Monday, November 9, 2026

Data Science Based Industrial Engineering - Cost Reduction in Cement Plant

 ÇimSA Çimento Sanayi ve Ticaret A.S  needed a decision optimization solution to help manage its supply chain network and enable management to find opportunities to improve service while cutting costs.


ÇimSA used CPLEX Optimization Studio to run complex “what-if” scenarios quickly, optimizing business decisions to improve customer service and reduce cement manufacturing, warehousing and distribution costs.


Results


Estimated 10% reduction  of inventory costs

An estimated 3% increase in working capital turnover

Estimated 3% reduction of manufacturing, warehousing and distribution costs



With several kilns, grinding mills and silos in more than 10 locations and seven international distribution terminals, ÇimSA established a complex network of facilities. The company also incurred significant overhead costs related to supply chain management, cement manufacturing and transportation. To optimize operations—while factoring in fluctuations in currency exchange rates, taxes, demand and manufacturing capacity changes—ÇimSA managers increasingly used a legacy computing tool for sales and operations planning.


The CPLEX Optimization Studio toolset uses data science and decision optimization technology to model and solve complex business optimization problems. It also enables users to develop and deploy optimization models quickly and create custom applications designed to improve business results.


After deciding to deploy the CPLEX Optimization Studio toolset, ÇimSA’s first step towards implementation was to acquire all the relevant business data necessary to develop accurate optimization models. “Preparing the data and ensuring data completeness was the  biggest challenge,”  “We then developed a mathematical model through the advanced modeling techniques of CPLEX. After the model was written, we could start the fine-tuning and testing processes to solve large-scale supply chain problems which include millions of decision variables and constraints.”


For the first modeling project, IBM Lab Services, IBM Data Science Elite and Lumtify tackled one of ÇimSA’s most important optimization priorities: the design of its global supply chain network. “With so many different production facilities in different locations, you need to make decisions based on different ‘what-if’ scenarios,”. “These questions are not easy to solve manually or with a spreadsheet, but CPLEX can run through millions of solution variables to make supply chain optimization successful.” 


In addition to expenses related to transportation and electricity, the cost of burning fuel to heat cement kilns to 2,000 degrees Celsius represents one of the largest cost inputs in the cement industry. Determining whether to use coal, petroleum coke, natural gas or other fuels at a production facility and deciding on the supplier, the price and delivery schedule is an especially difficult and ongoing challenge. Which is why ÇimSA made fuel-mix optimization its second major CPLEX optimization project. “CPLEX answers key fuel-mix questions like when, from whom, the amount and the price.”  “There are big cost savings when you can get that right.”


https://www.ibm.com/case-studies/cimsa-cplex-gbs










Industrial Engineering Case Studies 2024 Onwards

 


These case studies help you to make sense of current projects being undertaken by industrial engineers. They give you idea of typical cost savings and productivity improvements in IE projects. They motivate you think creatively and come up with IE studies and projects for various processes under your IE responsibility.


Case Report| September 11 2026

Enhancing productivity and efficiency in electrical device manufacturing: a case study on leveraging rapid improvement events (RIEs) and lean tools


Sustainable Manufacturing - Lean Manufacturing
Case Studies and Best Practices


A Study on Time and Motion Study for Improving Productivity at Manisha Enterprises

Productivity Improvement by Implementation of 5-S System: An Excellent Case Study in Indian Context


Productivity Improvement in Agricultural Equipment Manufacturing Industry

https://pmicorp.in/case-study/productivity-improvement-in-agricultural-equipment-manufacturing-industry/


2025


Assembly line balancing: a case study for increasing and optimizing productivity in the clothing sector

Published: 08 August 2025

https://link.springer.com/article/10.1007/s12046-025-02856-1

Improving Productivity on Machining Sector Based on Layout Arrangement Proposal Case Study of Mechanical Workshop in Ethiopia

https://www.sciencepublishinggroup.com/article/10.11648/j.ae.20250902.11


Implementation of a Lean Manufacturing Approach to Improving Productivity in SMEs: A Case Study in a Cloth Manufacturing Company

https://www.scielo.org.za/scielo.php?pid=S2224-78902025000300007&script=sci_arttext

Productivity improvement through lean tools in cement industry – A case study

Heliyon. 2025 Jan 22;11(3):e42057. doi: 10.1016/j.heliyon.2025.e42057

https://pmc.ncbi.nlm.nih.gov/articles/PMC11795787/

Productivity Improvement Through Lean Tools in Cement Industry – A Case Study

February 2025 Heliyon 11(11):e42057

https://www.researchgate.net/publication/388276241_Productivity_Improvement_Through_Lean_Tools_in_Cement_Industry_-_A_Case_Study


Productivity Improvement of Garments Industry by Implementing Line Balancing Algorithms—A Case Study

Mst. Anjuman Ara et al.

https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.70125


Cases of Genba Kaizen Consulting

[Case Study] Productivity Improvement Project at an Overseas Factory - A Multidimensional Approach Targeting a 30% Increase in Productivity

https://genbakaizen.com/results/13867/


Productivity-Transformation-in-SMEs-Case-Studies-from-Sri Lanka

https://www.apo-tokyo.org/wp-content/uploads/2025/11/Productivity-Transformation-in-SMEs-Case-Studies-from-Sri-Lanka_V5.pdf


Productivity Improvement in a Small-Scale Industry

29 Jan 2025 — The study aims to identify NVA activities, record cycle and idle time, assess workforce utilization, and provide recommendations for manpower planning.


