Quality Analytics in a Big Data supply chain: Commodity data analytics for quality engineering

摘要

While the world is experiencing a global shortage of natural resources, a new one in the form of Digital Data has emerged! The ability to harness this new resource has become a renewed basis for competitive advantage where leveraging Big Data effectively means winning in the marketplace. It is going to transform industries and professions around the world. However, traditional data management techniques and analytical methodologies that has taken us from the late 20th century and into the early 21st century are not sustainable in today's business environment where organizations are constantly being challenged to right size the work force, increase labor productivity, increase customer satisfaction and at the same time improving product quality and reliability. Business decision making processes today are also overwhelmed by massive amount of information where the realistic situation has gone beyond the natural cognitive ability of humans to cope. However, by embracing and effectively leveraging big data and analytical techniques, we can create unprecedented value that can significantly help achieve improved operational efficiency, gain competitive advantages over business rivals, generate or increase new revenue stream, deliver cost reductions and drive agile decision making from predictive insights. This paper discusses how organizations can investigate and implement such techniques for their modern enterprise with focus on how advanced big data tools can be applied to Quality Analytics for monitoring and improving quality in an electronic industry.