About Dr. K.V.S.S. Narayana Rao (Narayana Rao Kambhampati)
Dr. K.V.S.S. Narayana Rao (Narayana Rao Kambhampati) is presently Professor in National Institute of industrial Engineering (NITIE), Mumbai, India. He did his post graduation in industrial engineering from NITIE, Mumbai in 1979. His doctoral research is in the area of financial economics in the discipline of industrial management from IIT, Bombay. Prof. Rao published number of papers in both industrial engineering and investment analysis.
Prof. Narayana Rao made efforts in understanding the evolution of industrial engineering discipline since 1994, when he first heard adverse comments on the discipline in an alumni meeting of industrial engineering postgraduates. After making number of conference presentations and publishing papers, he presented the "Principles of Industrial Engineering" in the 2017 Annual Conference of IISE. He developed functions and focus areas of industrial engineering that are aligned to the principles. His blog, Industrial Engineering Knowledge Center, https://nraoiekc.blogspot.com, is a global top blog. The blog is visited by more than 106,000 industrial engineers. His YouTube Channel, having video presentations on industrial engineering, is also a popular channel. He is presently developing an online course on industrial engineering in his blog.
Dr. Rao advocates the objectives of industrial engineering as system efficiency engineering and human effort efficiency engineering. Industrial engineering is continuous improvement in engineering systems to increase productivity. It is an engineering discipline taking care of product and process life cycle engineering and management of productivity. In the area of work systems design and productivity improvement, he advocates two areas, machine work system and human work system. In the present engineering systems, the role of machine or engineering is very high and industrial engineers have to increase focus on engineering elements substantially compared to the current emphasis. Industrial engineers have to update their engineering knowledge throughout their life. Industrial engineering to make the transition to industrial engineering 4.0 and computer aided industrial engineering has to emerge as popular practice in industrial engineering.
Industrial Engineering 4.0 - Computer Aided Industrial Engineering: Work Systems Analysis in Industry 4.0
Important Points Covered in the Paper
Manufacturing execution systems (MES) and Industrial Engineering
1.Introduction
In the progress of engineering, the role of computers has increased in the engineering systems design, production and operation. Computer aids to industrial engineering also came into existence. But it did not evolve into “computer aided industrial engineering” (CAIE).
Manufacturing execution systems (MES) store product and process data and also record the manufacturing events. The recorded data can be utilized by IEs to develop process charts and information on machine effort and human effort to study the process to increase productivity. Computer aided industrial engineering (CAIE) can be made the main method in industrial engineering practice in industrial engineering 4.0.
2. What is Industrial Engineering?
Industrial engineering’s origin is F.W. Taylor’s productivity improvement in machine shops by developing science of metal cutting productivity. Taylor started a section with the name "Rate Fixing Department" that evolved into IE department.
Then it was generalized to other engineering activities and to purely manual activities.
In 1893, he presented paper on the redesign of belts to minimize the cost. In 1895, complete system of productivity improvement in machine shop was presented. It involved study of engineering elements: machine effort elements and human effort elements.
What are typical machine elements? You can understand it from the popular subject, "Design of Machine Elements." Cutting tools are elements in machining and machine tool. Even angles of cutting tools are elements. Even insert shape is an element. Industrial engineers have to go down to the lowest level to seek productivity improvement opportunities and modify every element where improvement is possible.The purpose is to identify the best alternatives to increase the productivity. The objective is to reduce machine time and man time.
Taylor recommended creation of a new department the “elementary rate fixing department.” It became industrial engineering department in due course.
James Gunn, a Harvard B-School faculty member, gave the name industrial engineer (IE). IE understands the costs created by engineering decisions and reduces them to the minimum by selecting the best engineering alternatives. Industrial engineering became continuous engineering improvement of processes based on shop floor studies.
The primary focus of industrial engineering is cost reduction and productivity improvement.
Other performance areas were also added or attempted by industrial engineers to augment their scope of activity. In the effort to increase productivity, Taylor and Gilbreth insisted that quality and human comfort should not suffer. Principles of industrial engineering presented in IISE 2017 Annual Conference by Narayana Rao, capture important issues that are part of industrial engineering discipline at the current time.
Now industry 4.0 is being welcomed by IEs and industrial engineering 4.0 was already proposed.
3.Computers in Industrial Engineering Methods
Ralph Barnes, in his seventh edition of Motion and Time Study devoted two chapters to explain the role of computers in industrial engineering. Chapter 25 is titled “computer aided time study.” Chapter 26 is titled “computerized machine and equipment downtime monitoring and reporting” [Barnes]. Time study is an important method in IE and it is important to notice that in 1980 itself, the role of computers was discussed in time study. In the area of layout planning also, computer methods existed during the 70s.