Case Study: Production Optimization

Company in Textile Manufacturing

https://lnkd.in/p/dH8CJb2H


Data Science Based Industrial Engineering - Cost Reduction in Cement Plant

https://nraoiekc.blogspot.com/2026/11/data-science-based-industrial.html



2024

Improving Space Utilization in Warehouse

https://www.productivity.ac.uk/news/improving-productivity-through-efficient-space-utilisation-a-case-study-with-jane-wallace-jones/

Enhancing productivity through work study - A case of electric power pole cross arm fabrication

Yonas Mitiku Degu

https://pmc.ncbi.nlm.nih.gov/articles/PMC11226887/


Study on Labor Productivity Improvement Based on Situational Awareness and Improved Value Stream Mapping

https://www.mdpi.com/2075-5309/14/5/1253


Productivity Improvement of Assembly Line-by-Line Balancing Technique: Case Study Textile Manufacturing Company Karachi Pakistan

https://journalsajsse.com/index.php/SAJSSE/article/view/814

APPLICATION OF LEAN MANUFACTURING TO IMPROVE PROCESSES AND INCREASE PRODUCTIVITY IN THE TEXTILE INDUSTRY OF PERU

https://sajie.journals.ac.za/pub/article/view/2932

IMPROVING PRODUCTIVITY IN AN SME IN THE METALWORKING SECTOR THROUGH LEAN MANUFACTURING AND TPM TOOLS - A CASE STUDY IN PERU

Vol. 35 No. 2 (2024): The South African Journal of Industrial Engineering / Case Studies

https://sajie.journals.ac.za/pub/article/view/3013


Kaizen: Improving Productivity and Reducing Waste in a Manufacturing Company: a Practical Case Study

https://openaccessojs.com/JBReview/article/view/4241


Plastics processing machinery manufacturing : A Kaizen Case Study in Productivity Enhancement - Summary Report by Concept Business Excellence Pvt. Ltd.

https://www.linkedin.com/pulse/plastics-processing-machinery-manufacturing-kaizen-aq4if/


Continuous Improvement of Productivity and Quality with Applying Lean Six Sigma: A case study

https://jte.edu.vn/index.php/jte/article/view/1586


Productivity Improvement through Work Measurement and Time Study Analysis

https://journal.kalibra.or.id/index.php/reswara/article/view/380






2021

A Case Study on Reducing Cycle Time and Improving Productivity in Leading Gear Manufacturing Company

December 2021  International Journal of Mechanical Engineering 6(3):3908-3921

https://www.researchgate.net/publication/377086295_A_Case_Study_on_Reducing_Cycle_Time_and_Improving_Productivity_in_Leading_Gear_Manufacturing_Company


Productivity Improvement by Cycle Time Reduction Using Time and Motion Study a Case Study of Electrode Manufacturing Company

https://www.academia.edu/81854107/Productivity_Improvement_by_Cycle_Time_Reduction_Using_Time_and_Motion_Study_a_Case_Study_of_Electrode_Manufacturing_Company


Productivity Improvement through Work Study The Case of Peacock Shoe Factory

https://etd.aau.edu.et/items/784a018b-4356-4fc7-8e1e-2ecac9e9c42e

https://www.linkedin.com/in/raj-sanne-a016a5/    shared many case studies in IE areas from his LinkedIn profile.

A print manufacturer example of activity based costing By Raj Sanne 

https://lnkd.in/p/d4xPaVXg


Ud. 9.11.2026

Pub. 21.10.2026







Robots in Tesla's Plants - Case Study

 



In late 2016 Musk set about converting an old GM / Toyota factory in Fremont, California into  Tesla assembly facility.  They invested in developing in-house robotics capabilities by acquiring two automation companies, Grohmann & Perbix.   Tesla  purchased over 1,000 robots including 6-axis arms from Kuka and Fanuc and automated guided vehicles from Omron. They applied them across the process tackling relatively routine tasks such as welding or painting through to entirely novel tasks like wire harness assembly.  By April 2018, everything was not going to plan. The company was far behind schedule, only producing an average of 2000 cars/week and haemorrhaging $100 million dollars a week. The period was described as “Production Hell”. But Tesla stabilized the process.

https://www.wevolver.com/article/how-tesla-used-robotics-to-survive-production-hell-and-became-the-worlds-most-advanced-car-manufacturer

https://www.themachinemaker.com/technologies/doug-walker-industrial-robots-2312

10 Sep 2024

Will robots take over Tesla factories? They could save Elon Musk's company $500 million a year, analysts say

Tesla could save $57,550 for every worker it replaces with a optimus humanoid robot by 2030.

https://x.com/Tesla_Optimus/status/1964425666834931730/photo/2

https://qz.com/tesla-reshape-industries-ev-ai-robotaxi-optimus-musk-1851644413

By the end of 2024, Musk he expects more than 1,000 robots will be working at Tesla. Two robots are already on the factory floor. The robots are expected to  sell for between $20,000 and $30,000.

Deutsche Bank (DB)  expects the robots to cost between $27,000 and $76,000 to make. The “cost savings” would come in the form of replacing  people with robots in Tesla factories.Assuming that a manufacturing worker is on the clock for 40 hours a week and gets paid $62,400 in 2027, Tesla could save $31,900 by replacing them with a robot. Replacing 10% of  workers would save $141 million per year. If that same worker was still at the company in 2030 and making $72,800 a year, Tesla could save $57,550 by putting a robot in their place. If 20% of workers are replaced, Tesla could save as much as $509 million annually. 



Yearwise Bibliography


2016

https://electrek.co/2016/07/31/tesla-gigafactory-robots-machines-battery-factory/

https://electrek.co/2016/09/01/tesla-model-3-robot-supplier-bring-up-production-line-time/

https://www.youtube.com/watch?v=f0P1Ikyz8To

https://partsolutions.com/innovative-engineering-how-a-tesla-model-s-is-made/

https://nypost.com/2016/11/08/tesla-acquires-robots-company-to-accelerate-car-production/

https://openhumanitiespress.org/feedback/science-technology/teslas-model-3-as-ikon-of-the-fourth-industrial-revolution/

https://archive.curbed.com/2016/7/29/12326402/gigafactory-manufacturing-robotics-technology

https://www.manufacturingtomorrow.com/article/2016/07/what-super-efficient-manufacturing-looks-like/8262

https://www.forbes.com/sites/alanohnsman/2016/11/08/tesla-buying-germanys-grohmann-engineering-to-automate-accelerate-production/

https://finance.yahoo.com/news/tesla-planning-build-futuristic-gigafactory-185200816.html

https://www.engadget.com/2016-08-06-six-tesla-inventions.html

2017

https://electrek.co/2017/04/25/tesla-model-3-robot-production-line-pictures/

https://electrek.co/2017/07/07/tesla-model-3-production-line/

https://cleantechnica.com/2017/04/27/tesla-army-kuka-robots-waiting-installed-model-3-assembly-line/

