Showing posts with label A to Z. Show all posts
Showing posts with label A to Z. Show all posts

August 17, 2025

Marketing Management Blogs



http://adcontrarian.blogspot.com

http://www.themarketingsage.com

https://blog.axiom.us.com

https://brightside.me

http://b2bmarketingdirections.blogspot.com

http://makemarketinghistory.blogspot.com/

https://marketingthatworksblog.blogspot.com
Interesting post:  https://marketingthatworksblog.blogspot.com/2018/03/meaty-messaging-messaging-inventory.html

http://saasmarketingstrategy.blogspot.com/

http://themwordblog.blogspot.com/
Library marketing

http://dranil-marketingmusings.blogspot.com/

http://marketdesigner.blogspot.com

http://mymarketingpicks.blogspot.com
Int post http://mymarketingpicks.blogspot.com/2013/01/top-20-marketing-gurus.html

http://www.uofadmissionsmarketing.com/

https://medialadder.ca/

http://fmcg-marketing.blogspot.com/

https://faculty.insead.edu/pierre-chandon/
Professor of Marketing, INSEAD

http://mktg-matters.blogspot.com/
http://mktg-matters.blogspot.com/2018/05/in-age-of-digital-content-is-king.html


search Google marketing site:blogspot.com


Ud. 18.8.20205
Pub. 27.2.2019

September 4, 2024

Evolution of Operations Management -Xenogamy (Cross Fertilization)





                                          Location: NITIE Office Room in Old Academic Building

Manufacturing has progressed from individual or cottage or craft based activity into industrial organisation and now into the post-industrial economy. There are many key thinkers and turning points in the development of modern operations management that plans, organizes, resources, directs and controls a net work of manufacturing, storing and transport facilities connected by information and financial flows and supplies goods and services at  prices market is willing to buy and at costs that give adequate profits to all the participants in the supply chain.

Production and Operations Management - The Beginning

1886 - ASME - Henry Towne - Shop Management and Works Management


American Society of Mechanical Engineers (ASME) made the beginning in the field of works management and shop management.

Henry Towne, in a paper presented to the society (ASME) in 1886 observed that  the work of all engineers, especially that of the mechanical engineers, includes the executive duties of organizing and superintending the operations of industrial establishments, and of directing the labor of the artisans whose organized efforts yield the fruition of his work.

To insure the best results, the organization of productive labor must be directed and controlled by persons having not only good executive ability, and possessing the practical familiarity of a mechanic or engineer with the goods produced and the processes employed, but having also, and equally, a practical knowledge of how to observe, record, analyze and compare essential facts in relation to wages, supplies, expense accounts, and all else that enters into or affects the economy of production and the cost of the product. 

It will probably not be disputed that the matter of shop management is of equal importance with that of engineering, as affecting the successful conduct of most, if not all, of our great industrial establishments, and that the management of works  has become a matter of such great and far-reaching importance as perhaps to justify its classification also as one of the modern arts. A vast amount of accumulated experience in the art of workshop management already exists, but there is no record of it available to the world in general. Surely this condition of things is wrong and should be remedied. The remedy should originate  from  engineers, and, for the reasons above indicated, particularly from mechanical engineers. So, Towne put forward the question, "why should it not originate from, and be promoted by The American Society of Mechanical Engineers?"

The discussion and the dissemination of useful knowledge in this specialty, group themselves under two principal heads, namely: Shop Management, and Shop Accounting. A third head may be named which is sub-ordinate to, and partly included in each of these, namely: Shop Forms  and Blanks. Under the head of Shop Management fall the questions of organization, responsibility, reports, systems of contract and piece work, and all that relates to the executive management of works, mills and factories. Under the head of Shop Accounting fall the questions of time and wages systems, determination of costs, whether by piece or day-work, the distribution of the various expense accounts, the ascertainment of profits, methods of book-keeping, and all that enters into the system of accounts which relates to the manufacturing departments of a business, and to the determination and record of its results.

This work, if undertaken by the society, may be kept separate and distinct from the present work of the society (engineering work) by organizing a new "section" (which might be designated the " Economic Section'').


In the case of shop information of  a manufacturing establishment, there is now in use, in connection with the manufacturing accounts and exclusive of the ordinary commercial accounts, some twenty various forms of special record and account books, and more than one hundred printed forms and blanks. .The primary object to which all of these contribute is the systematic recording of the operations of the different departments of the works, and the computation therefrom of such statistical information as is essential to the efficient management of the business, and especially to increased economy of production. All of these special books and forms have been the outgrowth of experience extending over many years, and represent a large amount of thoughtful planning and intelligent effort at constant development and improvement. The methods in use presently,  would undoubtedly be of great value to others engaged in similar operations, and particularly to persons engaged in organizing and starting new enterprises. The society can provide a platform for explaining the present practices and many would come forward to engage in such a dialogue to benefit from the idea generated in the discussions.

Costs of products were reduced by many companies without encroaching upon the earnings of the men engaged and the results we know are quite striking.

A portion of the cost reductions indicated resulted from improved appliances, larger product, and increased experience, but after making due allowance for all of these, there remains a large portion of the reduction which, to the writer's knowledge, is fairly attributable to the operations of the peculiar piece-work system adopted. Henry Towne, promised to present the details and operations of this system followed in his company in the proceedings of the new section of  the society, in due time. He expressed the hope that other, and probably much more valuable, information and experience relating to systems of contract and piece-work would doubtless be contributed by other members.

One can clearly see in the paper by Towne, the acceptance of the idea of "Xenogamy (Cross Fertilization)" to develop the subjects of shop management and works management.

For the full paper of Towne

The Engineer as an Economist- Henry Towne

Gain Sharing, Piecework and Day Work Systems


Henry Towne presented his ideas on involving labor in cost reduction work of the production organization in the paper "Gain Sharing" presented in 1889. This paper advocated bonus to all the employees based on the reduction achieved in the cost of production relative to a base year. Halsey in 1891 presented a paper and argued for production time as the basis for paying bonus to the individual workers. F.W. Taylor presented a more comprehensive system in 1895. It is very important to note that Taylor, proposed that organization of "Elementary Rate Fixing Department" as the fundamental step to achieve cost reductions. To implement the changes proposed by the rate fixing departments, differential piece rate system has to be introduced.


Elementary Rate Fixing Department (1895 - Taylor)

Taylor started this department of section in his company and its successful record was presented to the ASME in 1895. This department has to study the productive capabilities machines and men in a scientific manner and establish the speeds at which machines can work and men can work and based on the speed information has to decide the time required for completing various jobs or tasks. Such scientific information has to be used to set piece rates. This department must have status equal to the engineering department of the organization.  So Taylor organized the first industrial engineering department that is parallel to the engineering department of the company and is focused on the study of machines and men and in actual working on specific  jobs and in designing best methods of working that reduce cost of production.


Shop Management (1905 - Taylor)


Taylor responded to the call by Towne to described innovations in the field of management done by engineers who had done managerial work as part of engineer's functions. He contributed a paper on redesign of belts based on cost data (1893) and another paper on increasing productivity and reducing costs by organizing elementary rate fixing department and installing differential piece rate system.

In 1895, he presented a book length paper on shop management. He described many practices that will contribute to productivity improvement and effectiveness improvement. He also indicated the innovations of many others in the field of shop management. Taylor also contributed to discussions on shop accounting and its contribution to improving productivity.