4.Computer Aided Process Charting, Study and Improvement - Is It Possible?
In the current popular practice, process improvement or method studies have to be done by IEs by observing the process on the shop floor. In addition, they can collect more information from various files. The mechanization and automation have increased in engineering systems. Computer control, PLC control and DCS have emerged. Manufacturing execution systems (MES) came into existence to convert orders on the factory released by MRP system into jobs scheduled for individual machines. The output of MES is routed to machines through DCS/PLCs and each action taken by each device in the machine is now recorded. Hence the device identification and the action that it has taken are available in MES records. The manual activity is also collected by the MES through human machine interfaces (HMI). From these records, details of all elements of the process, the devices used and the parameters that were set by them in the operation/process can be extracted for method study or process improvement. Thus the recording activity of the method study can totally become computer aided activity. Each event has a time stamp and this can give a wealth of time data for each event.
5.Process Productivity Improvement Using Data From Manufacturing Execution Systems
Manufacturing Execution System (MES) is a process-oriented manufacturing planning, directing, recording and controlling information technology system. It maintains a database of products, various operations to be done to produce the products, various resources required to do the processing, orders received from customers or from MRP system, and other related data.
MES systems report the losses of machine time, operators’ time and material in real time to initiate supervisory actions to prevent furthers losses [MES]. The data recorded in MES about the losses facilitates root cause analysis. Industrial engineers can develop engineering changes and management policy changes based on the issues brought out in the root cause analysis. The problems in the equipment and the pain points of operators captured in the MES are addressed by industrial engineers as required by engineering changes in equipment as well as in the process. These modules of MES contribute to work systems analysis for productivity improvement.
Industrial engineers can prepare process charts and operation information sheets from the MES data and investigate the operations for further improvement based on the new engineering knowledge.
Thus, we can see clearly the role of MES in providing the data and visualization support for process improvement. As industrial engineers start using MES more actively, modifications can be proposed and got implemented for enhancing the utility in process improvement studies.
6. Process Productivity Improvement Through Process Mining
Process mining, a data science task, also supports process improvement. In process mining manifesto, van der Aalst W. et al. (2012) state that the idea of process mining is to discover, monitor and improve processes by extracting knowledge from the event logs readily available in today’s information systems like ERP and MES [PM]. Process mining includes current process discovery, conformance checking (i.e., monitoring deviations by comparing the specified process and actual events recorded in the log), organizational mining (activities of persons), automated construction of simulation models, model extension, model repair, case prediction, and history-based recommendations.
Process mining is an enabling technology for business process improvement (BPI). Process mining is successfully applied in number of manufacturing processes also. The objective of process mining is explicitly process improvement.
7. Process Productivity Improvement Using Digital Twins
Digital twin is a digital copy of a real factory, assembly line or machine, etc., that is created and automatically updated with the data sent by sensors on the machines and robots. Once created, the digital twin is globally available in real time to study the important aspects of the physical machine and simulate various changes. In the digital twin, the current information as well as historical information and simulations conducted are available to make use of in product and process designs as well as in planning operations related to doing jobs.
Descriptions of more applications of digital twins in manufacturing are available. The applications include improvement of product manufacturing process, production process optimization and fault diagnosis, and modeling for energy optimization. All there areas are the responsibility of industrial engineers to maintain and improve productivity and efficiency of production systems. Industrial engineers have to adopt digital twin in the process and work systems improvement and do further research to enhance the use of digital twin in process improvement.
8. Knowledge Management for Industrial Engineering
Industrial engineering has to generate alternatives for performing various elements of current processes to improve productivity and quality. In more general terms, it is to increase efficiency while maintaining the effectiveness of processes to produce products and services that are in demand. To do it successfully, the knowledge of engineering and technology developments are to be creatively combined with process requirements. There has to be a system that acquires technology developments on a continuous basis and makes it available to all persons in the organization to drive process improvement thinking. Further, artificial intelligence can be employed to locate the news and information that has direct application in specific operations of the processes.