https://futurism.com/leaked-photos-from-tesla-show-the-alien-robots-that-will-build-model-3s

https://www.seattletimes.com/business/teslas-model-3-production-hell-is-testing-elon-musks-fix-as-you-go-carmaking-model/

https://loupfunds.com/teslas-bedrock-in-ai-robotics-will-transform-the-industry-our-lives/

https://www.businessinsider.com/tesla-buying-perbix-automated-manufacturing-2017-11

https://www.youtube.com/watch?v=uZDBCpwk83A

https://www.mirror.co.uk/tech/elon-musk-assembling-robot-army-10314578

https://monroeengineering.com/blog/tesla-acquires-manufacturing-robot-supplier-perbix/

https://www.washingtonpost.com/news/innovations/wp/2017/11/03/sleepless-nights-broken-robots-and-mounting-pressure-musk-offers-rare-glimpse-inside-teslas-production-hell/

https://www.marketwatch.com/story/elon-musk-robot-software-will-make-tesla-worth-as-much-as-apple-2017-05-04

https://tech.hindustantimes.com/tech/news/robots-will-help-tesla-catch-up-to-apple-in-value-elon-musk-story-5Yu5DFQbS55SMZ3WLPLMfM.html

Tesla-made-only-260-model-3-cars-in-q3-but-is-confident-it-can-fix-bottleneck/

https://techcrunch.com/2017/10/02/tesla-made-only-260-model-3-cars-in-q3-but-is-confident-it-can-fix-bottleneck/

https://www.nytimes.com/interactive/2017/11/07/magazine/tech-design-future-autonomous-cars-factory-tesla-sustainability-gigafactory.html

https://hbr.org/2017/08/is-tesla-really-a-disruptor-and-why-the-answer-matters

https://www.ien.com/automation/video/20978879/tesla-reportedly-forced-to-make-parts-by-hand

https://insights.globalspec.com/article/4788/what-is-the-real-cost-of-an-industrial-robot-arm

https://one.oecd.org/document/DSTI/CIIE(2017)14/en/pdf

https://interestingengineering.com/video/drone-footage-of-teslas-fremont-factory-shows-new-buildings-for-increased-model-3-production

https://www.caranddriver.com/news/a15342218/teslas-musk-brags-on-model-3-gives-more-details-on-model-y-and-semi/

https://wccftech.com/terrifying-robotic-charger-tesla-model/

2018

https://www.theverge.com/2018/4/13/17234296/tesla-model-3-robots-production-hell-elon-musk

https://www.theguardian.com/technology/2018/apr/16/elon-musk-humans-robots-slow-down-tesla-model-3-production

https://electrek.co/2018/05/13/tesla-hackathon-robots-model-3-production/

https://www.business-standard.com/article/international/elon-musk-s-alien-dreadnought-an-insider-s-view-of-tesla-s-model-3-factory-118060900902_1.html

https://www.youtube.com/watch?v=zrAIQ_YZ_i4

https://interestingengineering.com/video/these-are-the-robots-making-your-tesla-s-vehicles

https://www.teslarati.com/tesla-model-3-paint-shop-robot-applicator/

https://www.technologyreview.com/2018/04/17/67012/tesla-shuts-down-model-3-production-to-improve-automation/

https://www.cbc.ca/news/science/tesla-model-3-sedan-automation-robots-manufacturing-delay-1.4734829

https://www.nytimes.com/2018/06/30/business/tesla-factory-musk.html

https://www.sfchronicle.com/business/article/Touring-Tesla-s-Model-3-production-line-the-13089341.php

https://qz.com/1294282/the-tesla-model-3-cost-28000-to-build-german-engineers-say-and-it-still-may-not-be-profitable/

https://cleantechnica.com/2018/06/18/tesla-2018-video-celebrating-workers-robots-speed/

https://cleantechnica.com/2018/06/30/automating-intelligently-is-teslas-manufacturing-advantage/

2019

https://techcrunch.com/2019/03/05/elon-musk-wasnt-wrong-about-automating-the-model-3-assembly-line-he-was-just-ahead-of-his-time/

https://electrek.co/2019/07/22/tesla-revolutionary-wiring-architecture-robots-model-y/

https://www.reuters.com/article/us-tesla-gigafactory-germany-strategy-idUSKBN1XO0LO

https://ieeexplore.ieee.org/iel7/6287639/8600701/08902005.pdf

https://www.forbes.com/sites/alanohnsman/2019/03/01/tesla-safety-violations-dwarf-big-us-auto-plants-in-aftermath-of-musks-model-3-push/

2020

https://electrek.co/2020/05/20/tesla-installs-production-robots-fremont-factory/

https://insideevs.com/news/408546/tesla-gigafactory-3-official-high-automation/

https://www.businessinsider.com/tesla-has-struggled-with-robots-breaking-down-at-car-factory-2020-3

https://www.cnbc.com/2020/09/16/why-amazon-warehouses-tesla-auto-plants-will-not-go-100percent-robot.html

https://www.torquenews.com/11826/elon-musk-says-tesla-s-production-lines-already-over-75-percent-automated

https://www.icmrindia.org/casestudies/catalogue/IT%20and%20Systems/ITSY104.htm

https://www.youtube.com/watch?v=WHo1PYV3OzY

https://cynlr.com/healing-manufacturings-trillion-dollar-migraine-part-1/

https://medium.com/@ashleylobo98/a-case-study-on-tesla-the-worlds-most-exciting-automobile-company-535fe9dafd30

2021

https://streetfins.com/inside-teslas-crazy-ai-manufacturing-revolution/

https://auto.hindustantimes.com/auto/cars/watch-robots-painting-tesla-cars-at-shanghai-gigafactory-41635922068352.html

https://www.iqsdirectory.com/resources/teslas-big-problem-excessive-automation/

https://techstory.in/teslas-new-automation-set-up-with-comau-robotics-equipment/

https://www.designnews.com/automotive-engineering/teslas-switch-giga-press-die-castings-model-3-eliminates-370-parts

https://www.teslarati.com/tesla-manufacturing-stellantis-owned-comau-robotics-new-automation-fremont-factory/

