To remain compliant and competitive in the medical device marketplace, manufacturers must excel at Supplier Quality Management (SQM). By ingraining high supplier quality standards first, then tracking and measuring key performance indicators in real-time, manufacturers are able to realize continuous improvement across their value chain.
Manufacturing quality is defined by how well production processes stay aligned with the demanding, diverse and unique requirements of customers while staying in compliance with regulatory requirements. In order to help enforce consistent quality across the supplier network, manufacturers must apply consistent, clear quality goals to ensure not one supplier lapses on quality standards.
In this eBook, you’ll learn how to strengthen your SQM strategies to drive collaboration and growth through your value chain.
Key insights delivered from the Supplier Quality Management eBook include:
What is the most effective cost reduction strategy when managing suppliers
What factors lead to higher supplier performance
Which analytics & metrics must be constantly tracked
How streamlining SQM provides long-term revenue growth
Prototyping (63%), proof of concept (27%) and production (26%) are the three most dominant uses of 3D printing in Europe today. The same priorities apply to America (prototyping (56%); proof of concept (43%); production (27%) and marketing samples (17%).
68% of respondents are forecasting their spending on additive manufacturing will increase in 2015.
Accelerating new product development and the ability to offer customized or limited-run products are the leading 3D printing priorities today.
These and other insights are from an extensive survey of 3D printing adoption published today by Sculpteo titled The State of 3D Printing (26 pp., opt-in). 1,118 respondents were contacted in sixteen vertical markets, with 91% being located in America (27%) and Europe (64%). Overall, the sample is comprised of companies and individuals in 50 countries working in 16 different industries. Please see page 3 of the study for an expanded description of their methodology. Sculpteo is based in Paris and San Francisco, offering 3D manufacturing on demand and of scale to start-ups, SMEs and design studios.
Key take-aways of the study include the following:
44% reported they will increase their spending on additive manufacturing by 50% or more this year. Overall, 68% of respondents are forecasting their spending on additive manufacturing will increase in 2015.
Accelerating product development (32%), offering customized products and limited series (28%) and increasing production efficiency/buying a 3D printer (13% each) are the top three priorities related to 3D printing in 2015. The following graphic compares the top priorities related to 3D printing in 2015 versus 2020.
Determining factors in the adoption of 3D printing globally that are most important center on machine consistency & capabilities and material & supply costs. The five most important factors include machine consistency & capabilities (60%), material and supply costs (53%), understanding customer needs (28%), clear legal framework (24%), reverse engineering (21%) and training teams (20%). The following graphic compares adoption factors by level of importance.
When respondents were asked if there are any trends that they anticipate having a major impact on 3D printing, materials (21.5%), new markets (17.2%) and easy 3D modeling (17%) emerged as most significant. The following graphic is based on textual analysis and multi-categorical semantic analysis of the responses. In all, twelve major themes recurred across all responses.
European 3D printing early adopters are more focused on attaining scale, while their Americas-based counterparts are focused on pragmatic factors of co-creation and buying a 3D printer. Europeans are more focused on offering customized products and limited series (21% in 2015 growing to 43% in 2020) and increasing production flexibility (9% in 2015 growing to 14% in 2020). The following graphic compares America and Europe along the dimensions of 3D printing priorities.
26% of European respondents consider themselves advanced or professional at 3D printing techniques compared to 23% of Americas-based respondents. Europeans see 3D printing as the defined domain of trained specialists. Americans perceive 3D printing can be used for everything and is accessible to everyone in the company. The following comparison provides insights into how each geographic group of respondents classify themselves in terms of 3D printing expertise.
3D printing power users have significant competitive advantages over their peers in accelerating product development and offering customized products and limited series today through 2020. Power users have a 19% advantage versus their peers in accelerating product development in 2015 (32% versus 51%), and a 15% in 2020 (31% versus 46%). The following graphic compares power users versus peers on 3D printing strategies.
50% of 3D printing power users are relying on these technologies, systems and processes to support production today. Power users dominate proof of concept (59%), prototyping (83%), and in the Americas the development of marketing samples (31%). The following graphic compares the total respondent base and power users.
Bottom line: Wearables are redeeming themselves by embracing a more service-oriented strategy that seeks to deliver manufacturing intelligence from the factory floor to the top floor.
From Google Glass to proprietary wearables designed for manufacturing, this category of the devices has received an underwhelming response in the market. What’s missing is a more service-oriented approach that transforms captured data into a broader, more contextually relevant manufacturing strategy and service.
