Showing posts with label IE Concepts. Show all posts
Showing posts with label IE Concepts. Show all posts

Wednesday, October 21, 2026

Data Analytics Period in Productivity Improvement - Productivity Industrial Engineering and Management

New. Popular E-Book on IE,

Introduction to Modern Industrial Engineering.  #FREE #Download.

In 0.5% on Academia.edu for the year 2024. 15.000+ Downloads/Views  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


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? 


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


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Further Developments




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.

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


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). 



© 2020

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

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


https://books.google.co.in/books?id=G2keDQAAQBAJ

HBR Top 10 on AI
https://books.google.co.in/books?id=ckBbDwAAQBAJ

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

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

Machining developments 2019
https://books.google.co.in/books?id=WTarDwAAQBAJ


https://books.google.co.in/books?id=0pY4DwAAQBAJ

https://books.google.co.in/books?id=4GHUxQEACAAJ

https://books.google.co.in/books?id=Qct2DwAAQBAJ



https://books.google.co.in/books?id=5FNwBQAAQBAJ


https://books.google.co.in/books?id=Q-OOBAAAQBAJ

https://books.google.co.in/books?id=5jqkBgAAQBAJ

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

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




Updated 12.4.2023,   10.3.2023, 4.5.2022, 11 Aug 2021, 16 July 2021


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


Needs update


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Saturday, September 5, 2026

Industrial Engineering - Foundation of Toyota Production System

New. Popular E-Book on IE,

Introduction to Modern Industrial Engineering.  #FREE #Download.

In 0.1% on Academia.edu. 12750 Downloads so far.

https://academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0



Lesson 40 - Taiichi Ohno on Industrial Engineering. Toyota style Industrial Engineering - Waste Elimination 


Video Presentation by Prof. N.Rao
____________________


https://www.youtube.com/watch?v=xUDpFpNKuKQ
____________________




Taiichi Ohno repeats what Taylor said. Improve every element of an operation/process for total cost reduction.


Improve machining processes,  install autonomous systems, improve tools,  rearrange machines,  improve  transportation methods. Examine available resources and  the materials at hand for manufacturing. optimize their use.

Prevent the recurrence of defective products, operational mistakes, and accidents, and by incorporate  workers' ideas."  


Toyota Industrial Engineering that is Ohno's Industrial Engineering is improving every element of the process and reducing every delay, defect and machine breakdown (Naryana Rao)


Toyota style Industrial Engineering - Ohno


"We have eliminated waste by examining available resources, rearranging machines, improving machining processes, installing autonomous systems, improving tools, analyzing transportation methods and optimizing the materials at hand for manufacturing. High production efficiency has also been maintained by preventing the recurrence of defective products, operational mistakes, and accidents, and by incorporating workers' ideas." Taiichi Ohno (P. 21)

Source: Taiichi Ohno, Toyota Production System: Beyond Large Scale Production, pp. 21-22.

Taiichi Ohno on Industrial Engineering. Toyota style Industrial Engineering - Waste Elimination
https://nraoiekc.blogspot.com/2013/11/taiichi-ohno-on-industrial-engineering.html

Toyota Production System Industrial Engineering (TPS IE) Part 1


Toyota Production System - Main Objectives

Elimination of Waste
Low Inventory
Low Order to Delivery Period using low cycle time.


Toyota Production System - Tools or Techniques


SMED
Poka Yoke

Now popular

TIE - Total Industrial Engineering.
TQM – Zero defects
TPM – High OEE
TPMgmt – Annual Planned Cost Reduction

Shigeo Shingo

A Study of Toyota Production System from an Industrial Engineering Viewpoint by Shigeo Shingo

Book published by Productivity Press

Two Pillars of TPS - Jidoka - JIT


Toyota Production System

A production system based on the philosophy of achieving the complete elimination of all waste in pursuit of the most efficient methods.
Toyota Motor Corporation's vehicle production system is a way of making things that is sometimes referred to as a "lean manufacturing system," or a "Just-in-Time (JIT) system," and has come to be well known and studied worldwide.