2022

https://www.thehindu.com/sci-tech/technology/giga-casting-and-robots-how-volkswagens-trinity-aims-to-catch-up-with-tesla/article65280747.ece

https://www.programatium.com/what-percentage-of-tesla-manufacturing-is-done-by-robotics/

https://www.autodaily.com.au/how-car-manufacturing-is-changing-for-evs/

https://cleantechnica.com/2022/04/11/inside-giga-texas-the-machine-that-builds-the-machine/

https://spectrum.ieee.org/tesla-4680-battery

https://www.forbes.com/sites/alanohnsman/2022/04/07/elon-musk-opens-teslas-giga-texas-that--he-says-is-the-biggest-us-plant/

https://www.clarify.io/learn/industry-5-0

https://www.barrons.com/articles/tesla-tsla-stock-cyber-rodeo-51649321149

Tesla Model 3 at $25K

https://www.nextbigfuture.com/2022/06/improved-lfp-batteries-will-make-a-25k-tesla-model-3-in-2026.html

Tesla installing automated EINES quality control robots at Fremont factory that are already in use at their other Gigafactories

https://www.reddit.com/r/teslamotors/comments/10l19qe/tesla_installing_automated_eines_quality_control/


2024

https://www.youtube.com/watch?v=ZJykJxeB95E


https://amdmachines.com/blog/tesla-optimus-robots-begin-limited-factory-deployment/

https://www.businessinsider.com/tesla-says-two-optimus-humanoid-robots-working-in-factory-autonomously-2024-6

10 Sep 2024

Will robots take over Tesla factories? They could save Elon Musk's company $500 million a year, analysts say

Tesla could save $57,550 for every worker it replaces with a optimus humanoid robot by 2030.

https://qz.com/tesla-reshape-industries-ev-ai-robotaxi-optimus-musk-1851644413

By the end of 2024, Musk he expects more than 1,000 robots will be working at Tesla. Two robots are already on the factory floor. The robots are expected to  sell for between $20,000 and $30,000.

Deutsche Bank (DB)  expects the robots to cost between $27,000 and $76,000 to make. The “cost savings” would come in the form of replacing  people with robots in Tesla factories.Assuming that a manufacturing worker is on the clock for 40 hours a week and gets paid $62,400 in 2027, Tesla could save $31,900 by replacing them with a robot. Replacing 10% of  workers would save $141 million per year. If that same worker was still at the company in 2030 and making $72,800 a year, Tesla could save $57,550 by putting a robot in their place. If 20% of workers are replaced, Tesla could save as much as $509 million annually. 



2025

https://www.bbc.com/news/articles/cdrz2rdlykdo


https://www.youtube.com/watch?v=HbPvNDnyN_o


2026

https://www.automotivemanufacturingsolutions.com/automation/teslas-humanoid-claim-meets-the-reality-of-the-line/2749967

https://www.therobotreport.com/from-evs-to-robotics-tesla-targets-10m-optimus-units-with-new-texas-plant/

https://www.assemblymag.com/articles/99853-tesla-to-end-model-x-s-production-convert-fremont-space-for-optimus-robots

https://arstechnica.com/ai/2026/09/tesla-workers-balk-at-training-optimus-humanoid-robots-as-replacements/

https://futurism.com/advanced-transport/tesla-cant-get-hands-work-optimus-robot




Ud. 9.11.2026

Pub. 24.7.2022



Sunday, November 8, 2026

Work-Related Limb Disorders - Musculoskeletal Disorders - Topic in Human Effort Industrial Engineering



Operator Comfort and Health - Principle of Industrial Engineering.

Principles of Industrial Engineering - Most Popular IISE Conference Presentation Video.

10,895+ Views so far.

https://www.youtube.com/watch?v=pU8CdWfZZdU





Source

OHTA STUDENT  MANUAL   

Ergonomics Essentials  

April   2009 

This manual was originally developed by BP and University of Wollongong.  The Occupational Hygiene Training Association Ltd would like to acknowledge the contribution of these organisations in funding and developing the material and is grateful for their permission to use and modify it. 

Supported by  OHTA, IOHA 

This work is licensed under a Creative Commons Attribution-No Derivative Works Licence  



WORK-RELATED UPPER LIMB DISORDERS (WRULD) 

 

4.2.1 The Nature and Causes of WRULD/Repetitive Strain Injuries/Cumulative Trauma Disorders  

The phrase “work-related upper limb musculoskeletal disorders” includes a variety of upper limb degenerative and inflammatory diseases and disorders, which result in pain and functional impairment.  Affected areas typically include the neck, shoulders, elbows, forearms, wrists and hands (Buckle & Devereux, 2002).  Names for these types of disorders include: cumulative trauma disorder (CTD), repetitive strain disorder (RSI), occupational cervicobrachial disease (OCD), occupational overuse syndrome (OOS) and more commonly work related upper limb disorder (WRULD).  

These types of occupational injuries have been well known over history, examples of occupations include tailors, shoe makers, milk maids; and more recently VDU workers involved with repetitive data entry and vibration induced hand and wrist disorders, such as jack hammer operators.  

Colombini et al. (2002) state that the incidence of occupational illnesses of the upper limbs has been constantly increasing and that in 1990, ‘cumulative trauma disorder’ accounted for over 60% of all occupational illnesses in the USA.   

From the evidence it is clear that posture is a definite risk factor for the neck and shoulder, while a combination of factors are a risk for the elbow and wrist/hand.    

Use of our upper limbs in functional activity, whether at work or at home usually involves static loading of the postural muscles and active use of the dynamic muscles for task completion.  For example, when typing on a keyboard, the shoulder, neck and scapular musculature is stabilising the arms for dynamic work of the hands and forearms.  However, using the evidence such as the findings of Bernard, allow us to determine whether or not manual tasks involving the upper limbs pose a risk to musculoskeletal health for workers.    

To add to the complexity of WRULD, other research into the causes of WRULD indicates that age, gender (female), psychosocial factors and work organisational factors are also significant.  