In the paper "Shop Management", Taylor wrote, "The art of management has been defined, "as knowing exactly what you want men to do, and then seeing that they do it in the best and cheapest way.""  No concise definition can fully describe an art, but the relations between employers and men form without question the most important part of this art. In considering the subject, therefore, until this part of the problem has been fully discussed, the other phases of the art may be left in the background. Once again, we have to carefully note this sentence. Taylor said, the many other items are left in the background and issues related to managing men are highlighted.

For more details of shop management

F.W. Taylor - Shop Management - With Appropriate Sections and Themes


Works Management - Harvard Business School and New York University


Works management was taught at Harvard Business School (HBS). James Gunn who first mentioned the word industrial engineering in 1901 worked in HBS. C.B. Thompson who wrote many papers and book on Scientific Management worked in HBS.

A New York University Engineering College, Walter Rautentrauch, organized a course on works management during 1908 to 1911. C.B. Going taught Industrial Engineering as a part of that course.


Industrial Engineering Course - Penn State College


Hans Diemer is the first full time industrial engineering faculty. He started the four year industrial engineering course in Penn State College. He published his proposed 4 year program in an article. Walter Rautentrauch criticized the course for lack sufficient attention to manufacturing, the key focus of industrial engineering.

In the modern era, Elwood Buffa is given the credit for developing Production Management subject.




A detailed writeup on Operations Management

Operations Management by Martin Spring in Oxford Handbook of Management
https://books.google.co.in/books?id=1dk-DgAAQBAJ&pg=PA57#v=onepage&q&f=false

Books on Shop Management, Works Management, Production Management and Operations Management


The Commercial Management of Engineering Works
by Francis G. Burton
Publication date 1899
Publisher The Scientific publishing co.
https://archive.org/details/commercialmanag00burtgoog/page/n4

Production factors in cost accounting and works management
by Church, A. Hamilton (Alexander Hamilton), 1866-1936
Publication date 1910
Topics Cost accounting, Factory management
Publisher New York, The Engineering magazine
https://archive.org/details/productionfacto00churgoog/page/n7

The human factor in works management
by Hartness, James, 1861-1934
Publication date 1912
https://archive.org/details/humanfactorinwo01hartgoog/page/n7

The Science Of Works Management
by Batey, John
Publication date 1914
https://archive.org/details/dli.bengal.10689.10563/page/n5





Theory development remains the most fertile research area in the field of operations management (Westbrook, 1995; Pannirselvam et al., 1999). 

A number of attempts have been made to develop and propose theories and theory-like principles of
operations management. 

These attempts include: trade-off theory (Skinner, 1969), the process-product matrix (Hayes and Wheelwright, 1979), the customer-contact model (Chase and Tansik, 1983), the TOC (Goldratt and Cox, 1984; Boyd and Gupta, 2004), the cumulative theory (Ferdows and DeMeyer, 1990), the theory of production competence (Cleveland et al., 1989; Vickery, 1991), priority management theory (Westbrook, 1994), the theory of TQM (Flynn et al., 1994; Handfield and Melnyk, 1998), the theory of swift and even flow, and the theory of performance frontiers (Schmenner and Swink, 1998).
This list is not exhaustive but rather an attempt to highlight major initiatives undertaken in the academic OM literature. Schmenner and Swink (1998) further suggested that these theories in operations management should be carefully examined, refined and, if warranted, abandoned.

Cox et al. (2003) goes beyond other OM textbooks in developing a “business systems model” incorporating organization structure, business processes and management direction as a framework for discussing the use and impact of TOC concepts on the whole organization.


Ud. 5.9.2024.
Pub. 27.4.2019

September 1, 2024

Quality Engineering and Management (Product and Process) - Quotes from Juran's Quality Handbook

This is my #AtoZchallenge  Roadtrip Post. It is an important topic and by reading the handbook and collecting excerpts, I learnt this important subject of quality in more depth. Juran is one of the three celebrated quality management gurus. The others two are Crosby and Deming.


 #AtoZchallenge bloggers can indicate their Roadtrip Post in the file included in A to Z Challenge Site post. http://www.a-to-zchallenge.com/2022/05/the-2022-post-to-z-challenge-road-trip.html  You will get support from A to Z Challenge Bloggers for more views and comments. Visit the post and enter your blog details.


Levels of  Industrial Engineering (Productivity Improvement) in an Enterprise -  Enterprise Level to Engineering Element Level Industrial Engineering

Process Quality Improvement is more popularly understood as Productivity Improvement - J.M. Juran

Process quality improvement by a specialist foreman termed inspector was recommended by F.W. Taylor as part of functional supervision plan.

There are three principal dimensions for measuring process quality: effectiveness, efficiency, and
adaptability. The process is effective if the output meets customer needs. It is efficient when it is
effective at the least cost. The process is adaptable when it remains effective and efficient in the face
of the many changes that occur over time.


Industrial Engineering Strategy - Enterprise Level Industrial Engineering

https://nraoiekc.blogspot.com/2014/11/industrial-engineering-strategy.html


Facilities Industrial Engineering

https://nraoiekc.blogspot.com/2020/05/facilities-industrial-engineering.html


Process Industrial Engineering - Process Machine Effort Industrial Engineering - Process Human Effort Industrial Engineering.

https://nraoiekc.blogspot.com/2021/11/process-industrial-engineering-process.html


Operation Industrial Engineering.

https://nraoiekc.blogspot.com/2013/11/approach-to-operation-analysis-as-step.html


Element Level Analysis in Industrial Engineering

Taylor's Industrial Engineering System - First Proposal 1895 - Productivity Improvement of Each Element of the Process



Engineers and Engineering supervisors have to contribute to Quality engineering in their organizations.



Quotes from Quality Handbook, 5 Edition,  Dr. J.M. Juran

In the preface to the Fourth Edition of this handbook, Dr. Juran commented on the events of the four decades between signing the contract for the First Edition of this handbook (1945) and the publication of the Fourth Edition (1988).

The main impetus for the growing importance of quality in the past decade has been the realization of the critical role quality plays as the key to competitive success in the increasingly globalized business environment. Upper managers now understand much more clearly the importance of quality—convinced by the threat of the consequences of product failure, by the rapid shift of power to the buyers and by the demands of global competition in costs, performance, and service.


5th Edition special features

1. We have changed the name from Juran’s Quality Control Handbook, to Juran’s Quality Handbook. The new name signals the change in emphasis from quality control, traditionally the concern of those working on the manufacturing floor, to an emphasis on the management of quality generally, a concern of managers throughout an organization.

2. We have changed the structure to reflect the new emphasis on managing quality. The Fifth Edition has 48 sections, arranged in five groups: Managerial, Functional, Industry, International, and Statistical.


Chapters 1 to 17 deal with management issues.


Page 2.2.

The Meanings of “Quality.” Of the many meanings of the word “quality,” two are of critical importance to managing for quality:

1. “Quality” means those features of products which meet customer needs and thereby provide

customer satisfaction. In this sense, the meaning of quality is oriented to income. The purpose of

such higher quality is to provide greater customer satisfaction and, one hopes, to increase income.

However, providing more and/or better quality features usually requires an investment and hence

usually involves increases in costs. Higher quality in this sense usually “costs more.”


2. “Quality” means freedom from deficiencies—freedom from errors that require doing work

over again (rework) or that result in field failures, customer dissatisfaction, customer claims, and so

on. In this sense, the meaning of quality is oriented to costs, and higher quality usually “costs less.”


In the above one can be interpreted as product quality and 2 can be interpreted as process quality.