Wearables need to get beyond personal productivity tasks that don’t scale and become more engrained into those that do, starting with manufacturing intelligence. Automating work instructions, integrating scanners for quick bar code reading, and other personal productivity tasks aren’t pushing wearables to the limit of what they are capable of. Building manufacturing intelligence services that are subscription-based will.
Capturing Data And Creating Subscription Services Is The New Black
The recent conversations with Tien Tzuo, Founder and CEO of Zuora and four of the company’s leading customers including Arrow Electronics, Scheinder Electric, Unify and Yellow Pages Canada at Subscribed 2015 underscore how the Internet of Things (IoT) has turned into a catalyst for entirely new subscription-based business models. Schneider Electric is using dynamic pricing to sell energy management subscription services based on sensor data. Arrow Electronics is working on a variety subscription services based entirely on IoT-based data. Yellow Pages, YP.CA has redefined their business model using online subscriptions. Based on the conversations with these Zuora customers, it became clear that wearables can deliver so much more value as a sensor versus just a personal productivity device. Over time, they could help turn manufacturing into a service subscribed to Zuora (which provided airfare and hotel to the event and is not a client or customer).
Here are the key take-aways from the discussion:
Wearables need to be an integral part of a manufacturer’s IoT strategy to deliver value. Instead of using wearables to electronically deliver the latest work instructions to an operator, the same wearable device needs to deliver data back on machine-level compliance, reliability and quality data. Having all this data is the beginning of a manufacturing intelligence subscription service.
Redefining wearables as sensors that learn and teach needs to happen now. Having wearables take on more tasks specifically in the areas of incoming inspection, traceability, ,machine-level compliance, and also how each step in a production process is running is unknown in many manufacturers. Capturing all this data, aggregating it and providing analytics apps to gain contextual insight further adds value to subscriptions.
Gaining insights into how to improve plant safety, production workflows and supplier coordination needs is another area wearables need to contribute. Today manufacturers are doing the majority of these things manually, if at all. Wearables, from glasses to sensors for monitoring forklifts and other heavy equipment, could capture this data and feed it into a subscription service.
Wearables need to become an integral part of a manufacturing subscription service that spans product lifecycles.Arrow Electronics and Schneider Electric have projects underway today that are relying on a variety of sensors, wearables and IoT-based technologies to fuel subscription services than span product generations. PwC’s recent blog post, Field service workers could fix wearables’ PR problem shows how a full-scale manufacturing subscription service integrating wearables, IoT sensors and 3D printing can revolutionize field service. This is what’s possible in a subscription economy when wearables become tightly integrated into production – not just left as personal productivity devices.
To compile the list, the World Economic Forum’s Meta-Council on Emerging Technologies, a panel of 18 experts, draws on the collective expertise of the Forum’s communities to identify the most important recent technological trends. You can find the 18 members of the Meta-Council on Emerging Technologies here.
The top ten emerging technologies for 2015 include the following:
1. Fuel cell vehicles
The World Economic Forum analysis found that mass-market fuel cell vehicles are one of several emerging technologies that have the potential to scale rapidly and deliver sustainability and cost advantages. Revolutionizing large-scale freight, logistics and supply networks using fuel cell vehicles would lead to enterprises’ long-term sustainability goals being achieved faster, with the potential of seeing a drop in operating and maintenance costs over the long-term as well.
2. Next-generation robotics
The study found that the new age of robotics takes machines away from just automating the most manual manufacturing assembly line tasks and orchestrates them to collaborate in creating more advanced assemblies, subassemblies and complete products. Collaborative robotics can accelerate time-to-market, improve production accuracy and reduce rework. The study also showed how using GPS technology that is commonly available in smartphones, robots are beginning to be used in precision agriculture for weed control and harvesting. In Japan, robots are being tested in nursing roles: they help patients out of bed and support stroke victims in regaining control of their limbs. The study also acknowledges that the next-generation of robotics poses novel questions for fields from philosophy to anthropology about the human relationship to machines
3. Recyclable thermoset plastics
Thermoset plastics has historically only been capable of being heated and shaped once due to molecular changes that lead to their retaining their shape and strength. Having recyclable thermoset plastics will contribute to greater levels of sustainability and a big reduction in landfill waste. The Council expects recyclable thermoset polymers to replace unrecyclable thermosets within five years, and to be ubiquitous in newly manufactured goods by 2025.
