Bottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.
Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.
Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.
The ten ways big data is revolutionizing supply chain management include:
- Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions. Big data is revolutionizing how supplier networks form, grow, proliferate into new markets and mature over time. Transactions aren’t the only goal, creating knowledge-sharing networks is, based on the insights gained from big data analytics. The following graphic from Business Ecosystems Come Of Age (Deloitte University Press) (free, no opt-in) illustrates the progression of supply chains from networks or webs, where knowledge sharing becomes a priority.
- Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace. These are the top four supply chain capabilities that Delotte found are currently in use form their recent study, Supply Chain Talent of the Future Findings from the 3rd Annual Supply Chain Survey (free, no opt-in). Control tower analytics and visualization are also on the roadmaps of supply chain teams currently running big data pilots.
- 64% of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations. SCM World’s latest Chief Supply Chain Officer Report provides a prioritization of the most disruptive technologies for supply chains as defined by the organizations’ members. The following graphic from the report provides insights into how senior supply chain executives are prioritizing big data analytics over other technologies.
- Using geoanalytics based on big data to merge and optimize delivery networks. The Boston Consulting Group provides insights into how big data is being put to use in supply chain management in the article Making Big Data Work: Supply Chain Management (free, opt-in). One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from the article. Combining geoanalytics and big data sets could drastically reduce cable TV tech wait times and driving up service accuracy, fixing one of the most well-known service challenges of companies in that business.
- Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives. Supply chain visibility often refers to being able to see multiple supplier layers deep into a supply network. It’s been my experience that being able to track financial outcomes of supply chain decisions back to financial objectives is attainable, and with big data app integration to financial systems, very effective in industries with rapid inventory turns. Source: Turn Big Data Into Big Visibility.
- Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant. Big data has the potential to provide improved traceability performance and reduce the thousands of hours lost just trying to access, integrate and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted.
- Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data. IBM has developed a quality early-warning system that detects and then defines a prioritization framework that isolates quality problem faster than more traditional methods, including Statistical Process Control (SPC). The early-warning system is deployed upstream of suppliers and extends out to products in the field.
- 69% of enterprises expect to make moderate-to-heavy cloud investments over the next three years as they migrate core business functions to the cloud.
- 44% of enterprises are relying on cloud computing to launch new business models today, predicting this will increase to 55% in three years.
- 32% are using cloud computing to streamline their supply chains today. Senior executives predict this figure will increase to 56% in three years, a 24% increase.
- 59% say they use cloud-based applications and platforms to better manage and analyze data today, reflecting the increasing importance of analytics and big data enterprise-wide.
These and other insights are from a recent Oxford Economics and SAP study of cloud computing adoption, The Cloud Grows Up. You can find the study here (no opt-in). In late 2014, Oxford Economics and SAP collaborated on a survey of 200 senior business and IT executives globally regarding the adoption and use of cloud technology. Oxford Economics’ analysts compared the latest survey with one completed in 2012 looking for leading indicators of cloud adoption in enterprises. They found many C- and VP-level executives are taking a more pragmatic, realistic view of what cloud technologies can contribute. Enterprises are moving beyond the hype of cloud computing, putting in the hard work of launching new business models while driving top-line revenue growth.
Oxford Economics has made two interactive infographics available from the study here. The first details cloud adoption, and the second, on how enterprises see cloud computing changing their business models over the next three years. As cloud platforms and applications become a scalable, secure and for the most part reliable, once-elusive enterprise goals and new business models become attainable.
Key take-aways from the study include the following:
- Top–line growth (58%), collaboration among employees (58%), and supply chain (56%) are the three areas enterprises expect cloud computing to impact most in three years. The greatest gains will be in the areas of supply chain (a 24% jump), collaboration among employees (20%) and increased agility and responsiveness to customers (17%). The following graphic compares where enterprises are seeing cloud computing’s impact today and a prediction of each areas’ impact in three years.
- Developing new products & services (61%), new lines of business (51%) and entering new markets (40%) are three key areas cloud computing is transforming enterprises. With a 35% increase, developing new products and services is the most dominant strategy enterprises are relying on to grow their businesses. See the comparison below for further details.
- 58% of enterprises predict their use of cloud computing will increase top-line revenue growth in three years. 67% see the cloud changing skill sets and transforming the role of HR. The following graphic illustrates the first of two interactive infographics Oxford Economics and SAP are providing with the report. You can access the infographic here.