Page 2.5

Managing for quality makes extensive use of three such managerial processes:

Quality planning

● Quality control

● Quality improvement


Page 2.12


The Factory System:  The goals of the factories were to raise productivity and reduce costs.  To reach their goals, the factories reengineered the manufacturing processes. Under the craft system, an artisan performed every one of the numerous tasks needed to produce the final product—pins, shoes, barrels, and so on. Under the factory system, the tasks within a craft were divided up among several or many factory workers. Special tools were designed to simplify each task down to a short time cycle. A worker then could, in a few hours, carry out enough cycles of his or her task to reach high productivity.

Adam Smith, in his book, The Wealth of Nations, was one of the first to publish an explanation of the striking difference between manufacture under the craft system versus the factory system. He noted that pin making had been a distinct craft, consisting of 18 separate tasks. When these tasks were divided among 10 factory workers, production rose to a per-worker equivalent of 4800 pins a day, which was orders of magnitude higher than would be achieved if each worker were to produce pins by performing all 18 tasks (Smith 1776). For other types of processes, such as spinning or weaving, power-driven machinery could outproduce hand artisans while employing semiskilled or unskilled workers to reduce labor costs. The broad economic result of the factory system was mass production at low costs. 

Page 2.13


The Taylor System of Scientific Management.  This originated in the late nineteenth century when Taylor, an American manager, wanted to increase production and productivity by improving manufacturing planning. His solution was to separate planning from execution. He brought in engineers to do the planning, leaving the shop supervisors and the work force with the narrow responsibility of carrying out the plans.

Taylor’s system was stunningly successful in raising productivity. It was widely adopted in the United States but not so widely adopted elsewhere. It had negative side effects in human relations, which most American managers chose to ignore. It also had negative effects on quality. The American managers responded by taking the inspectors out of the production departments and placing them in newly created inspection departments. In due course, these departments took on added functions to become the broad-based quality departments of today. (For elaboration, see Juran 1995, chap. 17.)

(I totally disagree with the above description by Juran.)


2.16


QUALITY TO CENTER STAGE


Except for Japan, the needed quality revolution did not start until very late in the twentieth century. To make this revolution effective throughout the world, economies will require many decades—the entire twenty-first century. Thus, while the twentieth century has been the “century of productivity,” the twenty-first century will be known as the “century of quality.”


2.17

Inventions Yet to Come. Many of the strategies adopted by the successful companies are

without precedent in industrial history. As such, they must be regarded as experimental. They did

achieve results for the role model companies, but they have yet to demonstrate that they can achieve

comparable results in a broader spectrum of industries and cultures. It is to be expected that the

efforts to make such adaptations will generate new inventions, new experiments, and new lessons

learned. There is no end in sight.


3.3

Quality Planning 


• Establish the project

• Identify the customers

• Discover the customer needs

• Develop the product

• Develop the process

• Develop the controls and transfer to operations


SECTION 4. THE QUALITY CONTROL PROCESS

J. M. Juran, A. Blanton Godfrey

4.2
 “Quality control” is a universal managerial process for conducting operations so as to provide stability—to prevent adverse change and to “maintain the status quo.”
To maintain stability, the quality control process evaluates actual performance, compares actual
performance to goals, and takes action on the difference.

The term “control of quality” emerged early in the twentieth century (Radford 1917, 1922). The
concept was to broaden the approach to achieving quality, from the then-prevailing after-the-fact
inspection, to what we now call “defect prevention.” For a few decades, the word “control” had a
broad meaning which included the concept of quality planning. Then came events which narrowed
the meaning of “quality control.” The “statistical quality control” movement gave the impression that
quality control consisted of using statistical methods. The “reliability” movement claimed that quality control applied only to quality at the time of test but not during service life.

 In Japan, the term “quality control” retains a broad meaning.
Their “total quality control” is roughly equivalent to our term “total quality management.” In 1997
the Union of Japanese Scientists and Engineers (JUSE) adopted the term total quality management
(TQM) to replace total quality control (TQC) to more closely align themselves with the more common terminology used in the rest of the world.

Quality assurance’s main purpose is to verify that control is being maintained.

A further common form of feedback loop involves office clerks or factory workers whose work
is reviewed by umpires in the form of inspectors. This design of a feedback loop is largely the
result of the Taylor system of separating planning from execution. The Taylor system emerged a century ago and contributed greatly to increasing productivity. However, the effect on quality control was negative.

(Once again I do not agree with the above statement. What Taylor did was to recommend multiple foremen organization in place of one foreman  in the military system. The system foreman and workers working under him was not initiated by Taylor. If the foreman is doing planning, Taylor suggested a foreman to take care of planning.)

Establish Standards of Performance: Product Goals and Process Goals. For each control subject it is necessary to establish a standard of performance—a quality goal (also called targets, objectives, etc.). A standard of performance is an aimed-at achievement toward which effort is expended.



The processes which produce products have two sets of quality goals:
1. To produce products which do meet customer needs. Ideally, each and every unit of product
should meet customer needs.
2. To operate in a stable and predictable manner. In the dialect of the quality specialist, each process
should be “under control.”

A study in one small company employing about 350 people found that there were over a billion
things to be controlled (Juran 1964, pp. 181–182).
There is no possibility for upper managers to control huge numbers of control subjects. Instead,
they divide up the work of control, using a plan of delegation somewhat as depicted in Figure 4.7.
This division of work establishes three areas of responsibility for control: control by nonhuman
means (automated controls), control by the work force, and control by the managerial hierarchy.

Planning for quality control of critical processes has traditionally been the responsibility of those
who plan the operating process. For noncritical processes the responsibility was usually assigned to
quality specialists from the Quality Department. Their draft plans were then submitted to the operating heads for approval.

Process Capability. One of the most important concepts in the quality planning process is
“process capability.” The prime application of this concept is during planning of the operating
processes.

Does the process conform to its quality goals? The umpire answers this question by interpreting the
observed difference between process performance and process goals. When current performance
does differ from the quality goals, the question arises: What is the cause of this difference?

Responsibility for results should, of course, be keyed to controllability. However, in the past
many managers were not aware of the extent of controllability as it prevailed at the worker level.
Studies conducted by Juran during the 1930s and 1940s showed that at the worker level the proportion of management-controllable to worker-controllable nonconformances was of the order of 80 to
20. These findings were confirmed by other studies during the 1950s and 1960s. That ratio of 80 to
20 helps to explain the failure of so many efforts to solve the companies’ quality problems solely by
motivating the work force.

(Do quality people appreciate Taylor when he said manager is responsible for 50% of the task's success)

Self-Inspection. We define “self-inspection” as a state in which decisions on the product are
delegated to the work force. The delegated decisions consist mainly of: Does product quality conform to the quality goals? What disposition is to be made of the product?
Note that self-inspection is very different from self-control, which involves decisions on the
process.
The merits of self-inspection are considerable:

SECTION 5
THE QUALITY IMPROVEMENT PROCESS
J. M. Juran

WHAT IS IMPROVEMENT?
 “Improvement” means “the organized creation of beneficial change; the attainment of
unprecedented levels of performance.” A synonym is “breakthrough.”

Two Kinds of Beneficial Change. Better quality is a form of beneficial change. It is applicable to both the kinds of quality.  

Product features: These can increase customer satisfaction. To the producing company, they are
income-oriented.

Freedom from deficiencies created in the production process: These can create customer dissatisfaction and chronic waste. To the producing company, they are cost-oriented.

Quality improvement to increase income may consist of such actions as
Product development to create new features that provide greater customer satisfaction and hence
may increase income.

