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Bottom Line: Cleanrooms provide the potential for plastics manufacturers to increase the average machine rate per hour, average markups and profit per production run by charging a price premium based on their certifications, compliance and industry expertise.

Cleanrooms Provide A Path Out Of Price Wars & Commodity Pricing

Plastics manufacturers are relying on and investing in cleanrooms to capture new customers and gain a greater share of revenues from existing accounts. Many are creating their business cases for creating a new cleanroom based on projected increases in production volume that require a cleanroom environment that is operating in compliance with regulatory and industry standards.

Cleanrooms are providing plastics manufacturers with an opportunity to differentiate themselves and charge a higher average machine rate per hour & higher average order markup while also providing short-notice, specialized production runs to their cleanroom customers. Plastics manufacturers with cleanrooms tend to be in the Commodity Zone of attempting to compete on price and availability versus those in the Premium Price Zone. Those in the latter group can sell cleanroom services at a higher average machine rate per hour and a higher average markup. The following is a correlation comparing the average machine rate per hour by average markup based on the 2019 survey of plastics manufacturers by the Manufacturer’s Association for Plastics Processors (MAPP).

The challenge for many manufacturers running cleanrooms today is how to successfully escape from the Commodity Zone into the Premium Price Zone.  Based on interviews with plastics manufacturers and the MAPP Survey, the following 5 strategies provide manufacturers guidance on how they can increase their revenues using a cleanroom-driven strategy. The following five strategies will help any plastics manufacturer with a clean room scale up and charge a higher average machine rate per hour, average markup, capture new customers and increase sales to existing accounts.

Proven Strategies For Improving Cleanroom Manufacturing

Manufacturers combining their expertise in a specific area of plastics manufacturing, for example, aerospace, automotive, food & beverage, medical, or surgical products while complying with industry and regulatory requirements are more likely to be in the Premium Price zone. Those manufacturers with ISO 13485, FDA cGMP, and 21 CFR Part 11 compliance are selling their industry expertise, cleanroom availability, and compliance successfully to gain higher average machine rates and average machine hours.  

Cleanrooms can cost up to $500K and over a year to construct, require daily cleaning and often also need to be inspected every two weeks by 3rd party specialist companies ensure they stay in compliance to regulatory and industry standards. Building a solid business case for investing in one needs to start with the five proven strategies shown here:  

  1. Making compliance certifications pay and win new customers. MAPP’s 2019 survey shows that the greater the level fo compliance expertise, the higher machine rates per hour plastics manufactures can earn. When plastics manufacturers were asked,, “What is your average markup for manufacturing products in cleanroom vs. open production environment?”  Across all 40 manufacturers interviewed, the average market was 15%.  The machine rate per hour in a cleanroom environment ranges from $50.80 for Internal Medical Devices (peacemaker, heart monitor, etc.) to a high of $61.00 for Food & Beverage Containers. Those manufacturers able to achieve higher levels of process and system standardization throughout the entire supplier networks reduce operating costs and increase gross margins per order.
  2. Prioritizing audits to improve quality, scale, and visibility. A regular cadence of internal quality audits provides invaluable data not available through any other process. Cleanroom manufacturers who are the most adept at translating open production time into revenue have streamlined and improved audits to provide only the most valuable findings. They’re doing this by integrating  Supplier Quality Management (SQM), Document Control, Training, Production Scheduling, regulatory compliance, and product returns to gain a 360-degree view of product and process quality. They’re also finding that audits surface new metrics and key performance indicators (KPIs) that provide insights into a new are of process improvement and manufacturing quality in cleanrooms.
  3. Closely tracking cleanroom costs to maintain profitability. Cleanrooms need to be tracked on separate financial statements to the income statement and balance sheet level to know their true revenue contributions. That’s because the largest percentage of operating costs are electrical and utilities.  Electrical costs are typically 75% of operating expense. A typical manufacturing cleanroom has air change rates of 15 to 100 or more, requiring an exceptional amount of electricity making utilities a large percent of variable costs (gas, water). There are also the many direct and indirect labor costs, cleaning, maintenance and repair costs, water disposal, and compliance costs to keep a cleanroom in operating condition. Having their financial statements separate makes managing cleanroom operations more efficient and profitable.
  4. Standardizing on an integrated quality management system to reduce scrap rates, improve supplier quality.  Due to industry and regulatory compliance requirements, quality standards in cleanrooms often require a higher level of operational accuracy, transparency, and verifiability compared to open floor production environments. One of the main benefits of cleanrooms having an integrated quality management system is the ability to troubleshoot why reject rates are high on one product versus another, and also seeing how to reduce reject and scrap rates based on production and audit data.
  5. Extending track-and-traceability multiple layers deep in their supply chains.  Track-and-trace is invaluable for troubleshooting supplier quality problems and averting larger quality challenges in the future. In cleanroom production, track-and-trace saves tens of thousands of production hours a year by ensuring components introduced into production meet quality standards. Track-and-trace is relied on by plastics manufacturers in the Premium Price zone to stay competitive from a compliance standpoint too. Track-and-trace is essential for cleanrooms to meet FDA 21 CFR Part 820 compliance.

The post Cleanroom Manufacturing Strategies That Pay appeared first on IQMS Manufacturing Blog.