4. Precise genetic engineering techniques
Genetically engineering crops for higher yields using RNA interference (RNAi) has proven effective against viruses and fungal pathogens, and can also protect plants against insect pests, reducing the need for chemical pesticides. Despite this and many other benefits, genetically engineering crops continues to be a catalyst of global controversy. The council mentioned this as one of the top ten emerging technologies given its ability to scale crop yields, reduce waste, and improve crop quality. The ethical dilemmas this technology raises needs to also be addressed for genetic engineering to reach critical mass globally.
5. Additive manufacturing (3D Printing)
By definition, additive manufacturing starts with loose material, either liquid or powder, and then builds it into a three-dimensional shape using a digital template. The study makes the point of how 3D products can be uniquely tailored to specific customer needs, alleviating the constraints of mass production methods. The study’s authors mention Invisalign, a company that uses computer imaging of customers’ teeth to make near-invisible braces tailored to their mouths. The study found that an important next stage in additive manufacturing would be the 3D printing of integrated electronic components, such as circuit boards.
6. Emergent artificial intelligence
The study defines artificial intelligence, in contrast to normal hardware and software, as the series of technologies that enables a machine to perceive and respond to its changing environment. Emergent AI is the nascent field of how systems can learn automatically by assimilating large volumes of information. An example of this is how IBM’s Watson system is now being deployed in oncology to assist in diagnosis and personalized, evidence-based treatment options for cancer patients.
7. Distributed manufacturing
The study defines distributed manufacturing as the continual strategy to replace as much of the material supply chain as possible with digital information. In manufacturing a chair for example, instead of sourcing wood and fabricating it into chairs in a central factory, digital plans for cutting the parts of a chair can be distributed to local manufacturing hubs using computerized cutting tools known as CNC routers. Parts can then be assembled by the consumer or by local fabrication workshops that can turn them into finished products. Cloud-based platforms supporting two-tier ERP systems that have distributed order management capability will be one of many catalysts of distributed manufacturing growth.
8. ‘Sense and avoid’ drones
The study’s authors see “sense and avoid” drones as having the potential to complete tasks too dangerous or remote for humans to do. These include checking electric power lines and delivering medical supplies in an emergency for example. The meta-council also sees the potential for autonomous drones to improve agricultural yields by collecting and processing vast amounts of visual data from the air, allowing precise and efficient use of inputs such as fertilizer and irrigation.
9. Neuromorphic technology
Neuromorphic technology will be the next stage in machine learning according to the study’s authors. IBM’s million-neuron TrueNorth chip, revealed in prototype in August 2014, has a power efficiency for certain tasks that is hundreds of times superior to a conventional CPU (Central Processing Unit), and more comparable for the first time to the human cortex. The challenge will be creating code that can realize the potential of the TrueNorth chip, which is an area IBM continues investing in today.
10. Digital genome
The study sees digital genomes as a means of gaining greater insights and intelligence into many of the most challenging, costly and complex diseases to treat today. From heart disease to cancer, all have a genetic component. The study’s authors point out that cancer is best described as a disease of the genome. With digitization, doctors would be able to make decisions about a patient’s cancer treatment informed by a tumor’s genetic make-up. The study concludes that this new knowledge is also making precision medicine a reality by enabling the development of highly targeted therapies that offer the potential for improved treatment outcomes, especially for patients battling cancer.
McKinsey & Company recently published How Big Data Can Improve Manufacturing which provides insightful analysis of how big data and advanced analytics can streamline biopharmaceutical, chemical and discrete manufacturing.
The article highlights how manufacturers in process-based industries are using advanced analytics to increase yields and reduce costs. Manufacturers have an abundance of operational and shop floor data that is being used for tracking today. The McKinsey article shows through several examples how big data and advanced analytics applications and platforms can deliver operational insights as well.
The following graphic from the article illustrates how big data and advanced analytics are streamlining manufacturing value chains by finding the core determinants of process performance, and then taking action to continually improve them:
Big Data’s Impact on Manufacturing Is Growing
In addition to the examples provided in the McKinsey article, there are ten ways big data is revolutionizing manufacturing:
Increasing the accuracy, quality and yield of biopharmaceutical production. It is common in biopharmaceutical production flows to monitor more than 200 variables to ensure the purity of the ingredients as well as the substances being made stay in compliance. One of the many factors that makes biopharmaceutical production so challenging is that yields can vary from 50 to 100% for no immediately discernible reason. Using advanced analytics, a manufacturer was able to track the nine parameters that most explained yield variation. Based on this insight they were able to increase the vaccine’s yield by 50%, worth between $5M to $10M in yearly savings for the single vaccine alone.
