In this new insideBIGDATA Guide to Manufacturing, the goal is to provide enterprise thought leaders in the manufacturing sector ways to obtain greater value from their company’s data assets through use of big data technology.
This article is the first in a series that explores the benefits that manufacturing firms can achieve by adopting big data technologies. The complete insideBIGDATA Guide to Manufacturing is available for download from the insideBIGDATA White Paper Library.
Big Data for Manufacturing – An Overview
Manufacturing concerns consistently have sought ways to reduce waste and variability in their production processes to dramatically improve product quality and yield (e.g. the amount of output per unit of input). Further, these companies need a granular approach toward recognizing and correcting manufacturing process flaws. Big data technology provides just such an approach and many high tier manufacturers possess a significant degree of interest and motivation in adopting the big data technology stack.
Big data analytics refers to the application of tools based on principles of computer science, statistics, data mining algorithms and mathematics to enterprise data assets with the fundamental goal to assess and improve business practices. In manufacturing, operations managers can use big data to drill down into historical process data, discover previously unidentified knowledge among discrete process steps and inputs, and then optimize the factors that are shown to have the greatest effect on yield. Many manufacturers across a broad range of industries now have an abundance of real-time shop floor data and the capability to conduct sophisticated statistical learning assessments. They are taking previously siloed data sets, aggregating and joining before analyzing them to reveal key insights.
Even when considering manufacturing operations that are thought to be best in class, the use of big data may reveal further opportunities to increase yield above industry benchmarks. In addition, companies can reduce their waste of raw materials, reduce energy costs and thus increase profitability by rigorously assessing production data, all without having to make additional capital investments or implementing major change initiatives.
The essential first step for manufacturers that want to use big data to increase yield is to consider how much data the enterprise has at its disposal. Most manufacturers collect vast troves of process data but typically use them only for tracking purposes, not as a basis for improving operations. The challenge is for these players to invest in the systems and skill sets that will allow them to enhance their use of existing process statistics. For example, it might be prudent to index information from multiple sources so it can be analyzed more easily and hire data scientists who are trained in identifying patterns and drawing actionable business insights from the data.
Some manufacturers, particularly those with lengthy production cycles, have too little data to be statistically meaningful when put under a data scientist’s magnifying glass. The challenge for thought leaders at these companies will be taking a long-term focus and investing in systems and practices to collect more data. They can invest incrementally, e.g. gathering information about one particularly important or particularly complex process step within the larger chain of events, and then applying sophisticated analytics to that part of the process.
Big data is becoming a strong competitive advantage in the manufacturing industry. In fact, big data savvy is nearly a necessity these days because many companies feel if they don’t adopt a data-centric business strategy it could affect their competitive landscape in the next 3-5 years. As a result, manufacturing businesses, across a wide range of categories and verticals, are exploring ways to adopt big data. Here are just a few areas of interest for big data under the manufacturer’s prism:
- Manufacturers are seeing a higher degree of visibility into supplier quality levels, and improved accuracy in predicting supplier performance over time. Using big data, 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.
- 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 big data, 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.
- 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. Big data 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.
- Quantify how daily production impacts financial performance with visibility to the machine level. Big data is 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.
For an important perspective on this new found awareness, the “Industrial Internet Insights Report for 2015” sponsored by GE and Accenture considers the rise of the “Industrial Internet,” the combination of big data analytics with the Internet of Things (IoT), and how it is producing huge opportunities for manufacturers. The report includes a number of telling highlights for how manufacturers see big data affecting their bottom lines:
- 67% of manufacturers report strong board level support as primary influencer for big data initiatives.
- 87% of manufacturers report big data is one of the top three priorities.
- By introducing big data analytics and more flexible production techniques, manufacturers could boost their productivity by as much as 30%.
- 70% of manufacturers report interest in using big data to “gain insights into customer behaviors, preferences and trends”—more than any other industry.
The goal for this Guide is to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.
Over the next few weeks we will explore these manufacturing topics:
- Big Data for Manufacturing – An Overview
- Common Big Data Pain Points for Manufacturers
- Big Data Technology for Manufacturing
- Adopting Big Data for Manufacturing
- Case Study: Omneo
If you prefer the complete insideBIGDATA Guide to Manufacturing is available for download in PDF from the insideBIGDATA White Paper Library, courtesy of Dell and Intel.
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