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  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations is achievable with machine learning.
  • Reducing supply chain forecasting errors by 50% and lost sales by 65% with better product availability is achievable with machine learning.
  • Automating quality testing using machine learning is increasing defect detection rates up to 90%.


Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level.

Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

The ten ways machine learning is revolutionizing manufacturing in 2018 include the following:

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations are is achievable with machine learning. Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimization are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.
  • Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.
  • Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC. Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers
  • McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.
  • Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.
  • Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%. AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimization across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.
  • Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D
  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more. Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017
  • A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning. The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services
  • Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE). OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Additional reading:

Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi

Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute

Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services

Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)

Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone

Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge

Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Get started with the Connected factory preconfigured solution, Microsoft Azure

Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum

Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group

Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell

Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK

Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. Ai Edam, 28(1), 83-97.

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company

Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney

The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press

The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017

The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45.

The post Manufacturing Gains From Machine Learning appeared first on IQMS Manufacturing Blog.

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  • The majority of enterprises are prioritizing their blockchain pilots that concentrate on supply chains improvements (53%) and the Internet of Things (51%) according to Deloitte’s latest blockchain survey.
  • By 2023, blockchain will support the global movement and tracking of $2T of goods and services annually based on a recent Gartner
  • By 2020, Discrete Manufacturing, Transportation & Logistics and Utilities industries are projected to spend $40B each on IoT platforms, systems, and services.
  • The Supply Chain Management enterprise software market is growing from $12.2B in 2017 to $20.4B in 2022, achieving a 10.7% Compound Annual Growth Rate (CAGR) according to Gartner’s latest market forecast.
  • Of the many blockchain and IoT Proof of Concept (POC) pilots running today, track-and-trace shows the most significant potential of moving into production.

Combining blockchain’s distributed ledger framework with the Internet of Things’ (IoT) proven real-time monitoring and tracking capability is redefining supply chains. Blockchain shows potential for increasing the speed, scale, and visibility of supply chains, eliminating counterfeit-goods transactions while also improving batching, routing and inventory control. Blockchain’s shared, distributed ledger architecture is becoming a growth catalyst for IoT’s adoption and commercial use in organizations.

Blockchain and IoT are defining the future of supply chains based on the initial success of Proof of Concept (POC) pilots focused on the logistics, storage and track-and-trace areas of supply chains across manufacturing. Supply-chain centric pilots are the most popular today, with enterprises looking at how they can get more value out of IoT using blockchain. One CIO told me recently his company deliberately spins up several POCs at once, adding “they’re our proving grounds, we’re pushing blockchain and IoT’s limits to see if they can solve our most challenging supply chain problems and we’re learning a tremendous amount.” The senior management team at the manufacturer says the pilots are worth it if they can find a way to increase inventory turns just 10% using blockchain and IoT. They’re also running Proof of Concept pilots to optimize batching, routing and delivery of goods, reduce fraud costs, and increase track-and-trace accuracy and speed. Of the many pilots in progress, track-and-trace shows the greatest potential to move into production today.

IoT in Supply Chains

The following are the top 10 ways IoT and blockchain are defining the future of supply chains:

  • Combining IoT’s real-time monitoring support with blockchain’s shared distributed ledger strengthens track-and-trace accuracy and scale, leading to improvements across supply chains. Improving track-and-trace reduces the need for buffer stock by providing real-time visibility of inventory levels and shipments. Urgent orders can also be expedited and rerouted, minimizing disruptions to production schedules and customer shipments.  The combination of blockchain and IoT sensors are showing potential to revolutionize food supply chains, where sensors are used to track freshness, quality, and safety of perishable foods.  The multiplicative effects of combining IoT and blockchain to improve track-and-traceability are shown in the context of the following table from the Boston Consulting Group. Please click on the graphic to expand for easier reading.

  • Improving inventory management and reducing bank fees for letters of credit by combining blockchain and IoT show potential to deliver cost savings. A recent study by Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, completed a hypothetical analysis of how much a $1B electronics equipment company implementing blockchain-as-a-service, a decentralized track-and-trace application, and 30 nodes that share among key supply chain stakeholders could save. The study found that the electronics equipment company could save up to $6M a year or .6% of annual sales. A summary of the business case is shown here:

  • Combining blockchain and IoT is providing the pharmaceutical and healthcare industry with stronger serialization techniques, reducing counterfeit drugs and medical products. Pharmaceutical serialization is the process of assigning a unique identity (e.g., a serial number) to each sealable unit, which is then linked to critical information about the product’s origin, batch number, and expiration date. According to the World Health Organization (WHO) approximately 1 million people, each year die from counterfeit drugs, 50% of pharmaceutical products sold through rogue websites are considered fake, and up to 30% of pharmaceutical products sold in emerging markets are counterfeit according to a recent study by DHL Research. DHL and Accenture are finalizing a blockchain-based track-and-trace serialization prototype comprising a global network of nodes across six geographies. The system comprehensively documents each step that a pharmaceutical product takes on its way to the store shelf and eventually the consumer. The following graphic illustrates the workflow.

  • Improving distribution and logistics, tracking asset maintenance, improving product quality, preventing counterfeit products and enabling digital marketplaces are the use cases Capgemini predicts blockchain will have the greatest impact. IoT’s potential contribution in each of these five use case areas continues to accelerate as real-time monitoring dominates manufacturing. Tracking provenace, contracts management, digital threads, and trade financing also show potential for high adoption. The following graphic illustrates blockchain use cases in the supply chain.