- 74% of enterprises say innovation and R&D is somewhat or mostly cloud-based. 61% say they will have developed new products and services in three years as a result of adopting cloud technologies. The following graphic illustrates the second of two interactive infographics Oxford Economics and SAP are providing with the report. You can access the infographic here.
- Enterprise cloud security strategies are maturing rapidly. From 2012 to 2014, strategies for ensuring the security of API and interfaces increased 24%, from 20% to 44%. Additional concerns that increased include virus attacks (up 19%), and identity theft (up 16%). The following figure compares the top concerns enterprises have in the area of cloud security.
- 31% of respondents say the cloud computing has had a transformative impact on their business. 48%, nearly half, state that cloud computing has had a moderate impact on business performance. The majority believe cloud computing will have a significant impact on top-line revenue growth in three years.
- 67% of enterprises say that marketing, purchasing, and supply chain are somewhat and mostly cloud-based as of today. Cloud-based adoption has reached an inflection point in enterprises, with functional areas having the largest percentage of workloads running on cloud-based apps. Enterprise senior executives see the potential to improve innovation, R&D, and time-to-market via greater collaboration using cloud technologies.
- Salesforce (NYSE:CRM) estimates adding analytics and Business Intelligence (BI) applications will increase their Total Addressable Market (TAM) by $13B in FY2014.
- 89% of business leaders believe Big Data will revolutionize business operations in the same way the Internet did.
- 83% have pursued Big Data projects in order to seize a competitive edge.
Despite the varying methodologies used in the studies mentioned in this roundup, many share a common set of conclusions. The high priority in gaining greater insights into customers and their unmet needs, more precise information on how to best manage and simplify sales cycles, and how to streamline service are common themes.
The most successful Big Data uses cases revolve around enterprises’ need to get beyond the constraints that hold them back from being more attentive and responsive to customers.
Presented below is a roundup of recent forecasts and estimates:
- Wikibon projects the Big Data market will top $84B in 2026, attaining a 17% Compound Annual Growth Rate (CAGR) for the forecast period 2011 to 2026. The Big Data market reached $27.36B in 2014, up from $19.6B in 2013. These and other insights are from Wikibon’s excellent research of Big Data market adoption and growth. The graphic below provides an overview of their Big Data Market Forecast. Source: Executive Summary: Big Data Vendor Revenue and Market Forecast, 2011-2026.
- IBM and SAS are the leaders of the Big Data predictive analytics market according to the latest Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015. The latest Forrester Wave is based on an analysis of 13 different big data predictive analytics providers including Alpine Data Labs, Alteryx, Angoss Software, Dell, FICO, IBM, KNIME.com, Microsoft, Oracle, Predixion Software, RapidMiner, SAP, and SAS. Forrester specifically called out Microsoft Azure Learning is an impressive new entrant that shows the potential for Microsoft to be a significant player in this market. Gregory Piatetsky (@KDNuggets) has done an excellent analysis of the Forrester Wave Big Data Predictive Analytics Solutions Q2 2015 report here. Source: Courtesy of Predixion Software: The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015 (free, no opt-in).
- IBM, KNIME, RapidMiner and SAS are leading the advanced analytics platform market according to Gartner’s latest Magic Quadrant. Gartner’s latest Magic Quadrant for advanced analytics evaluated 16 leading providers of advanced analytics platforms that are used to building solutions from scratch. The following vendors were included in Gartner’s analysis: Alpine Data Labs, Alteryx, Angoss, Dell, FICO, IBM, KNIME, Microsoft, Predixion, Prognoz, RapidMiner, Revolution Analytics, Salford Systems, SAP, SAS and Tibco Software, Gregory Piatetsky (@KDNuggets) provides excellent insights into shifts in Magic Quadrant for Advanced Platform rankings here. Source: Courtesy of RapidMiner: Magic Quadrant for Advanced Analytics Platforms Published: 19 February 2015 Analyst(s): Gareth Herschel, Alexander Linden, Lisa Kart (reprint; free, no opt-in).
- Salesforce estimates adding analytics and Business Intelligence (BI) applications will increase their Total Addressable Market (TAM) by $13B in FY2014. Adding new apps in analytics is projected to increase their TAM to $82B for calendar year (CY) 2018, fueling an 11% CAGR in their total addressable market from CY 2013 to 2018. Source: Building on Fifteen Years of Customer Success Salesforce Analyst Day 2014 Presentation (free, no opt in).