As outlined in Section 4.1, the causes of manual handling injury relates to demands exceeding tissue tolerances.  In WRULD, the link between cause and effect is more difficult to tease out.  The nexus between the physical risk factors, psychosocial factors, and individual risk factors is complex.  

 

a) The Upper Limb  

Let us now examine the structure of the upper limb; it comprises of the hand, wrist, forearm, upper arm and shoulder. The hand is comprised of 19 bones with a further eight in the wrist. It is highly flexible but also delicate and has evolved to manipulate and feel small items with a great degree of sensitivity and skill. It does not have intrinsic strength or mechanical power as the muscles of the hand are very small and are adapted to fine movements and precision.   

Some mechanical power, however, can be achieved through the larger forearm muscles acting on the fingers and through body leverage. Therefore the hand can perform two different types of grasp - the pinch or precision grip and the palmar or power grasps.   

Both require the forearm and particularly the hand to be stabilised.  This is achieved by forearm, upper arm, shoulder and trunk muscles which for the most part are working statically.  

The manipulative ability of the hand is improved by the full range mobility of the shoulder joint, the hinge action of the elbow and by the rotation of the forearm at the elbow and wrist.   

b) Injury Mechanisms  

The hand is a delicate and highly complex machine at the end of a highly flexible lever on a mobile body but its ability to perform depends to some extent on the rest of the body. Where the shoulder or the trunk cannot be positioned for optimum movement of the hand all three areas may suffer strain. This possibility must be considered in relation to the physical layout of work or the demands of the task.   

Similarly where joints are in their outer or inner positions (away from neutral position) repeatedly or long periods, all structures – capsules (connective tissue around a joint), ligaments (strengthening tissue in a capsule), tendons (joining muscles to bones) and muscles – may be stressed. For most people such positions are held for short periods and are desirable intermittently. They are not difficult or damaging unless maintained for long periods or repeated many times. Sitting, reading or sewing with the neck bent is an extreme posture and may lead to considerable discomfort if continued over any length of time.  

Work that requires the human body to adopt fixed postures and to repeat movements has become common over the last two to three hundred years. In such jobs the body must work in a way that it was never evolved to do. Repeated movements, be they light or forceful, and fixed work postures, such as at a computer terminal, a work bench or conveyor line put mechanical stress on the body. This cumulative loading on muscles, their capsules and ligaments sooner or later results in fatigue and perhaps strain, and eventually WRULD.  

When considering work activities and potential risk for WRULD, it is useful to consider the postural clues that can be observed.  Ranney (1997, p. 54,55) provides a list of risks to look for in work tasks for the shoulder, and then the hand and forearm. He notes that the potential damage these risk factors may cause is dependent upon exposure. Possible risk signals outlined by Colombini et al (2002) have also been included in Tables 4.5 and 4.6 outlined below:   


Table 4.6 - Risk Factors for the Hand and Forearm 

Risk Factors for the Shoulder  High moments at the shoulder • Hands far from body, especially if with load • Arms out from body or above mid-torso (weight of arms alone ~ 4 kg.) Static load on the shoulder (NB continuous low loads can be just as fatiguing and lead to injury as infrequent high loads • Arms held out from body continuously without support • Shoulder girdle elevated • Tools held continuously Awkward shoulder posture • Working above shoulder height (eg: painting) • Working with arm behind the trunk (eg: reversing machinery) No time for tissue recovery • Continuous repetition of same activity • More than 1/3 strength required for 1/3 of task time.  This may lead to incomplete recovery  

(Source: UOW - adapted from Ranney 1997 & Colombini et al. 2002)  


 

Risk Factors for the Hand and Forearm 

 High forces and very repetitive work  • Cycle time of up to 15 seconds for at least 4 hours within a shift • Little ‘rest’ time between cycles (arms in constant movement) High forces required by task • Use of fine grips rather than gross grips • Fine grip required to lift object > 900g • Individual fingers used for task (eg: pressing action) • Fingers in hyperextension • Gloves used for task (increases grip requirements due to fit issues) • Lifting activity with palm down (pronation) • Handling of objects > 2.5 kg 

 

Risk Factors for the Hand and Forearm 

 Non-optimal postures (extreme, end of range) 

• Sustained flexion/extension > 30º • Rapid, continual wrist movements • Sustained ulnar or radial deviation  • Jerky, flicking or tossing movements of the wrist • Sustained full pronation Static loads • Use of gloves for task • Activity requires wrist to remain in extension • Continuously holding an object Power tools used • High vibration • High torques/poor torques • ‘Kickback’ observed from power tool • Vibration – leads to mechanical injury of tissue and may lead to nerve damage Sharp edges and hard surface • Contact with sharp/hard surfaces by fingers or palm • Hitting/hammering trim or parts with palm of hand High precision placement requirements • Increases time • Increases static loading • Increases force requirements • Holding parts to fit together • Sustained awkward posture to assemble parts 

(Source: UOW - adapted from Ranney 1997 & Colombini et al. 2002)  

The following illustrations and photographs demonstrate potentially ‘unsafe’ postures of limbs and joints. 

 

4.2.2 Risk Assessment 

 

From the evidence discussed in the previous section, several indicators for risk of WRULMD in the workplace emerge: weight and load (force), frequency of activity (repetition), distances moved (force, posture), workplace layout (posture, vibration), duration of tasks, psychosocial factors, work organisational factors and personal variables. 

As in any risk management process, the first step is to identify, then assess, control and recheck to ensure there is no residual risk, or the new controls do not in fact create another, unforseen, risk.  

a) Risk Assessment Techniques  

i) Simple Techniques  

The simple risk assessment described in the standard has four components:  

1. Gather preliminary information of the job task 

2. Conduct hazard identification and risk estimation procedure and checklist 

3. Overall evaluation of the risk 

4. Remedial action required  

The standard rates risk with a three colour zone approach: green (acceptable risk), yellow (conditionally acceptable) and red (not acceptable).  Should the initial risk assessment produce yellow or red zones, the higher order and more complex risk assessment is to be undertaken, and this is to be done with a prescribed tool, the Occupational Repetitive Action Index (OCRA Index), Colombini, et al 2002.   

