Industrial Engineering Knowledge Center: Data Analytics Period in Productivity Improvement

Wednesday, April 12, 2023

Data Analytics Period in Productivity Improvement - Productivity Engineering and Management

New. Popular E-Book on IE,

Introduction to Modern Industrial Engineering. #FREE #Download.

In 0.1% on Academia.edu. 3600+ Downloads so far.

https://academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0

Lesson 446 of Industrial Engineering ONLINE Course - Applied Industrial Engineering Module

The Wave of Productivity Driven by Data and Analytics

Analytics is a new technology, a software based technology that give us support for improved decisions in the area of productivity improvement. So there is applied industrial engineering task of using analytics for improving productivity in processes and systems.

Intelligent Asset Management for Productivity

https://www.ibm.com/resources/guides/intelligent-asset-management/

Using Industry 4.0 Technologies

Industry 4.0 has tremendous potential for productivity improvement in the world. The quantum is in trillions as per estimates and forecasts. This industrial revolution offers potential for totally new products. But its role in redesign of existing products and production systems producing them itself has great potential. Hence engineers working in new products and processes area and engineers working in productivity area have ample work and challenge.

McKinsey 2021 Report on IoT Opportunity During 2020 to 2030

Internet of Things: Sensors and actuators embedded in things connected by networks to computing systems. These systems can monitor or manage the health and actions of connected objects and machines. Connected sensors can also monitor the natural world, people, and animals apart from parts and machines.

During 2015 - 2020, the IoT has faced headwinds related to change management, cost, talent, and cybersecurity, particularly in enterprises despite potential benefits. These hurdles have prevented some organizations from scaling their IoT ambitions beyond pilots, with projects still stuck as pilots or abandoned to lack of technical and economic feasibility.

McKinsey re-estimated the benefits, the estimated impact for all participants in the economic system in more than 100 unique applications in nine settings. The estimates go beyond the pure GDP impact of IoT applications and include various forms of consumer surplus, which are not measured in GDP.

From a tailwind perspective, the verdict is clear. Customers, who are using, see real value in deploying the IoT. Compared with 2015, the technology and networks needed to implement the IoT are available and sufficient. In general, technology is not the constraining factor (Economics could still be a barrier. IEs have to work in IoT supplier companies to reduce costs and prices).

11 Clusters of use cases

Operations optimization

Human productivity

Health

Condition-based maintenance

Sales enablement

Energy management

Autonomous vehicles

Environment management

Safety and security

Product development

Inventory management

The estimates in 2021-22 were revised downward from 2015 estimates at both the low and high scenarios. The present estimates for 2025 are: $2.8 trillion to $6.3 trillion in potential economic value in 2025 versus $3.9 trillion to $11.1 trillion from the 2015 work.

5G is expected to cover about 60 percent of the global population by 2026. We estimate that by 2030, up to 90 percent of the global population will have some level of 5G coverage.

IoT systems capture lot of data and big data analysis can capture lot of additional information from the data that can be used to increase effectiveness of the product or service and productivity. Data analytics, the computer based real time data analysis based machine intelligence will play a significant role in the industry 4.0 product and production systems and other engineering systems of enterprises.

Digital Analytics - Fuel for the next 20 percent Productivity Rise. Will your company achieve it in five years or ten years?

https://www.mckinsey.com.br/en/our-insights/ops-4-0-fueling-the-next-20-percent-productivity-rise-with-digital-analytics

Four Areas for Productivity Improvement

Product Design

Manufacturing

Complete Value Chain

Planning and Budgeting Costs

Product Design

Product Part Complexity Management. Due to product proliferation, small variants in specifications generate hundreds of mostly overlapping SKUs. Current methods take vast amounts of time and effort still fall short. New digital analytics tools allowed a conglomerate to complete an analysis, in just two weeks. The company could use to reduce variations among product families, subsystems, and components.