Accelerating the integration of IT, manufacturing and operational systems making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Big data is already being used for optimizing production schedules based on supplier, customer, machine availability and cost constraints. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are making rapid strides with Industrie 4.0 today. As this initiative serves as a catalyst to galvanize diverse multifunctional departments together, big data and advanced analytics will become critical to its success.
Better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%) are the top three areas big data can improve manufacturing performance. These findings are from a recent survey LNS Research and MESA International completed to see where big data is delivering the greatest manufacturing performance improvements today. You can find the original blog post here.
Integrating advanced analytics across the Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) framework to fuel continuous improvement. Getting greater insights into how each phase of a DMAIC-driven improvement program is working, and how the efforts made impact all other areas of manufacturing performance is nascent today. This area shows great potential to make production workflows more customer-driven than ever before.
Greater visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time. Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real-time, making trade-offs on which suppliers receive the most time-sensitive orders. Managing to quality metrics becomes the priority over measuring delivery schedule performance alone.
Measuring compliance and traceability to the machine level becomes possible. Using sensors on all machinery in a production center provides operations managers with immediate visibility into how each is operating. Having advanced analytics can also show quality, performance and training variances by each machine and its operators. This is invaluable in streamlining workflows in a production center, and is becoming increasingly commonplace.
Selling only the most profitable customized or build-to-order configurations of products that impact production the least. For many complex manufacturers, customized or build-to-order products deliver higher-than-average gross margins yet also costs exponentially more if production processes aren’t well planned. Using advanced analytics, manufacturers are discovering which of the myriad of build-to-order configurations they can sell with the most minimal impact to existing production schedules to the machine scheduling, staffing and shop floor level.
Breaking quality management and compliance systems out of their silos and making them a corporate priority. It’s time for more manufacturers to take a more strategic view of quality and quit being satisfied with standalone, siloed quality management and compliance systems. The McKinsey article and articles listed at the end of this post provide many examples of how big data and analytics are providing insights into which parameters matter most to quality management and compliance. The majority of these parameters are corporate-wide, not just limited to quality management or compliance departments alone.
Quantify how daily production impacts financial performance with visibility to the machine level. Big data and advanced analytics are delivering the missing link that can unify daily production activity to the financial performance of a manufacturer. Being able to know to the machine level if the factory floor is running efficiently, production planners and senior management know how best to scale operations. By unifying daily production to financial metrics, manufacturers have a greater chance of profitably scaling their operations.
Service becomes strategic and a contributor to customers’ goals by monitoring products and proactively providing preventative maintenance recommendations. Manufacturers are starting to look at the more complex products they produce as needing an operating system to manage the sensors onboard. These sensors report back activity and can send alerts for preventative maintenance. Big data and analytics will make the level of recommendations contextual for the first time so customers can get greater value. General Electric is doing this today with its jet engines and drilling platforms for example.
Additional sources of information on Big Data in Manufacturing:
The Rise of Industrial Big Data: Leveraging large time-series data sets to drive innovation, competitiveness and growth — capitalizing on the big data opportunity, GE Intelligent Platforms White Paper, April 2012. https://www.ge-ip.com/library/detail/13170
81% of CEOs see mobile technologies as being strategically important for their enterprises.
The top three technology priorities of industrial manufacturing CEOs are mobility (73%), cybersecurity (72%) and data mining and analysis (70%).
86% of CEOs say a clear vision of how digital technologies including mobile can create competitive advantage is key to the success of their investments.
These and many other insights are from PwC’s 18th Annual Global CEO Survey (free, no opt in). The report provides insights into the priorities, plans and technology adoption trends of CEOs for 2015. Based on interviews with 1,322 CEOs located in 77 countries, the survey provides valuable insights into the strategic direction enterprises are taking with technology investments. The following two graphics from the report illustrate the strategic importance CEOs are placing on mobile technologies in 2015:
Exploring How Mobility Is Revolutionizing Manufacturing
CEOs prioritizing the strategic importance of mobile technologies are driving a revolution in manufacturing today. Designing mobility into new production strategies, processes and procedures is bringing greater accuracy & speed to production centers. Augmenting existing processes with mobility is delivering solid efficiency gains. The net result is greater communication, collaboration and responsiveness to customer-driven deadlines and delivery dates than has been possible before. Manufacturers are staying away from BYOD on the shop floor due to security, scalability and support challenges. Instead, the focus is on how to standardize on industrial-grade mobile devices including tablets designed for manufacturing environments.
Based on my visits with manufacturers, here are the ten ways they are using mobility to revolutionize manufacturing:
Integrating mobile CRM systems with distributed order management, pricing and fulfillment to improve customer responsiveness. Providing information to sales teams, prospects and customers when, where and how they need it is driving greater mobile CRM adoption. Respecting prospects’ time and delivering a real-time response can make the difference between making a sale or not.
