YOU NEED A DATA MANAGEMENT STRATEGY? Most businesses today suffer from a data problem. Yet many don t even know it. How do you know if you have a data

More information

The Value of Vendor Compliance Optimization Technology

The Value of Vendor Compliance Optimization Technology Supply Chain Executive Brief The Value of Vendor Compliance Optimization Technology Dozens of retailers, and in some cases other types of companies such as wholesalers and manufacturers, manage vendor

More information

B2B Market Segmentation

B2B Market Segmentation Circle Research White Paper B2B Market Segmentation B2B Market Segmentation IN SUMMARY This paper on B2B market segmentation: Outlines the different approaches to segmentation in B2B environments Provides

More information

Why Marketing Automation is a Must-Have For Every B2B

Why Marketing Automation is a Must-Have For Every B2B Why Marketing Automation is a Must-Have For Every B2B VP of Sales Robert M. Walmsley President and CEO, Tailwind Strategies In the age of Internet marketing there is no salesmarketing alignment issue more

More information

NAS: A REFERENCE IN THE STAINLESS STEEL INDUSTRY

NAS: A REFERENCE IN THE STAINLESS STEEL INDUSTRY NAS: A REFERENCE IN THE STAINLESS STEEL INDUSTRY Cristobal Fuentes CEO of NAS Investor and Analyst s Day London, 8 th November 2011 Table of Contents I. Company Overview II. Raw Materials III. Integrated

More information

B2B Buyers Are Customers, Too: How Wholesalers Can Improve the Customer Experience

B2B Buyers Are Customers, Too: How Wholesalers Can Improve the Customer Experience Brief By Matthew Petersen and Rob O Regan NO. 56 B2B Buyers Are Customers, Too: How Wholesalers Can Improve the Customer Experience SAP Center for Business Insight Brief Q&A Case Study Inquiry E-Book 1

More information

Building Efficient and Consistent Marketing

Building Efficient and Consistent Marketing Building Efficient and Consistent Marketing This document sets out to help senior managers understand the key steps that need to be taken to achieve consistency and efficiency in package labelling what

More information

Steel supply chain transformation challenges Key learnings

Steel supply chain transformation challenges Key learnings IBM Global Business Services White Paper Industrial Products Steel supply chain transformation challenges Key learnings 2 Steel supply chain transformation challenges Key learnings Introduction With rising

More information

A better way to calculate equipment ROI

A better way to calculate equipment ROI page 1 A better way to calculate equipment ROI a West Monroe Partners white paper by Aaron Lininger Copyright 2012 by CSCMP s Supply Chain Quarterly (www.supplychainquarterly.com), a division of Supply

More information

Generating analytics impact for a leading aircraft component manufacturer

Generating analytics impact for a leading aircraft component manufacturer Case Study Generating ANALYTICS Impact Generating analytics impact for a leading aircraft component manufacturer Client Genpact solution Business impact A global aviation OEM and services major with a

More information

2012 AnnuAl MArket OutlOOk & FOrecAst SUMMARY REPORT

2012 AnnuAl MArket OutlOOk & FOrecAst SUMMARY REPORT 2012 Annual Market Outlook & Forecast SUMMARY REPORT SECTION TITLE 04 Executive Summary 06 Introduction 07 A closer look at online study respondents Table of Contents 09 Going a step further in-depth interviews

More information

Motorola Solutions and SAP : Extend the value of your investments in SAP software with mobile apps

Motorola Solutions and SAP : Extend the value of your investments in SAP software with mobile apps Motorola Solutions and SAP : Extend the value of your investments in SAP software with mobile apps 2 Motorola Solutions and SAP: Manufacturing The challenge: Improving efficiency and accuracy throughout

More information

Supply Chain Acceleration: Our Offering for Enabling Growth

Supply Chain Acceleration: Our Offering for Enabling Growth Supply Chain Acceleration: Our Offering for Enabling Growth Supply Chain Acceleration Services Supply Chain Acceleration (SCA) brings together 30 years of supply chain knowledge and domain expertise, that

More information

Small Data, Big Impact

Small Data, Big Impact Small Data, Big Impact The importance of extracting actionable insights from lift truck telematics to improve efficiency and protect the bottom line Yale Materials Handling Corporation Small Data, Big