An alternate and extremely useful tool for assessing the risk of WRULD in the workplace is that of the HSE in the UK.  The HSE Risk Assessment Worksheet focuses on the key risk factors of repetition; working posture for each of the upper limb body segments; force; working environment; psychosocial factors and individual differences.  It provides prompts for specific issues, control options and an action plan.  

ii) Complex Techniques  

More complex tools include RULA (all discussed in Section 4.1.4), REBA, QEC, and OWAS and the tool used for detailed risk assessment in the international standard, ISO 11228-3 Ergonomics – Manual Handling – Part 3: Handling of low loads at high frequency, OCRA (occupational repetitive action).  OCRA has been selected for use in the standard, as it is based on epidemiological data, considers the known risk factors and can assess ‘multitask jobs’. (ISO 11228-3:2007 (E), p. 8).  Ergonomists will select the tool most appropriate for the task(s) under examination.  


REBA

https://www.linkedin.com/pulse/rapid-entire-body-analysis-reba-vinayak-shete/   

4.2.3 Principles of Control, Preventive and Protective Measures  

The principles of control, prevention and protective measures reflect the risk factors that have been discussed in Section 4.2.  This may entail some complex changes to work organisation as well as modification of actual work stations and areas.  Colombini et al.  (2002, p. 137) categorises the types of changes required for prevention as structural, organisational, and training and retraining.  The suggestions by Colombini have been combined with those of Bridger (2003) and Helander (2006) to provide the following guidelines and suggestions:  

a) Structural Modifications  

• Use of ergonomic work tools (appropriately designed tools, handles, grips, etc)  

- Bend tool handles to 5-10º (to avoid wrist deviation) 

- Avoid extreme ulnar and radial deviation (utilise a neutral wrist posture) 

- Use low forces when rotating or flexing wrist 

- For finger pinch movements, keep forces below 10N, as this represents 20% of weakest operator maximal pinch strength  

• Optimal/ergonomically appropriate workstation layout, equipment, etc.  

- Angle parts to be assembled for optimal postures 

- Ensure appropriate bench heights 

- Ensure suitable chair for task 

- Ensure adequate work areas  

• Reduce identified excessive use of force, awkward postures, and any compression of tissues (eg: wrist permanently resting while hand is working)  

- Encourage use of large muscle groups rather than fine, intrinsic muscles of hands 

- Design products for gripping with hand rather than requiring a pinch grip  

b) Organisational Modifications  

• Ergonomically designed job (appropriate pace, rest breaks/pauses, task variation)  

- Rotate workers between high-repetition and low-repetition tasks/jobs 

- Use machinery for repetitive jobs and use workers for variable tasks 

- Allow workers to set own pace to minimise time or pacing pressure 

- Use ergonomic criteria when planning work systems and purchasing equipment   

• Reduce duration of frequent and repetitive movements  

- Design out tasks requiring rapid movements  

- Limit repetitive movements to 2000 per hour or less 

- Eliminate very repetitive tasks/jobs (< 30 second cycle time)  

• Ensure adequate recovery time  

- Eliminate unnecessary overtime 

- Avoid repetitive work in extreme temperatures 

- Build in pauses in work cycles  

c) Training and Retraining (in addition to above strategies)  

• Strategies for task variation, rest breaks, work pauses  

- Train management/those responsible for work system  

• Provide appropriate information on specific risks and injuries  

- Inform of risks and damage associated with repetitive tasks  

• Provide techniques for task performance in line with ergonomics principles  

- Train the workers to perform tasks in  → Required order 

→ Use both limbs whenever possible 

→ Avoid unnecessary movements/actions 

→ Grip objects correctly 

→ Communicate with Supervisor regarding changes in work, or signs of discomfort 

 

THE QUANTIFICATION OF HUMAN EFFORT AND MOTION FOR THE UPPER LIMBS

J. D. RAMSEY

International Journal of Production Research, Volume 7, 1968 - Issue 1, Pages 47-59 

https://www.tandfonline.com/doi/abs/10.1080/00207546808929796




Ud. 13.11.2024

Pub. 8.11.2021

AI - LLM - Token Optimization - Industrial Engineering

2945+ Downloads. 3550+ Downloads (Versions 1,2,3). Most popular IE publication on Academia-Edu platform. Now in Top 0.5 percent.

INTRODUCTION TO MODERN INDUSTRIAL ENGINEERING. EBook. FREE Download.

https://www.academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0



Token optimization: Backbone of effective prompt engineering

Learn how token optimization drives cost-efficient and accurate LLM prompt engineering

By Supal Kanti Chowdhury, IBM August 2026

https://developer.ibm.com/articles/awb-token-optimization-backbone-of-effective-prompt-engineering/


Token optimization is a key driving factor for prompt engineering because it directly impacts the efficiency, cost, and performance of LLMs.


Token optimization focuses on minimizing the number of tokens used in a prompt without changing its natural meaning or intent to reduce costs and improve performance.


Token usage directly impacts the cost of running LLMs. Each token processed (input or output) incurs a cost, making it essential to optimize token usage to manage expenses effectively. Efficient token usage can enhance the model's performance by reducing the computational load and improving response times.


Improve performance: Fewer tokens mean less computational load, which can lead to faster response times. This is essential for real-time applications such as chatbots, virtual assistants, and interactive systems.


Optimize resources: Minimizing the token count helps in optimizing the use of computational resources, which is especially important in large-scale applications. Efficient resource utilization can lead to better scalability and reliability.


