Sunday, August 27, 2017

TRIZ - Creative Thinking for Inventing and Innovating

New Books and Articles on TRIZ
2016 to

Systematic Innovation Toolkit
March 25, 2016


40 Inventive principles of TRIZ

Described in Part 2-3 of the Book  The Innovation Algorithm

Also See

40 Principles: TRIZ Keys to Innovation

Genrich Altshuller, Lev Shulyak, Steven Rodman
Technical Innovation Center, Inc., 2002 - 135 pages

The book has one page for each principle with pictures illustrating the explanation of the principle.

Principle 1: Segmentation:

Principle 2: Taking out or Extraction:

Principle 3: Local quality: development; local anaesthesia.

Principle 4: Asymmetry:

Principle 5: Merging, Consolidation or combining:

Principle 6: Universality:

Principle 7: Nested doll:

Principle 8: Anti-weight:

Principle 9: Preliminary anti-action:

Principle 10: Preliminary action:

Principle 11: Beforehand cushioning:

Principle 12: Equipotentiality:

Principle 13: The other way round:

Principle 14: Spheroidality – Curvature:

Principle 15: Dynamics:

Principle 16 : Partial or Excessive actions:

Principle 17: Another dimension:

Principle 18: Mechanical vibration:

Principle 19: Periodic action:

Principle 20: Continuity of useful action:

Principle 21: Skipping or Rushing Through:

Principle 22 : Blessing in disguise - Harm into benefit:

Principle 23: Feedback:

Principle 24: Intermediary/Mediator:

Principle 25: Self-Service:

Principle 26: Copying:

Principle 27: Cheap short-living objects:

Principle 28: Mechanics substitution:

Principle 29: Pneumatics and hydraulics:

Principle 30: Flexible shells and thin films:

Principle 31: Porous materials:

Principle 32: Color changes:

Principle 33: Homogeneity:

Principle 34: Rejecting, Discarding – Recovering, Regeneration:

Principle 35: Parameter Changes:

Principle 36 : Phase transitions:

Principle 37: Thermal expansion:

Principle 38 : Accelerated oxidation:

Principle 39 : Inert atmosphere:

Principle 40: Composite materials:

40 Principles - Pdf List

Examples of 40 principles - from automotive sector

TRIZ: Systematic Innovation in Manufacturing

Yeoh Teong San, Yeoh Tay Jin, Song Chi Li
Firstfruits Publishing, 2009 - Engineering - 180 pages

The Ideal Result: What It Is and How to Achieve It

Jack Hipple
Springer Science & Business Media, 26-Jun-2012 -  208 pages

The Ideal Final Result introduces the TRIZ Inventive Problem Solving Process in a way that allows readers to make immediate use of its most basic concepts. The Ideal Final Result reviews the basics of this left brained, but at the same time, very creative process for problem solving that uses a basic algorithm developed through the study of millions of patents. As opposed to psychologically based tools relying on the generation of hundreds of ideas to be sorted through to find the few of value, TRIZ rigorously defines the problem and assists the problem owner in identifying the existing inventive principles that are already known to solve that class of problems. This book reviews the most basic of the TRIZ algorithm tools and provides templates for readers to use in analyzing their difficult problems and provides a mental framework for their solution. It also describes TRIZ techniques for basic strategic planning in a business sense.

TRIZ for Engineers: Enabling Inventive Problem Solving

Karen Gadd
John Wiley & Sons, 11-Feb-2011 - 504 pages

TRIZ is a brilliant toolkit for nurturing engineering creativity and innovation. This accessible, colourful and practical guide has been developed from problem-solving workshops run by Oxford Creativity, one of the world's top TRIZ training organizations started by Gadd in 1998. Gadd has successfully introduced TRIZ to many major organisations such as Airbus, Sellafield Sites, Saint-Gobain, DCA, Doosan Babcock, Kraft, Qinetiq, Trelleborg, Rolls Royce and BAE Systems, working on diverse major projects including next generation submarines, chocolate packaging, nuclear clean-up, sustainability and cost reduction.

Engineering companies are increasingly recognising and acting upon the need to encourage successful, practical and systematic innovation at every stage of the engineering process including product development and design. TRIZ enables greater clarity of thought and taps into the creativity innate in all of us, transforming random, ineffective brainstorming into targeted, audited, creative sessions focussed on the problem at hand and unlocking the engineers' knowledge and genius to identify all the relevant solutions.

For good design engineers and technical directors across all industries, as well as students of engineering, entrepreneurship and innovation, TRIZ for Engineers will help unlock and realise the potential of TRIZ. The individual tools are straightforward, the problem-solving process is systematic and repeatable, and the results will speak for themselves.
This highly innovative book:

Satisfies the need for concise, clearly presented information together with practical advice on TRIZ and problem solving algorithms
Employs explanatory techniques, processes and examples that have been used to train thousands of engineers to use TRIZ successfully
Contains real, relevant and recent case studies from major blue chip companies
Is illustrated throughout with specially commissioned full-colour cartoons that illustrate the various concepts and techniques and bring the theory to life
Turns good engineers into great engineers.

TRIZ - Systematic Innovation in Business & Management

Yeoh Teong San
First Fruits Sdn. Bhd., 01-Oct-2014 - Business & Economics - 238 pages

TRIZ (Theory of Inventive Problem Solving) is a powerful methodology which is able to improve a company's top-line and bottom-line. The top-line refers to a company's gross sales or revenues, whereas the bottom-line is a company's net earnings or net profits. The uniqueness of TRIZ is its ability to provide a structured and systematic approach, coupled with a suite of tools to enhance both top-line and bottom-line results. TRIZ can be used for creating new products to generate sales or making processes more efficient and effective to reduce operating costs and expenses.

TRIZ also enhances management capabilities by transforming a good manager to a great manager by acquiring tools to recognize contradictions when they arise and solve them without compromise.