Business process improvement to reduce the cycle time for providing better service to customers
Creation of “one-stop shopping” to reduce customer frustration over having to deal with multiple personnel to get service

Quality improvement to reduce deficiencies created by the production process that create chronic waste may consist of such actions as

Increase of the yield of factory processes
Reduction of the error rates in offices
Reduction of field failures



Quality improvement to increase income starts by setting new goals, such as new product features, shorter cycle times, and one-stop shopping. Meeting such new goals requires several kinds
of planning, including quality planning. 

In the case of chronic waste, the product goals are already in place; so are the processes for meeting those goals. However, the resulting products (goods and services) do not all meet the goals. Some
do and some do not. As a consequence, the approach to reducing chronic waste is different from the
quality planning roadmap. Instead, the approach consists of (1) discovering the causes—why do
some products meet the goal and others do not—and (2) applying remedies to remove the causes. 

Continuing improvement is needed for both kinds of quality, since competitive pressures apply
to each. Customer needs are a moving target. Competitive costs are also a moving target. However,
improvement for these two kinds of quality has in the past progressed at very different rates. The
chief reason is that many upper managers, perhaps most, give higher priority to increasing sales than
to reducing costs. 


Unstructured Reduction of Chronic Waste. In most companies, the urge to reduce chronic waste has been much lower than the urge to increase sales.

As a result:
The business plan has not included goals for reduction of chronic waste.
Responsibility for such quality improvement has been vague. It has been left to volunteers to initiate action.
The needed resources have not been provided, since such improvement has not been a part of the
business plan.

The quality managers have contributed to this unawareness by presenting their reports in the language of quality specialists rather than in the language of management—the language of money.

5.5
The most decisive factor in the competition for quality leadership is the rate of quality improvement.

Quality improvement should be directed at all areas that influence company performance—
business processes as well as factory processes.

5.11
Higher quality in the sense of improved product features (through product development) usually
requires capital investment. In this sense, it does cost more. However, higher quality in the sense of
lower chronic waste usually costs less—a lot less. Those who are responsible for preparing proposals for management approval should be careful to define the key words—Which kind of quality are
they talking about?

Companies that have become the quality leaders—the role models—all adopted the practice of
enlarging their business plan to include quality-oriented goals.

5.20
Deployment of Goals. Goals are merely a wish list until they are deployed—until they are
broken down into specific projects to be carried out and assigned to specific individuals or teams
who are then provided with the resources needed to take action.

5.39
The Two Journeys. The universal sequence includes a series of steps that are grouped into two journeys:

1. The diagnostic journey from symptom to cause. It includes analyzing the symptoms, theorizing
as to the causes, testing the theories, and establishing the causes.
2. The remedial journey from cause to remedy. It includes developing the remedies, testing and
proving the remedies under operating conditions, dealing with resistance to change, and establishing controls to hold the gains.

Diagnosis is based on the factual approach and requires a firm grasp of the meanings of key
words. 

5.41

FORMULATION OF THEORIES

All progress in diagnosis is made theory by theory— about causes. The theory development and testing  process consists of three steps: generating theories, arranging theories in some order, choosing theories to be tested and testing theories.

Generating Theories. Securing theories should be done systematically. Theories should be
sought from all potential contributors—line managers and supervisors, technologists, the work force,
customers, suppliers, and so on )based on the data recorded. If it based on knowledge, the extensive knowledge is to be gathered first by many participants.) Normally, the list of theories has to be  extensive, 20 or more. If only 3 or 4 theories have emerged, it usually means that the theorizing has been inadequate.

One systematic way of generating theories is called “brainstorming.” 

Another systematic approach—“nominal group technique”—is similar to brainstorming.
Participants generate their theories silently, in writing. Each then offers one theory at a time, in rotation. After all ideas have been recorded, they are discussed and then prioritized by vote.

5.49
Design of Experiments. Test of theories through experiment usually involves producing trial
samples of product under specially selected conditions. The experiment may be conducted either in
a laboratory or in the real world of offices, factories, warehouses, users’ premises, and so on.


5.55
 Choice of remedy then depends on the extent to which the proposals meet certain essential criteria. The proposed remedies should
Remove or neutralize the cause(s)
Optimize the costs

Special remedies.
Increase the factor of safety through additional structural material, use of exotic materials, design
for misuse as well as intended use, fail-safe design, and so on. Virtually all of these involve an
increase in costs.
Increase the amount and severity of test. Correlation of data on severe tests versus normal tests
then provides a prediction of failure rates.
Reduce the process variability. This applies when the defects have their origin in manufacture.
Use automated 100 percent test. This concept has been supported recently by a remarkable
growth in the technology: nondestructive test methods, automated testing devices, and computerized controls.



SECTION 6 PROCESS MANAGEMENT
James F. Riley, Jr.

Why Process Quality Management? The dynamic environment in which business is conducted today is characterized by what has been referred to as “the six c’s:” change, complexity, customer demands, competitive pressure, cost impacts, and constraints.

A business process is the logical organization of people, materials, energy, equipment, and information into work activities designed to produce a required end result (product or service).


There are three principal dimensions for measuring process quality: effectiveness, efficiency, and
adaptability. The process is effective if the output meets customer needs. It is efficient when it is
effective at the least cost. The process is adaptable when it remains effective and efficient in the face
of the many changes that occur over time. A process orientation is vital if management is to meet
customer needs and ensure organizational health.

By mid-1985, many organizations and industries were managing selected major business
processes with the same attention commonly devoted to functions, departments, and other organizational entities. Early efforts bore such names as Business Process Management, Continuous Process
Improvement, and Business Process Quality Improvement.

Much has been published on process management. AT&T (1988), Black (1985), Gibson
(1991–92), Hammer and Champy (1993), Kane (1986 and 1992), Pall (1987), Riley (1989),
Rummler (1992), Schlesiona (1988), and Zachman (1990) have all proposed similar methodological
approaches that differ from one another in minor details. The specific details of the methodology presented in this section were developed by consultants at the Juran Institute, Inc. [Gibson et al. (1990);
Riley et al. (1994)], based on years of collective experience in a variety of industries.

6.11
Process measures based on cost, cycle time, labor productivity, process yield, and the like are
measures of process efficiency.

6.13
Analyzing the Process. Process Analysis is performed for the following purposes:
● Assess the current process for its effectiveness and efficiency.
● Identify the underlying causes of any performance inadequacy.
● Identify opportunities for improvement.
● Make the improvements.

The goal for process efficiency is that all key business processes operate at minimum total
process cost and cycle time, while still meeting customer requirements.


Process effectiveness and efficiency are analyzed concurrently. Maximizing effectiveness and efficiency together means that the process produces high quality at low cost; in other words, it can provide the most value to the customer.

Process decomposition—Identification of of process elements disclosed within  business process.

6.14
The “Process Analysis Summary Report” is the culmination and key output of this process analysis
step. It includes the findings from the analysis, that is, the reasons for inadequate process performance
and potential solutions that have been proposed and recorded by owner and team as analysis progressed.

SECTION 7  QUALITY AND INCOME
J. M. Juran

Consumer Products. Numerous researchers have tried to quantify the correlation between
product quality and product price. (See, for example, Riesz 1979; also Morris and Bronson 1969.)


SECTION 8. QUALITY AND COSTS
Frank M. Gryna

The underlying theme in the section is the use of quality-related costs to support a quality improvement effort rather than as a system of reporting quality costs.

The bulk of the costs were the result of poor quality. Such costs had been buried in the standards,
but they were in fact avoidable.

While these quality costs were avoidable, there was no clear responsibility for action to reduce
them, neither was there any structured approach for doing so.