Analytics and Automation. Data from the procurement and engineering department’s own bills of materials can be analyzed more effectively. New tools can combine thousands of records, held around the world in dozens of local languages and part-numbering structures, to find potential commonalities and opportunities to negotiate better pricing. Robotic process automation, allows software “robots” to take over tedious processes, such as collating information from disparate systems for complex forms. Technical and purchase persons can to focus on work that uses their judgment and experience. Sophisticated algorithms optimize in real time dynamically many decisions, enabling constant adjustments as conditions change.

Powerful portfolio analysis. Revenue and cost analysis of an entire product portfolio can be done periodically to adjust prices and volumes. A conglomerate found it could eliminate 15 to 80 percent of product variants within a category and reduce cost by approximately 30 percent.

Design optimzation

R&D - Productivity Improvement through Machine Learning

Data Analytics for Product Design - Product Industrial Engineering

Manufacturing

Yield, Energy, Throughput Analysis
OEE Analysis
Experiential Learning for Digital Manufacturing (Simulators for Learning)
Smart Predictive Maintenance
Supply Chain 4.0

Complete Value Chain

End to End Digitization
Robotic Process Automation

Planning and Budgeting Costs

Smart Capital Investment Decisions
Zero Based Budgeting for All activities

Industrial Engineering in the Big Data Era: Selected Papers from the Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2018, June 21–22, 2018, Nevsehir, Turkey

Fethi Calisir, Emre Cevikcan, Hatice Camgoz Akdag

Springer, 23-Jan-2019 - Technology & Engineering - 513 pages

This book gathers extended versions of the best papers presented at the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), held in Nevsehir, Turkey, on June 21-22, 2018. They reports on industrial engineering methods and applications, with a special focus on the advantages and challenges posed by Big data in this field. The book covers a wide range of topics, including decision making, optimization, supply chain management and quality control.

https://books.google.co.in/books?id=O_aEDwAAQBAJ

2016

The wave of productivity driven by data and analytics - McKinsey senior partners

"You had the wave of lean, you had the wave of outsourcing, and now we’re seeing the wave of productivity driven by data and analytics, enabling organizations to refine the way that people work together, the way that processes perform, and the way assets are productive. If you think about an oil well, for example, you’ve got more than 300 sensors downhole that are spewing out data at the rate of about a gigabit a second, in some cases." - Bill Wiseman, McKinsey senior partner

How advanced analytics can drive productivity
Podcast August 2016
Podcast transcript
http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-advanced-analytics-can-drive-productivity

How Data Analytics Increases Productivity?

Technology and innovation drive productivity. But transaction costs of new technology implementation are considerable. Innovation requires capital and labor investment incurring transaction costs to build infrastructure and grow the market.

Analytics and decision science could provide the means to reduce the transaction costs and increase innovations to improve productivity in the economy.

Analytics provide a process to monitor, measure, and benchmark performance. By integrating diverse datasets from remote monitoring (IoT), measuring, and benchmarking can be accomplished automatically and economically. Reporting by anomaly and exception could free resources and thereby reduce costs and improve productivity.

Application of analytics to monitoring energy through sensors resulted in 17% reduction in energy costs for the NJ DOT main office complex. In another application, propensity models were constructed from customer profile and behavior data to identify candidates with highest conversion rates for sending sales promotion communications. Such examples are now many, to illustrate applying analytics to reduce costs and improve operations.

Low cost cloud computing together with large, diverse, and growing datasets, from the web to internal sources, and available statistical visualization tools, have dramatically changed the cost of data analytics. The low cost of data analytics is allowing more companies to use analytics and improve the productivity.