More information

THE NEXT NORMAL Five reasons why e-invoicing is fast becoming business as usual

THE NEXT NORMAL Five reasons why e-invoicing is fast becoming business as usual WHITE PAPER THE NEXT NORMAL Five reasons why e-invoicing is fast becoming business as usual October 2014 TABLE OF CONTENTS 1. E-INVOICING AND TODAY S BUSINESS NORMS 2. WHERE WE ARE TODAY 3. HOW WE GOT

More information

Manufacturing Analytics: Uncovering Secrets on Your Factory Floor

Manufacturing Analytics: Uncovering Secrets on Your Factory Floor SIGHT MACHINE WHITE PAPER Manufacturing Analytics: Uncovering Secrets on Your Factory Floor Quick Take For manufacturers, operational insight is often masked by mountains of process and part data flowing

More information

Agile Project Portfolio Management

Agile Project Portfolio Management C an Do GmbH Implerstr. 36 81371 Munich, Germany AGILE PROJECT PORTFOLIO MANAGEMENT Virtual business worlds with modern simulation software In view of the ever increasing, rapid changes of conditions,

More information

Making Machines More Connected and Intelligent

Making Machines More Connected and Intelligent White Paper Making Machines More Connected and Intelligent Overview It s no secret that technology is dramatically transforming the manufacturing arena. We re witnessing a new industrial revolution, led

More information

A report from the Economist Intelligence Unit. Views from the C-suite Who s big on BIG DATA? Sponsored by

A report from the Economist Intelligence Unit. Views from the C-suite Who s big on BIG DATA? Sponsored by A report from the Economist Intelligence Unit Views from the C-suite Who s big on BIG DATA? Sponsored by Executive summary The way that big data pervades most organisations today creates a dynamic environment

More information

One View Of Customer Data & Marketing Data

One View Of Customer Data & Marketing Data One View Of Customer Data & Marketing Data Ian Kenealy, Head of Customer Data & Analytics, RSA spoke to the CX Network and shared his thoughts on all things customer, data and analytics! Can you briefly

More information

Global Survey: What s creating tension between IT and business leaders? April 2014

Global Survey: What s creating tension between IT and business leaders? April 2014 Global Survey: What s creating tension between IT and business leaders? April 2014 1 Research & Insights Executive summary If the marketing department is making its own decisions about an email marketing

More information

How to Structure the Right Industrial Vending Contract for Your Needs (VMI vs. CMI)

How to Structure the Right Industrial Vending Contract for Your Needs (VMI vs. CMI) By Rick Barrera How to Structure the Right Industrial Vending Contract for Your Needs (VMI vs. CMI) The benefits of vending are well known an immediate 0%-40% reduction in consumption, better inventory

More information

MARKETING AUTOMATION:

MARKETING AUTOMATION: MARKETING AUTOMATION: A Proven Way to Become Indispensable TABLE OF CONTENTS MARKETING GETS A MAKEOVER... 4 AUTOMATION IN ACTION... 4 ADVANCING MARKETING AUTOMATION TO THE HIGHER UPS... 5 MONEY TALKS...

More information

guides The contact centre guide in association with

guides The contact centre guide in association with s contact centre in association with Foreword Richard Mill, Managing Director Business Systems (UK) Ltd 3 Steps to using this Guide. 1. Clear out a special space on your bookshelf for this, you are going

More information

European Migration Survey

European Migration Survey European Migration Survey Key challenges in IT migration Survey conducted by IDG Connect on behalf of Dell Summary of Research Yes to Windows 7, no to the cloud as businesses strive to migrate from Windows

More information

Business Success Blueprints Choosing and using a CRM

Business Success Blueprints Choosing and using a CRM Business Success Blueprints Choosing and using a CRM YOUR BLUEPRINT TO SUPER SUCCESS The definitive Entrepreneurs Circle Guide to managing your customer data. Choosing and Using a CRM Still trying to

More information

Are You Using Word, Excel or PowerPoint to Create and Manage Your Assembly Work Instructions and Supporting Documentation?

Are You Using Word, Excel or PowerPoint to Create and Manage Your Assembly Work Instructions and Supporting Documentation? A Special Report for Production, Manufacturing and Continuous Improvement Executives. Are You Using Word, Excel or PowerPoint to Create and Manage Your Assembly Work Instructions and Supporting Documentation?

More information

BlackBerry Business Solutions for Manufacturing. Ideal solutions from the top floor to the shop floor.