AI token optimization: How to build cost-efficient AI

The rapid adoption of large language model agents has shifted the central engineering constraint from model capability to inference economics. Token efficiency, or tokenomics, is now a primary design concern. To get ahead, organizations should build a framework of four composable levers, or thinking modes, applied across the agent stack to cut token expenditure without degrading output quality.

https://www.infosys.com/iki/perspectives/ai-token-optimization.html


Guide to AI Tokenomics: Eleven Principles for Token Efficient Software Engineering

July 17, 2026

https://cloud.google.com/blog/topics/developers-practitioners/guide-to-ai-tokenomics-eleven-principles-for-token-efficient-software-engineering


1. Start with a balanced model

2. Use skills from the beginning

3. Automate with scripts and CLI tools

4. Delegate output-heavy tasks

5. Divide and conquer

6. Shift verification left

7. Undo when adrift

8. Be specific with context

9. Iterate on rules

10. Avoid uncontrolled loops

11. Start new sessions for each new topic



LinkedIn Course - Reduce AI Costs: Token Optimization Techniques

Advanced

44m 7s

Released: July 30, 2026

https://www.linkedin.com/learning/reduce-ai-costs-token-optimization-techniques


LLM Token Optimization at SAP: A Practical Guide for Developers and Business Users

Rahul_Karda

Product and Topic Expert 

6.11.2026

https://community.sap.com/t5/technology-blog-posts-by-sap/llm-token-optimization-at-sap-a-practical-guide-for-developers-and-business/ba-p/14517397


Token Optimization in Generative AI Prompts
Wipro Tech Blogs
Dr. Magesh Kasthuri, Distinguished Member of Technical Staff, Wipro Limited
4 min read. Feb 24, 2026

https://wiprotechblogs.medium.com/token-optimization-in-generative-ai-prompts-8cfec71331f9


Tutorial

Optimizing your AI usage to maximize efficiency and reduce cost

Learn how to choose the right models, structure your prompts, and add guardrails so that Copilot completes tasks more efficiently and uses fewer AI credits.

https://docs.github.com/en/copilot/tutorials/optimize-ai-usage


https://intl.cloud.baidu.com/en/article/9161108


Effective context engineering for AI agents

https://www.anthropic.com/research/effective-context-engineering-for-ai-agents


AI teams waste $50K-200K yearly on unnecessary token spend.- Here's how to cut costs by 60%:

Token Usage Optimization

→ Compress prompts without losing context

→ Remove redundant instructions

→ Use shorter system messages

By Aditya Santhanam

https://lnkd.in/p/d3FiUy_h


OpenAI Developer Community


A Simple UX Prompt Optimization That Could Save OpenAI Millions in Token Costs

https://community.openai.com/t/a-simple-ux-prompt-optimization-that-could-save-openai-millions-in-token-costs/1261847



License: CC BY-NC-SA 4.0

arXiv:2606.24616v1 [cs.AI] 10 Jun 2026

AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models

Quanyan Zhu

https://arxiv.org/html/2606.24616v1


What is AI tokenomics?

AI tokenomics is the economic framework used to manage the cost, value, and scale of artificial intelligence by treating the token as the fundamental unit of work and the primary currency of the modern IT stack.

CISCO

With token consumption projected to grow 24X by 2030, organizations are moving toward a model where they can precisely measure the "cost-to-value" ratio of every autonomous workflow. The formalization of this discipline through the Tokenomics Foundation signals that AI spend must now be managed with the same rigor as traditional infrastructure. Without this framework, organizations risk “bill shock”, where firms exhausted annual budgets in months because they tracked tokens as a generic expense rather than an investment in specific economic outcomes.

Three pillars of an AI tokenomics framework

1. Measure and attribute every token
2. Predict and budget for future consumption
3. Optimize the unit cost of intelligence

NVIDIA Whitepaper

AI Tokenomics: A Framework for Deploying and Monetizing Inference at Scale

What is tokenomics? It is the economics of token generation and usage and is a term frequently used by the ecosystem, but it can lack precision. To drill down into what tokenomics really means, it’s helpful to frame it as how tokens are valued, consumed, supplied, and monetized.

Tokenomics encompasses four interconnected pillars:

Token Utility: Describes how a token is valued and how the required value varies by use case
Token Demand: Covers how to forecast the volume of tokens a deployment will require, accounting for workload characteristics and operational conditions
Token Supply: Addresses how to maximize token availability while minimizing the cost to produce them
Token Monetization: Explores how to turn token output into revenue and sustainable margins


Search Results on Github

https://github.com/topics/token-optimization




Pub. 7.11.2026











Saturday, November 7, 2026

Artificial Intelligence (AI) Facilitation Data Science Transformation and Productivity Improvement

 


How Artificial Intelligence Is Transforming Data Science

https://cranesvarsity.com/how-artificial-intelligence-is-transforming-data-science/


AI in Data Science: Uses, Examples, and Tools to Consider

Written by Coursera Staff • Updated on Sep 24, 2026


Learn how artificial intelligence is used in data science and explore ways to prepare your team to use the latest AI technologies.

https://www.coursera.org/articles/ai-in-data-science



How AI is transforming Data Science Model Development

Driving higher-value activities, democratization, and operational excellence

https://modern-cdo.medium.com/how-ai-is-transforming-data-science-model-development-67802b28fd35



AI Made Data Scientists Faster. Now It’s Expanding the Job.

The real shift is bigger than productivity: AI is reshaping ownership, judgment, and the career path of data scientists.

Yu Dong

September 29, 2026

https://towardsdatascience.com/ai-made-data-scientists-faster-now-its-expanding-the-job/



https://cloud.google.com/use-cases/ai-data-analytics


https://www.microsoft.com/en-us/copilot/ai-knowledge/ai-for-data-analysis


https://www.turing.com/blog/how-data-scientists-leverage-ai-for-enhanced-efficiency-and-effectiveness


https://www.databricks.com/blog/how-ai-transforming-data-analytics


https://www.linkedin.com/pulse/how-artificial-intelligence-transforming-data-science-a67ac


https://plotly.com/blog/practical-guide-ai-in-data-science-workflows/


https://solutions.nextcomputing.com/ai/how-to-use-ai-for-data-science-techniques-and-tools/?srsltid=AYCX9vI7aBd2jne0A3UUxkxFNKkjAlQTlq2w9tFIeUMq_4729g0CQYRs


https://www.researchgate.net/publication/404831816_AI-Powered_Data_Science_A_Review_of_Current_Developments_and_Emerging_Opportunities


https://www.simplilearn.com/ai-in-data-science-article


https://towardsdatascience.com/beyond-code-generation-ai-for-the-full-data-science-workflow/

https://www.usdsi.org/data-science-insights/how-can-ai-enhance-data-science-with-automation


https://www.iiba.org/business-analysis-blogs/how-ai-is-rewriting-the-rules-of-data-analysis/


https://www.wdsai.org/blogs/ai-and-data-science-working-together-for-better-results


https://www.nvidia.com/en-us/solutions/ai/


https://www.eicta.iitk.ac.in/knowledge-hub/data-science/ai-data-science-in-2025




Applied Industrial Engineering - IE Applications in AI and Agentic AI

 

Applied Industrial Engineering  - IE in New Technologies

Industrial Engineers have to develop productivity science, productivity engineering and productivity management for new technologies.   Are they doing it effectively? No industrial engineers are not doing it adequately.