  • Combining blockchain and IoT is enabling manufacturers to pursue and excel at digital twin initiatives across their value chains. A digital twin is a dynamic, digital representation of a physical asset which enables companies to track its past, current and future performance throughout the asset’s lifecycle. The asset, for example, a vehicle or spare part, sends performance data and events directly to its digital twin, even as it moves from the hands of the manufacturer to the dealer and ultimately the new owner. Blockchain can be used to securely document everything related to the asset and IoT provides real-time monitoring and updates. Microsoft and VISEO are partnering to use blockchain to connect each new vehicle’s maintenance events to the vehicle’s digital twin. The graphic below illustrates how digital twins streamline additive manufacturing.

  • 54% of suppliers and 51% of customers are expecting the organizations they do business with to take a leadership position on blockchain and IoT. The majority of suppliers and customers expect the manufacturers, suppliers, and vendors they do business with to take a leadership position on these two emerging technologies and define a vision with them in it. Deloitte’s excellent study, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey, provides insights into how supplier and customer expectations are a factor in driving blockchain and IoT adoption, further helping to shape the future of supply chains.

  • Consumer products and manufacturing lead adoption of blockchain today, followed by life sciences according to the latest Deloitte estimates. IoT adoption is flourishing in manufacturing, transportation & logistics and utilities. By 2020, each of these industries is projected to spend $40B each on IoT platforms, systems, and services. The following graphic compares blockchain adoption levels by industry. Given how dependent manufacturers are on supply chains, the high adoption rates for blockchain and IoT make sense. Please click on the graphic to expand for easier reading.

  • Blockchain has the potential to deliver between $80B and $110B in value across seven strategic financial sectors when supported by IoT, redefining their supply chains in the process. McKinsey completed an extensive analysis of over 60 viable use case for blockchain in financial services where IoT would provide greater visibility across transactions. The combination of technologies has the potential to deliver over $100B in value.

  • Reducing product waste and perishable foods’ product margins while increasing traceability is attainable by combining blockchain and IoT. IBM’s Food Trust uses blockchain technology to create greater accountability, traceability, and visibility in supply chains. It’s the only consortium of its kind that connects growers, processors, distributors, and retailers through a permissioned, permanent and shared record of food system data. Partners include Carrefour, Dole, Driscoll’s, Golden State Foods, McCormick and Co., McLane Co., Nestlé, ShopRite parent Wakefern Food Corp.,  grocery group purchasing organization Topco Associates  The Kroger Co., Tyson Foods, Unilever and Walmart. An example of the Food Trust’s traceability application is shown below:

 

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  • Industrial Internet of Things (IIoT) presents integration architecture challenges that once solved can enable use cases that deliver fast-growing revenue opportunities.
  • ISA-95 addressed the rise of global production and distributed supply chains yet are still deficient on the issue of data and security, specifically the proliferation of IIoT sensors, which are the real security perimeter of any manufacturing business.
  • Finding new ways to excel at predictive maintenance, and cross-vendor shop floor integration are the most promising applications.
  • IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations.

These and many other fascinating insights are from an article McKinsey published titled IIoT platforms: The technology stack as value driver in industrial equipment and machinery which explores how the Industrial Internet of things (IIoT) is redefining industrial equipment and machinery manufacturing. It’s based on a thorough study also published this month, Leveraging Industrial Software Stack Advancement For Digital TransformationA copy of the study is downloadable here (PDF, 50 pp., no opt-in). The study shows how smart machines are the future of manufacturing, exploring how IIoT platforms are enabling greater machine-level autonomy and intelligence.

The following are the key takeaways from the study:

  • Capturing IIoT’s full value potential will require more sophisticated integrated approaches than current automation protocols provide. IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations and support the design-to-manufacturing process. Digital manufacturing platforms are already enabling real-time monitoring to the machine and shop floor level. The data streams real-time monitoring is delivering today is the catalyst leading to greater real-time analytics accuracy, machine learning adoption and precision and a broader integration strategy to the PLC level on legacy machinery. Please click on the graphic to expand for easier reading.

  • Inconsistent data structures at the machine, line, factory and company levels are slowing down data flows and making full transparency difficult to attain today in many manufacturers. Smart machines with their own operating systems that orchestrate IIoT data and ensure data structure accuracy are being developed and sold now, making this growth constraint less of an issue. The millions of legacy industrial manufacturing systems will continue to impede IIoT realizing its full potential, however. The following graphic reflects the complexities of making an IIoT platform consistent across a manufacturing operation. Please click on the graphic to expand for easier reading.

  • Driven by price wars and commoditized products, manufacturers have no choice but to pursue smart, connected machinery that enables IIoT technology stacks across shop floors. The era of the smart, connected machines is here, bringing with it the need to grow services and software revenue faster than transaction-based machinery sales. Machinery manufacturers are having to rethink their business models and redefine product strategies to concentrate on operating system-like functionality at the machine level that can scale and provide a greater level of autonomy, real-time data streams that power more accurate predictive maintenance, and cross-vendor shop floor integration. Please click on the graphic for easier reading.

  • Machines are being re-engineered starting with software and services as the primary design goals to support new business models. Machinery manufacturers are redefining existing product lines to be more software- and services-centric. A few are attempting to launch subscription-based business models that enable them to sell advanced analytics of machinery performance to customers. The resulting IIoT revenue growth will be driven by platforms as well as software and application development and is expected to be in the range of 20 to 35%. Please click on the graphic to expand for easier reading.

