Mining and data in 2017 Pt 2
Yesterday we heard from Ashley Bosworth, Director of Analytics and Innovation at Pulse Mining on the year ahead for the mining industry, today we bring you part two - Farnaz Erfan, Senior Director of Product Strategy at Birst
What do you think will be the biggest change in the mining industry in 2017?
With expanding production of certain major commodities over the past decade, and a collapse in mining profitability over the past three years, areas such as efficiency and productivity are now considered a major priority. This means more concentration on the impact of decisions and more use of data across the production cycle.
In mining, data-driven decisions reach all areas under management: from invested capital, equipment and materials, to labour, the production processes for operating the mine, and the spending on goods and services.
What do you see happening in the Internet of Things, and how will this affect the mining sector?
The mining sector has already used sensors on devices and assets to provide updates on status. The opportunity here is to bring this data into a wider context. The Internet of Things pushes this concept further, providing more insight into how those devices perform over time. This can then be used for predictive maintenance and to reduce problems like downtime.
For example, companies like Pulse Mining create applications that target specific mining pain points based on Birst. Using data from 300 sensors that capture and transmit information every second, this application helps monitor mine operations in real-time, enabling firms to drive millions worth of additional production. In one instance, there has seen a 3.2 percent increase in operating rate and an increase from $2.5 million to $3 million per year in the value of tonnes produced.
What role will analytics play in companies during 2017?
Looking at the Ernst and Young report on the mining sector for 2016-2017, cash optimization, capital access, and productivity are the most important areas for companies globally.
Linking up data from finance, with the production and operations departments will therefore be important to boost operational cash flow for long-term profitability. This includes using data to identify areas of cost reduction such as supplier consolidation across different regions or countries. The same goes for using data to increase the life of working capital. Predictive maintenance can identify the lifetime and sustainability of assets and move companies towards replacing their equipment on a need-base instead of a calendar-base routine, extracting more value from existing assets to present opportunities of savings.
To make this happen, mining companies will have to network their different teams of operations, finance, repair, production, and supply chain together to promote information sharing and impact analysis.
Will more employees in the mining sector start using data in their daily lives?
I think so. When margins are tight, it’s more important that each decision made is based on relative data. Providing more context around the decision is a good first step, followed by greater decision support and automation of recommendations based on data.
Additionally, the use of analytics supports companies where there has been a loss of experience out of the industry. Many experienced professionals have retired, and their knowledge has gone with them. While they might have naturally understood the best approaches to a particular environment, their successors don’t have those nuances. Using data analytics, that expertise and production efficiency can be embedded into everyday processes for the business to benefit from.
How close to the “coal face” will data get, and how much difference will it make to day-to-day activities?
Pretty close. We have seen this uptake with leading mining companies. Analytics for the mining industry has potential that is already being realised in other market sectors. The efficiency of resources, staffing, time, material and production processes can have a direct impact on profitability. Using big data, IoT and networked analytics, it’s possible to make decisions that lead to higher profitability and better business performance.
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