Generating quotes for build-to-order products that reflect the latest pricing and delivery dates available. A VP of Sales at a local manufacturer told me that when his team delivers the first complete quote immediately following in-depth discussions with a prospect, they win 70% of the time. Mobile integration of their configure, price and quote (CPQ) system to pricing and inventory systems makes it possible for a sales rep to get a complete quote done and delivered within hours of leaving a prospect.
Making distributed order management more transparent to sales while increasing order fulfillment accuracy. The more complex the product being built, the more the purchasing and procurement teams on the customer side want updates. One global leader in high tech distribution created a series of mobile applications their sales reps give to customers so they can request order status, delivery dates and configure order alerts that are delivered 24/7, anywhere in the world. The result: 76% reduction in order status calls to the enterprise sales teams and 13% increase in sales the first six months these apps were available.
Improving supplier traceability and quality levels using real-time analysis and reporting. Too often quality systems and processes are manually integrated or isolated from manufacturing systems. Mobility is starting to have an impact here, making it possible for supplier traceability, quality, non-conformance & corrective action (NC/CA), corrective and preventative action (CAPA), Statistical Process Control (SPC) and genealogy traceability data to be immediately made available plant-wide. Forward-thinking manufacturers are using this data to benchmark suppliers in real-time, all over mobile devices.
Replacing manually-intensive inventory management systems with enterprise-wide mobile inventory tracking, traceability and reporting systems.An aerospace manufacturer producing mid-range personal and commercial aircraft is using an enterprise-wide mobile inventory tracking, traceability and reporting system. This manufacturer has worked so closely with the Federal Aviation Administration (FAA) they can now report production status to the work instruction level electronically, saving thousands of hours a year in government-mandated reporting paperwork. Mobility is saving this manufacturer thousands of hours and dollars a year.
Monitoring production workflow performance using dashboards accessible from mobile devices. A build-to-order engine manufacturers in the rust belt of the United States found that to complete just one customized engine, the entire order traveled six miles inside the building. By integrating mobile-based systems to provide real-time updates and propagate data through the production center, four miles was trimmed off the typical order workflow, saving two weeks of production time.
Tracking machine-level compliance and providing alerts to production engineering when maintenance is required. In highly regulated manufacturing industries including medical products, production machinery and systems need to be regularly calibrated to stay in compliance. Manufacturers are starting to use mobile-based sensors to capture this data and report it in real time. Production and quality engineering teams get the alerts immediately and can plan on how to keep an entire shop floor continuously in compliance.
Reducing Field Service call cancellations and delays by accurately communicating parts and staffing requirements. There is nothing more frustrating from a customer’s perspective than waiting for a field service technician to show up, only to find they don’t have the necessary parts or were told the problem was completely different than the one that needs to be solved. Cloud-based mobile platforms show significant potential here. Combining emerging mobile platforms with service optimization apps, manufacturers can get the right technician to the right customer problem with the right parts the first time.
Improving logistics and supply chain coordination with suppliers using mobile technologies. Manufacturers whose business models rely on rapid inventory turns, tight production schedules and thin margins are the leading early adopters of mobile technologies for logistics and supply chain coordination. High tech hardware manufacturers are a case in point, as are many distributors whose business models are shifting to value-added services over pick, pack and ship operations.
Making Manufacturing Intelligence the new normal in production operations. The CFO at a well-known auto parts manufacturer told me recently that her greatest challenge is taking shop floor data and interpolating it to financial results fast. Mobility is helping with the data collection, and this manufacturer is using advanced pattern detection and predictive analytics to get in front of production cost trends. Their financial models also include cost analysis, cost formulation tools, cost and defective monitoring analysis and comparative financial analysis tools. All of these can be accessed from a secured tablet by her and her staff anytime.
Bottom line: Mobility is forcing manufacturers to compete in their prospects’ and customers’ timeframes while delivering greater value in less time than before.
79% of Internet of Things (IoT) app developers spend at least 25% of their time with analytics or databases, and 42% work on Big Data or advanced analytics projects.
55% of IoT developers primarily connect devices through the Cloud, with 32% connecting through a hub or middle tier.
26% of IoT developers most associate cloud computing with the Internet of Things and are 3X more likely to use the Cloud as a development environment.