This production control system was established based on many years of continuous improvements, with the objective of making the vehicles ordered by customers in the quickest and most efficient way, in order to deliver the vehicles as swiftly as possible. The Toyota Production System (TPS) was established based on two concepts: "jidoka" (which can be loosely translated as "automation with a human touch"), as when a problem occurs, the equipment stops immediately, preventing defective products from being produced; and the "Just-in-Time" concept, in which each process produces only what is needed for the next process in a continuous flow.

Based on the basic philosophies of jidoka and Just-in-Time, TPS can efficiently and quickly produce vehicles of sound quality, one at a time, that fully satisfy customer requirements.


Shigeo Shingo said
80% of the TPS is waste elimination in facilities, processes and inventory, that is industrial engineering (facilities and processes improvement, Jidoka)),
15% - production management (production batch quantities planning, JIT material and production planning) and
5% - kanban (sign board) communications

What is Industrial Engineering?

Industrial Engineering is eliminating Muda, Muri and Mura (Japanese explanation).

IE is developing science that explains productivity (Productivity Science development)

IE is improving technical processes  for cost reduction (Machines and Men) (Fundamental - Productivity Engineering).

IE is improving management processes of planning, organizing, resourcing, executing (accepting and releasing orders, leading, directing, communicating) and controlling (associated activity - Productivity Management).

IE is improving business processes (Augmented).

For some more details visit:  Functions of Industrial Engineering



In Preface to the Japanese Edition

Shigeo Shingo had written that management consultants were not allowed to disclose any confidential or proprietary information. Taiichi Ohno authored two books describing Toyota Production System (TPS).  That allowed Shigeo Shingo, to use the published material as the basis to explain industrial engineering principles behind TPS.

Shigeo had as his objectives in writing the book:

1. Explaining the principles of the Toyota Production System based on Industrial Engineering Theory.
2. Explanation of the system of practicing these principles.
3. Description of the practical application of the methods following these principles.

Chapter 1 Introduction


Production is a network of processes and operations.
Process – transforming material into product is accomplished through a series of operations.
Process – flow of material in time and space.
Process analysis examines the flow of material or product.

In an operation a transformation occurs.
Process analysis questions whether that transformation is required.

Operation Analysis
Operation analysis examines the work performed on products by workers, machines and tools.
Process analysis, operation analysis, motion study and time study form part of methods efficiency engineering.
Process analysis and operation analysis are engineering activities specific to each branch of engineering.
http://nraoiekc.blogspot.in/2012/04/method-study-methods-efficiency.html

Shingo wrote "When we look at operations, we see the work performed to accomplish the transformation of the material - the interaction and flow of equipment and operators in time and space."

I keep insisting that after a process chart is made, for each operation in the process chart, the machine effort and human effort are to be recorded in detail. Motion study, provides two handed operator chart for recording work of every operator involved. For machine effort, for each machine a machine effort sheet has to be prepared to improve machine work.

Chapter 2 Improving Process

Improve process before improving individual operations.
Process is flow of material through operations.

Process Chart - Gilbreth

Processing operation
Inspection operation
Transport operation
Storage operation – Temporary, Permanent (Delay operation)

Process Improvement
Process can be improved in two ways.
The first improves the product itself through design efficiency engineering (value engineering, design for manufacture, design for assembly, and design optimization techniques).
The second improves manufacturing method through methods efficiency engineering, motion studies and production optimization and variability reduction methods.