- 89% of business leaders believe big data will revolutionize business operations in the same way the Internet did. 85% believe that big data will dramatically change the way they do business. 79% agree that ‘companies that do not embrace Big Data will lose their competitive position and may even face extinction.’ 83% have pursued big data projects in order to seize a competitive edge. The top three areas where big data will make an impact in their operations include: impacting customer relationships (37%); redefining product development (26%); and changing the way operations is organized (15%).The following graphic compares the top six areas where big data is projected to have the greatest impact in organizations over the next five years. Source: Accenture, Big Success with Big Data: Executive Summary (free, no opt in).
- Customer analytics (48%), operational analytics (21%), and fraud & compliance (21%) are the top three use cases for Big Data. Datameer’s analysis of the market also found that the global Hadoop market will grow from $1.5B in 2012 to $50.2B in 2020, and financial services, technology and telecommunications are the leading industries using big data solutions today. Source: Big Data: A Competitive Weapon for the Enterprise.
- 37% of Asia Pacific manufacturers are using Big Data and analytics technologies to improve production quality management. IDC found manufacturers in this region are relying on these technologies to reduce costs, increase productivity, and attract new customers. Source: Big Data and Analytics Core to Nex-Gen Manufacturing.
- Supply chain visibility (56%), geo-location and mapping data (47%) and product traceability data (42%) are the top three potential areas of Big Data opportunity for supply chain management. Transport management, supply chain planning, & network modeling and optimization are the three most popular applications of Big Data in supply chain initiatives. Source: Supply Chain Report, February 2015.
- Finding correlations across multiple disparate data sources (48%), predicting customer behavior (46%) and predicting product or services sales (40%) are the three factors driving interest in Big Data analytics. These and other fascinating findings from InformationWeek’s 2015 Analytics & BI Survey provide a glimpse into how enterprises are selecting analytics applications and platforms. Source: Information Week 2015 Analytics & BI Survey.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
- Enterprises are only realizing 35% of the total potential value of their cloud deployments according to a recent Bain & Company study.
- Companies that moved development to IaaS and PaaS clouds from Amazon Web Services (AWS) reduced downtime by 72% and improved application availability by 3.9 hours per user per year.
These and other key take-aways are from the recent Bain & Company study, Tapping Cloud’s Full Potential. The full report PDF is available for download here (free, no opt-in). The following graphic from the report illustrates the currently realized value of cloud deployments in enterprises today according to Bain & Company.
The researchers found several critical drivers of cloud value with one of the most important being the strengthening and clarifying of a product and service focus. The following graphic illustrates the critical drivers of cloud value.
Cloud Service Providers Give Manufacturers The Ability To Stay Competitive
Cloud-first strategies designed to accelerate and strengthen shifts in emerging business models is paying off according to Bain’s research results.
Manufacturers choosing to pursue a cloud-first strategy are focusing on evolving their business models, processes, systems and performance quickly to stay in step with customers’ needs. For many manufacturers, their customers’ pace is faster than internal IT organizations can anticipate and react to. CSPs are helping to close that gap.
Here are five ways CSPs are making manufacturers more competitive:
- Bringing industry expertise to the shop floor level. The best CSPs serving manufacturers today have management teams that have decades of combined manufacturing experience in specific industries. The CEO of a specialty tools manufacturer remarked that his company’s cloud strategy was more focused on accelerating plant floor performance first. Working with a CSP that had expertise in their industry, this manufacturer was able to gain greater supply chain visibility and improve forecast accuracy, all with cloud-based apps.
- Solving legacy and 3rd party system integration problems so that cloud-based ERP, CRM, supply chain management (SCM) systems can scale quickly. When a rust-belt based manufacturer of heating, ventilation and air conditioning (HVAC) systems had the opportunity to grow their business by expanding into build-to-order customized products, their CSP partner made it possible to integrate an entirely new product configurator and cloud-based ERP system module to manage quote-to-cash. Today, 30% of corporate-wide profits are from build-to-order selling strategies.
- Knowledge-sharing supplier networks are becoming more attainable for manufacturers thanks to cloud technologies and CSPs. All manufacturers have strategic plans that include greater integration of their supplier networks, with many seeking to create knowledge-sharing networks. One of the best studies of how to create a knowledge-sharing network is from Dr. Jeffrey Dyer and Dr. Kentaro Nobeoka based on their intensive work with Toyota. Their study, Creating And Managing A High Performance Knowledge-Sharing Network: The Toyota Case is a great read. The following graphic from the study illustrates the evolution of a knowledge-sharing network. Manufacturers are relying on cloud platforms and CSPs to enable shifts in network structures and nurture change management to create self-sustaining systems.