Source: Onoly Analytics and Analytics 2 Insight - White Paper
Analytics at the Speed of Insight: Simple, Fast and Actionable Tools to Improve Productivity

-------------------------------------------------------------------------

Further Developments

Industrial Engineering through Process Mining

https://nraoiekc.blogspot.com/2020/11/industrial-engineering-through-process.html

Industrial Engineering through Digital Twins

https://nraoiekc.blogspot.com/2020/11/industrial-engineering-through-digital.html

Industrial Engineering through MES Data

https://nraoiekc.blogspot.com/2020/11/industrial-engineering-through-mes-data.html

Harvard Data Science Review
https://hdsr.mitpress.mit.edu/

2023

Stardog's Enterprise Knowledge Graph platform delivers 320% ROI

Forrester determined an ROI of 320% and total benefits of over $9.86 million over three years for the Stardog Enterprise Knowledge Graph Platform.

Read this study to learn how several customers turned their data into knowledge, completed their data analytics projects faster, saved on infrastructure costs, and unlocked new business opportunities with Stardog.

https://www.stardog.com/resources/forrester-tei/

Data science productivity tools you need to have in 2023

Data scientists across the globe process approx 2.5 quintillion bytes of data everyday. Here are some of the most useful productivity tools that help them stay on top of their demanding roles.

https://www.oslash.com/blog/data-science-productivity-tools-you-need-to-have

Workplace Productivity Analytics – The Complete Guide

https://squeezegrowth.com/workplace-productivity-analytics/

2021

Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety, and Veracity on Firm Performance*Francesco Cappa , Raffaele Oriani, Enzo Peruffo, and Ian McCarthy

J PROD INNOV MANAGEMENT, 2021;38(1):49–67

Big data can be a valuable resource when firms create and capture value from it that exceeds the costs of collecting, managing, and analyzing it (Björkdahl and Holmén, 2018; Chesbrough, Lettl, and Ritter, 2018; Lepak, Smith, and Taylor, 2007; Wamba et al., 2017).

While previous studies focused on the impact of big data on firm productivity (Müller et al., 2018; Tambe, 2014), short-term financial performance (Corte-Real et al., 2017; Wamba et al., 2017), or new product development (Johnson et al., 2017) to assess the impact of big data, we use a market-based proxy of firm performance, that is, Tobin’s Q.

Big data are central to digitalization of the busi-ness environment because it can provide informa-tion about customers’ preferences, feedback about a firm’s product and service performance, and insights about emerging trends

our study focuses in particu-lar on how private big data collected from customers through mobile applications (referred to as big data in the rest of the paper) impacts firm performance.

One estimate of the costs for the cre-ation, management, and analysis of a three-Terabyte database is approximately U.S. $ 1 million per month (CoolaData, 2014).

Data Science and Productivity Analytics

Editors: Charles, Vincent, Aparicio, Juan, Zhu, Joe (Eds.)

Table of contents (15 chapters)

Data Envelopment Analysis and Big Data: Revisit with a Faster Method Pages 1-34

Khezrimotlagh, Dariush (et al.)

Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry Pages 35-56

Dulá, José H.

An Introduction to Data Science and Its Applications Pages 57-81

Rabasa, Alex (et al.)

Identification of Congestion in DEA Pages 83-119

Mehdiloo, Mahmood (et al.)

Data Envelopment Analysis and Non-parametric Analysis Pages 121-160

Villa, Gabriel (et al.)

The Measurement of Firms’ Efficiency Using Parametric Techniques Pages 161-199

Orea, Luis

Fair Target Setting for Intermediate Products in Two-Stage Systems with Data Envelopment Analysis

Pages 201-226

An, Qingxian (et al.)

Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems Pages 227-249

Ding, Tao (et al.)

Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using PISA 2015 Pages 251-277

Cordero, Jose Manuel (et al.)

A DEA Analysis in Latin American Ports: Measuring the Performance of Guayaquil Contecon Port

Pages 279-309

Morales-Núñez, Emilio J. (et al.)

Effects of Locus of Control on Bank’s Policy—A Case Study of a Chinese State-Owned Bank

Pages 311-335

Xu, Cong (et al.)

A Data Scientific Approach to Measure Hospital Productivity Pages 337-358

Daneshvar Rouyendegh (B. Erdebilli), Babak (et al.)

Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis Pages 359-389

Yu, Anyu (et al.)

Pension Funds and Mutual Funds Performance Measurement with a New DEA (MV-DEA) Model Allowing for Missing Variables Pages 391-413

Badrizadeh, Maryam (et al.)

Sharpe Portfolio Using a Cross-Efficiency Evaluation Pages 415-439

Landete, Mercedes (et al.)

https://www.springer.com/gp/book/9783030433833

Special Issue on Data Science for Better Productivity

Data science for better productivity

Vincent Charles,Juan Aparicio &Joe Zhu

Journal of the Operational Research Society

Volume 72, 2021 - Issue 5: Special Issue Data Science for Better Productivity

Afsharian, M. (2019). A frontier-based facility location problem with a centralised view of measuring the performance of the network. Journal of the Operational Research Society, 72(5), 1058–1074. https://doi.org/10.1080/01605682.2019.1639476

Bougnol, M.-L., & Dulà, J. (2020). Improving productivity using government data: The case of US Centers for Medicare & Medicaid's ‘Nursing Home Compare. Journal of the Operational Research Society, 72(5), 1075–1086. https://doi.org/10.1080/01605682.2020.1724056

Del Vecchio, M., Kharlamov, A., Parry, G., & Pogrebna, G. (2020). Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries. Journal of the Operational Research Society, 72(5), 1110–1137. https://doi.org/10.1080/01605682.2019.1705194

Grimaldi, D., Fernandez, V., & Carrasco, C. (2019). Exploring data conditions to improve business performance. Journal of the Operational Research Society, 72(5), 1087–1098. https://doi.org/10.1080/01605682.2019.1590136

Ihrig, S., Ishizaka, A., Brech, C., & Fliedner, T. (2019). A new hybrid method for the fair assignment of productivity targets to indirect corporate processes. Journal of the Operational Research Society, 72(5), 989–1001. https://doi.org/10.1080/01605682.2019.1639477

Jiang, R., Yang, Y., Chen, Y., & Liang, L. (2019). Corporate diversification, firm productivity and resource allocation decisions: The data envelopment analysis approach. Journal of the Operational Research Society, 72(5), 1002–1014. https://doi.org/10.1080/01605682.2019.1568841

Li, Y., & Chen, W. (2019). Entropy method of constructing a combined model for improving loan default prediction: A case study in China. Journal of the Operational Research Society, 72(5), 1099–1109. https://doi.org/10.1080/01605682.2019.1702905

Lin, S.-W., Lu, W.-M., & Lin, F. (2020). Entrusting decisions to the public service pension fund: An integrated predictive model with additive network DEA approach. Journal of the Operational Research Society, 72(5), 1015–1032. https://doi.org/10.1080/01605682.2020.1718011

Routh, P., Roy, A., & Meyer, J. (2020). Estimating customer churn under competing risks. Journal of the Operational Research Society, 72(5), 1138–1155. https://doi.org/10.1080/01605682.2020.1776166

Shi, Y., Zhu, J., & Charles, V. (2020). Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations. Journal of the Operational Research Society, 72(5), 975–988. https://doi.org/10.1080/01605682.2020.1860661

Summerfield, N. S., Deokar, A. V., Xu, M., & Zhu, W. (2020). Should drivers cooperate? Performance evaluation of cooperative navigation on simulated road networks using network DEA. Journal of the Operational Research Society, 72(5), 1042–1057. https://doi.org/10.1080/01605682.2019.1700766

Zhu, J. (2020). DEA under big data: Data enabled analytics and network data envelopment analysis. Annals of Operations Research, 1–23. In press. https://doi.org/10.1007/s10479-020-03668-8

Zhu, W., Liu, B., Lu, Z., & Yu, Y. (2020). A DEALG methodology for prediction of effective customers of internet financial loan products. Journal of the Operational Research Society, 72(5), 1033–1041. https://doi.org/10.1080/01605682.2019.1700188 [Taylor & Francis On

https://www.tandfonline.com/doi/full/10.1080/01605682.2021.1892466

A Balanced Perspective on Prediction and Inference for Data Science in Industry

by Nathan Sanders
https://hdsr.mitpress.mit.edu/pub/a7gxkn0a

Reporting, analytics, harmonization, productivity and data access solutions for the SAP®-run enterprise.
Realize the operational efficiencies of your SAP® environment.