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  • Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.
  • IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies.
  • Connected IoT technologies are enabling a new era of smart, connected products that often expand on the long-proven platforms of everyday products. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.

These and many other fascinating insights are from IoT Analytics’ study, IIoT Platforms For Manufacturing 2019 – 2024 (155 pp., PDF, client access reqd). IoT Analytics is a leading provider of market insights for the Internet of Things (IoT), M2M, and Industry 4.0. They specialize in providing insights on IoT markets and companies, focused market reports on specific IoT segments and Go-to-Market services for emerging IoT companies. The study’s methodology includes interviews with twenty of the leading IoT platform providers, executive-level IoT experts, and IIoT end users. For additional details on the methodology, please see pages 136 and 137 of the report. IoT Analytics defines the Industrial loT (lloT) as heavy industries including manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet.

What You Most Need To Know About IIoT In Manufacturing

The seven things you need to know about IIoT in manufacturing include the following:

  • IoT Analytics’ technology architecture of the Internet of Things reflects the proliferation of new products, software and services, and the practical needs manufacturers have for proven integration to make the Industrial Internet of Things (IIoT) work. IoT technology architectures are in their nascent phase, showing signs of potential in solving many of manufacturing’s most challenging problems. IoT Analytics’ technology architecture shown below is designed to scale in response to the diverse development across the industry landscape with a modular, standardized approach.

  • IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies. IoT Analytics is seeing IIoT platforms begin to replace existing industrial software systems that had been created to bridge the IT and OT gaps in manufacturing environments. Their research teams are finding that IIoT Platforms are an adjacent technology to these typical industrial software solutions but are now starting to replace some of them in smart connected factory settings. The following graphic explains how IoT Analytics sees the IIoT influence across the broader industrial landscape:

  • Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years. IoT Analytics is finding that manufacturing is the largest IoT platform industry segment and will continue to be one of the primary growth catalysts of the market through 2024. For purposes of their analysis, IoT Analytics defines manufacturing as standardized production environments including factories, workshops, in addition to custom production worksites such as mines, offshore oil gas, and construction sites. The lloT platforms for manufacturing segment have experienced growth in the traditionally large manufacturing-base countries such as Japan and China. IoT Analytics relies on econometric modeling to create their forecasts.

  • In 2018, the industrial loT platforms market for manufacturing had an approximate 60%/40% split for within factories/outside factories respectively. IoT Analytics predicts this split is expected to remain mostly unchanged for 2019 and by 2024 within factories will achieve slight gains by a few percentage points. The within factories type (of lloT Platforms for Manufacturing) is estimated to grow from a $1B market in 2018 to a $1.5B market by 2019 driven by an ever-increasing amount of automation (e.g., robots on the factory floor) being introduced to factory settings for increased efficiencies, while the outside factories type is forecast to grow from $665M in 2018 to become a $960M market by 2019.

  • Discrete manufacturing is predicted to be the largest percentage of Industrial IoT platform spending for 2019, growing at a CAGR of 46% from 2018. Discrete manufacturing will outpace batch and process manufacturing, becoming 53% of all IIoT platform spending this year. IoT Analytics sees discrete manufacturers pursuing make-to-stock, make-to-order, and assemble-to-order production strategies that require sophisticated planning, scheduling, and tracking capabilities to improve operations and profitability. The greater the production complexity in discrete manufacturing, the more valuable data becomes. Discrete manufacturing is one of the most data-prolific industries there are, making it an ideal catalyst for IIoT platform’s continual growth.

  • Manufacturers are most relying on IIoT platforms for general process optimization (43.1%), general dashboards & visualization (41.1%) and condition monitoring (32.7%). Batch, discrete, and process manufacturers are prioritizing other use cases such as predictive maintenance, asset tracking, and energy management as all three areas make direct contributions to improving shop floor productivity. Discrete manufacturers are always looking to free up extra time in production schedules so that they can offer short-notice production runs to their customers. Combining IIoT platform use cases to uncover process and workflow inefficiencies so more short-notice production runs can be sold is driving Proof of Concepts (PoC) today in North American manufacturing.

  • IIoT platform early adopters prioritize security as the most important feature, ahead of scalability and usability. Identity and Access Management, multifactor-factor authentication, consistency of security patch updates, and the ability to scale and protect every threat surface across an IIoT network are high priorities for IIoT platform adopters today. Scale and usability are the second and third priorities. The following graphic compares IIoT platform manufacturers’ most important needs:

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Bottom Line: The majority of manufacturers are prioritizing shop floor productivity as their most valuable growth strategy, relying on manufacturing software to be the catalyst to get them there.

What is Manufacturing Software?

Best defined by its many benefits, manufacturing software provides real-time visibility into the details of a manufacturing operation. With greater visibility, manufacturers attain greater control of daily production, machine yield rates, supplier performance, quality, and ultimately, profit margins.

State-of-the-art manufacturing software interprets and analyzes real-time data from machines running on the shop floor, providing invaluable insights into daily operations. The best manufacturing software focuses on how modules in an integrated system can be orchestrated to improve efficiency, quality, and profits continually.