 In this handbook, the term “quality costs” means the cost of poor quality

Identify major opportunities for reduction in cost of poor quality throughout all activities in an organization. Costs of poor quality do not exist as a homogeneous mass. Instead, they occur in specific segments, each traceable to some specific cause.


Cost of poor quality = Cost of nonconformities + Cost of inefficient processes+ Cost of lost opportunities for sales revenue


Note that this framework extends the traditional concept of quality costs to reflect not only the costs of nonconformities but also process inefficiencies and the impact of quality on sales revenue. Sometimes, the term “economics of quality” is employed to describe the broader concept and differentiate it from
the traditional concept of “quality cost.”

We must emphasize the main objective in collecting this data, i.e., to energize and support quality improvement activities.

Cost of Inefficient Processes. Some of the subcategories are

Variability of product characteristics: Losses that occur even with conforming product (e.g.,
overfill of packages due to variability of filling and measuring equipment).

Unplanned downtime of equipment: 

Inventory shrinkage: Loss due to the difference between actual and recorded inventory amounts.

Variation of process characteristics from “best practice”: Losses due to cycle time and costs
of processes as compared to best practices in providing the same output. 

Best practice or method doing a task is developed by industrial engineering department or process planning department. They may use benchmarking to identify best practice internally or in external organization. See:  Process Industrial Engineering - Methods and Techniques 


Non-value-added activities: Redundant operations, sorting inspections, and other non-value-added activities. 


International Standards and Quality Costs. The issue of quality costs is addressed in
ISO 9004-1 (1994), Quality Management and Quality System Elements—Guidelines, Section 6,
“Financial Considerations of Quality Systems.”

Three approaches to data collection and reporting are identified (but others are not excluded):
1. Quality costing approach: This is the failure, appraisal, and prevention approach described above.
2. Process cost approach. This approach collects data for a process rather than a product. All process costs are divided into cost of conformity and cost of nonconformity.
3. Quality loss approach: Under this approach the costs can be estimated by using the Taguchi quality loss function.

SECTION 9
MEASUREMENT, INFORMATION,
AND DECISION MAKING
Thomas C. Redman

A critical step in obtaining needed information is measurement. To measure is “to compute, estimate, or ascertain the extent, dimensions, or capacity of, especially by a certain rule or standard”
(Webster 1979). Measurement, then, involves the collection of raw data. For many types of measurements, specialized fields have grown up and there is a considerable body of expertise in making
measurements. Chemical assays and consumer preference testing are two such areas. Data collection
may involve less formal means—searching a library, obtaining data originally gathered for other
purposes, talking to customers, and the like. For our purposes, all such data collection shall be considered measurement.

Top 10 Measurement System Principles:
1. Manage measurement as an overall system, including its relationships with other systems of the
organization.
2. Understand who makes decisions and how they make them.
3. Make decisions and measurements as close to the activities they impact as possible.
4. Select a parsimonious set of measurements and ensure it covers what goes on “between functions.”
5. Define plans for data storage and analyses/syntheses/recommendations/presentations in
advance.
6. Seek simplicity in measurement, recommendation, and presentation.
7. Define and document the measurement protocol and the data quality program.
8. Continually evolve and improve the measurement system.
9. Help decision makers learn to manage their processes and areas of responsibility instead of the
measurement system.
10. Recognize that all measurement systems have limitations.


10. COMPUTER APPLICATIONS TO QUALITY SYSTEMS

Fredric I. Orkin, Daniel Olivier

TESTING AND VALIDATION

Testing Environment. Testing must ensure that the system operates correctly in the actual environment or, where such testing is not possible, in an environment that simulates the conditions of actual use. Stress testing in the actual-use environment is very effective in identifying errors that may otherwise remain undetected until after product release. Effective techniques to assure correct operation in the user environment must include “beta”-type testing, where early product versions are provided for customer-use testing to assure that the system functionality is consistent with the actual use environment.

Quality software programs exhibit certain attributes across programming languages and applications.

Correctness: Extent to which a program satisfies its specifications and fulfills the user’s mission 
objectives
Reliability: Extent to which a program can be expected to perform its intended function with required
precision
Efficiency: Amount of computing resources and code required by a program to perform a function
Integrity: Extent to which access to software or data by unauthorized persons can be controlled
Usability: Effort required to learn how to operate, prepare input, and interpret output of a program
Maintainability: Effort required to locate and fix an error in an operational program
Testability: Effort required to test a program to ensure that it performs its intended function
Flexibility: Effort required to modify an operational program 
Portability: Effort required to transfer a program from one hardware configuration and/or software 
system environment to another
Reusability: Extent to which a program can be used in other application—related to the packaging and
scope of the functions that programs perform
Interoperability: Effort required to couple one system with another

Sources of Statistical Software. Quality Progress annually publishes commercial sources
for software. The 1996 issue lists 183 companies that supply statistical software products covering
(Struebing 1996):
● Capability studies
● Design of experiments
● Sampling
● Simulation
● Statistical methods
● Statistical process control

Many industries are increasingly accepting inspection systems that are integrated with automated manufacturing systems. “This step completes the computer-integrated manufacturing (CIM)
loop” (Reimann and Sarkis 1993).
Generally, automatic inspection will couple a transducer to a computer. Transducers can take the
form of dimensional position indicators or indicators of physical effects such as force, flow, vibration,
electrical properties, and magnetic properties. An American National Standards Institute (ANSI) 
standard for integrating the CAD and dimensional measuring instruments was published in 1990
(ANSI/CAM-I 1990).

Page 10-11

Potential Applications for Automated Inspection


Industry applications 
Equipment type  - Transducer type - Computer function

Dimensional gauging    

Automatic high-speed, noncontact video inspection, and optical comparators -    Optical, laser, video, solid-state camera -   inspection of  unaligned parts


Coordinate measurement machine - Touch probe - Geometrical tolerance programming, tolerance 
analysis, multiple probe calibration, laser calibration, contouring, operator prompting,  accept/reject decision

Computer-assisted gauging (lab) -  Touch probe, electronic, air - Supervised prompting, automatic mastering,  counting, spec comparison, diagnostic testing 

Electronic gauges and measuring systems with computer interface - Calipers, micrometers, snap gauges, bore gauges, indicator probes, height gauges, air gauges, ultrasonic gauges, magnetic gauges, etc. -   Direct digital output


In-cycle gauging on numerical  control (NC) machines -      Touch probe - On machine measurements, tool wear  compensation, temperature compensation automatic check of tool offset, work location, table and spindle relationship

Bench laser micrometer - Laser - Automatic laser scan, data handling, statistical dimension calculations, part sorting, accept/reject decision

Holography - Laser - Automatic stress, strain, displacement, image processing

Laser interferometer - Laser - Automatic temperature and humidity compensation data handling and storage, math processing

3-D theodolite, coordinate, measurement -  Optical - Interactive operator prompting, automatic angular 
 measurement, data handling

Scanning laser acoustic microscope (SLAM) -  Laser, acoustic - Beam scanning, data processing

To be edited
Electrical and electronic Temperature measurement Thermocouple, thermistor, resistance Calibration; data acquisition, analysis, and processing
instrumentation temperature detector (RTD)
Robotic-printed circuit board test Electronic Robot control, fully automatic board test
Weight and balance, filling Electronic Automatic tare, statistical processing, data recording
and packaging, inspection
Circuit analyzers Electronic Special-purpose test systems
Automatic test equipment All Special-purpose test systems with complete
functional testers real-time input, processing and output data
Cable testers Electrical Automated harness continuity and high-potential
testing
Semiconductor testers Automated test of standard and special-purpose
chips
Lab devices and equipment Chromatographs Optical Fully automatic preprogrammed sampling and data
recording
Strength of materials Probe, force, displacement, Preprogrammed cycle operation; data, chart, and
strain gauge graphic output records; multichannel recording;
on-line data processing