BlackBerry Business Solutions for Manufacturing. Ideal solutions from the top floor to the shop floor. BlackBerry Business Solutions for Manufacturing Ideal solutions from the top floor to the shop floor. About BlackBerry Business Solutions. BlackBerry Business Solutions are about much more than wireless

More information

The Rise of Industrial Big Data

The Rise of Industrial Big Data GE Intelligent Platforms The Rise of Industrial Big Data Leveraging large time-series data sets to drive innovation, competitiveness and growth capitalizing on the big data opportunity The Rise of Industrial

More information

Successful Knowledge Management: Does It Exist?

Successful Knowledge Management: Does It Exist? Successful Knowledge Management: Does It Exist? Karl M. Wiig Manuscript for the August 1999 Issue of the European American Business Journal Knowledge Research Institute, Inc. kmwiig@krii.com Introduction

More information

Your Complete CRM Handbook

Your Complete CRM Handbook Your Complete CRM Handbook Introduction Introduction Chapter 1: Signs You REALLY Need a CRM Chapter 2: How CRM Improves Productivity Chapter 3: How to Craft a CRM Strategy Chapter 4: Maximizing Your CRM

More information

Big Data. Fast Forward. Putting data to productive use

Big Data. Fast Forward. Putting data to productive use Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize

More information

Big Data better business benefits

Big Data better business benefits Big Data better business benefits Paul Edwards, HouseMark 2 December 2014 What I ll cover.. Explain what big data is Uses for Big Data and the potential for social housing What Big Data means for HouseMark

More information

If your company had an extra $41 million, what would you do with it? For every $1 billion in revenue,

If your company had an extra $41 million, what would you do with it? For every $1 billion in revenue, CASH ADVANTAGE Put Working Capital Back to Work There s never been a better time to reduce working capital requirements to speed financing and invest in future growth. By Lisa Higgins If your company had

More information

Why CRM implementations fail and what to do about it.

Why CRM implementations fail and what to do about it. Why CRM implementations fail and what to do about it. By Peter R. Chase About the Author Peter R. Chase is Executive Vice President and founder of Scribe Software Corporation. With over 1,500 customers,

More information

Bandsaw Performance Monitoring

Bandsaw Performance Monitoring Bandsaw Performance Monitoring Bruce F. Lehmann, P.Eng., Ph.D. Senior Engineer Thin Kerf Technologies Inc. Surrey, B.C., Canada Introduction Saw deviation sensing equipment is no longer an exotic or unusual

More information

When big data goes lean

When big data goes lean FEBRUARY 2014 When big data goes lean Rajat Dhawan, Kunwar Singh, and Ashish Tuteja The combination of advanced analytics and lean management could be worth tens of billions of dollars in higher earnings

More information

QUANTIFIED THE IMPACT OF AGILE. Double your productivity. Improve Quality by 250% Balance your team performance. Cut Time to Market in half

QUANTIFIED THE IMPACT OF AGILE. Double your productivity. Improve Quality by 250% Balance your team performance. Cut Time to Market in half THE IMPACT OF AGILE QUANTIFIED SWAPPING INTUITION FOR INSIGHT KEY FIndings TO IMPROVE YOUR SOFTWARE DELIVERY Extracted by looking at real, non-attributable data from 9,629 teams using the Rally platform

More information

BIG DATA HOW IS IT IMPACTING SALES IN THE AUTOMOTIVE INDUSTRY?

BIG DATA HOW IS IT IMPACTING SALES IN THE AUTOMOTIVE INDUSTRY? BIG DATA HOW IS IT IMPACTING SALES IN THE AUTOMOTIVE INDUSTRY? The market for Big Data is set to grow to a staggering $47bn by 2017. How many times have you heard the term Big Data so far this year? More

More information

PARADIGMS THAT DRIVE COSTS IN MANUFACTURING. The whole purpose of a business enterprise is pretty simple to make a profit by selling

PARADIGMS THAT DRIVE COSTS IN MANUFACTURING. The whole purpose of a business enterprise is pretty simple to make a profit by selling PARADIGMS THAT DRIVE COSTS IN MANUFACTURING The whole purpose of a business enterprise is pretty simple to make a profit by selling products or services to persons who desire those particular goods or