In summary, TRIZ is a philosophy, process, and suite of tools. A total of 11 TRIZ tools (Function Analysis, Cause & Effect Chain Analysis, Perception Mapping, Ideality, S-curve, Trends of Engineering System Evolution, Trimming, Feature Transfer, Function Oriented Search, 9-Windows, and Engineering Contradiction) are discussed in detail.

Numerous examples and case studies are used to illustrate TRIZ applications in accelerating the ability to predict product, process, and service trends; identify unique value propositions for new products or services; circumvent patents of competitors; and solve age-old or chronic problems in both business and management fields.

Innovation Management System - Presentation - Simon Tong
Hong Kong Society for Quality

Updated on  28 August 2017,  17 February 2017,  22 October 2016

Saturday, August 26, 2017

Productivity Science - Some Hypothesis like Statements

"When large companies get Agile right, the results can be stunning. Productivity can improve by a factor of three. Employee engagement, measured in quantitative surveys, increases dramatically too. New product features can be released within weeks or months rather than quarters or years. Rates of innovation rise, while the number of defects and do-overs declines. In the first year after going Agile, one bank’s development team increased the value delivered per dollar spent by 50%, simultaneously cutting development time in half and improving employee engagement by one-third."

Five Secrets to Scaling Up Agile
FEBRUARY 19, 2016 by Kaj Burchardi, Peter Hildebrandt, Erik Lenhard, Jérôme Moreau, and Benjamin Rehberg

Wednesday, August 23, 2017

Six Sigma - Contribution to GE - 1997

Excerpts from  GE Annual Report 1997

The centerpiece of our dreams and aspirations "the drive for Six Sigma quality.

 “Six Sigma” is a disciplined methodology, led and taught by highly trained GE employees
called “Master Black Belts” and “Black Belts,” that focuses on moving every process that touches our
customers — every product and service — toward near-perfect quality.

Six sigma projects usually focus on improving our customers’ productivity and reducing their capital outlays,
while increasing the quality, speed and efficiency of our operations.

We didn’t invent Six Sigma — we learned it.

Motorola pioneered it and AlliedSignal successfully embraced it. The experiences of these two
companies, which they shared with us, made the launch of our initiative much simpler and faster.

GE had another huge advantage that accelerated our quality effort: we had a Company that was
open to change, hungry to learn and anxious to move quickly on a good idea.

At GE today —finding  the better way, the best idea, from whomever will share it with us, has become our central focus.

Nowhere has this learning environment, this search for the better idea, been more powerfully
demonstrated than in our drive for Six Sigma quality. Twenty-eight months ago, we became con-
vinced that Six Sigma quality could play a central role in GE’s future; but we believed, as well, that it
would take years of consistent communication, relentless emphasis and impassioned leadership
move this big Company on this bold new course.

We were wrong!
 Projections of our progress in Six Sigma, no matter how optimistic, have had to be junked every few months as gross underestimates. Six Sigma has spread like wildfire across the
Company, and it is transforming everything we do.

We had our annual Operating Managers Meeting — 500 of our senior business leaders
from around the globe — during the first week of January 1998, and it turned out to be a wonderful
snapshot of the way this learning Company — this new GE — has come to behave; and now, with Six Sigma, how it has come to work.

Today, in the uncountable number of business meetings across GE — both organized and “in-
the-hall” — the gates are open to the largest flood of innovative ideas in world business. These ideas
are generated, improved upon and shared by 350 business segments — or, as we think of them, 350
business laboratories. Today, these ideas center on spreading Six Sigma “best practices” across our
business operations.

At this particular Operating Managers Meeting, about 25 speakers, from across the Company and
around the world, excitedly described how Six Sigma is transforming the way their businesses work.

They shared what they had learned from projects such as streamlining the back room of a credit card
operation, or improving turnaround time in a jet engine overhaul shop, or “hit-rate” improvements
in commercial finance transactions. Most of the presenters focused on how their process improve-
ments were making their customers more competitive and productive:

• Medical Systems described how Six Sigma designs have produced a 10-fold increase in the life of CT scanner x-ray tubes — increasing the “uptime” of these machines and the profitability and level of patient care given by hospitals and other health care providers.

• Superabrasives — our industrial diamond business — described how Six Sigma quadrupled
its return on investment and, by improving yields, is giving it a full decadeÕs worth of capacity despite growing volume — without spending a nickel on plant and equipment capacity.

• Our railcar leasing business described a 62% reduction in turnaround time at its repair shops: an enormous productivity gain for our railroad and shipper customers and for a business that’s now two to three times faster than its nearest rival because of Six Sigma improvements. In the next phase, spread across the entire shop network, Black Belts and Green Belts, working with their teams, redesigned the overhaul process, resulting in a 50% further reduction in cycle time.

• The plastics business, through rigorous Six Sigma process work, added 300 million pounds of new capacity (equivalent to a “free plant”), saved $400 million in investment and will save another $400 million by 2000.

At our meeting, zealot after zealot shared stories of customers made more competitive, of credit
card and mortgage application processes streamlined, of inventories reduced, and of whole facto-
ries and businesses performing at levels never believed possible.

The sharing process was repeated at another level two weeks later in Paris, as 150 Master Black
Belts and Black Belts, from every GE business throughout Europe, came together to share and
learn quality technology. This learning is done in the boundaryless, transcultural language of Six
Sigma, where “CTQ’s” (critical to quality characteristics) or “DPMO’s” (defects per million oppor-
tunities) or “SPC” (statistical process control) have exactly the same meaning at every GE operation
from Tokyo to Delhi and from Budapest to Cleveland and Shanghai.