Hardness testing Probe Robotic, fully automatic testing and recording,
results analysis, and prediction
Analyzers All Automatic calibration, testing, and recording
Electron microscopes Electromagnetic Processing and materials analysis, preprogrammed
for failure analysis
Optical imaging Video borescope, fiber-optic inspection Optical Digital data image processing documentation
Photographic Optical Fully automatic strobe, photographic sequencing
and processing
Video microscopes Optical Video image processing data documentation
High-speed video recording Optical Automatic 200–12,000 frames per second 
stop-motion recording of machine and manual
processes; motion analysis; data processing
Environmental and Test chamber controls Temperature, humidity, altitude Preprogrammed cycle controls, time and 
functional test equipment data records
Leak detection Vacuum, gas, acoustic Automatic zeroing, built-in calibration, automatic
sequencing, tolerance checking, data processing
and display
Shock and vibration testing Accelerometer Automatic cycle control, built-in calibration, data
logging and display
Built-in equipment Electrical, electronic Preprogrammed part and system functional and
environmental cycling, recording
EMI measurement Electronic, magnetic Data processing, math analysis, recording
Materials testing equipment Surface and roughness measurement Stylus follower, air flow Operator prompting, data analysis
Coating thickness, sheeting thickness Electronic, video, ultrasonic, Calculation and math processing; display; self-beta backscatter calibration; automatic filter changing and positioning; prompting self-diagnostics; feedback;
accept/reject decision


Industry applications Equipment type Transducer type Computer function
Paper, plastic, and coated product process Laser Automatic high-speed processing, feedback 
inspection for holes, particulates, controls, data analysis, and alarms
streaks, thickness
Nondestructive test equipment Magnetic particle, eddy current Probe Self-regulation, calibration, data handling,
defect recognition
Ultrasonic flaw detection Sonic, vibration Automated quantitative analysis, curve 
matching, automated procedures, graphics data 
acquisition and storage
Scanning laser acoustic microscope Laser, acoustic Beam scanning, data processing, flow detection
(SLAM) flaw detection
X-ray, fluoroscopic Optical, electronic Automatic calibration, operator prompting, data handling, statistics, stored programming, defect 
recognition
Acoustic emission Acoustic Independent channel monitoring and display, linear,
zone location, tolerance comparison, preprogrammed
tests, graphics output, triangulation, source location
Infrared test systems Optical, video Calibration, system control
Radiographic, gamma Optical, gamma Programmable, automatic, self-diagnostic, safety 
malfunction interrupts, automatic defect recognition,
robotic part handling, automatic detection of 
missing parts
Computer-aided tomography (CAT) X-ray Data acquisition, processing, interpretation and 
imaging
Nuclear magnetic resonance Magnetic Data acquisition, processing, interpretation and 
(NMR) scanner imaging




FUTURE TRENDS
Although the future is impossible to predict precisely, one thing is certain: Computer systems will
continue to revolutionize the definition of quality practices. Some current trends include:
● Data from disparate quality tracking systems will be increasingly integrated to provide system-wide
measures.
● The cost of scrap, rework, warranties, and product liability will impart continuing importance to
monitoring of the system, the process, and the machines that assure quality of output (McKee 1983).
● Evaluation of the effectiveness of software quality systems will become an increasing responsibility of the quality professional.


SECTION 13 STRATEGIC DEPLOYMENT
Joseph A. DeFeo

In recent years, total quality management (TQM) has become a pervasive change process and a
natural candidate for inclusion in the strategic plan of many organizations.

What Is Strategic Deployment? Strategic deployment is a systematic approach to integrating customer-focused organization-wide improvement efforts with the strategic plan of an organization. More specifically, strategic deployment is a systematic process by which an organization
defines its long-term goals with respect to quality, and integrates them—on an equal basis—with
financial, human resources, marketing, and research and development goals into one cohesive business plan. The plan is then deployed throughout the entire organization. (The quality emphasis can be given the term strategic quality policy deployment).

Strategic deployment has evolved during the 1990s as an integral part of many organizational
change processes, especially total quality management. Strategic deployment is part of the foundation
that supports the broader system of managing total quality throughout an organization.

The criteria for these awards stress that customer-driven quality and operational performance excellence are key strategic business issues which need to be an
integral part of overall business planning.

 In earlier versions of the Malcolm Baldrige National Quality Award this was referred to as the strategic quality
plan (SQP). 

Projects are the day-to-day, month-to-month activities that link quality improvement activities, re-engineering efforts, and quality planning teams to the organization’s business objectives.

Project: An activity of duration as long as 3 to 9 months that addresses a deployed goal, and
whose successful completion contributes to assurance that the strategic goals are achieved. A project most usually implies assignment of selected individuals to a team which is given the responsibility and authority to achieve the specific goal.

Deployment plan: To turn a vision into action, the vision must be broken apart and translated
into successively smaller and more specific parts—key strategies, strategic goals, etc.—to the
level of projects and even departmental actions. The detailed plan for decomposition and distribution throughout the organization is called the “deployment plan.” It includes the assignment of
roles and responsibilities and identification of resources needed to implement and achieve the
project goals.


SECTION 14 TOTAL QUALITY MANAGEMENT

A. Blanton Godfrey

Juran stated that, “Just as the twentieth century was the century of productivity, the twenty-first century will be the quality century.”

 Total quality management (TQM) is probably the most frequently used term in the United States, while total quality control (TQC) was until recently most often used in Japan, although this may be changing. “The term TQC (total quality control) has begun to be replaced in Japan by the term TQM (total quality management)” (Kondo 1995, p. vi). Kondo himself uses the equivalent term “Companywide Quality Management” in his recent book (Kondo 1995). Another term sometimes encountered is “continuous quality improvement” (CQI). In 1997, JUSE announced a formal change from the term TQC (total quality control) to TQM (total quality management) (The TQM Committee 1997a, p. 1). 

In JUSE’s view, TQM is a management approach that strives for the following in any business
environment:
● Under strong top-management leadership, establish clear mid- and long-term vision and strategies.
● Properly utilize the concepts, values, and scientific methods of TQM.
● Regard human resources and information as vital organizational infrastructures.
● Under an appropriate management system, effectively operate a quality assurance system and
other cross-functional management systems such as cost, delivery, environment, and safety.
● Supported by fundamental organizational powers, such as core technology, speed, and vitality,
ensure sound relationships with customers, employees, society, suppliers, and stockholders.
● Continuously realize corporate objectives in the form of achieving an organization’s mission,
building an organization with a respectable presence, and continuously securing profits.
In any discussion of total quality it is useful to start with the basics: the results we expect, the
three fundamental concepts, the three strong forces, the three critical processes, and the key elements
of the total quality infrastructure.

The Results of Total Quality. The almost universally accepted goals of total quality are lower costs, higher revenues, delighted customers, and empowered employees. These goals need little explanation.

The Three Fundamental Concepts. In the past few years many leading companies throughout the world have begun to revisit the fundamental concepts of quality management: customer focus, continuous improvement, and the value of every individual.

The Three Strong Forces. There are three primary drivers of performance excellence: alignment, linkage, and replication. 

The Three Critical Processes for Quality Management.
Quality Planning. Quality Control. Quality Improvement.