- Two-tier ERP adoption in manufacturing is growing as CSPs master cloud ERP systems. CSPs are moving beyond providing basic services, specializing in cloud ERP, CRM, SCM, pricing, services and legacy system integration to keep pace with manufacturers’ demands. In one high tech manufacturer, their CSP partner orchestrated the procuring and launch of their cloud-based two-tier ERP system integrated to an SAP instance in their headquarters. Today they operate production centers in Asia, North America and Australia, all coordinated through the main SAP instance in the U.S. headquarters.
- Making Service Level Agreements (SLAs) more relevant to manufacturing business models. Instead of just getting SLAs for uptime, security and system stability, manufacturers are getting advanced manufacturing intelligence dashboards that provide visibility to the plant or production center level.
Bottom Line: Manufacturers are increasingly relying on CSPs’ cloud, industry and integration expertise to support the transition many are making to new business models and get greater than 35% of the value from their cloud investments.
Additional resources on Cloud ERP systems:
according to MoneyTree Report, a collaborative research initiative between PricewaterhouseCoopers and the National Venture Capital Association. A graphic from the latest available data shows how software investments were 39% of all investments in Q4, 2014.
To determine which enterprise software startups have gained the greatest amount of funding since they were founded, Mattermark was used to rank order all enterprise start-ups. Mattermark uses a combination of artificial intelligence and data quality analysis to provide insights into over 1M companies, over 470K with employee data, and over 100,000 funding events.
Mattermark uses their Growth Score is the default ranking for all companies tracked in their service. This score is not meant to provide guidance on which startup to invest in. Rather it’s a measure of momentum across the metrics and KPIs that Mattermark measures.
Using their free trial, I completed the following analysis of cloud-based enterprise software startups. I’m not a consultant to Mattermark and never have been. As many Forbes readers find software investment data fascinating, I contacted Mattermark and asked for a free trial, which they graciously provided. You can download the list in Microsoft Excel format here.
A Goldman Sachs study published earlier this year projects that spending on cloud computing infrastructure and platforms will grow at a 30% CAGR from 2013 through 2018 compared with 5% growth for the overall enterprise IT.
Centaur Partners and other firms mentioned in this roundup are seeing more enterprise-size deals for cloud computing infrastructure and applications. While each of these consultancies and research firms have varying forecasts for the next few years, all agree that cloud computing adoption is accelerating in enterprises on a global scale.
Key take-aways from the roundup are provided below:
- By 2018, 59% of the total cloud workloads will be Software-as-a-Service (SaaS) workloads, up from 41% in 2013. Cisco is predicting that by 2018, 28% of the total cloud workloads will be Infrastructure-as-a-Service (IaaS) workloads down from 44% in 2013. 13% of the total cloud workloads will be Platform-as-a-Service (PaaS) workloads in 2018, down from 15% in 2013. The following graphic provides a comparative analysis of IaaS, PaaS and SaaS forecasts from 2013 to 2018. Source: Cisco Global Cloud Index: Forecast and Methodology, 2013–2018. (PDF, free, no opt-in).
- Centaur Partners’ analysis of SaaS & cloud-based business application services revenue forecasts the market growing from $13.5B in 2011 to $32.8B in 2016, attaining a 19.5% CAGR. Centaur provides a useful overview of current market conditions including M&A activity in their latest market overview published this month, Introduction to Centaur Partners: SaaS Market Overview, (PDF, free, no opt-in).
- Global SaaS software revenues are forecasted to reach $106B in 2016, increasing 21% over projected 2015 spending levels. Spending on integration, storage management, and database management systems are projected to experience the greatest growth in 2015. These and other key insights are from Forrester’s SaaS software subscription revenue by category show below. Source: Enterprise software spend to reach $620 billion in 2015: Forrester.
- $78.43B in SaaS revenue will be generated in 2015, increasing to $132.57 in 2020, attaining a compound annual growth rate (CAGR) of 9.14%. The following graphic and table provides an overview of Forrester’s Global Public Cloud Computing market size analysis and forecast for the years 2011 to 2020. Source: Institut Sage.
- IDC predicts that by 2016, there will be an 11% shift of IT budget away from traditional in-house IT delivery, toward various versions of cloud computing as a new delivery model. By 2017, 35% of new applications will use cloud-enabled, continuous delivery and enabled by faster DevOps life cycles to streamline rollout of new features and business innovation. Source: 2015-2017 Forecast: Cloud Computing to Skyrocket, Rule IT Delivery.
