Manufacturers face thousands, if not millions, of potential problems and challenges in the coming year. Over the past few months, the media has given a surmountable amount of attention to the water problems of Flint, Michigan. In fact, an auto manufacturer in Flint made the decision to refrain from using the city supply of water in the manufacture of automobiles. This could not have been an easy switch, but the company was able to reroute water supplies from other locations to maintain production. Knowing what’s going on and how operations can be bettered are critical factors in improving the productivity and responsiveness to problems for manufacturers and increasingly, manufacturing companies are turning to advanced analytics to achieve these goals.
Data collection, analysis, and appropriation will be defining factors for manufacturers this year. Advanced analytics offer manufacturers the ability to look beyond what’s currently happening on the surface and uncover what trends, processes, and applications may impact the overall volume and quality of production. According to Bala Deshpande, the worldwide value of manufacturing maintenance is estimated to be worth $0.5 trillion. Yet, some manufacturers continue to wonder if production value can be bolstered through the use of advanced analytics, and the trends throughout the industry seem to point to a growing inclination to use advanced analytics across the scope of an entire organization.
Let’s take a look at how advanced analytics are playing into the creation, reorganization, and dramatic changes in production value for manufacturers in the coming year.
The Industrial Revolution gave birth to the concept of statistical quality reporting, the ultimate precursor to all data processing and analysis aspects in business. As the world became increasingly reliant on computers and technology, the ideas of aggregating data will focus more on electronics and capabilities. However, the modern consumer demands more than top-notch products; they want products that will make recommendations, perform autonomous updates, notify the appropriate party when a piece of equipment or part starts to fail and identify ways to work more efficiently; i.e. consumers want manufacturers to leverage predictive analytics to ensure a more satisfying product, elucidates David Gillman.
Predictive analytics is considered to be the hallmark of advanced analytics. Essentially, predictive analytics refer to the use of information to generate a way to improve the efficiency and productivity of a given business process. For manufacturers, predictive analytics pose major implications for machine maintenance and how a product is produced. This data can then be used to break down organizational silos, which 20 percent of manufacturers hope to achieve in 2016, asserts Louis Columbus of Forbes.
The Internet of Things (IoT) is fundamentally responsible for the ability to generate all of this data. It allows algorithms to identify similar patterns in isolated data bits and recognize how these processes can be consolidated. Powered by the IoT, a connected car can generate up to 25 gigabytes of data per hour, and a fully instrumented jet engine can generate up to 50 terabytes of data per hour. All this data generation goes back to how manufacturers have designed products to communicate back with the manufacturer, as well as the end user. As a result, both consumers and manufacturers can find ways to increase the value of the original product.
Vitria, a provider of an advanced analytics platform that unifies real-time streaming analytics, historical analytics, predictive analytics, prescriptive analytics, and intelligent actions, offers in their blog post, “Can Advanced Analytics for IoT Drive Significant Business Value for Manufacturers?” a chart from a TATA communications study of why manufacturers are using more advanced analytics:
Once, digital programming was considered to be the universal language of the human species. However, today’s infinite number of programming languages clearly show this ideal’s inadequacy. Manufacturers have realized different programming languages hinder analytics capabilities, especially for businesses that operate with many different products, across many different lands, in many different languages. The logical reasoning leads manufacturers to create a means of reading data and accessing analytics software universally, as explained by Thor Olavsrud.
For manufacturers who have sought to expand production and reduce waste, especially for those who have limited raw resources for production, this naturally leads to the next trend in advanced analytics, expanding the use of cloud-based technologies.
Modern manufacturing has grown exceedingly complex, and organizational silos have become a fundamental part of modern manufacturing. Silos usually result an isolation of data for that particular part of the organization.
For example, a warehouse management system that is not currently connected to the other warehouses in the network cannot accurately generate a demand forecast for a given time period. as a result, this warehouse may be more likely to suffer setbacks from not having enough product on hand, which could lead a consumer to work with a competitor. As explained in McKinsey Quarterly, advanced analytics allows a company to “fine-tune the mix of raw materials and finished products, as well as the routing of manufacturing flows, in real-time,” allowing an organization to make changes on a recurring, frequent basis.
Although these organizational silos worked for decades, the rise of cloud-based technologies has changed the model of thinking. The use of the cloud enables an organization to monitor manufacturing operations across a variety of settings. In the aforementioned example, cloud-based technology may be applied to help the warehouse management system, transportation management system, and ERP communicate between one another, which further assists the manufacturer in producing a more accurate forecast of consumer demand. Consequently, the use of cloud-based technologies could be applied to this example to generate a means of reorganization to current manufacturing processes to better meet consumer demands.
Modern consumers do not want the same, stale, and mundane products of yesteryear. As consumers have become increasingly aware and customized experiences with products or services have grown, manufacturers must change current processes to ensure the consumers have everything they need, at any time, and from virtually any location. As consumers become more self-aware, manufacturers must become more self-aware of their processes and capabilities in a near-stretched industry, and advanced analytics is the tool that’s needed.
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