The meeting stories are anecdotal; big companies can make great presentations and impressive
charts. But the cumulative impact on the Company’s numbers is not anecdotal, nor a product of
charts. It is the product of 276,000 people executing ... and delivering the results of Six Sigma to our
bottom line.

Operating margin, a critical measure of business efficiency and profitability, hovered around
the 10% level at GE for decades.  With Six Sigma embedding itself deeper into Company operations, GE in 1997 went through the “impossible” 15% level — approaching 16% — and we are optimistic about the upside.

Six Sigma, even at this relatively early stage, delivered more than $300 million to our 1997
operating income. In 1998, returns will more than double this operating profit impact.
Six Sigma is quickly becoming part of the genetic code of our future leadership. Six Sigma
training is now an ironclad prerequisite for promotion to any professional or managerial position
in the Company — and a requirement for any award of stock options.

Senior executive compensation is now heavily weighted toward Six Sigma commitment and suc-
cess — success now increasingly defined as “eatable” financial returns, for our customers and for us.
There are now nearly 4,000 full-time, fully trained Black Belts and Master Black Belts: Six
Sigma instructors, mentors and project leaders. There are more than 60,000 Green Belt part-time
project leaders who have completed at least one Six Sigma project.

Already, Black Belts and Master Black Belts who are finishing Six Sigma assignments have become
the most sought-after candidates for senior leadership jobs in the Company, including vice presidents and chief financial officers at some of our businesses. Hundreds have already moved upward
through the pipeline. They are true believers, speaking the language of the future, energized by
successful projects under their belts, and drawing other committed zealots upward with them.

In the early 1990s, we efined ourselves as a company of boundaryless people with a thirst for learning and a compulsion to share

Now it is Six Sigma that is  permeating much of what we do all day.

We are feverish on the subject of Six Sigma quality as it relates to products, services and people — maybe a bit unbalanced —  because we see it as the ultimate way to make real our dreams of
what this great Company could become.

Six Sigma has turned up the voltage in every GE business across the globe, energizing and
exciting all of us and moving us closer than ever to what we have always wanted to become: more than a hundred-billion-dollar global enterprise with the agility, customer focus and fire in the belly of a small company.

In our 1994 letter to you, we addressed the perennial question put to management teams, which is “how much more can be squeezed from the lemon?” We claimed, then, that there was in fact unlimited juice in this “lemon,” and that none of this had anything to do with “squeezing” at all.
We believed there was an ocean of creativity and passion and energy in GE people that had no bottom and no shores. We believed that then, and we are convinced of it today. And when we said that
there was an “infinite capacity to improve everything,” we believed that as well — viscerally — but
there was no methodology or discipline attached to that belief. There is now. It’s Six Sigma quality,
along with a culture of learning, sharing and unending excitement.


Six Sigma is based on the following basic principles.

1. Y=f(X) + ε: All outcomes and results, the dependent variable (the Y) are determined by inputs (the Xs) with some degree of uncertainty (ε).

2. To change or improve results (the Y), you have to focus on the inputs (the Xs), modify them. (In the six sigma method, values of different variables X are changed systematically and resulting output is recorded and analyzed to find the best combination of values.

3. Variation is everywhere, and it degrades consistent, good performance. Your job is to find it and minimize it!

4. You get minimum variation for a particular combination Xs for given set of X and some times by including more input variables.

5. Valid measurements and data are required foundations for consistent, breakthrough improvement.

6. Only a critical few inputs have significant effect on the output. Concentrate on the critical few. There is some effort involved in determining the set of Xs that have significant effect on the output.

Philosophy – Process inputs control the outputs and determine their level of quality.

Focus – An unending quest for improving business processes.

Methods – Known as DMAIC (define, measure, analyze, improve, and control) and DMADV (define, measure, analyze, design, verify).

Measure of Success – Ultimately reducing defects to 3.4 per one million opportunities, through iterative application of six sigma methodology to understand the process better.

Updated 24 August 2017, 3 March 2012

Tuesday, August 22, 2017

Data Analytics Period in Productivity Improvement - Productivity Engineering and Management


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

How Data Analytics Increases Productivity?

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

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

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

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

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

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


Further Developments


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

Be agile, Be focused on results and Take up manageable data analytics projects

Digital transformation-driven productivity
Don't underestimate the incredible power of DX turned inward, toward enhancing internal organizational productivity.

Digital Transformation Can Resolve the Productivity Paradox
EITN MALAYSIA, April 27, 2017
By Hu Yoshida, Chief Technology Officer, Hitachi Data Systems

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

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.

IoT use cases with 2 year pay back period in global auto company mentioned.

Microsoft Workplace Analytics helps managers understand worker productivity



November 2016

Smart data is the way to boost mining productivity

2 September 2016

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.


Production Data Analytics – To identify productivity potentials
Master Thesis - 2015
Department of Product and Production Development


Data Analytics and Continuous Productivity
September 18, 2013

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.


MIT professor Erik Brynjolfsson discusses how companies can increase their productivity by making better use of their data.
by David Talbot  February 16, 2011

MIT professor Erik Brynjolfsson
Book Chapter: Business practices that enhance productivity along with IT investments.

Updated 23 August 2017,  16 August 2017,  10 July 2017,  30 June 2017, 22 June 2017

Industrial Engineering - Foundation of Toyota Production System

Toyota Production System or Lean Philosophy

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

Toyota Production System - Lean Tools or Techniques

Poka Yoke
Value stream mapping to find cycle time and processing time.
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
Components of Lean System

Shigeo Shingo said
80% of the lean system (TPS) is waste elimination that is industrial engineering,
15% - production management 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.
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.

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.
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.
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.
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).

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
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.

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

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.

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.