The Total Quality Management Infrastructure. The elements include the quality system, customer-supplier partnerships, total organization involvement, measurement and information, and education and
training.

The Malcolm Baldrige National Quality Award Criteria. The core values and concepts described previously are embodied in seven categories:
1.0 Leadership
2.0 Strategic Planning
3.0 Customer and Market Focus
4.0 Information and Analysis
5.0 Human Resource Focus
6.0 Process Management
7.0 Business Results

SECTION 15 HUMAN RESOURCES AND QUALITY
W. R. Garwood 
Gary L. Hallen


The purpose of this section is to present concepts, structures, methods, and tools which have helped successful organizations manage human resources effectively in directing their efforts toward the pursuit of high-quality
products (including services).

Major TQM elements (as embodied in the criteria of the Malcolm Baldrige National Quality
Award and other major state, national, and regional quality awards around the world) which relate
directly to human resources, and the Baldrige points associated with them are
4.1 Human resource planning and evaluation 20 of 1000
4.2 High-performance work systems 45 of 1000
4.3 Employee education, training, and 50 of 1000
development
4.4 Employee well-being and satisfaction 25 of 1000
6.3 Human resource results 35 of 1000

Employee empowerment is an advanced form of employee involvement. Empowerment is a condition in which the employee has the knowledge, skills, authority, and desire to decide and act within prescribed limits.

Empowerment = alignment x authority x capability x commitment

DESIGN PRINCIPLES OF WORK AND ORGANIZATION

Design Work for Optimum Satisfaction of Employee, Organization, and Customer. 

Successful organizations are designed to achieve high employee commitment and
organizational performance focused on satisfying, and even delighting, the customers. A proper work
design allows people to take action regarding their day-to-day responsibilities for customer satisfaction and employee satisfaction.

Design a System that Promotes High Levels of Employee Involvement at All Levels in Continuous Improvement.


TRAINING IN A TOTAL QUALITY ORGANIZATION

An attribute that successful organizations have in common is commitment to extensive training of employees.

Multiskilled workers increase the organization’s flexibility and facilitate teamwork. A multiskilled work force is a key feature of the desired organization and a key objective of the training
activity.

Training should focus on developing technical skills and social skills. Technical skills are the job-related skills to do the technical tasks of the job. Social skills are the skills of personal interaction and
administration which, together, enable team members to work collaboratively to manage their business.

Examples of Positive Reinforcement. Successful teams celebrate their success. The
sports world is filled with examples of how positive reinforcement drives continuous improvement:
A football player who scores is immediately congratulated by fellow players; a baseball player who
hits a home run is congratulated by fellow base runners who await him at home plate;

Lester Thurow (1992) states in his book Head to Head: “The skills of the workforce are going to be
the key competitive weapon in the twenty-first century. Brainpower will create new technologies, but
skilled labor will be the arms and legs that allow one to employ—to be the low-cost masters of—the
new product and process technologies that are being generated.”

Those organizations that get the highest performance from employees who can work together effectively with the technology of their systems are projected to be long-term maximizers.
This is not easy to implement. If it were easy, every good company would be working to make
itself a high-performing organization.






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


Industrial Engineering, Productivity and Quality


F.W. Taylor: Industrial Engineers to Guard Against Deterioration of Quality Due to Increase in Output.


One of the dangers to be guarded against, when the pay of the man or woman is made in any way to depend on the quantity of the work done, is that in the effort to increase the quantity the quality is apt to deteriorate.

It is necessary ... to take definite steps to insure against any falling off in quality before moving in any way towards an increase in quantity.

https://nraoiekc.blogspot.com/2013/08/illustrations-of-success-of-scientific_9321.html



Evolution of The Quality Management Philosophy and Practice

https://nraomtr.blogspot.com/2017/03/evolution-of-quality-management.html




Updated frequently

Pub 2.9.2024,  23.5.2022, 6.5.2022,  20.4.2022










April 22, 2024

Health of Organization



WHAT IS A HEALTHY COMPANY?
May 19, 2021
by Dan Markovitz
https://www.markovitzconsulting.com/blog/what-is-a-healthy-company


12 April 2018
______________

______________


Uploaded Mile Madinah  - A Management Institute
Presentation by Jim Laub
https://www.servantleaderperformance.com/about/dr-jim-laub/

Organizational health is also a concept of importance for top management.


Definitions of Organizational Health


For the organi­zation, the definition of health  may be stated as: a healthy organization is an organization which establishes and maintains a mutually beneficial relationship with its environment.

Organizational health is  the capacity to deliver—over the long term—superior financial and operating performance. (McKinsey)

The capacity to deliver has to be improved along with using it deliver performance.
http://www.mckinsey.com/business-functions/organization/our-insights/the-hidden-value-of-organizational-health-and-how-to-capture-it
April 1, 2014 article

Even though many authors have not mentioned it, we can describe some characteristics of the organization as disease. An organization where the CEO is despised is a sick organization. No body can be a healthy CEO, who develops hatred in the employees of the organization. Leaders that means managers in the organization must have the capability to assemble followers. They should not become group leaders in organizations and create conflict between employees and create a context for fights, cases and dismissals. In majority of the commercial organizations where conflict among employees is started by the leaders are doomed to fail. There are leaders who join organizations of relative peace and stability but convert them into organizations of strife, mistrust and dissatisfaction.

Shaomin Huang and  Gerald Ramey have proposed a method of measurement of organizational health through four propositions

PROPOSITION 1 (organizational effectiveness):
a)  Strong attention to task orientation will lead to higher organization effectiveness.
b)  A more involved work force will lead to higher organization effectiveness.
c)  An increase in innovative practices among workers will lead to higher organization effectiveness.
d)  Optimal interaction among the variables of innovation, involvement and task orientation (collectively, not individually) within  the work force will improve organization
effectiveness.

PROPOSITION 2 (leadership):
a)  An increase in appropriate supervisor support will lead to more leadership within the
organization.
b)  A decrease in excessive control mechanisms will create more leadership within the
organization.
c)  An increase in autonomy will increase leadership within the organization.
d)  Optimal interaction among the variables of autonomy, control, and supervisor support
(collectively, not individually) within the work force will improve leadership in the
organization.

PROPOSITION 3 (team work efficiency):
a)  Increased clarity in all communication will reduce destructive conflict in the organization.
b)  An increase in cohesiveness among peers within the work force will decrease destructive
conflict in the organization.
c)  A reasonable release in work pressure  will decrease destructive conflict in the organization.
d)  Optimal interaction among the variables of clarity, peer cohesion,and work pressure (collectively, not individually) within  the work force will reduce excessive and
destructive conflict in the organization.

PROPOSITION 4:
Assessment on organization effectiveness, leadership, and team work efficiency showing multiple causalty effects is a valid and appropriate method of evaluating organizational health.


McKinsey - Measurement of Health of Organizations


McKinsey helps you to measure your Organizational Health Index

Healthy organizations perform better. In healthy companies, employees know where the organization is headed; understand how they fit into the strategy to get there; have the tools, capabilities, and motivation to execute; and are empowered to innovate and change.