More information

Speed and quality under pressure: How financial services are different

Speed and quality under pressure: How financial services are different The challenge of speed About the research The report is based on a survey of 461 senior, Europe-based executives. Of these, 72 were from the financial services sector, including retail, corporate and investment

More information

TPM at the heart of Lean - March 2005 Art Smalley

TPM at the heart of Lean - March 2005 Art Smalley TPM at the heart of Lean - March 2005 Art Smalley Total Productive Maintenance (TPM) has been a very important tool for equipment intensive manufacturing sectors. It is a key means for increasing machine

More information

YOUR ERP PROJECT S MISSING LINK: 7 REASONS YOU NEED BUSINESS INTELLIGENCE NOW

YOUR ERP PROJECT S MISSING LINK: 7 REASONS YOU NEED BUSINESS INTELLIGENCE NOW YOUR ERP PROJECT S MISSING LINK: 7 REASONS YOU NEED BUSINESS INTELLIGENCE NOW THERE S NO GOOD REASON TO WAIT Enterprise Resource Planning (ERP) technology is incredibly useful to growing organizations,

More information

Customer Activation. Marketing with a Measurable Purpose

Customer Activation. Marketing with a Measurable Purpose Customer Activation Marketing with a Measurable Purpose INTRODUCTION As a marketing leader, you need to think about the lifecycle that each of your customers progresses through from potential customer

More information

The challenge of speed in healthcare as technology puts its foot on the accelerator

The challenge of speed in healthcare as technology puts its foot on the accelerator The challenge of speed: Healthcare About the research The report is based on a survey of 461 senior, Europe-based executives. Of these, 59 work in the healthcare sector, including hospitals, medical equipment

More information

BT s supply chain carbon emissions a report on the approach and methodology

BT s supply chain carbon emissions a report on the approach and methodology BT s supply chain carbon emissions a report on the approach and methodology April 2015 1 BT s supply chain emissions metrics approach and methodology 1 Are supply chain emissions really the essential,

More information

Using Mobile Solutions to Drive CONTENTS. Efficiency and Innovation in Manufacturing. Infuse mobility into all operations

Using Mobile Solutions to Drive CONTENTS. Efficiency and Innovation in Manufacturing. Infuse mobility into all operations Using Mobile Solutions to Drive Efficiency and Innovation in Manufacturing CONTENTS INFUSE MOBILITY INTO ALL OPERATIONS MANAGE INVENTORY THROUGHOUT THE SUPPLY CHAIN DRIVE EFFICIENCIES THROUGH M2M MOBILITY

More information

Datasheet Electronic Kanban Ultriva vs. ERP ekanban Modules By Narayan Laksham

Datasheet Electronic Kanban Ultriva vs. ERP ekanban Modules By Narayan Laksham Datasheet Electronic Kanban Ultriva vs. ERP ekanban Modules By Narayan Laksham Summary: Several ERP vendors offer rudimentary Kanban modules to compliment their MRP systems. However, these Kanban modules

More information

Moving Mountains. Erik Berggren Senior Director of Customer Results & Global Research. Keith Messick Senior Manager, Customer Results

Moving Mountains. Erik Berggren Senior Director of Customer Results & Global Research. Keith Messick Senior Manager, Customer Results Moving Mountains by: Erik Berggren Senior Director of Customer Results & Global Research Keith Messick Senior Manager, Customer Results Executive Summary: Most companies place priority on strategy over

More information

Time Management Systems: Helping you to make an informed choice

Time Management Systems: Helping you to make an informed choice www.mitrefinch.co.uk Time Management Systems: Helping you to make an informed choice United Kingdom Registered Office: Mitrefinch Ltd Mitrefinch House Green Lane York YO30 5YY Head Office:01904 693115

More information

Nordex SE. Capital Markets Day Production & efficiency - Dr. Marc Sielemann

Nordex SE. Capital Markets Day Production & efficiency - Dr. Marc Sielemann Nordex SE Capital Markets Day Production & efficiency - Dr. Marc Sielemann Rostock, 13 October 2011 WELL ESTABLISHED PRESENCE IN GROWTH REGIONS Global production, sales and distribution network Chicago

More information

Small Business Brief How to Build and Use Credit Policies to Your Advantage

Small Business Brief How to Build and Use Credit Policies to Your Advantage How to Build and Use Credit Policies to Your Advantage Customers are your bread and butter, but they can also be your biggest risk. Now more than ever, small businesses need to take a page from some of

More information