Summary of Remaining Chapters of the Book

Updated 22 August 2017,  9 Sep 2015
First published 9 Sep 2014

Behavioral Approach to Productivity - Behavioral Variables and Productivity

"Behavioral strategies to improve productivity"
Gary P.Latham, Larry L.Cummings, and Terence R.Mitchell,
Organizational Dynamics
Volume 9, Issue 3, Winter 1981, Pages 5-23

Hum Resour Manage. 1983 Jan-Feb;13(1):1-5.
"Behavioral science approaches to improving productivity."

Shortell SM.

We have suggested that improving the productivity of an individual or of one group of workers is not the same thing as improving the productivity of the organization overall. Further, because work units in hospitals are interdependent, attempts to improve productivity ultimately involve a reexamination of the organization's values and culture. Until this fundamental realization occurs, little can be done to improve the organization's productivity. As such, productivity is not simply a day-to-day managerial issue as a long-term leadership issue. Thus, we ought to begin mapping out productivity strategies for the long run. Some of the elements of such a strategy have been highlighted.

Aubrey C. Daniels "Performance management: The behavioral approach to productivity improvement"
Global Business and Organizational Excellence
Volume 4, Issue 3, Summer 1985, Pages 225–236

S.K. Srivastava & Kailash Chandra Barmola
SMS Varanasi,  Vol. VII, No. 1; June, 2011

Evidence-Based Productivity Improvement: A Practical Guide to the Productivity Measurement and Enhancement System (ProMES)
Robert D. Pritchard, Sallie J. Weaver, Elissa Ashwood
Routledge, 04-May-2012 - Psychology - 316 pages
This new book explains the Productivity Measurement and Enhancement system (ProMES) and how it meets the criteria for an optimal measurement and feedback system. It summarizes all the research that has been done on productivity, mentioning other measurement systems, and gives detailed information on how to implement this one in organizations. This book will be of interest to behavioral science researchers and professionals who wish to learn more about the practical methods of measuring and improving organizational productivity.

Enhancing Strategies to Improve Workplace Performance
Thesis by Francine Williams Richardson
Walden University

Nudge management: applying behavioural science to increase knowledge worker productivity
Philip Ebert and Wolfgang Freibichler
Journal of Organization Design2017  6:4
Published: 21 March 2017


Culture and Productivity - Bibliography

Seven Wastes Model

Seven Wastes - Taiichi Ohno - page 19 - Toyota Production System

   Waste                          -                   Method

Waste of overproduction - One cannot produce without a production Kanban

Waste of time on hand (waiting)  - Multiple machines to an operator, all producing as per tact time.

Waste in transportation - Machines in line or flow placed close together

Waste of processing itself - Standardized Methods

Waste of stock on hand (inventories) - JIT system - low inventory

Waste of movement (of workers) - Machine layout changes so that an operator handling multiple machines does not waste movement. Can there be control unit for all the machines at one place only?

Waste of making defective products - Problem solving approach to produce zero defects. 5 Why approach to find where the problem or defect occurred in the earliest stage. Educating and training operators by other team members and management.

Seven Waste Model is also expressed as TIMWOOD

T – Transport – Movement of material, people
I – Inventory – Stock of materials, parts, and finished items
M – Motion – movement of hands and other body parts in operating machines of hand tools
W – Waiting – Men and machines waiting for parts or instructions
O – Over production – Making more than is IMMEDIATELY required
O – Over processing – Tighter tolerances or higher grade materials than are necessary
D – Defects – Items scrapped and rework

Compare the seven waste model with flow process chart

Flow process chart recommends recording and examining 5 items.

Process -   will examine  1.Waste of processing itself,  2. Waste of overproduction

Inspection -  3. Waste of making defective products  - The inspection is only shown as a stage in flow process chart, It needs to be augmented with a record of defects or defectives found during inspection.

Transport - 4. Waste in transportation, 5. Waste of movement (of workers)

Temporary delay - 6. Waste of time on hand (waiting)

Permanent storage - 7. Waste of stock on hand (inventories)

Thus we can see, flow process charts has provided the foundation for analyzing the seven wastes proposed by Taiichi Ohno.

But subsequent persons have indicated Eighth Waste.

Wastage of physical and mental skills of people.

Alan Mogensen identfied this gap in industrial engineering theory and introduced work simplication workshops to involve operators and supervisors in productivity improvement. Subsequently, suggestions scheme became popular. Japanese managers brought more improvements and made operators given large number of suggestions and provided forums for participation.

Narayana Rao proposed Ninth waste.

9th Waste - Wastage of Machine Potential, Capability and Power - Wasting Machine's Potential Productivity

Industrial engineering has ignored wastage of potential of machines and equipment even though Taylor has advocated right from his piece rate system paper that for productivity improvement both machine and man are to be analyzed and improved.

9th Waste - Wasting Machine's Potential Productivity -- Elimination - Essential Industrial Engineering Activity


Seven Wastes

Seven Wastes Tool

Lean for Government: Eliminating the seven wastes

Lean in Government

Seven Wastes in Preventive Maintenanee Programs

Updated 23 August 2017, 15 November 2013

Sunday, August 20, 2017

Poka-Yoke - Shigeo Shingo

For Zero defects, Shigeo Shingo came up with an industrial engineering solution. Industrial engineering needs efficiency sense and focus. They have to use engineering knowledge to improve the efficiency of engineering systems and reduce costs. The solution proposed by Shingo for zero defect production is Poka-Yoke. The features built into the machine and associated devices that prevent defects from happening.  The features inform the operator that a mistake has happened and provide him an opportunity to correct the mistake.

Familiar examples of Poka Yoke

1) Warning about missing attachment file you get while composing email using Gmail.

2) Websites showing password strength indicator to show password strength. So weak passwords are avoided.

3) Google search engine feature to auto-suggest spelling corrections for user search query. This helps uses to avoid making inadvertent mistakes during search.