These and many other insights are from the Internet of Things Study 2015, Volume I by Evans Data Corporation. Evans Data Corporation (EDC) maintains an international panel of developers who were contacted for this study. 578 in-depth interviews were conducted with developers who are currently planning and working on projects for connected devices and sensors. Only those developers who are currently writing apps for connected devices or plan to within the next six months are included in this survey.
Key take-aways include the following:
26% of Asia Pacific and 23% of North American app developers are actively working on IoT projects today. An additional 26% of Asia Pacific app developers are planning to develop IoT applications. The Asia Pacific region is a strong catalyst of IoT research and development globally, with Samsung, Fujitsu and many other leading technology companies based there. EDC found that this region is growing quickly due to developers being involved with the Sensing China Initiative and the partnership China has with the European Union to create fifteen smart cities. India and South Korea’s partnership to bring IoT to the former nation is also reflected in the following distribution of IoT development activity:
Commercial, ISV applications (36.5%), custom apps for system integrator and Value-Added Reseller (VAR) use (31.8%) and enterprise apps (18.3%) are the three most common app areas IoT developers are working on today. Corporate workgroup (6.8%), Original Equipment Manufacturer (2.8%), scientific (2.6%) and other (1.2%) are the types of applications IoT developers are working on today.
IoT app developers are primarily focused on analytics on aggregated data (25.3%), middleware development (20.5%), and backend/server development (18.6%). Additional areas of focus include firmware or preloaded software for the client device (16.3%), downloadable applications for the client device (8.7%) and Web application or web-based user interface development (8.7%). The following graphic compares where developers are primarily focusing their efforts.
34.2% of IoT developers spend 50% or more of their time working with analytics and databases. IoT developers are more likely to spend 25% of their development time working with analytics and databases across all app categories as well. Clearly analytics and databases are an essential design element of current and future IoT applications.
37.9% of IoT apps are being developed in the Cloud, and 49.6% of developers plan to begin development there in the next twelve months. Only 5.5% of IoT developers sruveyed have no plans to build their apps in the Cloud.
IoT developers are priortizing their development efforts on apps that include multiple devices or sensors connected to the Internet 54.9% of the time. Additional app development efforts include attaching a single device or sensor to the Internet (24.8%), multiple devices or sensors of the same kind to each other (13.7%), and multiple device or sensor types to each other (3.9%).
55.4% of IoT app developers are integrating to connected devices through the Cloud. 31.9% report their apps are integrated to connected devices through a hub or middle tier, and 11.2% are integrating connected devices directly to each other.
Office productivity and office appliances lead connected device app development (15.8%) followed by e-commerce (B2B) (12.8%), and transpiration (not car-based) (12.3%). Additional connected device projects include security and surveillance (11.1%), public utilities (10.7%), home and home appliance (6.1%), logistics (5.5%) and connected car (4.9%). The graphic below explains the connected device projects IoT developers are working on today.
IoT developers most often associate cloud computing (26.1%), Big Data (17.4%) and real-time event processing (17.2) with IoT and the development efforts they are working on. Also included are cognitive computing (11.1%), Wi-Fi enablement (10.9%), machine-to-machine communication (10.7%) and Near Field Computing (6.1%).The following graphics show the distribution of responses and a breakdown of responses by region.
Security (21.2%), technology will exceed demand (15.8%), and variety of data (12.6%) are the top three concerns of IoT developers creating new apps. Additional concerns include privacy (11.9%), amount of data (10.2%) and tools do not meet requirements (8.2%). Additional concerns including insufficient standards (7.2%), fragmentation of platforms (6.1%) and fragementation of devcices (5.6%).
51% of IoT developers have management and leadership positions in their organizations, with 15% self-identifying themselves as project leads or team leaders. EDC found IoT developers self-identify themselves into job descriptions and titles that reflect a more fragmented, pluralistic development community globally than other app development areas. 25% of IoT developers defined themselves in a specialist role, including business analyst, data architect, software architect or Web developer.
94% of IoT developers are using one of a series of Microsoft Windows operating systems as their primary development host. 43.7% are developing on Windows 7, 37.6% on Windows 8/8.1, and 6.6% on Windows 10. 3.1% are using Linux as their operating system and 1.9%, Apple Mac OS X.
79% of enterprises surveyed have Internet of Things (IoT) initiatives in place today to better understand customers, products, the locations in which they do business with customers, or their supply chains.
45% of enterprises use IoT technologies to monitor production and distribution operations.
40% of Enterprises Are Growing Their Services Businesses With Internet of Things Initiatives.
Manufacturers expect Internet of Things initiatives to drive an average 27.1% revenue increase by 2018.