Look for manufacturing software providers whose main focus is on shop floor control and end-to-end visibility. These two factors are the fuel that enables manufacturing software systems to deliver benefits to your business, a few of which are listed below. They’re achieved by having a manufacturing system that integrates accounting and finance, manufacturing operations, Manufacturing Execution System (MES), production management, and quality management and control all on the same platform.

  • Eliminate unplanned downtime
  • Prevent bottlenecks in the manufacturing process
  • Operate with near 100% machine utilization
  • Optimize scheduling and resource allocation regardless of the plant location
  • Have complete unimpaired visibility and traceability of the entire manufacturing process from raw material, through customer delivery
  • Comply with quality standards

Why Is Manufacturing Software So Hot Today?

  • The global ERP software market is expected to grow from $32.6B in 2017 to $61.2B by 2025, achieving a Compound Annual Growth Rate (CAGR) of 8.27% during the forecast period.
  • Gartner predicts the ERP market will increase from $31.4B in 2017 to $44.3B in 2022, growing at a CAGR of 7%.
  • Spending on Manufacturing ERP software worldwide will increase from $5.87B in 2017 to $7.26B in 2022.

There’s a combination of long-term trends continuing to nurture the growth of manufacturing software. Lead times are becoming shorter, quality and compliance requirements are becoming more complex, and automation is becoming more affordable. The need to compete globally is top of mind for many manufacturers. The long-term labor shortage is also motivating manufacturers to automate more of their operations. All of these factors contribute to manufacturing software growing in importance over the long-term.

A few of the many benefits manufacturing software is providing include the following:

Audits And Compliance

  • Audit every step of the production process and provide that data to customers and regulatory agencies by running a report instead of having to tabulate and format it manually.
  • Increase the level of compliance through automated reporting.

Customer Relationship Management

  • Gain new customers by providing complete, accurate quotes and delivery dates faster than competitors.
  • Increase customer satisfaction by meeting delivery dates.
  • Reduce order errors by building the right product & delivering on schedule.

Manufacturing Execution System

  • Assure every operator on the shop floor has up-to-date training and certifications.
  • Better manage shop floor teams by assigning each team member to a process they are excellent at.
  • Create and use work instructions that are tested and shared across the shop floor, reducing costly production errors.
  • Increase production capacity with existing machines and staff as part of capacity planning.
  • Increase the efficiency of shop floor operations and get more done.
  • Measure Overall Equipment Effectiveness (OEE) to improve individual machinery performance.

Supplier Quality Management

  • Improve inbound supplier quality and measure how that improves production efficiency.
  • Improve supplier quality levels, so production isn’t delayed.
  • Use supplier data to improve supplier selection and evaluate their performance to goals.
  • Improve overall product quality.

Production Scheduling

  • Update production schedules in real-time instead of only a few times a week.
  • Find out which areas of manufacturing operations need attention before a problem occurs.
  • Improve inventory management and costs.

Conclusion

By providing real-time visibility and control, improving communication, and enabling greater collaboration across production locations, manufacturing software is digitally transforming manufacturing today. Be sure to get a copy of the ebook, The Ultimate Guide to Manufacturing Software: Increase Efficiency & Profit Margins to learn more about the many advantages of manufacturing software, how to evaluate manufacturing systems including key questions to ask. The ebook also provides a needs assessment and requirements gathering section to help you select the best manufacturing software for your organization. 

The post The Ultimate Guide to Manufacturing Software appeared first on IQMS Manufacturing Blog.

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Kaddas Enterprises turns to IQMS (DELMIAWORKS) to improve efficiency on the shop floor, reduce scrap rates, and improve planning and reporting at every level.

 Headquartered in Salt Lake City, Utah, Kaddas Enterprises, Inc. is the industry leader in wildlife and asset protection, manufacturing more than 300 BirdguarD products, which are used by power utility companies worldwide. Since 2013, the woman-owned company has grown its export business by an average of 400 percent.

Business Growth Limited by Lack of Real-Time Data

While Kaddas is best known for BirdguarD, the company specializes in thermoform plastic manufacturing for a range of industries, including utility, aviation, transportation and medical—continuing to focus on new products and areas to expand its custom solutions. For years, Kaddas managed the manufacturing of these various products via a legacy enterprise resource planning (ERP) system. However, as the manufacturer continued its rapid growth, employees increasingly were affected by limitations of the system.

A top concern with the old ERP system was the inability to get real-time information from Kaddas’ machines, which impeded decision-making. Using the old system, employees also were unable to track scrap efficiently, and they had to make too many manual calculations on the pulls of its machines in determining production capacity. Moreover, data from the legacy ERP system had to be imported into separate accounting software, making it difficult to get a unified view of the business.

Over time, it became clear that Kaddas would need to migrate to a new ERP system in order to get the detailed information needed to make better business decisions. Because the manufacturer’s Director of Operations previously had good experiences in implementing and using IQMS (now renamed DELMIAWORKS), Kaddas’ executives decided to evaluate it first before looking at other solutions.

“After reviewing our requirements for a new ERP system and conducting our internal due diligence, it was clear that the comprehensive functionality of IQMS ERP software ticked off all our boxes,” said John R. Adams, Kaddas Enterprises CFO / General Counsel.

Moving forward, Kaddas expects IQMS to play a central role in driving improvements across five areas: efficiency on the shop floor, scrap rates, planning and scheduling of machines, inventory tracking, and reporting at every level.