With a focus on enabling you to unify data from multiple applications and sources, delivering insights for critical – and trusted – business decisions, and extending the value of your infrastructure investments, Magnitude and our subsidiaries offer data management and productivity solutions for SAP-run businesses.
https://magnitude.com/solutions/sap-solutions/

Email Analytics
https://emailanalytics.com/21-productivity-analytics-tools-every-business-needs-to-be-using/

2020

Productivity of Aircraft increased on the basis of analytics

Lockheed Martin
Keeping aircraft ready for critical mission

ARTIFICIAL INTELLIGENCE FROM SAS HELPS TURN 35 DAYS OF DOWNTIME INTO 35 DAYS OF FLYING TIME – PER PLANE.

600 sensors on each C-130J

72,000 rows of data per flight hour

95% reduction in data cleanup time
With customer data streaming in, the SAS Platform enables Lockheed Martin to go from reactive to predictive and give its customers confidence through intelligent diagnostics.
https://www.sas.com/en_us/company-information/discover/lockheed-martin.html

Industry 4.0 based process data analytics platform: A waste-to-energy plant case study
James Clovis Kabugoa Sirkka-LiisaJäms ä-Jounela Robert Schiemann Christian Binder
Power & Energy Systems
Volume 115, February 2020, 105508
Open Access Paper
https://www.sciencedirect.com/science/article/pii/S0142061518336731

January 2020

Data sharing in the supply chain

https://www.bcg.com/publications/2020/manufacturers-unlock-value-from-data-sharing

2019

Mobilaris Productivity Analytics

Tunneling Productivity
https://mobilaris.se/mining-civil-engineering/Improvements-by-analytics-tunneling/

SEPTEMBER 17, 2019
Accenture Leverages Data Analytics Solutions to Help Deutsche Bahn Cargo and Mars Inc. Increase Productivity and Reduce Risk

New Splunk-based solutions help clients grow revenue and optimize operations
https://newsroom.accenture.com/news/accenture-leverages-data-analytics-solutions-to-help-deutsche-bahn-cargo-and-mars-inc-increase-productivity-and-reduce-risk.htm

Aug 11, 2019
10 Ways Machine Learning Is Revolutionizing Manufacturing In 2019
https://www.forbes.com/sites/louiscolumbus/2019/08/11/10-ways-machine-learning-is-revolutionizing-manufacturing-in-2019

Exponential Productivity: Why IRPA Is Here To Stay

IRPA: Intelligent Robotic Process Automation
February 27, 2019
Hosted by Bonnie D. Graham
https://www.voiceamerica.com/episode/113504/exponential-productivity-why-irpa-is-here-to-stay

https://blog-sap.com/analytics/2019/05/02/exponential-productivity-why-irpa-is-here-to-stay/

Exploiting Big Data and analytics to improve productivity in manufacturing
By TIM LAWRENCE, GLOBAL HEAD OF MANUFACTURING, PA CONSULTING . Feb 22, 2019,
https://www.manufacturingglobal.com/technology/exploiting-big-data-and-analytics-improve-productivity-manufacturing

How is Data Science Used in Manufacturing Companies?
May 30, 2019 | Manufacturing Analytics
https://www.sensrtrx.com/how-is-data-science-used-in-manufacturing-companies/

Prediction of Productivity and Energy Consumption in a Consteel Furnace Using Data-Science Models
International Conference on Business Information Systems
BIS 2019: Business Information Systems pp 85-99
https://link.springer.com/chapter/10.1007/978-3-030-20485-3_7