9. Conclusion
Multiple ways of collecting data for visualizing processes are available now in software systems being used in manufacturing. Some of these systems explicitly state “supporting process improvement” as one of their objectives. These systems were introduced to industrial engineers in some articles published in IISE magazine. The Industrial Revolution 4.0 (Industry 4.0) has increased digitization in manufacturing and there is a trend toward collecting data from all machines and related accessories through embedded sensors. Industrial engineers can utilize more computer assistance now to do process data recording and to simulate improvement ideas. In the industrial engineering practice in industry 4.0 systems, a structured computer assisted industrial engineering is possible and the profession has to deliberate on it and develop a suggested initial practice which can be further improved after evaluating the experience.
The Industrial Revolution 4.0 (Industry 4.0) has increased digitization in manufacturing.
Data from all machines, related accessories, jobs and operators is collected through embedded sensors and MES.
Industrial engineers can utilize more computer assistance now to do process data recording, process charting, experimenting with improvement ideas through simulation and time standard setting.
Software modules and systems to be included in Computer Aided Industrial Engineering System
1. Work Measurement Software.
2. Shop Layout Software
3. Machine Downtime Monitoring
4. MES
5. Process Mining
6. Digital Twins
7. Knowledge Management
8. Simulation
Presentation - Computer Aided Industrial Engineering (CAIE) - Prof. Narayana Rao K.V.S.S.
Industrial Engineering 4.0 – Computer Aided Industrial Engineering: Work Systems Analysis in Industry 4.0 - IISE 2021 Annual Conference Paper
Prof. K.V.S.S. Narayana Rao (NITIE,India)
(Principles and Functions of IE)
Aniket Rathod (NITIE Alumnus)
Industrial Engineering Knowledge Center
Blog maintained by Prof. Narayana Rao
Global Number One IE Blog
Utilized by 106,000 industrial engineers
1. Introduction
In the progress of engineering, the role of computers has increased in the engineering systems design, production and operation.
Computer aids to industrial engineering also came into existence.
But it did not evolve into “computer aided industrial engineering” (CAIE).
MES and Computer Aided Industrial Engineering
Manufacturing execution systems (MES) store product and process data and also record the manufacturing events.
The recorded data can be utilized by IEs to develop process charts and information on machine effort and human effort to study the process to increase productivity.
Computer aided industrial engineering (CAIE) can be made the main method in industrial engineering practice in industrial engineering 4.0.
2. What is Industrial Engineering?
Industrial engineering’s origin is F.W. Taylor’s productivity improvement in machine shops by developing science of metal cutting productivity.
Then it was generalized to other engineering activities and to purely manual activities.
In 1893, he presented paper on the redesign of belts to minimize the cost.
In 1895, complete system of productivity improvement in machine shop was presented.
Redesign of Engineering Elements
It involved study of engineering elements: machine effort elements and human effort elements.
The purpose is to identify the best alternatives to increase the productivity and do productivity engineering (process IE) and productivity management.
The objective is to reduce machine time and man time.
Taylor recommended creation of a new department the “elementary rate fixing department.” (Became IE in due course)
James Gunn – Industrial Engineer – Understands Costs
James Gunn, a Harvard B-School faculty member, gave the name Industrial Engineer (IE).
IE understands the costs created by engineering decisions and reduces them to the minimum by selecting the best engineering alternatives.
Industrial engineering became continuous engineering improvement of processes based on shop floor studies (Basic design and Redesign).
The primary focus of industrial engineering is cost reduction and productivity improvement.
Other Performance Areas – Continuous Improvement
Other performance areas were also added or attempted by industrial engineers to augment their scope of activity.
In the effort to increase productivity, Taylor and Gilbreth insisted that quality and human comfort should not suffer.
Principles of industrial engineering presented in IISE 2017 Annual Conference by Narayana Rao capture important issues that are part of industrial engineering discipline at the current time.
Now industry 4.0 is being welcomed and industrial engineering 4.0 was already proposed.
3. Computer Support in Industrial Engineering Methods
Ralph Barnes
Motion and Time Study (7 ed.) by Ralph Barnes contains,
Chapter 25 - “computer aided time study,” and
Chapter 26 “Computerized machine and equipment downtime monitoring and reporting.”
In the area of layout planning, computer methods existed during the 70s.
4. Computer Aided Process Charting, Study and Improvement - Is It Possible?
In the current popular practice, process improvement or method studies have to be done by IEs by observing the process on the shop floor.
The mechanization and automation have increased in engineering systems.
Computer control, PLC control and DCS have emerged.
Manufacturing execution systems (MES) came into existence to schedule jobs for individual machines and operators and transmit instructions to them.
MES Input Data – Recorded Data
MES records each action taken by each device/machine and each operator.
From these records, details of all elements of the process, the devices used and the parameters that were set by them in the operation/process can be extracted for method study or process improvement.