Adams notes, “We are confident that IQMS is the best fit for our business, and it will give us the flexibility to add more as we grow.”

About Kaddas Enterprises, Inc.

Since 1966, Kaddas Enterprises, Inc. has been a leader in thermoform plastic manufacturing. From its headquarters in Salt Lake City, Utah, Kaddas utilizes high-quality materials and innovative design to develop products and solutions for a wide variety of industries, including utility, aviation, transportation and medical, while maintaining its dedication to sustainable business practices. The industry leader in wildlife and asset protection, Kaddas manufactures more than 300 BirdguarD products used by power utility companies around the globe. To learn more, visit www.kaddas.com.

The post Enhancing Business Decisions via Real-Time Data appeared first on IQMS Manufacturing Blog.

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  • 99% of mid-market manufacturing executives are familiar with Industry 4.0, yet only 5% are currently implementing or have implemented an Industry 4.0 strategy.
  • Investing in upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES) are manufacturers’ top three priorities based on interviews with them.
  • Mid-market manufacturers getting the most value out of Industry 4.0 excel at orchestrating a variety of technologies to find new ways to excel at product quality, improve shop floor productivity, meet delivery dates, and control costs.
  • Real-time monitoring is gaining momentum to improve order cycle times, troubleshoot quality problems, improve schedule accuracy, and support track-and-trace.

Demystifying Industry 4.0 is an excellent objective for any research effort. Delving into the specifics of Industry 4.0 delivers fascinating insights into adoption trends and future direction of the market. Those factors are what makes the latest BDO study,  Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver ValueBDO is a leading provider of assurance, tax, and financial advisory services and is providing the report available for download here (PDF, 36 pp., no opt-in).

The survey was conducted by Market Measurement, Inc., an independent market research consulting firm. The survey included 230 executives at U.S. manufacturing companies with annual revenues between $200M and $3B and was conducted in November and December of 2018. Please see page 2 of the study for additional details regarding the methodology. One of the most valuable findings of the study is that mid-market manufacturers need more evidence of Industry 4.0, delivering improved supply chain performance, quality, and shop floor productivity.

Early Indicators of Industry 4.0 Adoption: Machine Upgrades, Smart Machines, Real-Time Monitoring & MES Lead Investment Plans

In the many conversations I’ve had with mid-tier manufacturers located in North America this year, I’ve learned the following:

  • 61% of manufacturers are making large-scale operational improvements to their shop floors, including manufacturing process automation systems in 2019 to continue the strong growth they achieved in 2018.
  • Improving shop floor productivity is 1.4 times more important to manufacturers than making marketing improvements to drive more sales leads.
  • Manufacturers are turning to robotics as over half of them (58%) are unable to meet production demand due to a lack of skilled labor availability.
  • Investing in improving product and service quality fuels growth, keeps customers for life, and leads all technology spending by manufacturers in 2019.

These and many other insights are from the recent Decision Analyst study completed in conjunction with IQMS/Dassault Systemes, Shop Floor Productivity Investments That Drive Manufacturing Growth (PDF, 7 pp., opt-in). Based on interviews with 150 manufacturers distributed across ten industries, the survey provides invaluable insights into their current and future manufacturing process automation roadmaps and plans.

Key Takeaways from BDO’s Industry 4.0 Study
  • Manufacturers are most motivated to evaluate Industry 4.0 technologies based on the potential for growth and business model diversification they offer. Building a business case for any new system or technology that delivers revenue, even during a pilot, is getting the highest priority by manufacturers today. Based on my interviews with manufacturers, I found they were 1.7 times more likely to invest in machine upgrades and smart machines versus spending more on marketing. Manufacturers are very interested in any new technology that enables them to accept short-notice production runs from customers, excel at higher quality standards, improve time-to-market, all the while having better cost visibility and control. All those factors are inherent in the top three goals of business model diversification, improved operational efficiencies, and increased market penetration.
Source: Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value.
  • For Industry 4.0 technologies to gain more adoption, more use cases are needed to explain how traditional product sales, aftermarket sales, and product-as-a-service benefit from these new technologies. Manufacturers know the ROI of investing in a machinery upgrade, buying a smart, connected machine, or integrating real-time monitoring across their shop floors. What they’re struggling with is how Industry 4.0 makes traditional product sales improve. 84% of upper mid-market manufacturers are generating revenue using Information-as-a-Service today compared to 67% of middle market manufacturers overall.
Source: Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value.

  • Manufacturers who get the most value out of their Industry 4.0 investments begin with a customer-centric blueprint first, integrating diverse technologies to deliver excellent customer experiences. Manufacturers growing 10% a year or more are relying on roadmaps to guide their technology buying decisions. These roadmaps are focused on how to reduce scrap, improve order cycle times, streamline supplier integration while improving inbound quality levels, and provide real-time order updates to customers. BDOs’ survey results reflect what I’m hearing from manufacturers. They’re more focused than ever before on having an integrated engagement strategy combined with greater flexibility in responding to unique and often urgent production runs.
Source: Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value. Industry 4.0 Needs To Stay Focused On Customers To Succeed
  • Industry 4.0’s potential to improve supply chains needs greater focus if mid-tier manufacturers are going to adopt the framework fully. Manufacturing executives most often equate Industry 4.0 with shop floor productivity improvements while the greatest gains are waiting in their supply chains. The BDO study found that manufacturers are divided on the metrics they rely on to evaluate their supply chains. Upper middle market manufacturers are aiming to speed up customer order cycle times and are less focused on getting their total delivered costs down. Lower mid-market manufacturers say reducing inventory turnover is their biggest priority. Overall, strengthening customer service increases in importance with the size of the organization.
Source: Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value.