WeldCloud - Esab

WeldCloud is an online management system that connects welding power supplies to a software platform that manages data to be analyzed for maximum productivity.
https://nraoiekc.blogspot.com/2019/01/productivity-drivers-productivity.html

Smart welding – a practical research project
https://www.hera.org.nz/smart-welding/

2018

Data analytics and expert insights increase paper mill productivity
ABB Ability™ Collaborative Operations helps leading paper producer in Indonesia to optimize mill operations
https://new.abb.com/pulp-paper/abb-in-pulp-and-paper/articles/previous/data-analytics-and-expert-insights-increase-paper-mill-productivity

Main facts
Customer Major paper mill in East Java
Country Indonesia
Customer need: Automate production: Identify and address production, quality and cost issues to achieve better consistency, fewer rejects and more sales
Solution ABB Ability™ Collaborative Operations

Benefits:
Reduced cycle times on product grade changes
Stabilized moisture and other additive levels
Higher equipment availability, fewer sheet breaks
Lower chemical costs
Reduced paper quality variation
Improved return on capital
Year: 2018

Improve Employee Productivity With Internal Analytics
John Choi, Jul 27, 2018
https://www.cmswire.com/digital-workplace/improve-employee-productivity-with-internal-analytics/

Uncovering cost savings hidden in administrative and operational data

Analyzing benchmarking data helped the company to save $26 million in a rational manner and maintain its competitiveness.
By Sally Akers, RN, MSN, CNS and Mike Chamberlain - April 26, 2018
https://www.ibm.com/blogs/watson-health/uncovering-cost-savings-hidden-administrative-operational-data/

2017

Ops 4.0: Fueling the next 20 percent productivity rise with digital analytics
By Mercedes Goenaga, Philipp Radtke, Kevin Speicher, and Rafael Westinner
Article April 2017
http://www.mckinsey.com/business-functions/operations/our-insights/ops-4-0-fueling-the-next-20-percent-productivity-rise-with-digital-analytics

Be agile, Be focused on results and Take up manageable data analytics projects
https://www.bcg.com/publications/2017/digital-transformation-transformation-data-driven-transformation.aspx

Digital transformation-driven productivity
Don't underestimate the incredible power of DX turned inward, toward enhancing internal organizational productivity.
http://www.cio.com/article/3200807/leadership-management/digital-transformation-driven-productivity.html

Digital Transformation Can Resolve the Productivity Paradox
EITN MALAYSIA, April 27, 2017
By Hu Yoshida, Chief Technology Officer, Hitachi Data Systems
http://www.enterpriseitnews.com.my/digital-transformation-can-resolve-the-productivity-paradox/

How digital transformation improves productivity in manufacturing
By Alex Walker and Arnd Simon, Senior Directors, Industry Services, Global Manufacturing Practice, Microsoft Corporation; Contributor: Dr. Lei Liu, Digital Advisor, Microsoft Deutschland GmbH on May 5, 2017
https://enterprise.microsoft.com/en-us/articles/industries/discrete-manufacturing/digital-transformation-improves-productivity-manufacturing/

Increasing Chemical Industry Productivity through Digital Transformation

May 12, 2017 | Showcase
Learn how Capgemini's ChemPath SAP – Certified Business All-in-One (BAIO) solution, will help your organization streamline and synchronize the operations, while achieving greater visibility and control over your core business processes.
https://www.capgemini.com/resources/video/increasing-chemical-industry-productivity-through-digital-transformation

IoT use cases with 2 year pay back period in global auto company mentioned.
https://www.linkedin.com/pulse/chinas-factories-want-digitize-heres-what-need-do-karel-eloot

Microsoft Workplace Analytics helps managers understand worker productivity
https://techcrunch.com/2017/07/05/microsoft-workplace-analytics-helps-managers-understand-worker-productivity/