Thus the recording activity of the Process improvement/method study can totally become computer aided activity.
Each event has a time stamp and this can give a wealth of time data for each event.
5. Process Productivity Improvement Using Data From Manufacturing Execution Systems
Manufacturing Execution System (MES) is a process-oriented manufacturing planning, directing, recording and controlling “information system.”
It maintains a database of products, process plans, resource requirements, orders received, and other related data.
MES systems report the losses of machine time, operators’ time and material in real time.
The data about the losses facilitates root cause analysis.
MES Supports Work Systems Analysis
Industrial engineers can develop engineering changes and management policy changes to prevent losses.
The problems in the equipment and the pain points of operators can be addressed by IEs.
The modules of MES contribute to work systems analysis for productivity improvement.
Process Charts from MES Data – Possible
Industrial engineers can prepare process charts and operation information sheets from the MES data and investigate the operations for further improvement based on the new engineering knowledge.
To summarize, MES provides the data for developing process charts to support process improvement.
As industrial engineers start using MES more actively, modifications can be proposed and got implemented for enhancing the utility in process improvement studies.
6.Process Productivity Improvement Through Process Mining
Process mining, a data science task, also supports process improvement.
Process mining is used to discover, monitor and improve processes by extracting knowledge from the event logs available in ERP and MES.
Process mining is an enabling technology for business and manufacturing process improvement (BPI).
The objective of process mining is explicitly process improvement.
7. Process Productivity Improvement Using Digital Twins
Digital twin is a digital copy of a real factory, assembly line or machine.
It is created and automatically updated with the data sent by sensors on the machines, robots, workpieces.
Once created, the digital twin is globally available in real time to study the important aspects of the job, physical machine and its operation.
It can be used to simulate various changes.
Applications of Digital Twins in Manufacturing
In the digital twin, the current and historical information and simulations conducted are available to make use of in product and process designs as well as in planning operations.
The applications of digital twins include improvement of product manufacturing process, production process optimization and fault diagnosis, and modeling for energy optimization.
All there applications help industrial engineers to improve productivity and efficiency of production systems.
8. Knowledge Management for Industrial Engineering
Industrial engineering has to generate better engineering alternatives
To do it successfully, the knowledge of engineering and technology developments are to be creatively combined with process requirements.
IE requires a system that helps it to record and monitor technology developments.
Artificial intelligence can be employed to locate the news and information that has direct application in specific operations of the processes from the knowledge bases maintained.
Software Systems Indicated
1. Work Measurement Software.
2. Shop Layout Software
3. Machine Downtime Monitoring
4. MES
5. Process Mining
6. Digital Twins
7. Knowledge Management
8. Simulation
9. Conclusion and Thanks
The Industrial Revolution 4.0 (Industry 4.0) has increased digitization in manufacturing.
Data from all machines, related accessories, jobs and operators is collected through embedded sensors and MES.
Industrial engineers can utilize more computer assistance now to do process data recording and process charting.
A structure for computer assisted industrial engineering is to be developed.
----------------------
Full Schedule of the Conference
https://www.iise.org/Annual/details.aspx?id=10042
Digital Twins of CNC Machines - Bibliography
CEU Credits for IISE Annual Virtual Conference & Expo 2021 - K.V.S.S. Narayana Rao
Credits given for the sessions attended and feedback given.