Iris D. Tommelein
Director, Project Production Systems Laboratory,, and Professor,
Engineering and Project Management Program, Civil and Environmental Engineering Department,
215-A McLaughlin Hall, University of California, Berkeley, CA 94720-1712,

2008 Paper on Shingo System

Mistake-Proofing for Operators: The ZQC System

Shigeo Shingo, Productivity Press Development Team
Productivity Press, 01-Jan-1997 - Business & Economics - 80 pages

The Zero Quality Control System (ZQC) is a mistake-proofing approach that prevents defects by monitoring processing conditions at the source and correcting errors that cause defects. Since it is human nature to make mistakes, ZQC does not blame people for errors, but instead finds ways to keep errors from becoming defects. In this breakthrough approach, mistake-proofing devices called poka-yoke are used to check and give feedback about each product or operation in the process, not just a sample. This book introduces operators and assembly workers to the basic methodology of ZQC in an easy-to-read format that covers all aspects of this important manufacturing improvement strategy.

Mistake-Proofing for Operators includes the instructional features that are the signature of the Shopfloor Series. In this series Productivity Press has taken the lead in adult education by teaming with instructional designers to develop complete programs for frontline learning. The goal: to place powerful and proven improvement tools such as ZQC and mistake-proofing in the hands of your company's entire workforce.

Winner of the 1990 Shingo Prize for Excellence in Manufacturing, Mistake-Proofing for Operators is based on Zero Quality Control: Source Inspection and the Poka-Yoke System by Shigeo Shingo

Poka-Yoke: Improving Product Quality by Preventing Defects
Nikkan Kogyo Shimbun, Factory Magazine
Productivity Press, 1988 - Business & Economics - 282 pages

If your goal is 100% zero defects, here is the book for you — a completely illustrated guide to poka-yoke (mistake-proofing) for supervisors and shop-floor workers. Many poka-yoke ideas come from line workers and are implemented with the help of engineering staff or tooling or machine specialists. The result is better product quality and greater participation by workers in efforts to improve your processes, your products, and your company as a whole.

The first section of the book uses a simple, illustrated format to summarize many of the concepts and main features of poka-yoke. The second section shows 240 examples of poka-yoke improvements implemented in Japanese plants.

The book:

Organizes examples according to the broad issue or problem they address.
Pinpoints how poka-yoke applies to specific devices, parts and products, categories of improvement methods, and processes.
Provides sample improvement forms for you to sketch out your own ideas.
Use Poka-yoke in study groups as a model for your improvement efforts. It may be your single most important step toward eliminating defects completely. (For an industrial engineering perspective on how source inspection and poka-yoke can work together to reduce defects to zero, see Shigeo Shingo's Zero Quality Control.)

Training Programmes

Shingo Institute of Japanese Management, Bangalore and Hyderabad


Updated   21 August 2017,  25 February 2017,  21 December 2013

Friday, August 18, 2017

UK Productivity


The UK currently lags behind its G7 competitors’ average productivity levels by an average of 18% - and 35% behind Germany. Poor management is estimated to cost the UK £84bn in lost productivity a year.

Chartered Management Institute (CMI) proposed a management manifesto to improve management in UK and reclaim the 84 billion pounds lost in productivity due to poor management.

You can download the manifesto document from:

Tuesday, August 15, 2017

Productivity Improvement Through Smart Machines

IIOT, Industry 4.0, 4IR (Fourth industrial revolution) etc. are  frameworks to merge the operational technology (OT) with the information technology (IT) to provide new solutions for automating and networking industrial machines and systems for performance and productivity improvement.

What is a Smart Machine?

It is self-­aware, reacts autonomously and provides information about the product being produced, production parameters utilized, production time taken production quantity in unit time periods, production history,  configuration, condition, quality and Overall Equipment Efficiency (OEE) to other machines.

Smart Machine Control
Machine vision and advanced motion control are key components of smart automation. Engineers can increase throughput and build highly efficient modern machines that sharpen accuracy and easily adapt to new products and processes.

Today, the keys to smart machine control are gaining insight into the machine’s operation and the ability to adjust control outputs. Engineers rely on sensor information to monitor the condition of mechanical parts, which gives them the ability to observe the process for quality control or closed-control loops for applications like force feedback or precise positioning.

Today, LabVIEW (of NI.Com) graphical programming helps leading machine builders master this increasing system complexity with add-on modules for motion control, machine vision, and control design and simulation.


Leonardo Smart Rotational Moulding Machine

Smart Machine
Bringing intelligence into the milling process is the intended aim of "smart machine".

Optimization of the Machining Process

Patented: The Original OSS
The intelligent Operator Support System optimises the machining process according to the specifications of the workpiece. The target variables – speed, accuracy and surface quality – as well as the workpiece weight and the complexity of the machining application can be selectively defined and modified at any time via an intuitive user interface.

Setting priorities
The user can define the machine settings via the OSS, depending on the operation. He can choose from 12 predefined settings or extend them as desired.

Priority: Time

Only the time is of interest when roughing. If the operator selects the time as the highest priority, then OSS extends the tolerance band. In addition to this, the control system adapts the speed profile to the geometry that is to be processed.

Priority: Surface quality

In order to maintain the best possible surface quality despite poor NC program quality, the tolerance band is enlarged and the transitions are smoothed

Priority: Accuracy

If the workpiece demands extremely high precision, the tolerance band selected is narrower, in order to achieve the required accuracy. The control system is configured in such a way that the best possible geometrical accuracy is guaranteed.

User-defined setting

Additional settings can be saved in a library and can then be used again at any time. Several positions can be selected between the extremes (surface quality, accuracy, time).