ECR Method of Process Improvement

Eliminate the operation – sometimes it is found to be not necessary or sometimes it is due to improvement of earlier operation.
Combine operations with earlier one or latter one.
Rearrange the sequence of operations


Processing Operations Analysis

Examples in the book
Manufacturing operations can be improved by alternatives related to proper melting or forging temperatures, cutting speeds or tool selection.
Examples related to vacuum molding, plating and plastic resin drying are given in the book.
Eliminating Flashing in Castings (Die)
Flashing in die castings occurs due to escape of air.
Removing the air in mould with a vacuum pump eliminated flashing.
Removing Foam in High-Speed Plating
Spraying or showering the surface to be painted resulted in a 75% reduction.
Drying Plastic Resin
Letting the resin dry a little at a time by allowing it to float to the surface resulted in a 75% reduction of electric power consumption.


Analysis of Inspection Operations

Shingo said normal inspection is judgment inspection.
It separates good and defective items.
Rework done on defective items if possible
Informative inspection asks for process improvement.
It is like medical examination that leads to treatment.
Statistical Process Control
SPC is sampling based informative inspection.
But Shingo says even it is not sufficient to assure zero defects.
To assure zero defects we need to inspect every item but at low cost per item.
Shingo’s Suggestions
Informative Inspections

Self Inspection
Successive Inspection
Enhanced Self Inspection – Inspection enhanced with devices  - poka-yoke

Example 2.4 – Vacuum Cleaner Packing
Cleaner along with attachments and leaflets to be packed.
When a leaflet is taken from the pile,  a limit switch is operated.
When attachments are taken from the container, a limit switch is operated.
Then only, the full package is allowed to be sealed.
Principle
The purpose of inspection is prevention of the defect.
Quality can be assured when it is built in at the process and when inspection provides immediate and accurate feedback at the source to prevent the defective item to go further.
Self Inspection
It provides the most immediate feedback to the operator.
He can improve the process and also rework on the item.
Disadvantage inherent.
There is potential for lack of objectivity.
He may accept items that ought to be rejected.
Successive Inspection
The operator inspects the item for any defect in the previous operation before processing it.
Shingo says, when this was introduced defects dropped to 0.016% in Moriguchi Electric Company in television production
Inspection enhanced by Poka Yoke
Human operation and inspection can still make errors unintentionally.
Poka Yoke will take care of such errors.
Ex: Left and right covers are to be made from similar components with a hole in different places.
The press was fitted with a poka yoke which does right cover pressing only when the hole is in proper place.
Source Inspection
This is answering the question: What is the source of the defect in the process/operation?
Two types proposed.
Vertical
Horizontal
Source Inspection – Vertical, Horizontal
Vertical source inspection traces problems back through the process flow to identify and control conditions external to the operation that affect quality.
Horizontal source inspection identifies and controls conditions within an operation that affect quality.
Poka-yoke Inspection Methods
Poka-yoke achieves 100% inspection through mechanical or physical control.
Poka-yoke can either be used as a control or a warning.
As a control it stops the process so the problem can be corrected.
As a warning, a buzzer or flashing lamp alerts the worker to a problem that is occurring.

Three types of control poka-yoke
Contact method - identify defects by whether or not contact is established between the device and some feature of the product's shape or dimension
Fixed value method - determines whether a given number of movements have been made


Motion step method - determines whether the established steps or motions of a procedure are followed

Choosing/Designing  Poka Yoke
First decide stage of inspection – Self or Successive
Second – Type of regulation
Control or warning.
Third decide Error Sensing type – Contact, fixed number or motion step


Analysis of Transport Operations

Transport within the plant is a cost that does not add value.
Hence real improvement of the process eliminates the transport function as much as possible.
This involves improving the layout of process.

Ex – 7. Transport Improvement
Tokai Iron Works – process layout -  presses, bending machines, embossing
Layout Change: Flow based layout.
A 60 cm wide belt conveyor with ten presses on either side.
WIP reduced. Production time shortened. Delays disappeared.
200% increase in productivity.
Principle
Only after opportunities for layout improvement have been exhausted should the unavoidable transport work that remains be improved through mechanization.