(967146) Guiding Principles for Physical Store Redesign 0.03
(964859) Industrial Engineering 4.0 - Computer Aided Industrial Engineering: Work Systems Analysis in Industry 4.0 0.03
Keynote Session: Andres Medaglia 0.1
(973546) Innovations in Service Systems Engineering: Best Practice Case Studies 0.13
(973206) Benchmarking Industry: Best Practices in Deploying ISE to Engineer Performance Excellence 0.13
(969847) Large Scale Parallel 3D Printing of Patterns for Metal Casting 0.03
(969213) Maximizing Storage Density of Steel Pipes: Optimization Modeling 0.03
(968890) Assessment of the Maturity of Research on Kaizen Events in Hospitals 0.03
(974293) A Practical Aid for Material Handling Equipment Selection 0.03
(972886) 5As of Problem Solving Techniques for Game Changers 0.03
(974416) Optimal Shelves Design for Assembly Line in an OEM Industry 0.03
(970134) The Challenge of Becoming a Worker 4.0 - A Human-centered Maturity Model for Industry 4.0 Adoption 0.03
(971269) Multi-objective Optimization of Peel and Shear Strengths in Ultrasonic
Metal Welding Using Machine Learning-based Response Surface Methodology 0.03
(974210) A Preliminary Study on Manufacturing Systems Resiliency in the Industry 4.0 Era 0.03
Keynote Presentation: Nadine Sarter 0.1
(Special Session) Panel: The Present and Future of Work Measurement 0.13
(973385) Industry 4.0: Migration Strategies and Case Examples 0.13
Captains of Industry Forum 0.07
Wellington Award Lecture 0.13
Operational Excellence Best Practice Presentations 0.13
(969275) Laser Fabrication of Polymeric Microneedles for Transdermal Drug Delivery 0.03
(968280) Effects of Directed Energy Deposition Parameters on Bond Strength Between Stainless Steel Deposition and Cast Iron Substrate 0.03
(973359) Open Educational Resources for Industrial Engineering Undergraduate Courses 0.03
(968948) Metallic Nanoparticle Ink Formulation Development and Optimization for Electrohydrodynamic Inkjet Printing 0.03
(974981) Realtimepc 2020: Overcoming the Challenge of Offering an Interactive and Virtual Experience 0.03
(969113) Rheological Study of Highly Thixotropic Hydrogels for 3D Bio-printing Processes 0.03
(974668) The Influence of Study Habits on the Academic Performance of Industrial Engineering Students: Survey Research 0.03
(974868) Cheating in Industrial Engineering College Student: Reasons, Techniques, and Mitigations 0.03
(970893) Decision-making Framework for Systematic Evaluation of Design Alternatives for Metal Additive Manufacturing 0.03
(965236) Continuous Improvement in Capstone Course Delivery 0.03
(969560) Integrating Additive Manufacturing and Robotics in Interdisciplinary Freshman Engineering Education 0.03
(974576) Linkage, Innovation, and Technology Transfer Capacities in Technological Institutes for Industry 4.0: case Yucatán, Mexico 0.03
(973816) Inventory Models, Work Systems and Project-based Learning Combined for a Didactic Strategy 0.03
(969708) Understanding Change in Public Organizations: A Systematic and Integrative Review 0.03
(974459) Online Monitoring of Geometric Accuracy of Additively Manufactured Parts with Point Clouds 0.03
Keynote Session: Walt Ehmer 0.1
BlockChain Tutorial 0.13
(965004) The Impact of Design and Manufacturing Complexity on an Additive Manufacturing System 0.03
(969223) Optimal Process Plan of Manufacturing Product Variants Using Hybrid Manufacturing Technology 0.03
(967662) Identification of Circumstances in Which Zone Formation Is Beneficial in a Manual Order Picking E-commerce Warehouse 0.03
(977691) Designing Optimal Unit-load Warehouses: Use Deterministic or Probabilistic Models? 0.03
Total Credits: 2.2
Interesting Content from Papers Read and Presentations Viewed - list above
A Practical Aid for Material Handling Equipment Selection
Database for Selection of Material Handling Equipment.
Machine Learning Methods
• Polynomial regression (ridge)
• Spline regression
• Gaussian process regression (GPR)
Ø radial basis function (RBF)
Ø rational quadratic
• Support vector regression (SVR)
Ø radial basis function (RBF)
Ø quadratic
Laser Fabrication of Polymeric Microneedles for Transdermal Drug Delivery
An Ytterbium laser (200W) was utilized to study the effect of five input factors (laser power, pulse width, number of repetitions, laser waveform, and interval time between laser pulses) on two output factors (diameter and height) of the fabricated microneedles.
Effects of Directed Energy Deposition Parameters on Bond Strength Between Stainless Steel Deposition and Cast Iron Substrate
Directed Energy Deposition (DED) is a metal additive manufacturing technology that has found applications in the repair of difficult-to-weld cast iron. Significant effort has been placed into understanding the relationship between process parameters and the resultant track geometry and dilution in DED-based cast iron repairs. This study presents a method for measuring bond strength in DED repairs. Stainless steel was deposited into cast iron grooves, and bond strength was measured using tensile testing transverse to the groove length. Dilution and residual stresses are also reported to understand the effects of distinct thermal conditions and their relationship to bond strength. Larger track volumes were generally observed to show lower dilution and subsequently poorer bonding. Additionally, residual stresses were found to vary along the length of the groove repair, with the groove ends showing high tensile stresses. The results suggest that superior repair quality can be achieved through parametric control.
Updated 24.3.2022, 19 Sep 2021, 26 June 2021 15 May 2021
Pub 13 May 2021
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