The world’s capacity to store, broadcast and compute information is growing exponentially. In this digital age, the scale and speed of data gathering is unprecedented and the amount of data in our society has exploded, thus analyzing large data sets which is called Big Data—will become a key basis for competition, growth of productivity, innovation, and increase customer base. This increasing volume of data and detail of information captured by enterprises, multimedia usage, social media sites, and the Internet of Things will fuel exponential growth in data for the predicted future.
The ability to store, manipulate, process and combine data and then perform deeper analyses on the result has become even more accessible. The last decade has seen the vast scale of data sets. These data sets arised from the faster growth of transformative technologies such as the Internet and mobiles, along with the development of powerful computational methods to process such datasets. These methods, developed in the related fields of artificial intelligence and data processing techniques provide a powerful set of tools for intelligent problem-solving and data-driven policy analysis. Such methods have the full potential to dramatically improve the public welfare by guiding policy decisions and interventions, and their participation into intelligent information systems will improve public services in domains ranging from medicine and public health to homeland security.
Digital data is now everywhere—in all sector, all economy, all organizations and user of today’s digital technology. There are too many ways today that big data can be used to create value across sectors of the global economy. Whereas the use of big data will matter across the sectors, some of the sectors are set for better gains. Several US industry sectors have the potential to capture value from Big Data with opportunities and challenges vary from sector to sector. The sectors with computer and electronic products and information, finance and insurance as well, and government are poised to get substantially from the use of Big Data.
Data-driven discovery is revolutionizing scientific exploration and company innovations. So many of the Big Data tools and processing techniques pioneered in the private sector will have a role to play in the public sector as well. The White House has a Big Data Research and Development initiative to accelerate the use of Big Data in science and engineering, to strengthen up the national security, and transform teaching as well as learning. To launch this initiative, six federal departments and agencies announced more than $200 million in new commitments to promise to greatly improve the tools and techniques needed to access and organize discoveries from huge volumes of digital data. Big Data analytics can have a real and direct impact on the way policymakers work and citizens interact with governments.
We are about to take a detour from the traditional ways of using computers to solve the problems. Future market leaders will be those companies able to leverage their intellectual property, unique knowledge about products and data mining skills to create products that understand the context, predict future outcomes, and continue to improve from vast amounts of information collected daily.
Today’s corporate computers are designed to give business leaders precise information about operations from sales to customer support with supply chain details. These systems were built with a rigid set of processes and assumptions about the information managers require to build competitive products. But the arrival of data analytics has exposed their limitations. Companies can see the value in having the flexibility to view relationships and patterns across data captured in a variety of forms including text, images, and other data available.
Big Data is all about this, and it is changing our view of the value of information. It’s also providing a hint at what will be next: the ability to analyze vast amount of data in context with related information and expertise. In computer world, this approach is known as Cognitive Computing. It is the technology which allows a search engine such as Google to anticipate what you mean by the question you ask. Unlike old traditional systems, cognitive computing systems automatically learn from how they are used and adjust their rules and results on the fly as well. Cognitive Computing is the commercialization of Artificial Intelligence, which took such a beating in the 1980s that no one uses that terminology. In early nineties, the over-hyped technology wasn’t mature enough for what businesses wanted to do.
There’s a place for the traditional static programs, but can you imagine the advantages of today’s custom software that can leverage proprietary knowledge and continue to learn? Companies keep growing because they can base their business decisions on information that is often in the minds of experts but rarely in digital form. Consumers will benefit because corporate services will get smarter and can be tailored to specific issues, instead of offering generic solutions that hardly address the source of a problem.
We are in the early stages in the evolution of Big Data. We will need to make substantial investment of time, money, and resources which needed to make this style of computing commonplace. That’s why this is the right time to start challenging your executive team to rethink the business value of software that powers your enterprise.
47% of manufacturers expect big data analytics to have a major impact on company performance making it core to the future of digital factories. 36% expect mobile technologies and applications to improve their company’s financial performance today and in the future. 49% expect advanced analytics to reduce operational costs and utilize assets efficiently.
These and additional take-aways are from the well-researched and written report from SCM World. SCM World and MESA International recently collaborated on a joint survey to define the landscape of manufacturing technology tools and defining an investment priority timeline of industry. Online surveys were sent to corporate people of SCM World and MESA International, with responses from professional services and software sectors excluded from the analysis. Manufacturing & Production (22%), IT Technology (21%), Operations and Engineering (14% each) and General Management (8%) are the most common job functions of survey respondents. Respondents were from all over the world including Asia & Australia (22%), Europe, Middle East & Africa (40%) and North & South America (38%).
Conclusions from the taken survey include the following:
- Mobile technologies and applications (75%), big data analytics (68%) and advanced robotics (64%) are considered the three most disruptive technologies by manufacturers today. SCM World study shows that mobile technologies and applications are being progressively adopted across the plant floor, greatly changing the way manufacturing operations are measured, controlled and supervised to make better products. This survey also found manufacturers globally have high expectations for big data analytics providing greater insights into how manufacturing operations can be improvised. Cloud computing got a low rank as a disruptive technology as manufacturers increasingly see it as an enabling technology.
- Big Data analytics (42%), advanced robotics (30%), mobile technologies and applications (36%), Internet of things/cyber-physical systems (36%) and digital manufacturing (29%) are the four top technologies manufacturers are relying on to improve speed, responsiveness and reliability of their operations. SCM World survey first looked at which technologies will most impact the Supply Chain Operations Reference (SCOR) Models’ five Key Performance Indicators (KPIs) of reliability, responsiveness, speed, costs and asset utilization. SCM World grouped the five Key Performance Indicators into speed (combination of agility, responsiveness and reliability) and efficiency (combining as costs and assets utilization). The results of the survey are presented below.
- 58% of manufacturers are either piloting or planning to invest in mobile technologies and applications, followed by big data analytics (49%). The following investment priority timeline illustrates how manufacturers are prioritizing their technology investments in future, emerging, current and mature technologies.
- Comparing the investment priority timeline and level of technology disruption in the following technology investment priority grid further clarifies the impact of each technology on manufacturing. The size of the bubbles in the chart indicate the overall impact on SCOR Model KPIs. SCM World survey shows that the five technologies to the right of the red curve will drive the greatest disruption in manufacturing in the next few years.
- Real-time factory performance analysis (57%), real-time planning (including MRP and factory scheduling) (53%), real-time supply chain performance analysis (42%) and production quality and yield management (40%) are the four most likely use cases for big data analytics in the digital factory of the future. Only 4% of manufacturers see no use case for big data analytics in the future.
- Intel’s big data analytics strategy is paying off. In 2012, Intel saved $3M in manufacturing costs by implementing preventative analysis on just a single line, and is now planning to enhance this process to more chip lines and save an additional $30M in the next few years.
- Estimation of GE Aviation shows that using big data analytics to enable “in process” inspection could increase production speeds by 25%, while cutting down on inspection after the building process is complete by another 25%.
- Production tracking and remote factory monitoring (60%), track and trace across the supply chain (46%), and extended plant floor automation via machine-to-machine communication (40%) are the three most likely use cases of the Internet of Things in the digital factory of the future. SCM World found only 6% of manufacturers found no use case of the Internet of Things on the plant floor.