- By 2018, IDC forecasts that public cloud spending will more than double to $127.5 billion. This forecast is broken down as follows: $82.7 billion in SaaS spending, $24.6 billion for IaaS and $20.3 billion in PaaS expenditures. Source: Forecasts Call For Cloud Burst Through 2018.
- By 2016 over 80% of enterprises globally will using IaaS, with investments in private cloud computing showing the greater growth. Ovum forecasts that by 2016, 75% of EMEA-based enterprises will be using IaaS. These and other insights are from the presentation, The Role of Cloud in IT Modernisation: The DevOps Challenge (free PDF, no opt in). The graphic below provides an analysis of cloud computing adoption in EMEA and globally.
- By 2018, more than 60% of enterprises will have at least half of their infrastructure on cloud-based platforms. These and other are insights are from the keynote Cloud Business Summit presentation Digital Business, Rethinking Fundamentals by Bill McNee, Founder and CEO, Saugatuck Technology. Source: Digital Business, Rethinking Fundamentals.
and 85.40% for Computer and Information Research Scientists.
Demand for Python programming expertise increased 96.9% in big-data related positions in the last twelve months.
These and other key insights are from a recent analysis completed of big data hiring trends using WANTED Analytics, the leading provider of data analytics on the workplace. For purposes of this analysis, the term “big data” is comprised of the four skill sets of data analysis, data acquisition, data mining and data structures. The WANTED Analytics taxonomy references these skill sets when queries are made on the term “big data”.
The company currently maintains a database of more than one billion unique job listings and is collecting hiring trend data from more than 150 countries. WANTED Analytics has never been a client, they provided complimentary access based on my requesting a trial account. Many Forbes readers are interested in staying current on big data hiring trends, which led me to complete this analysis.
Key Take-aways include the following:
- Demand for big data expertise across a range of occupations saw significant growth over the last twelve months. There was a 123.60% jump in demand for Information Technology Project Managers with big data expertise, and an 89.8% increase for Computer Systems Analysts. The following table provides an overview of the distribution of open positions by occupation and the percentage growth in job demand over time.
- The five leading industries with the most job openings requiring big data expertise include Professional, Scientific and Technical Services (27.14%), Information Technologies (18.89%), Manufacturing (12.35%), Retail Trade (9.62%) and Sustainability, Waste Management & Remediation Services (8.20%). The following graphic shows the distribution of open positions between September 1, 2014 to today, December 29, 2014:
- The Hiring Scale is 76 for jobs that require big data skills with 12 candidates per job opening as of December 29, 2014. The higher the Hiring Scale score, the more difficult it is for employers to find the right applicants for open positions. Nationally an average job posting for an IT professional with cloud computing expertise is open just 47 days.
- The median salary for professionals with big data expertise is $103,000 a year. Sample jobs in this category include Big Data Solution Architect, Linux Systems and Big Data Engineer, Big Data Platform Engineer, Lead Software Engineer, Big Data (Java, Hadoop, SQL) and others. The distribution of median salaries across all industries shown below:
- San Jose – Sunnyvale – Santa Clara, CA, San Francisco – Oakland – Fremont, CA, and Washington – Arlington – Alexandria, DC are the top three U.S. employment markets for big data related jobs as of today. Mapping the distribution of job volume, salary range, candidate supply, posting period and hiring scale by Metropolitan Statistical Area (MSA) or states and counties is supported by WANTED Analytics and shown in the following graphic. A summary of the top twenty employment markets is also shown following the map:
- Cisco (NASDAQ:CSCO), IBM (NYSE: IBM) and Oracle (NYSE:ORCL) have the most open big data-related positions today. Cisco, its supplier, partner and support ecosystem companies have 3,613 related big data positions available. The following table shows the top ten big data employers today, the distribution of jobs, and the number of new jobs added over the last year.