Productivity & Precision

Your benefit
The intelligent system sets the dynamic behaviour of the machine exactly according to the workpiece requirements
Shorter machining times
Better surface quality
Intuitive handling of complex settings
Reduced complexity when setting up a workpiece


Cat (Caterpillar) Connect Technology: Hardware and software available for equipment to arm customers with information designed to help them optimize their operations. Specific construction technologies include:

        o  LINK, a solution that captures vital performance and product health data and makes that data available on the web to guide decision-making.

        o  GRADE and COMPACT, two productivity solutions that help operators move material faster, more accurately and with fewer passes.

        o  PAYLOAD, an on-board system for trucks and loading tools that drives higher efficiency, shorter cycle times and lower cost per ton.

Smart Asphalt Compaction


Caterpillar’s new B-Series tandem vibratory asphalt roller models include a range of advancements in intelligent compaction technology to deliver higher quality compaction. These models — the CB64B, CB66B, and CB68B — feature technological improvements made possible through Cat Compaction Control, Caterpillar’s intelligent compaction suite. The suite consists of dual air-purged, infrared temperature sensors that are integrated into the front and rear of the machine.


Smart Food Processing

A Cisco project involving Sugar Creek Packing Co. , Washington Court House, Ohio is making machines and processes smarter. The company recently commissioned a brownfield project in Cambridge City, Ind., Harvesting large amounts of data and feeding it back as actionable information is used for  for establishing a high-performance work team structure at the plant. “High-performance work teams are semi-autonomous teams work with little supervision, with production, maintenance and HR issues handled by team members. They need meaningful feedback and they get it from the smart features of the system. A sous vide cooking system is an illustration of it. It is a disruptive cooking technology and a highly automated system, with hundreds of sensors to control the process. To access the data, team members use a Cisco mobile app called Jabber, “essentially an IP phone. This feature substitutes installing the system based on ordinary mobiles with a booster system that would have added $300-500 million to project cost. Jabber radios essentially function like a desk phone and integrate easily with plant software. The wireless network also will track worker locations within one meter via RFID tags embedded on protective headgear.


“Smart” ProSlab 155 Automated Turf Harvester

National Instruments Corporation, Austin, Texas describes the machine as smart machine.

The machine uses LabVIEW software and CompactRIO hardware supplied by National Instruments. The smart machine harvests turf 20 percent faster, and uses half the diesel fuel than other turf harvesting machines on the market. It uses more electric systems. FireFly engineers can also remotely monitor, diagnose, update (and control) the sensor-laden ProSlab 155



The technologies for building a large-scale and diverse scope of smart machines are coalescing and being tested by "first movers." Once they offer significant  cost reduction and productivity improvement, companies will embrace them by employing smart machines in place of humans.

IT cost is typically about four percent of annual revenue of the companies, whereas the labor costs that can be rationalized by smart machines are as high as 40 percent of revenue in some knowledge and service industries. This gives considerable scope for deploying further IT systems.

The firms that have not begun to develop programs and policies for a "digital workforce" by 2015 will not perform in the top quartile for productivity and operating profit margin improvement in their industry by 2020. As a direct result, the careers of CIOs who do not begin to champion digital workforce initiatives with their peers in the C-suite by 2015 will be cut short by 2023.

Books on Smart Machines

Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation: Methods for System Self-Organization, Learning, and Adaptation

Zhou, Zude
IGI Global, 31-Mar-2010 - Computers - 407 pages

The manufacturing industry has experienced dramatic change over the years with growing advancements, implementations, and applications in technology.

Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation focuses on the latest innovations for developing, describing, integrating, sharing, and processing intelligent activities in the process of manufacturing in engineering. Containing research from leading international experts, this publication provides readers with scientific foundations, theories, and key technologies of manufacturing intelligence.

Manufacturing in Real Time: Managers, Engineers and an Age of Smart Machines

Gian F. Frontini, Scott Kennedy, Scott L. Kennedy
Butterworth-Heinemann, 2003 - Business & Economics - 206 pages

The development of self-operating machines is the foundation of modern manufacturing. This current manufacturing environment is based on automation and smart machines that have the ability to make things with a level of accuracy and consistency that humans cannot match. In order to maximize efficiency, engineers and managers need to change their outlooks, processes and strategies and as a result, adopt new methods and management systems.

The authors demonstrate what is needed by first presenting a brief history of manufacturing and the changes we have already seen and then make their way into the current manufacturing environment. Topics covered include supply chain management, product streams, the role of automation in the supply chain, the relationships between machines and people in automated product streams (looking at what machines do best and what humans do best), variation and quality control, statistical process control, the flow of information in a supply chain and how all of these elements are effected by new technologies and need to be changed to allow for maximum efficiency as we move more toward automation in factories.

*Discover the impact of new technologies on the future shape on the manufacturing industry
*Excellent examples used throughout to demonstrate each idea and process
*Includes a CD with lectures, slides, tutorials, dynamic models and much more!


High Productivity Through Smart Factories

Updated 16 August 2017, 13 July 2017

Sunday, August 13, 2017

Process Analysis - Questions/Check List

Book: Productivity Through Process Analysis by Jinichi Ishiwata
Four basic principles for process improvements
1. Eliminate processes whenever possible.
2. Simplify them. (Operations analysis)
3. Combine them
4. Change the sequence
1. Eliminate  - Can this be eliminated? What will happen if we eliminate it?
2. Simplify - Can this made simpler?  - the task of operations analysis
3. Combine - Can two or more processes be consolidated into one?
4. Change sequence - Can this operation be switched with another one?
Big three problems in process: waste, irrationality, and inconsistency
5W1 Analysis for Product Process Analysis
Operation - Why - Who is doing it - Which machine - where - when - How
Can the layout be changed to reduce the transportation?
Can number of inspections be reduced?
Are any inspections unnecessary?
Can necessary inspections be done while the product is being processed?
Can number of delays be reduced?
Book: Motion and Time Study - Improving Productivity by Marvin E. Mundel
Checklist for Process Chart - Product Analysis
basic principles
1. Reduce number of steps.
2. Arrange steps in best order.
3. Make steps as economical as possible (operation analysis).
4. Reduce handling.
5. Combine steps if economical.
6. Shorten moves.
7. Provide most economical means for moving (operation analysis)
8. Cut in-process inventory to workable minimum
9. Use minimum number of control points at most advantageous places
1. Can any step be eliminated?

a. as unnecessary. (Ask: Why is it done?)
b. By new equipment (Ask: Why is present equipment used?)
c. By changing the place where it is done or kept. (Ask: Why is it done there?)
d. By changing the order of work. (Ask: Why is it done in its present order?)
e. By changing the product design. (Ask: Why is it made as it is?)
f. By changing the specifications of the incoming supply. (Ask:  Why is it ordered in its present form or used at all)
2. Can any step be combined with another?

a. By changing the specifications of supplies, or of any raw material?
b. By changing the design of the product, even if only the tolerances?
c. By changing the order of the steps of production, or doing inspection at any operation station so as to avoid an inventory of faulty product?
d. By changing the equipment used (e.g., using a multifunction machine, or creating a multimachine work cell served by a single person or by a robot)/
e. By redesigning one or more work places?
3. Can steps be rearranged so as to make any shorter or easier?
4. Can any step be made easier?
Book: Motion and Time Study: Design and Measurement of Work by Ralph M. Barnes
Following approaches should be considered in developing preferred work method
A. Eliminate all unnecessary work.
B. Combine operations or elements.
C. Change the sequence of operations
D. Simplify the necessary operations

Process Analysis, Process Improvement, and Cost Reduction


Updated  15 August 2017, 10 February 2012

Saturday, August 5, 2017

Cloud Computing - Productivity Science

Ozdemir A, Asil H (2017)
The Optimization of Query Processing in Sea Base Cloud Databases Based on CCEVP Model.
Ind Eng Manage 6:208. doi:10.4172/2169-0316.1000208

The increase in data volume in many applications and the need for their calculations are the database challenges. Cloud computing and the use of Sea Base databases are a solution to integrate a variety of DBMSs and integrated access to tables in databases. The study tried to optimize query processing in the Sea Base cloud database and reduce query processing time. The method used adaptability for optimization. The purpose of this method is to make adaptive the execution plans of high-traffic queries sent to the Sea Base. For adaptability, the method uses three parts: separator, similarity detector and replacement policy.  The results show that the system optimizes query processing in the database and reduces response time by one percent. The response time can be further decreased by changing the replacement policy.

Productivity - Journals

Journal of Productivity Analysis

International Journal of Productivity and Quality Management

International Journal of Productivity and Performance Management
ISSN: 1741-0401
Previously published as: Work Study

Journal of Productivity Analysis

International Journal of Productivity Management and Assessment Technologies (IJPMAT)

Friday, August 4, 2017

Industrial Engineering Journals

Ranking of Journals in Industrial and Manufacturing Engineering

Journal of Industrial Engineering International

Journal of Industrial Engineering International  is a peer-reviewed open access journal published under the brand SpringerOpen, covering all aspects of industrial engineering. It is fully supported by the Islamic Azad University, who provide funds to cover all costs of publication, including the Article Processing Charges (APC’s) for all authors. Therefore the journal is both free to read and free to publish in.

Journal of Industrial and Production Engineering

Official Journal of the Chinese Institute of Industrial Engineers
Volume 32, Issue 2, 2015

International Journal of Applied Industrial Engineering (IJAIE)

Editor-in-Chief: Lanndon Ocampo (University of the Philippines Cebu, Philippines)
Indexed In: INSPEC and 10 more indices
Published: Semi-Annually |Established: 2012

Topics Covered
Business and strategy
Case studies in industry and services
Decision analysis
Engineering economy and cost estimation
Enterprise resource planning and ERPII
Facility location, layout, design, and materials handling
Forecasting, production planning, and control
Human factors, ergonomics, and safety
Industrial engineering education
Information and communication technology and systems
Innovation, knowledge management, and organizational learning
Inventory, logistics, and transportation
Knowledge and technology transfers in a globalized network
Manufacturing, control, and automation
Operations management
Performance analysis
Product and process design and management
Project Management
Purchasing and procurement
Reliability and maintenance engineering
Scheduling in industry and service
Service systems and service management
Supply chain management
Systems and service modeling and simulation
Technology transfer and management
Third party/fourth party logistics
Total quality management and quality engineering

European Journal of Industrial Engineering

International Journal of Industrial Engineering Computations

International Journal of Industrial and Systems Engineering

Journal of Industrial Engineering and Management

International Journal of Industrial Engineering : Theory Applications and Practice

South African Journal of Industrial Engineering

Jordan Journal of Mechanical and Industrial Engineering

International Journal of Industrial Engineering and Management

Journal of Japan Industrial Management Association

Engineering Optimization

Updated  5 August 2017, 18 June 2017, 9 April 2015

General - System Level Productivity Science


FEBRUARY 16, 2017 by Jaap Backx, Christoph Hilberath, Reinhard Messenböck, Yves Morieux, and Henning Streubel
BCG Perspectives

Businesses compete in a world that is growing ever more complex. Disruptive technologies emerge with increasing frequency. Customers’ needs and demands change at breakneck speed. New competitors are always entering the fray.

In their attempts to reduce uncertainty and reestablish control amid this new complexity, companies tend to introduce new reports, new rules, and new processes. Such reactions, however, simply translate external complexity into internal “complicatedness”—the counterproductive proliferation of cumbersome structures, processes, and systems. Complicatedness hinders productivity by creating a work environment that leaves employees disengaged and unmotivated.

Knowledge Worker Productivity - Productivity Science


Margaret Moussa, Mathew Bright, Maria Estela Varua, (2017) "Investigating knowledge workers’ productivity using work design theory", International Journal of Productivity and Performance Management, Vol. 66 Issue: 6, pp.822-834.