Forecasting demand with limited information using Gradient Tree Boosting

Stephan Chang and Felipe Meneguzzi
Copyright
c 2017, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/download/15508/14934

Related article to above
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
by Jason Brownlee on September 9, 2016 in XGBoost
Last Updated on August 21, 2019
https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/

2016

EXPLORING THE POTENTIAL OF DATA DRIVEN AGRICULTURE IN INCREASING FARM PRODUCTIVITY AND PROFITABILITY

November 2016

http://www.icrisat.org/exploring-the-potential-of-data-driven-agriculture-in-increasing-farm-productivity-and-profitability/

Smart data is the way to boost mining productivity

2 September 2016
https://home.kpmg.com/xx/en/home/insights/2016/09/smart-data-way-boost-mining-productivity.html

Improving dragline productivity and increasing reliability using big data

August 2016

Mining operations produce an enormous amount of data through numerous parallel, though diverse, monitoring systems. Data mining and analytics can be a major part of a successful mining improvement process.

In this case, the goal is to find a single target variable and its value that will drive operator behaviour to operate the dragline at maximum production capacity and speed while not exceeding machine fatigue.
https://www.ausimmbulletin.com/feature/improving-dragline-productivity-and-increasing-reliability-using-big-data/

A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
by Jason Brownlee on September 9, 2016 in XGBoost
Last Updated on August 21, 2019
https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/

2015

Analytics 4.0: The Scary Ago Of Automated Networks
Tom Davenport
December 31, 2015
https://www.iianalytics.com/blog/2015/12/31/analytics-40-the-scary-ago-of-automated-networks

Analytics 3.0
Thomas H. Davenport
FROM THE DECEMBER 2013 ISSUE of HBR
https://hbr.org/2013/12/analytics-30

Production Data Analytics – To identify productivity potentials
Master Thesis - 2015
MUKUND SUBRAMANIYAN
Department of Product and Production Development
CHALMERS UNIVERSITY OF TECHNOLOGY, Gothenburg, Sweden - 2015
http://publications.lib.chalmers.se/records/fulltext/225149/225149.pdf

Forecasting demand with limited information using Gradient Tree Boosting

Stephan Chang and Felipe Meneguzzi
Copyright
c 2015, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Mentioned by Mr. Saikat Chakraborty, NITIE IE Conclave 15 October 2019)

2013

Analytics 3.0
Thomas H. Davenport
FROM THE DECEMBER 2013 ISSUE of HBR
https://hbr.org/2013/12/analytics-30

Data Analytics and Continuous Productivity
September 18, 2013
https://www.tibco.com/blog/2013/09/18/data-analytics-and-continuous-productivity/

July 2013

Big-data analytics as a productivity tool McKinsey Global Institute

Sectors across the economy can harness the deluge of data generated by transactions, medical and legal records, videos, and social technologies, sensors, cameras, bar codes, and transmitters embedded in the world around us. Advances in computing and analytics can transform this sea of data into insights that create operational efficiencies.

By 2020, the wider adoption of big-data analytics could increase annual GDP in retailing and manufacturing by up to $325 billion.

Additionally as much as $285 billion savings can occur in the cost of health care and government services.

http://www.mckinsey.com/global-themes/americas/us-game-changers

2011

MIT professor Erik Brynjolfsson discusses how companies can increase their productivity by making better use of their data.
by David Talbot February 16, 2011
https://www.technologyreview.com/s/422753/using-it-to-drive-innovation/

2009
MIT professor Erik Brynjolfsson
Book Chapter: Business practices that enhance productivity along with IT investments.
https://books.google.co.in/books?id=WBYeChNzVo8C&pg=PA61#v=onepage&q&f=false

Google Books

20 March 2020, 28 October 2019, 16 October 2019, 13 October 2019, 17 August 2019, 4 August 2019, 18 July 2019, 23 August 2017, 16 August 2017, 10 July 2017, 30 June 2017, 22 June 2017