Eliminating - Storage Operations (Delay)


Process Delay – Permanent storage – Whole lot is waiting
Lot Delays – Temporary storage – One item is being processed. Other items in the lot waiting.
Another classification is storage on the factory floor and storage in a controlled store.
Eliminating - Storage Operations (Delay)
There are three types of accumulations between processes:

E storage - resulting from unbalanced flow between processes  (engineering)
C storage - buffer or cushion stock to avoid delay in subsequent processes due to machine breakdowns or rejects (control)
S storage - safety stock; overproduction beyond what is required for current control purposes

Eliminating E-Storage

E-storage is due to engineering/planning/design of the production-distribution  system
This can be eliminated through leveling quantities, which refers to balancing flow between high and low capacity processes and synchronization.

Leveling would mean running high-capacity machines at less than 100% capacity, in order to match flow with lower capacity machines that are already running at 100% on short interval basis.
At Toyota, the quantity to be produced is determined solely by order requirements (Takt time).

Principle
Presence of high capacity machines should not be used to justify large lot processing and resulting inventory.
Process capacity should serve customer requirements/production requirements and should not determine them
synchronization.
The lots especially one piece lot is processed without delay in a flow.
It is efficient production scheduling that ensures that once quantities are leveled (output is matched), inventories do not pile at any stage due to scheduling conflicts.
Synchronize the entire process flow.


Eliminating C storage - Cushion

Cushion stocks compensate for:
machine breakdowns,
defective products,
downtime for tool and die changes and
sudden changes in production scheduling.

Eliminate Cushion Storage
Prevent machine breakdowns:
Determining the cause of machine failure at the time it occurs, even if it means shutting down the line temporarily.
Total Productive Maintenance movement.

Eliminate Cushion Storage
Zero Defect Movement.
Total quality management.
Use better inspection processes:
Self Inspection.
Successive Inspection.
Enhancement to inspection through Poka Yoke
Eliminate Cushion Storage
Eliminate Lengthy setups and tool changes
Implement SMED to eliminate long set-up times and tool changes
Running smaller batch sizes to allow for quick changes in production plans

Eliminate Cushion Storage
Absorb Change in Production Plan
Running smaller batch sizes allows for quick changes in production plans without disturbing flow production to significant extent.

Eliminating Safety (S) storage

Safety stock is kept not to take care of any predicted problem but to provide additional security
It may guard against delivery delays, scheduling errors, indefinite production schedules, etc.
Ex. 10 Delivery to stores
In example 2.10 Shingo mentions a company wherein vendors supply to store and from store components are supplied to assembly line.
Shingo suggested that vendors should directly supply the day’s requirements to assembly floor and in case of any problem, components in the store can be used.
Less Need for Safety Stock Observed
That practice led to the observation that very less safety stock is needed in the store.

Shingo recommends keeping a small controlled stock that is only used when the daily or hourly scheduled delivery fails or falls behind.
In case of unexpected defects also it can be used.


The safety stock can then be replenished when the scheduled materials arrive, but the supply of materials due for the process go directly to the line, rather than normally going into storage first.
This is the essence of the just-in-time supply method.


Eliminating lot delays
While lots are processed, the entire lot, except for the one piece being processed, is in storage (is idle).
The greatest reduction in production time can be achieved when transport lot sizes are reduced to just one; the piece that was just worked on.

SMED
Using SMED (single-minute exchange of dies), set up time is decreased so large lot sizes are no longer necessary to achieve machine operating efficiencies.
SMED facilitates one item lot sizes.




Layout Improvement - Flow
Transportation changes can be accomplished through flow  layout and using gravity feed Chutes which result in shorter production cycles and decreases in transport man-hours.

Reducing Cycle Time
Generally, semi-processed parts are held between processes 80% of the time in a production cycle time.
It quantity leveling is used and synchronization of flow is created, the cycle time can be reduced by 80%.
By shifting to small lot sizes will further reduce cycle time.