Applied Industrial Engineering: Prof. Narayana Rao's Vision for Systemic Efficiency and Profitability
https://nraoiekc.blogspot.com/2025/07/applied-industrial-engineering-prof.html

Applied Industrial Engineering - Industrial Engineering 4.0 - Online Course Module


AI - Generative AI - LLMs - Applications in Software Development for Productivity and Quality Improvement

https://nraoiekc.blogspot.com/2026/11/ai-generative-ai-llms-applications-in.html


AI - LLM - Cost Reduction - Token Optimization - Industrial Engineering

https://nraoiekc.blogspot.com/2026/11/ai-llm-token-optimization-industrial.html


IE Applied to Agentic AI

AI and AI Agents are new technologies with application potential in many processes and systems. Industrial engineers have to learn those technologies and develop IE for those technologies.



Operations Function - The key areas where AI agents are making a significant impact
 

The Business Case

The adoption of AI is not just an upgrade to the technology stack; it is a disruption to operational processes and cost structure. With the real-time decision-making process,  manufacturers are seeing improvements in operational efficiency that were very difficult to achieve with rules-engine-based automation.


The key areas where AI agents are making a significant impact include:


Autonomous manufacturing operations (Smart Manufacturing): AI agents can oversee entire production processes, ensuring robotic systems operate at peak efficiency and managing deviations in schedules. They can handle most real-time decisions, with human workers intervening only for issues requiring judgment.


Predictive maintenance: By continuously monitoring machine performance and sensor data, AI agents can predict equipment failures before they occur. This allows for scheduling  maintenance. It is observed that  plants significantly reduced unplanned downtime by up to 40% and cut maintenance costs by 20-25% using predictive maintenance agents.


Quality control and defect detection: AI agents can be used for real-time inspection, using machine vision, sensor fusion and anomaly detection to spot subtle defects that human inspectors might miss, especially in high-speed production. They can also adjust processes in real-time to correct issues, leading to a 30-50% reduction in defect rates.

Automated Inspection - Introduction and Bibliography



Supply Chain Agents: AI agents can predict and react to supply chain disruptions by monitoring raw material availability, adjusting production schedules, optimizing resource use and even identifying alternative suppliers. They streamline logistics, forecast demand and manage inventory, helping to avoid bottlenecks and material shortages.


Energy optimization and sustainability: Manufacturers can significantly reduce energy waste as AI agents monitor consumption across machines and make real-time adjustments to minimize usage without compromising production targets. From our observations the implementation of AI tools at our plants, this can lead to energy savings of 15-20% and supports green manufacturing objectives.


Process automation and optimization: Beyond traditional robotics, AI agents enable cognitive process automation by improving decisions and workflows that were previously manual or rule-bound. They can dynamically adjust parameters like temperature and pressure in real-time based on historical data, ambient conditions and input materials, leading to less waste, fewer mistakes and consistent quality.


Workplace safety: AI agents can monitor environmental factors and safety metrics on the factory floor, predicting potential hazards and automatically triggering safety protocols—such as shutting down machinery or alerting workers—to ensure safe operations.


Intelligent manufacturing assistants: These agents integrate design intelligence into the engineering process, using generative design algorithms to explore product variants, analyzing customer data to recommend product tweaks and evaluating manufacturability before prototyping.


End-to-end automation: Advanced "super AI agents" can manage complex, cross-functional tasks across the entire manufacturing process, from material procurement and production planning to quality control and shipment. They integrate data from all aspects of the supply chain and manufacturing floor to ensure seamless automation.


https://www.industryweek.com/technology-and-iiot/emerging-technologies/article/55362524/were-data-experts-at-ford-heres-how-we-see-ai-agents-reshaping-the-shop-floor



A Dilemma of Marketing Managers - The First Customer Could be an AI Agent

“How do we remain visible and persuasive when the first ‘customer’ in the funnel is not a human, but an AI agent?”

McKinsey & Company


Our research estimates that by 2030, agentic commerce could orchestrate $3 trillion to $5 trillion globally, as AI agents increasingly influence discovery, decision-making, and transactions across categories.


As AI quickly becomes the first stop in the shopping journey in Europe and among industry leaders, the strategic question is shifting to: “How do we remain visible and persuasive when the first ‘customer’ in the funnel is not a human, but an AI agent?” https://mck.co/3Q7kKIM

https://www.linkedin.com/posts/our-research-estimates-that-by-2030-agentic-share-7444669940797755392-d0zm


Interesting LinkedIn Posts on AI and Agentic AI

https://www.linkedin.com/posts/andreashorn1_anthropic-claude-skills-ugcPost-7444286437992103936-HP3D


100 AI  agents

https://www.linkedin.com/posts/adamdanyal_i-analyzed-100s-of-ai-agent-use-cases-from-activity-7443990342397296640-AOJn


6 Imp AI Reports

https://www.linkedin.com/posts/adamdanyal_i-read-2000-pages-of-ai-research-heres-activity-7444352724369969152-qb44


"Global AI Leadership Summit- Edition 1"

https://www.linkedin.com/posts/shaikabdulkhadar_global-ai-leadership-summit-virtual-edition-activity-7443523890301206528-OzUG


https://www.linkedin.com/posts/aiforenterprise_generative-vs-agentic-ai-vs-ai-agents-%F0%9D%97%95-activity-7444383150631194624-dwTt


𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁.

https://www.linkedin.com/posts/supplychainaipro_supplychain-ai-procurement-activity-7444635932672188416-Gw4X



Post included in


ud. 7.11.2026

Pub. 1.4.2026