- Python programming (96.90%), Linux expertise (76.60%) and Structured Query Language (SQL) (76%) are the three most in-demand skills in positions that mention big data as a requirement. The following table provides an overview of the top 10 most in-demand skills:
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:
- Attitudes on How Big Data will Affect Manufacturing Performance. Posted by Greg Goodwin on Thu, Mar 06, 2014. LNS Research. http://blog.lnsresearch.com/blog/bid/194972/Attitudes-on-How-Big-Data-will-Affect-Manufacturing-Performance-DATA From Value to Vision: Reimagining the Possible with Data Analytics, David Kiron, Renee Boucher Ferguson and Pamela Kirk Prentice, MIT Sloan Management Review, March 2013. http://sloanreview.mit.edu/reports/analytics-innovation/introduction/
- Merck Optimizes Manufacturing With Big Data Analytics, Information Week, Doug Henschen. April 2, 2014. http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/merck-optimizes-manufacturing-with-big-data-analytics/d/d-id/1127901
- Takeaways from the MIT/Accenture Big Data in Manufacturing Conference. Posted by Greg Goodwin on Wed, Nov 27, 2013. http://blog.lnsresearch.com/blog/bid/190482/Takeaways-from-the-MIT-Accenture-Big-Data-in-Manufacturing-Conference
- The Internet of Things and the future of manufacturing. McKinsey and Company. Executives at Robert Bosch and McKinsey experts discuss the technology-driven changes that promise to trigger a new industrial revolution. June 2013 | by Markus Löffler and Andreas Tschiesner. http://www.mckinsey.com/insights/business_technology/the_internet_of_things_and_the_future_of_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. http://www.ge-ip.com/library/detail/13170
- When Big Data Meets Manufacturing. Stephen Chick, Serguei Netessine, INSEAD Professors of Technology and Operations Management and Arnd Huchzermeier, Chaired Professor of Production Management at WHU-Otto Beisheim School of Management | April 16, 2014. http://knowledge.insead.edu/operations-management/when-big-data-meets-manufacturing-3297
87% of enterprises believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. 89% believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum.
These and other key findings are from a Accenture and General Electric study published this month on how the combination of Big Data analytics and the Internet of Things (IoT) are redefining the competitive landscape of entire industries. Accenture and GE define the Industrial Internet as the use of sensor, software, machine-to-machine learning and other technologies to gather and analyze data from physical objects or other large data streams, and then use those analyses to manage operations and in some cases to offer new, valued-added services.
Big Data Analytics Now Seen As Essential For Competitive Growth
The Industrial Internet is projected to be worth $500B in worldwide spending by 2020, taking into account hardware, software and services sales according to Wikibon and previously published research from General Electric. This finding and others can be found on the home page of the Accenture and GE study here: How the Industrial Internet is Changing the Competitive Landscape of Industries.
The study also shows that many enterprises are investing the majority of their time in analysis (36%) and just 13% are using Big Data analytics to predict outcomes, and only 16% using their analytics applications to optimize processes and strategies. Moving beyond analysis to predictive analytics and optimization is the upside potential the majority of the C-level respondents see as essential to staying competitive in their industries in the future.
A summary of results and the methodology used are downloadable in PDF form (free, no opt in) from this link: Industrial Internet Insights Report For 2015.
Key take-aways from the study include the following:
- 73% of companies are already investing more than 20% of their overall technology budget on Big Data analytics, and just over two in ten are investing more than 30%. 76% of executives expect spending levels to increase. The following graphic illustrates these results:
- Big Data analytics has quickly become the highest priority for aviation (61%), wind (45%) and manufacturing (42%) companies. The following graphic provides insights into the relative level of importance of Big Data analytics relative to other priorities in the enterprises interviewed in the study:
- 74% of enterprises say that their main competitors are already using Big Data analytics to successfully differentiate their competitive strengths with clients, the media, and investors. 93% of enterprises are seeing new competitors in their market using Big Data analytics as a key differentiation strategy. The single greatest risk enterprises see from not implementing a Big Data strategy is that competitors will gain market share at their expense. Please see the following graphic for a comparison of the risks of not implementing Big Data strategy.
- 65% of enterprises are focused on monitoring assets to identify operating issues for more proactive maintenance. 58% report having capabilities such as connecting equipment to collect operating data and analyzing the data to produce insights. The following graphic provides an overview of Big Data monitoring survey results:
- Increasing profitability (60%), gaining a competitive advantage (57%) and improving environmental safety and emissions compliance (55%) are the three highest industry priorities according to the survey. The following table provides an analysis of the top business priorities by industry for the next three years with the shaded areas indicating the highest-ranked priorities by industry:
- The top three challenges enterprises face in implementing Big Data initiatives include the following: system barriers between departments prevent collection and correlation of data for maximum impact (36%); security concerns are impacting enterprises’ ability to implement a wide-scale Big Data initiative (35%); and consolidation of disparate data and being able to use the resulting data store (29%), third. The following graphic provides an overview of the top three challenges organizations face in implementing Big Data initiatives: