Manufacturing companies have to focus hard on balancing innovation with operational efficiency in the current low spend climate. Technological advancements that once led internal consolidation have also spread across the global supply chain to enable methodologies like Just-In-Time delivery for those who can adapt. Rapid product development is already being accelerated by innovations like  additive manufacturing technologies that offer reproducibility at scales even larger than before allowing new entities to upstage existing establishments.

As markets mature, the design function needs to rely on extensive use of simulation methods to work through more choices and arrive at marketable products. The management function itself needs to be global and local at the same time. This is an industry-wide acknowledgement of the need to optimally use resources and skill sets where available in different regions, while also integrating with local cultures and work styles. Channel sales and direct sales in large markets, especially in emerging countries, are often decentralised with much less control on the last mile.

These challenges are all surmountable provided data is recognized as being one fundamental enabler of change, operational efficiency and quality, especially when coupled with the appropriate systems and processes.


Technology penetration has resulted in instrumentation of data collection right from the assembly line to the end customer’s interactions with products and services. The Internet-of-Things (IoT) is increasingly allowing measurability of processes, people and products at very fine granularities that was unimaginable even a few years ago. While sectors like financial services are only now waking up to the possibilities of exploiting data, manufacturing sectors, especially those of advanced high-technology specialisation, have had to deal with massive amounts of data for decades now. To truly benefit from the revolution driven by IoT, however, it becomes necessary to have a scalable end-to-end data processing pipeline that is able to collect, analyse and act on the mass of data in quick time.

Homologation of product designs and functionality across locales, managing product bill of materials complexity and quality are also becoming more data driven than before. Quality has always been measured using statistical approaches but with machine data available in troves from simulation and assemblies, product engineering can go a few steps further to carry out root cause analysis at a fine level without resorting to aggregate statistics or sampling methods.

Immediate analysis on live data streamed from deployments can allow to identify anomalous behaviours and predict failures facilitating timely maintenance thereby reducing downtime. Data-driven dashboards can be built in every stage of the manufacturing operation, allowing the management function to take decisions that drive value across the board and enable targeted sales to customers in a more personalized manner.

The Data Team can help manufacturing companies adopt data as the fundamental enabler in improving profitability, achieve operational efficiencies and drive innovation.

Our Industry View

The Internet of Things

The rapid emergence of IoT, simply put, is an outcome of the convergence of technologies, devices, and services. From RFID-like identification technologies a decade ago, to networked sensors and actuators today, innovation has been quite fast paced and widespread, but mostly focused on the specific areas of application (or within the domains at best). This has resulted in the proliferation of numerous options at every seam and every interface, without a proliferation of solutions that address da

Data and the Automotive Industry

The automotive industry is often considered a proxy for the performance of important economies, and as such, faces diverse challenges on the technological, supply chain and operational fronts. In this post, we explore some of these challenges, and the relevance of big data technologies, data science to these problems. We discuss the overarching features and themes of the industry's problems, while discussing broad data analytics solutions and approaches that could have a positive impact.