Disconnected Operations is a well-known and critical issue in the Manufacturing Industry. Over the last decade, the industry has spent to the tune of USD 200+ Billion each year on IT and OT assets. These would include Enterprise systems such as ERP and CRM at the top layer, all the way to automation systems on the shop floor generating 100,000+ time-series data streams from the sensors and actuators. And, the 25 to 50 odd plant-floor applications commonly known as MES (Manufacturing Execution System) that lie in between these two layers. Data accumulated in these silos over the last 10+ years is an untapped gold mine which is hitherto largely unutilized. Needless to say that new data from IoT sensors, usually replacing and complementing various manual inspection rounds to start with, are also rapidly adding to the data pile.

​In the real world, there is no segregation between IT, OT, and IoT as far as data is concerned. For example, a typical motor driving a pump has associated data such as rotational speed, winding temperatures, bearing vibration, lubrication oil sample analysis, engineering data, maintenance work orders, spares, financial information (purchase price, book value, etcetera.). While all these datasets describe one single physical object, they are segregated into automation, MES, and ERP data silos. ​​These multitudes of disparate systems from multiple vendors plague the industry with inefficiencies that lead to numerous operational blind spots and in turn, significant losses and accidents. Manufacturers have woken up to this challenge and now understand that leveraging these datasets through advanced analytics is the next frontier of value creation and competitive advantage. Operationalizing this data is the key to strategic and tactical operational excellence, lowering costs, curbing losses & inefficiencies, and improving safety. However, attempts to leverage these data through various Digital Transformation initiatives resulted mostly in failures and limited success at best. Most of these projects are long-drawn, costly, and complex. They are misaligned to business requirements and are leading to a newly emerging problem – “disparate analytics,” i.e., an additional layer on the silos. ​​

dDriven’s mission is to tackle this issue of Disconnected Manufacturing most feasibly and efficiently. The dDriven approach to Digitalization & Industrial Analytics, embodied in their digital manufacturing platform UNLSH, ensures a drastic reduction in time-to-value and therefore, risk. The approach intrinsically ensures cross-functional cognition of the business impact of any analytics. It accounts for the fact that all physical and non-physical entities are interlinked in any business or manufacturing operation. For example, in a petroleum refinery, a physical entity such as a pump may affect the inventory in a tank, thus affecting the working capital, berth utilization, demurrage paid, KPIs such as “Quality Giveaway”, etcetera. The impact of a pump failure ripples all the way up to the staffs’ and companies’ performances and rewards.​​​Manufacturing operations is “real-time”. There lies a lot of opportunity cost between knowing things “now” and later as “post-mortem” analytics. Therefore, a critical and most significant differentiation UNLSH delivers is that it ensures stakeholders have the analytical oversight, insight, and foresight within the opportunity window. This approach, and hence UNLSH, was purpose-built to tackle the issue of Disconnected Manufacturing. It is a major paradigm shift from the siloed and post-mortem nature of analytics of the so-called legacy digital transformation projects of today. It is essentially the manifestation of the foundational pillars & critical success factors associated with any large-scale Digital Transformation & Industrial Analytics deployment. We are at an early day of Digital Transformation as far as industry is concerned. They are sitting on large piles of siloed data. Most of the efforts are focussed on “solutioning” without solving the fundamentals at the ground level, leading to an emerging problem mentioned earlier – disparate analytics. The market need is enormous, going by any estimations. dDriven is solving it bottom-up, i.e., fundamentals first.

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