TPS – Reduction of Delays or Storage
Methods of reducing production time delays (JIT) is the foundation of Toyota Production System.
It clearly brings down production cycle time and thereby offers small order to delivery time.

Process Improvements in Toyota
Mixed model small lot production was attempted in Toyota to compete with American manufacturers.
First, inefficiencies in processing operations, inspection operations and transport operations were removed.
Then storage operations were attacked and inventories eliminated.
Toyota surpassed American manufacturers.

Now TPS is promoted as Lean System



Chapter 2 End

Ch. 3 Improving Operations

Operation may be classified as follows:

Set up operations - preparation
Principal operations - performance
Margin allowances - machine breaks
Personal allowances - worker breaks


Improving Setup
SMED

Improving principal operations
The easiest way to improve principal operations is to separate the worker from the machine.
Reduce involvement of man in machine running and production.
This involves the "one worker, many process" theory.
One worker attends 5-6 machines,
The principle is that cost reduction is more important than high machine operating rates.
Machines should not unnecessarily function and produce excess inventory.
But the operable time of the machine should be high.
Whenever needed machine must be ready for production.

Autonomation
Machine detects problem and stops.
Workers correct the problem.
The next step is to make the machine correct the problem

Improving margin allowances

Main operations are automated by marginal activities like removing chips, feeding materials and stocking products are still done by hand by men.
They also need to be automated.
Lubrication: Consider automatic lubrication, use of oil impregnated metals etc.
Cutting oil – Consider automatic oiling or cutting without oil.
Chip removal – Consider powdering chips or automatic lubrication and chip removal.

Workshop allowances

Automate the following:
Automate feeding for materials.
Automate product storage.
By adopting the SMED system, Toyota achieved dramatic reductions in setup time and inventory cost.
Adding multi-machine handling and autonomation further increased productivity.

Chapters 2 & 3 to be used in analysis of value stream map (VSM)

Toyota Production System Industrial Engineering (TPS IE) Part 2

Summary of Remaining Chapters of the Book

http://nraoiekc.blogspot.com/2013/12/toyota-production-system-industrial.html

Updated 11.7.2024,  11.9.2022,  16.2.2022,  11 July 2021,  14 June 2020,   21 August 2019,  22 August 2017,  9 Sep 2015
First published 9 Sep 2014

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Thursday, May 14, 2026

Practice Areas of Industrial Engineering - Productivity Science - Determinants of Productivity

Included in

Philosophy, Principles and Practices of Industrial Engineering - Modern Industrial Engineering May 2026 Issue

https://www.linkedin.com/pulse/philosophy-principles-practices-industrial-engineering-kvss-tedgf/



Practice Areas of Industrial Engineering



Productivity Science - Determinants of Productivity

https://nraoiekc.blogspot.com/2017/10/productivity-science-determinants-of.html

Productivity Science of Machine Effort

Productivity Science of Human  Effort


Frameworks for Productivity Science of Machine Effort and Human Effort

Rao, Kambhampati Venkata Satya Surya Narayana.IIE Annual Conference. Proceedings; Norcross (2020): 429-434.

https://www.proquest.com/openview/5786c4e6edff56abf808b4db26f083b3/1.pdf

Productivity Science of Material


Productivity Science of Energy

Productivity Science of  Information

Productivity Science of Production Processes

 - In Industrial engineering, a process is a sequence of operations (steps) that produce a part or subassembly or final assembly.

 - In production different ways of manufacturing are termed as processes. Turning process, Milling process etc.

Productivity Science of Production Operations

Productivity Science of Inspection Processes

Productivity Science of Inspection Operations

Productivity Science of Material Handling Processes

Productivity Science of Material Handling Operations

Productivity Science of  Storage Processes

Productivity Science of Storage Operationa

Productivity Science of Production Quantity Planning

Productivity Science of Production Plan Control

Productivity Science of Plant Layout or Facility Layout


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