Key take-aways from the study include the following:
Globally enterprises expect to increase revenues 16.3% between 2015 and 2018 using IoT initiatives, with North American companies projecting an average 18.1% revenue gain.Asia-Pacific companies expect a 17.9% revenue gain in the forecast period and Latin America, 17.8%.
Providing mobile apps to customers (46.5%), production and distribution operations to track product flow to customers (44.9%), digital sensors in products that send data to the company on how products are performing (25.2%) are the three leading uses of IoT technologies today. Digital sensors in distribution and supply chain locations (25.3%) and digital devices that are used for tracking customer usage (13.5%) are the remaining two of the top five ways enterprises are using IoT technologies today.
Industrial manufacturers predict IoT initiatives will increase revenue 27.1% from 2015 to 2018. Of the thirteen industries included in the study, industrial manufacturing is by far the most optimistic with regard to IoT’s ability to drive increased revenues from 2015 until 2018. High tech sees strong potential as well, predicting 19.4% revenue growth from today through 2018.
Travel, transportation and hospitality, industrial manufacturing and banking & financial services are the top three industries when ranked on average IoT spend per company in 2015.Travel, hospitality and transportation also leads IoT spend as a percentage of revenue (0.60%) across all thirteen industries surveyed.
54% of enterprises place sensors on products valued between $1M and $10M. The more expensive the product, the higher probability there is an integrated sensor designed to track the products’ performance over time. TCS found that overall only 26% of enterprises are using digital sensors today. This percentage skews to high end products as the following graphic illustrates. Manufacturers are using the data provided by these sensors to sell aftermarket and maintenance, repair and overhaul (MRO) services.
Enterprises who sell products valued more than $10M are the most likely to invest in IoT initiatives. Manufacturers of capital-intensive products are projected to spend $334.9M on IoT initiatives this year globally. Conversely, companies whose products sell for less than $100 are projected to spend just $39.1M of IoT initiatives in 2015. Manufacturers are seeing the opportunity to create information services for complex, expensive products based on each assets’ performance history, sold to their customers using a subscription –based revenue model.
Product monitoring (31.1%), customer monitoring (26.6%), supply chain monitoring (23.2%) and premises monitoring (19%) are the priorities enterprises are assigning to IoT initiatives. Product monitoring is dominated by manufacturers who sell products with prices from $1M to $10M and $10M and above. Customer monitoring includes fitness wearables and the subscription services offered to customers of these devices, providing them with insights into how they are progressing to health and fitness goals.
Increasing the service business (40%) and driving revenue with customer product usage data (27%) are the top two areas where business models are being redefined by IoT initiatives. Streamlining supply chains to make them more efficient and more leasing activity of company products including adoption of a product-as-a-service model (15.4%) are also starting to emerge as a catalyst of business model change.
By 2020, IoT initiatives are projected to increase the services business (40.3%), drive greater revenue with product usage data (28.7%), and bypass entities in the distribution channel (22.8%). The following graphic breaks down projected business model changes that TBS found from their analysis.
47.7% of market leaders are driving revenue from customer product usage data versus 20% of IoT follower companies. One of the most fascinating areas of this study is the section on Learning from the Leaders. This section provides a thorough analysis of enterprises leading their industries in IoT investment versus IoT followers. The following graphic compares IoT leaders and followers by business model changes made as a result of IoT initiatives.
The following video is an excellent summary of the study results and provides several useful insights into the industries analyzed and spending forecast of IoT initiatives. TCS provides a summary of average investments by enterprise and predictions of revenue growth through 2018 in this video:
42% of manufacturers say big data and analytics as their highest priority in 2015.
56% of power distribution providers rank big data and analytics within their top three priorities for 2015.
61% of aviation companies consider big data and analytics their highest priority this year.
Bottom line: Digital manufacturing strategies are gaining ground as manufacturers adopt big data and analytics to improve operational effectiveness, time-to-market, new product development and increase product quality and reliability.
Data Analytics Are Fueling Digital Manufacturing Growth
Big data and analytics adoption by manufacturers is the first step many are taking to create a galvanized, intelligent digital thread that unifies every aspect of their value chains. For aerospace manufacturers whose supply chains are exceptionally complex, big data and analytics are revolutionizing value chains starting with suppliers and progressing through all operations.
The majority of manufacturers are relying on analytics to improve order accuracy, shipment & cycle time performance, and product quality. Those excelling at digital manufacturing strategies are gaining additional analytical insights into how they can make decisions more accurately, quicker and with lower potential costs and risks.
