Effective FuelManagement for theIndustrial Fleet: A Cognitive Approach

Heavy industries across the world are at an inflection point, competing against low demand, declining commodity prices, and limited budgets for capital projects. Industry players are left with no choice but to seek out smart and cost effective ways for running operations, while gaining a competitive advantage. Asset-intensive industries, such as mining, construction, manufacturing and logistics, are inherently reliant on heavy industrial eets that run on diesel. Consumption of fossil fuel such as this puts signicant pressure on OPEX. Moreover, health, safety and environmental (HSE) regulations, irrespective of industries, are becoming increasingly stringent, putting diesel-powered vehicles on the regulatory watch list. The paper will try to explore possibilities for reducing diesel consumption using various cognitive levers and an integrated end-to-end approach that aims to optimizing equipment operations as well as reducing instances of pilferage and carbon footprint.


Introduction

For heavy industries, ‘assets’ mostly qualify as high-priced industrial equipment, also known as heavy earth moving machinery (HEMM). These machineries, which include haul trucks or dumpers, loaders, dozers and lifting, and fixing equipment, are intrinsic part of daily operations. Industries rely on efficiency and effective utilization of these assets to achieve higher productivity and lower operating cost (OPEX). Here, a majority of equipment (such as dumper trucks and loaders) run on diesel, and the remaining run on electricity (such as draglines for surface mining, and ‘longwall’ and ‘continuous miners’ used for underground mining operations). Increased fuel consumption and the subsequent OPEX pressure is a major concern for heavy industries. In fact, it can be a serious growth inhibitor for a sector already struggling with mounting costs and environmental concerns. Diesel powered vehicle causes more environmental pollutions, by emitting greenhouse gasses. The impact increases for aging vehicles. We have noticed that per regulatory mandates, diesel vehicles that are 10 years or older cannot be used in a number of metro cities. Although some fuel management solution providers operating at a limited scale, the real opportunity lies in OPEX reduction through fuel optimization. Most of the prevailing products or solutions are for the petro retail space, and are not suitable for managing complex heavy fleets typically found in mining, construction, or manufacturing industries. Moreover, existing solutions do not provide an integrated end-to-end view of fuel consumption for decision support. Smart Fleet Management System Imperatives Traditional fleet management systems (FMS) leverage various external sensors (GPS, payload on wheels, RFID tag, engine temperature, tire pressure, and more.) for tracking equipment and its health condition in real-time. These systems however often fail to provide insights into fuel consumption with reference to operating conditions and the operator’s performance. But times are changing.



Off late, the majority of the industrial equipment come embedded with various built-in sensors provided by original equipment manufacturers (OEMs), including various fuel level parameters. These sensors generate streams of data, but not all of it is used to extract actionable insights. Data alone can’t serve as decision support system, as there is a need to:

Tap required sensor data

Decide periodicity or frequency n Filter data (outliers and anomalies)

Correlate sensor data through analytics to interpret fuel consumption

Provide decision support system for fuel optimization


Embracing a Cognitive Approach for Fuel Optimization


The Internet of Things (IoT) unfolds endless possibilities for effective asset management in a connected ecosystem. Cloud on the other hand provides a platform to host and analyze historical data. Enterprises can look towards crafting a fully connected FMS by leveraging: IoT – A network of physical devices comprising sensors, actuators and connectivity to transfer data, which can be accessed through the internet. These sensors can capture realtime data from heavy equipment (for example, GPS, hour meter, speedometer, engine temperature, driver’s behaviour, payload, fuel level, and more). While IoT devices can transfer data over a wide range of network (ZigBee, Wi-Fi, 3G/4G, LTE, and so on), actuators are reactive components, which can enable assets to operate accordingly. Cloud – for collecting, filtering, correlating, and analyzing sensor data. Cognitive command centre – for gaining real-time view into operations and decision support.

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