The paper concludes by offering suggestions for a model suitable for examining the drivers of knowledge work productivity.

Survey instruments based on the recommended model potentially provide a valuable means for understanding and enhancing productivity in a variety of knowledge intensive service industries. The pronounced benefit of this model is that it is applicable in cross-industry and cross-occupational contexts, unlike many existing knowledge worker productivity models.


Peter F. Drucker gave a call for knowledge worker productivity.

Can IEs do some things in that area?

Raise the productivity of interaction workers—high-skill knowledge workers, including managers and professionals—by 20 to 25 percent by using social technologies internally and externally.

Updated 5 August 2017, 6 August 2013

Manufacturing System Digital Transformation and Reengineering

Information technology was applied with significant effect in business processes and during that phase of progress Business Process Reengineering (BPR) emerged as an important method. In manufacturing CNC, CAD-CAM, and CIM appeared. But the current development Internet of Things (IoT) seems to having a major impact on the manufacturing systems. It is getting the description of Industrial Revolution 4.0.

In Industrial engineering also, we can see IE 1.0 - Science of Man - Machine Systems (From Taylor to Taiichi Ohno), IE 2.0 - Low Inventory Manufacturing (Taiichi Ohno, Shigeo Shingo to Womack et al.) and IE 3.0 - Imaginative Use of New Technology by Understanding the Power of the Technology First (Michael Hammer, Thomas Davenport to current days).

Will IE 4.0 be driven by innovations by Industrial Engineers in the age of Digital Transformation of Manufacturing Processes?

Presently number of papers are being published by leading consultants to make digital transformation a big movement.  Their references are being collection in this post

March 2017

By 2018, 60 percent of the large manufacturers will bring in new revenue from information based products and services.

By 2020, manufacturers will claim 20% more after market revenue by offering more services through information (IoT) based support.


October 2016
IDC - Dassault Research Paper on Status of Industry 4.0 in Germany

April 2016

The Digital Transformation Playbook: Rethink Your Business for the Digital Age

By David Rogers
Columbia University

February 2016
Industry 4.0.
European Parliament

This study, prepared by Policy Department A at the request of the ITRE
committee, analyses the Industry 4.0 Initiative which encompasses the
digitalisation of production processes based on devices autonomously
communicating with each other along the value chain. It considers the potential of
the initiative and business paradigm changes and impacts of this transformation.
The study assesses the rationale for public intervention and outlines measures
that could be adopted to increase the gains and limit the threats from Industry

January 2016


Good number of examples (21) with source indicated for more information

Digital Futures, Digital Transformation - From Lean Production to Acceluction

Authors: Bounfour, Ahmed
Copyright - 2016, Publisher: Springer International Publishing
This book provides an integrated overview of key trends in digital transformation, taking into consideration five interrelated dimensions: strategy and business models, society, organization, technology and regulation. As such, it provides a framework for the analysis of digital business transformation and its emerging factors, analyzing twenty-five key trends in terms of their future impact. On that basis, the book then delineates a new approach centered on the mutually accelerating links between multiple value creation spaces. It proposes a new mode of production – accelerated production of links (acceluction) – and analyzes it with respect to the still-dominant concept of lean production. Based on the results of the international CIGREF research program ISD, the book presents a valuable perspective of the expected impact of the abundance of networks and data as critical resources for enterprises beyond 2020.

Digital Transformation of the Firm: New Strategies, New Structures

Jan 2016


Boosting Visibility, Agility and Profits with Digital Manufacturing

Digital Transformation: What It Means to Manufacturers

Cisco white Paper



CSC - Computer Sciences Corporation

Industry 4.0

Challenges and Solutions for the Digital Transformation and the Use of Exponential Technologies


Survey of more than 50 companies in Switzerland

How to Become a Digital Leader

Arthur D Little

67 Pages Report

September 2015

Mercedes-Benz as pioneer of the digital transformation: From Car Manufacturer to Networked Mobility Service Provider

Daimler AG Media Release

Detailed the initiatives at various stages in the manufacturing system and product

How Five Companies Launched Digital Transformations

SEPTEMBER 01, 2015 by Lars Fæste, Thomas Gumsheimer, and Matthias Scherer


Man and Machine in Industry 4.0

How Will Technology Transform the Industrial Workforce Through 2025?


SEPTEMBER 28, 2015

June 2015

Manufacturing’s next act

By Cornelius Baur and Dominik Wee
Industry 4.0 is more than just a flashy catchphrase. A confluence of trends and technologies promises to reshape the way things are made.


We define Industry 4.0 as the next phase in the digitization of the manufacturing sector, driven by four disruptions:
- the astonishing rise in data volumes, computational power, and connectivity, especially new low-power wide-area networks;
- the emergence of analytics and business-intelligence capabilities;
- new forms of human-machine interaction such as touch interfaces and augmented-reality systems;  and
- improvements in transferring digital instructions to the physical world, such as advanced robotics and 3-D printing.

May 2015

Industry 4.0: It's all about information technology this time
The so-called Industry 4.0 concept now being embraced in Europe predicts the Internet of Things will change manufacturing as we know it.

Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries


The Nine Pillars of Technological Advancement

Big Data and Analytics
Autonomous Robots
Horizontal and Vertical System Integration
The Industrial Internet of Things
The Cloud
Additive Manufacturing
Augmented Reality


Revamping Your Business Through Digital Transformation
MIT Sloan Management Review, Magazine: Spring 2015, Research Highlight February 18, 2015  Reading Time: 11 min
George Westerman and Didier Bonnet

First iOS Apps from Apple-IBM venture
January 5th, 2015

Updated 5 August 2017,  12 March 2017, 18 April 2016.