By enabling integration between engineering, supply chain management, Manufacturing Execution Systems (MES) and CRM systems, more manufacturers are achieving product configuration strategies at scale. A key growth strategy for many manufacturers is to scale beyond the limitations of their longstanding Make-to-Stock production strategies. By integrating engineering, supply chains, MES, and CRM, manufacturers can offer more flexibility to their customers while expanding their product strategies to include Configure-to-Order, Make-to-Order, and for highly customized products, Engineer-to-Order. The more Industry 4.0 can be shown to enable design-to-manufacturing at scale, the more it will resonate with senior executives in mid-tier manufacturing.

Source: Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value.
  • Manufacturers are more likely than ever before to accept cloud-based platforms and systems that help them achieve their business strategies faster and more completely, with analytics being in the early stages of adoption. Manufacturing CEOs and their teams are most concerned about how quickly new applications and platforms can position their businesses for more growth. Whether a given application or platform is cloud-based often becomes secondary to the speed and time-to-market constraints every manufacturing business faces. The fastest-growing mid-tier manufacturers are putting greater effort and intensity into mastering analytics across every area of their business too. BDO found that Artificial Intelligence (AI) leads all other technologies in planned use.
Source: Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value.

The post Industry 4.0’s Potential Needs To Be Proven On The Shop Floor appeared first on IQMS Manufacturing Blog.

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The progression manufacturers are making from MRP to ERP has its basis in how diversified their product lines have become in response to customers’ need for more versatile, configurable and customizable products. Many manufacturers got their start producing the same product using components they sourced locally or made themselves. Customers weren’t given the option of customizing products. Doubling down on manufacturing efficiency, the core process areas of Bills of Materials (BOM), Inventory Tracking and Master Production Scheduling dominated global manufacturing. Automating these three areas became the foundation of Material Requirements Planning (MRP).

Global competition became fierce fast, forcing manufacturers to rely on price and availability to win more sales. Offering more customized products and capitalizing on the ideas customers had for new, configurable products helped manufacturers escape price wars and becoming a commodity. More feature-rich products that often needed services also required a new production system, and Manufacturing Resource Planning (MRPII) was born. Gartner defines closed-loop MRP as a system built around MRP that also includes production planning, master production schedule, and capacity requirements planning. Once the planning phase is complete, and the plans have been accepted as realistic and attainable, the execution functions come into play. These include the shop floor control functions of input/output measurement, detailed scheduling and dispatching, as well as anticipated delay reports from both the shop and vendors, purchasing follow-up and control, and other functions.

MRP II was perfect for the expanding world of manufacturing that had become heavily dependent on suppliers. It’s common to find 60% or more of a given new products’ components are from suppliers and production partners outside the company. For Engineer-to-Order manufacturers, it’s close to 75% or more. Products are becoming complex exceptionally fast, with a typical new car having over 100 million lines of code according to the MIT Technology Review, not counting future Amazon Alexa integration.

To stay competitive products have to be managed over their lifecycles, not just month to month to meet production targets. The financial implications of changing production mix need to be predicted first and factored for customer demand, and supplier quality levels and inventories checked for availability. Every area of a manufacturing business needs to stay coordinated with production costs, direction, forecasts, and plans. Enterprise Resource Planning Systems (ERP) was designed specifically for this need and continue to expand to meet manufacturers’ requirements today.

Comparing The Benefits of MRP, MRPII, And ERP

Designed for an era when mass production dominated manufacturing, MRP systems are designed to make factories as efficient was possible on their own. The greater the product complexity, the more integrated manufacturing needed to be across an entire business, leading to MRP II. The proliferation of new products, greater intensity of global competition, shorter time-to-market, and need for greater cost control and visibility led to ERP platforms being created.  The following compares the benefits of MRP, MRP II, and ERP:

What Motivates Manufacturers To Change from MRP to ERP

It’s common for manufacturers to begin operations relying on Microsoft Excel for production scheduling and Intuit QuickBooks for their financials and it’s the same with how many progress from MRP to ERP. The more they become reliant on outside distributors and suppliers, the more the added advantages of ERP motivate them to migrate from MRP. Having two different systems that aren’t in sync with each other quickly leads to problems including higher than industry average scrap rates, missed customer shipments, limited visibility & control of manufacturing costs and more. Eldon James is an example of a leading plastics manufacturer who chose to migrate away from Microsoft Excel and QuickBooks to an IQMS EnterpriseIQ ERP software system. Here’s what they were able to accomplish:

  • On-time deliveries happened 50% of the time with siloed systems that didn’t communicate, and with the IQMS EnterpriseIQ ERP system integrated across their operations, they soared to 98% – a jump of 96%. By having a single system that organized all the data they needed for production scheduling, quality assurance, and shipping, Eldon James was able to increase their on-time delivery accuracy nearly 100%, jumping from 50% to 98% in less than a year.
  • Reject rates plummeted from 30% to 2% as Quality Assurance had real-time monitoring to track where scrap was being produced in the production process. Like on-time deliveries, scrap rates and reject rates drastically improved when Eldon James had real-time data from IQMS’ ERP system to manage production. Where and how to scrap was being produced was no longer a mystery; the answers to the most challenging quality assurance questions were in the IQMS system anytime they needed them.
  • The long-term goal of offering and producing over 6,000 products has been achieved with scale to spare. One of Eldon James’ strategic priorities is always to be innovating, always find new ways to improve, and this includes their new product development strategies. With IQMS’ ERP system in place today, they are now offering over 6,000 products to their customers located in 40 different countries.