The manufacturing industry generates more data than any other sector of the global economy on a consistent basis. The more complex a given manufacturers’ operations are, the more valuable the insights gained from big data and analytics. The following comparison of big data analytics priorities by industry from a recent speech given by Jeff Immelt, CEO and President of General Electric illustrates this point:
10 Ways Analytics Are Accelerating Digital Manufacturing
The ten ways analytics is accelerating digital manufacturing adoption globally include the following:
Providing real-time operator intelligence (70%), remote monitoring and diagnostics (66%), and condition-based maintenance (59%) are the three most valuable areas for analytics GE customers mentioned in a recent survey. GE’s industrial customers are looking to tailor pre-built applications that can deliver the eight different functional areas shown in the graphic below. Manufacturers are looking to asset performance management as an integral part of their digital thread’s analytics and insight.
Using data modeling to improve production workflows is improving Earnings Before Interest & Taxes (EBIT) by 55% for a chemical manufacturer. Using analytics and data modeling to make more accurate, efficient decisions encompassing making or buying ingredients, choosing to substitute an ingredient or not, optimizing equipment usage and/or reliability and gaining incremental sales through increased production capacity is leading to a significant improvement in EBIT for a leading chemical manufacturer on a consistent basis. The following graphic provides insights into the contributions of each factor in improving EBIT performance.
Planning-execution integration in production centers and real-time production integration are two areas where analytics are having the greatest impact on manufacturers’ operating expenses (OPEX). When analytics are integrated as part of a digital manufacturing strategy, supply chains benefit when Web-EDI (Electronic Data Interchange) and real-time order conformation are implemented and analyzed for continual improvement.
Optimization tools (56%), demand forecasting (53%), integrated business planning (48%) and supplier collaboration & risk analytics (46%) are being rapidly adopted by manufacturers today, setting the foundation for digital manufacturing growth.Deloitte recently interviewed supply chain executives regarding the thirteen fastest-moving technical capacities they are using today and expect to use in the future. The following graphic provides an overview of supply chain capabilities current in use and what percent of each they expect to use in the future.
Analytics is integral to making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Analytics is extensively used in manufacturing centers who are in the process of reengineering their entire operations to attain Industrie 4.0 compliance. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are also relying on analytics extensively to guide their Industrie 4.0 journey. A recent Deloitte study of Industrie 4.0 adoption found that research and development (43%) will see the greatest transformational contribution from Industry 4.0.
Analytics is enabling manufacturers to also scale real-time cloud-based operational intelligence, condition-based monitoring, monitoring & diagnostics and asset lifecycle management across global manufacturing centers. Capturing, aggregating, analyzing and taking action on analytics across all production centers using the GE Predix Cloud will also accelerate digital manufacturing growth over time. Integrating analytics, industrial and sensor data into a scalable series of data models and apps delivered as a Platform-as-a-Service (PaaS), GE will make this service commercially available in 2016. The following graphic illustrates how complex manufacturers could use Predix Cloud to improve operational efficiency and quality.
Analytics is providing greater insights into product, process, program and service quality, forcing manufacturers to revamp existing production centers and make them more efficient. Gaining greater insight into which production centers and factories are delivering the highest quality products and why is now possible. The vision of unifying quality across an enterprise quality management and compliance (ECQM) framework is now a reality, driving greater digital manufacturing growth as a result. The following graphic from Tableau is an example of a manufacturing quality dashboard.
Increasing production yields through the use of more effective supplier quality management and bill of material (BOM) planning integrated within production processes. Analytics is extensively being used today for supplier audits, supplier quality management and traceability. Capitalizing on the full value of these analytics is a strong catalyst for manufacturers to move closer to digitizing their operations.
Using analytics to predict machine failures before they occur reduces downtime, production costs and increase customer satisfaction. In highly regulated industries production equipment is periodically audited and reviewed for conformance to specific standards. Integrating even the simplest sensor into production equipment can deliver valuable insights into what factors cause it to fail. Analytics are providing Failure Mode and Effects Analysis (FMEA) in real-time today, providing manufacturers with a glimpse into which equipment and machinery will most likely fail when. Knowing this can save literally millions of dollars in lost production time.
Adopting Pareto Analysis to continually improve schedule, quality and cost performance to the cell or production center level is driving digital manufacturing adoption. Determining which factors are enhancing or reducing product, process and program quality is now possible using advanced manufacturing analytics. Differentiating between the many symptoms of a quality problem and its root cause is now becoming possible, especially for companies pursuing digital manufacturing strategies.
Additional sources of information on the impact of analytics on digital manufacturing: