What Data Analytics Really Means to the Industrial Sector

It goes beyond just positive trends and their causes in driving ROI, value and efficiency.

Io T

The fields of data analytics, science and engineering have proven a transformative force for businesses over the last few decades. Data is a complex and diverse medium that encapsulates everything from sales and customer data to information collected through IoT sensors. Scalable IT systems and powerful database infrastructure have ushered in the age of Big Data - the trend and practice of collecting colossal datasets for complex analysis and insight.

In a 2021 study by NewVantage Partners, a remarkable 99 percent of surveyed businesses cited that they were making efforts to invest in data and AI. Research by Statista found that Big Data investment is 10x higher now than it was 10 years ago in 2011, and is expected to push $100 billion by 2026.

Businesses can use data to analyze almost any internal or external process. From manufacturing to finance, sales to marketing and advertising, data intersects with almost every department in every sector and industry. But despite these eye catching headlines and significant investment, data remains quite an elusive force for most.

So how is data being used in the manufacturing industry right now?

Energy Efficiency and Process Refinement

The manufacturing industry is heavily process-based. Energy efficiency is often critically important and R&D investment is poured into refining processes to make them more efficient and cheaper. This has only intensified as manufacturing industries attempt to move towards greener energy sources. The International Renewable Energy Agency reported that renewables could constitute some 27 percent of all manufacturing-related power consumption by 2030.

Some governments already offer forms of tax relief to businesses that invest in cutting their energy consumption. This provides yet another incentive for businesses to take process refinement and energy efficiency more seriously than ever.

A research project conducted by Towards Data Science used data analytics to tweak the efficiency of HVAC air-conditioning units in factories. HVAC air conditioning accounts for some 50 percent of a building’s energy consumption and 10 percent of all global electricity usage. By measuring and analyzing data from their air-con units, the company used a reinforcement learning algorithm (RL) to optimize their air-con for 25 percent energy savings across the boards.

Manufacturing data can be similarly analyzed to uncover anomalies or inconsistencies that drive process refinement. For example, machines might operate best at specific temperatures, chemical processes could be accelerated by slightly tweaking intervals and workforce efficiency might be highest at certain times of the day. Data analytics is about uncovering these sorts of positive trends and analyzing their causes so they can be replicated to drive ROI, value and efficiency.

Data in IoT Systems

The Internet of Things is a collection of sensors and data platforms that feed data into central networks. The IoT networks physical objects - ‘things’ - together with the internet. With IoT systems, data from various disparate sources can be centralized for analysis and tracking. Perhaps one of the most prolific industrial uses of IoT is in the supply chain.

A report by Supply Chain 247 found that IoT implementation reduced supply chain labor costs by 30 percent whilst increasing the speed and efficiency of the industrial supply chain also by 30 percent. Data can be used to identify and plug supply chain gaps, optimizing increasing factory uptime and reducing supply chain issues. Deliveries can be made on-time and logistical costs can be cut. This frees up resources that can be redirected elsewhere in the business.

AI-Powered Analytics

One of the key problems with Big Data frameworks is that businesses may invest in data induction and collection techniques without being able to make use of their collected data. They then end up inundated with data that can’t be analyzed - a frustrating situation. Data analysis is also time-consuming and expensive. AI platforms have been developed to make light work of heavy data sets - AI is proficient at working with Big Data with speed and accuracy.

AI-powered data analytics platforms like Avora are able to work with huge datasets, analyzing data automatically to provide AI-driven insights, graphs and reports. This takes the legwork out of the data analysis process and provides businesses with the information that really matters for enhanced strategy, planning and management.

Implementing data systems in your industry will often take some creativity and pragmatism. Identifying areas of obvious inefficiency is often the first port-of-call. Data solutions start with a business problem - data is then prescribed as a means to solve that problem.


Sources

Towards Data Science, Reinforcement Learning based Energy Optimization in Factories, 2019: https://towardsdatascience.com/tagged/energy-efficiency

International Renewable Energy Agency, Renewable Energy in Manufacturing, 2014: https://www.irena.org/publications/2014/Jun/Renewable-Energy-in-Manufacturing

Statista, Forecast revenue big data market worldwide 2011-2027, 2021: https://www.statista.com/statistics/254266/global-big-data-market-forecast/

NewVantage Partners, Big Data and AI Executive Survey 2021, 2021: https://c6abb8db-514c-4f5b-b5a1-fc710f1e464e.filesusr.com/ugd/e5361a_76709448ddc6490981f0cbea42d51508.pdf

Supply Chain 247, How the Internet of Things Is Improving Transportation and Logistics, 2015: http://www.supplychain247.com/article/how_the_internet_of_things_is_improving_transportation_and_logistics/zebra_technologies

Avora, 2021: https://avora.com/

 

 

 

 

 

 

 

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