Conclusion

MRP began on the shop floor and has stayed within the four walls of factories as a system for continually improving production efficiency. Today many of the core elements of MRP systems are included in ERP systems including Material Requirements Planning, Labor Capacity Planning, Auxiliary Equipment Planning, Machine & Work Center Capacity Planning AND Rough Cut Capacity Planning. The more adept manufacturers become using the MRP functional areas of their ERP systems, the more efficient they become at achieving their manufacturing goals, especially those that rely on suppliers, partners, and distributors.    

The post MRP vs. ERP – What’s the Difference? appeared first on IQMS Manufacturing Blog.

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  • 61% of manufacturers are making large-scale operational improvements to their shop floors, including manufacturing process automation systems in 2019 to continue the strong growth they achieved in 2018.
  • Improving shop floor productivity is 1.4 times more important to manufacturers than making marketing improvements to drive more sales leads.
  • Investing in improving product and service quality fuels growth, keeps customers for life, and leads all technology spending by manufacturers in 2019.
  • Manufacturers are turning to robotics as over half of them (58%) are unable to meet production demand due to a lack of skilled labor availability.

These and many other insights are from the recent Decision Analyst study completed in conjunction with IQMS/Dassault Systemes, Shop Floor Productivity Investments That Drive Manufacturing Growth (PDF, 7 pp., opt-in). Based on interviews with 150 manufacturers distributed across ten industries, the survey provides invaluable insights into their current and future manufacturing process automation roadmaps and plans. Manufacturers are planning to invest more in manufacturing process automation than in previous years as the results indicate. Key insights from the survey include the following:

  • 76% of manufacturers are prioritizing improving shop floor productivity as their most valuable growth strategy today, setting a quick pace for manufacturing process automation investment. Looking for new insights into how they can offer short-notice production runs, improve product quality and reduce costs, manufacturers say improving shop floor productivity is 1.4 times more important than making marketing improvements to drive more sales leads (44%) or growing partnership-based revenue (31%). The following graphic compares top manufacturing priorities, growth strategies, and growth barriers.

  • Investing in improving product and service quality fuels growth, keeps customers for life, and leads all technology spending in 2019. Manufacturers are most focused on how they can excel as suppliers in 2019 while also overcoming the growth barrier of inconsistent supplier quality and delivery consistency. These two factors are motivating them to increase their investments in Quality Management. Taken together, the top technologies manufacturers are adopting in 2019 defines their roadmap for this and future years’ operations. The study found that Quality Management adoption is growing in Aerospace & Defense (A&D) this year, with 73% of manufacturers surveyed saying this is a priority. 68% of plastics manufacturers are adopting Quality Management to streamline audits. 59% of fabricated metal manufacturers are adopting Quality Management this year to stay competitive.

  • Where Manufacturing Process Automation is flourishing today is in the area of upgrading existing machinery and replacing fully depreciated machines with smart, connected next-generation production equipment. 73% of manufacturers surveyed have fully depreciated their machinery; the majority are interested in either upgrading them or replacing equipment. The survey found that 41% of manufacturers are investing in upgrading existing machinery, and an additional 41% are investing in new machinery. 29% of all manufacturers are investing in robotics to alleviate the skilled labor shortages that leave them unable to meet production demand.

  • Lack of skilled labor is manufacturers’ greatest barrier to growth, with 29% of them ranking robotics as a high investment priority to overcome this challenge. Robotics is becoming the most popular solution to the labor shortages facing mid-tier manufacturers today with 29% of them report that robotics is one of their top investment priorities for 2019. The more isolated a production facility is geographically, the higher the probability robotic stackers will be adopted initially to help with unloading production machinery. Robotics adoption continues from stacking to orchestrating multiple production machines across the shop floor, becoming a contributor to lights-out production shifts. Overcoming the barriers of inconsistent supplier quality and delivery consistency is leading manufacturers to invest heavily in Quality Management. The following are the leading growth barriers manufacturers face in 2019:

Conclusion

Manufacturing Process Automation is dominating manufacturers’ investment priorities for 2019. The Decision Analyst survey found that manufacturers growing 10% a year or faster are capable of quickly converting shop floor productivity into greater order accuracy, including perfect order performance, strengthening relationships with customers. Manufacturers are also integrating real-time monitoring, Quality Management, and MES to gain greater accuracy,  scale, and speed versus their competitors. The goal for many manufacturers in 2019 is to excel at selling short notice production runs, higher product quality levels, higher yields, and greater cost control and visibility. It’s common for the fastest growing manufacturers to receive 25 to 50 audit requests from customers a year. Having an Enterprise Resource Planning (ERP) system, MES, and Quality Management system (QMS) that can flex and support these is key to their growth.

The post The Era of Manufacturing Process Automation Is Here appeared first on IQMS Manufacturing Blog.

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