How To Leverage BigData & Analytics For Competitive Advantage
In the last two years, both the volume and number of data sources have increased by unprecedented rates. From combining statistics and other mathematical tools to business data in order to assess and improve practices to taking a deep dive into historical process data to identify patterns and relationships among discrete process steps and inputs to optimize the factors that prove to have the greatest effect on yield. Emerging technology and the availability of bigdata sources offers manufacturing industry new ways to optimize both their upstream and downstream.
How to best harness and utilize technology and bigdata to achieve higher top line, lower bottom line, larger market share, process improvements, minimizing inventory, continuous innovation, customer success, higher brand value, and new business opportunities, is not always clear. So how can manufacturing industry leverage technology and data sources to maximize their impact?
Asking the right questions
Industry 3.0 allowed manufacturing industry generate data (not necessarily digitized) at every step but unfortunately is worth nothing unless gathered, combined and shared for a particular purpose. That’s why the first step to harnessing the power of data is learning how to ask right questions, with clearly defined goals. Refining the questions is critical, as they become the key drivers of the data collection and analysis process.
For example, for a customer service manager overseeing more than 400 distribution channels in India, the initial question might be, “How is my equipment performing?” which would be refined to, “In which areas of productivity am I failing the most, as of one month ago, and which specific types of equipment are contributing the most to my non-performance?” The answer to the second question, if available in real-time at the manager’s fingertips, is more helpful for effective decision making.
Engineering easy to use dashboards as answers to key questions
Once we have the questions identified, the task of engineering teams (software) is to create and easy to use dashboard, which provides the business intelligence in real-time to make better, more informed and timely decisions. We believe that “Organizations don’t need more data, but answered the key questions in a structured manner – and they need them quickly.”
Adopting context-appropriate, low-grade technology for data collection
For manufacturers whose manufactured products/equipment’s work in areas that often lack regular access to electricity, internet, or even mobile network coverage, relying on “high tech” data collection methods are often impractical.
For nearly a decade, monitoring of the production and machine/equipment performance was a manual process. Although the collected data was potentially useful, a majority of it never got digitized leading to paperwork bottleneck of years’ worth of data.
The solution for these manufacturers was to collect the data from the field (machine usage, performance data capturing using embedded devices) in digital format (shifting to tools like SharePoint instead of using excel for logging data) for key performance indicators.
The collected data using complex analytics algorithms can help manufacturers predict wear and tear of the equipment under different usage / environmental conditions – helping them plan maintenance prior to failure, increasing productivity.
Sharing of data across departments to maximize impact
For years, the quality assurance, sales and customer service departments have been capturing multiple parameters from the field by multiple offices for analysis and report generation. The major challenge faced by all was common, “The data was difficult to get, and it was always in different formats. One person would prefer keeping it in their Google Drive, while another person would have the data on their thumb drive, or even in an email.” These departments are not the only ones struggling with this problem: Many are duplicating their data because they didn’t know it already existed, while some used outdated data when fresher real-time sources are available. To solve this problem, engineering an open platform in which entities can upload and download data sets in a standard, usable format is need of the hour.