The Organizational Health Index is a survey-based diagnostic. It  has been deployed by more than 1,300 organizations worldwide. The data collected from employees and executive, provide the consultants and company managers  to understand the underlying mind-sets and behaviors that drive performance. Country- and industry-specific benchmarks are available  to compare like with like.

http://www.mckinsey.com/business-functions/organization/how-we-help-clients/organizational-health-index

A detailed article

http://www.mckinsey.com/business-functions/organization/our-insights/the-hidden-value-of-organizational-health-and-how-to-capture-it



For the CEO

http://chiefexecutive.net/your-companys-health-performance-is-not-enough/

Shaomin Huang and  Gerald Ramey,    "Organizational Health Assessment: a Romania Firm Case Study"


This article is part of #AtoZChallenge 2017 for Blogging Posts. My Theme for the Challenge is Top Management Challenges - Full List of Articles  http://nraomtr.blogspot.com/2016/12/a-to-z-2017-blogging-challenge-top.html


To Know More About A to Z Blogging Challenge
http://www.a-to-zchallenge.com/

Blog posts visited today

Homemade: Theme values
https://queasypeasy.wordpress.com/2017/04/10/homemade-is-still-best/

A to Z of Happiness: H - Hope #atozchallenge @AprilA2Z
http://www.mysteriouskaddu.com/2017/04/a-to-z-of-happiness-hope.html

#AtoZchallenge Healthy Minds and Healthy Writers
https://operationawesome6.blogspot.com/2017/04/atozchallenge-healthy-minds-and-healthy.html

My comment on this blog.

Narayana Rao K.V.S.S. said...

Failing during learning. Failing during performing. Failing during learning is personal. We need to continue till we learn. Failing during performing has both personal and external components. We want the audience to enjoy. But they may not. We have to accept such failures as a part of the profession of performing. But personal failure to perform adequately is something we need to think through and pledge to avoid in the future. It may be through some relearning or advanced learning or through more rehearsals or through stricter quality control. We want some private audience to judge our performance. Whether it is up to standard or not. But we should not give up once we start any activity as a profession. For that matter even serious amateur initiatives. Perseverance is important for the ultimate success. There is nothing wrong in displaying learning board for a longer time. Thank you for the post. My post for the day for letter H is:
Health of Organization


H for Happiness- What is it like to be in the state of happiness and how to attain it?
http://www.medhanagur.com/state-of-happiness-how-to-be-happy/

H is for HORIZONS #AtoZ Poetry
https://poetryfromthelanai.blogspot.com/2017/04/h-is-for-horizons-atoz-poetry.html

#atozchallenge Letters to my Embryos: H is for Hospital
http://clicks-clan.blogspot.com/2017/04/atozchallenge-letters-to-my-embryos-h.html

Updated on 23.4.2024,  26.4.2022,  13 April 2018, 10 April 2017

August 7, 2023

Supply Chain Analytics - Introduction and Bibliography



What is a supply chain control tower?

A supply chain control tower is a connected, personalized dashboard of data, key business metrics and events across the supply chain. A supply chain control tower enables organizations to more fully understand, prioritize and resolve critical issues in real time. Supply chain control tower collects all data and utilizes supply chain analytics to present the data to decision makers in various formats and decision related levels.

Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics

Descriptive Analytics

Using Descriptive Analytics to Improve Supply Chain Visibility for Variability, Velocity, Volume, and Variety.

Diagnostic Analytics

Supply Chain Diagnostic: A Four-Step Process.
Insights on performing a supply chain diagnostics and improving supply chain planning.

Predictive Analytics

What Is Predictive Analytics?
As its name implies, predictive analytics is about predicting future trends such as sales demand, exchange rates and other important supply chain metrics. The technique relies on the application of statistical modeling and regression analysis to historical data to determine and understand trends and formulate future trends.
Supply Chain Predictive Analytics: What Is It and Who's Doing It?


Prescriptive Analytics

Prescriptive analytics: For supply chain planning processes that need recommendations for more efficient and data-based decision-making, prescriptive analytics is recommended. It can employ techniques like AI and machine learning. An example is a business that wants advice on a possible outcome and what suggested actions they should take.
Using Prescriptive Analytics for Supply Chain Planning

A smarter control tower should provide end-to-end visibility across the supply chain — particularly into unforeseen external events.

Supply chain executives are under enormous pressure to provide customers what they need — when and where they need it — while also optimizing supply operations and achieving cost-saving goals. This is especially challenging in times of unpredictable yet inevitable vulnerabilities and disruptions.

Types of supply chain control towers

Logistics/transportation control towers
Logistics/transportation control towers offer advance shipping notifications, delivery data and track-and-trace information — and visibility into inbound and outbound logistics.

Fulfillment control towers
Fulfillment control towers specialize in assisting package shipments, and are designed to help expedite orders while reducing the overall cost-to-serve.

Inventory control towers
Inventory control towers enable real-time insights into inventory management, with special emphasis on preventing inventory stock-outs and shortages.

Supply assurance control towers
Supply assurance control towers ensure there’s an adequate supply available, that more supply is planned for delivery, and other matters related to supply.

E2E supply chain control towers
E2E supply chain control towers are engineered to provide visibility across internal and external systems and processes, with applications for various departments or entities.



Supply Chain Analytics - Video Lectures - NPTEL



A good comprehensive essay

Big Data Analytics and Its Applications in Supply Chain Management


Deloitte on Supply Chain Analytics


Like in various others parts of business, analytics which is a combination of statistics and data processing power of computers has enabled the processing of supply chain related data to point out cost reduction or profit improving niches.

Deloitte consultants pointed out the following.

Parametric pricing: The new parts a manufacturer procures differ only in small, specific ways from earlier versions in number of cases. A company with good parametric price modeling ability can identify these parameters of change in new parts and use them to determine what the net price change should be. That expedites the negotiating process and helps a company avoid overpayment.

Commodities price volatility:  Raw materials fluctuate in price and makes business planning difficult. Unexpected price jumps can damage margin. Companies can use analytics to develop macroeconomic models and come out with better predictions – and use options, futures and contract provisions to hedge.

M&A integration: When the merger is between two companies in the same industry,   they may be using the same parts/materials in their operations, but may have different material numbers and most likely different purchasing prices. After merger, such parts can be identified using analytics so that buyers can rationalize their procurement and save money.
https://www2.deloitte.com/us/en/pages/operations/articles/supply-chain-analytics-how-hard-should-you-squeeze.html

https://www2.deloitte.com/tw/en/pages/deloitte-analytics/solutions/process.html





2021

Supply Chain 4.0: Improving Supply Chains with Analytics and Industry 4.0 Technologies

Emel Aktas, Michael Bourlakis, Ioannis Minis, Vasileios Zeimpekis
Kogan Page Publishers, 03-Feb-2021 - Business & Economics - 312 pages

Supply Chain 4.0 has introduced automation into logistics and supply chain processes, exploiting predictive analytics to better match supply with demand, optimizing operations and using the latest technologies for the last mile delivery such as drones and autonomous robots.

Supply Chain 4.0 presents new methods, techniques, and information systems that support the coordination and optimization of logistics processes, reduction of operational costs as well as the emergence of entirely new services and business processes.

This edited collection includes contributions from leading international researchers from academia and industry. It considers the latest technologies and operational research methods available to support smart, integrated, and sustainable logistics practices focusing on automation, big data, Internet of Things, and decision support systems for transportation and logistics. It also highlights market requirements and includes case studies of cutting-edge applications from innovators in the logistics industry.

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

2019



SIX TRENDS THAT WILL IMPACT PROCUREMENT IN THE DIGITAL AGE
September 9, 2019 Kumar Singh
https://supplychainanalytics.guru/2019/09/09/six-trends-that-will-impact-procurement-in-the-digital-age/

“Without the right tech investment, you aren’t optimized and you aren’t synchronized.”

Domo’s approach,  is that organizations need to link supply and demand in order to understand the customer in the middle and ensure transparent reporting throughout.

Synchronizing the two creates collaboration, and collaboration starts with data. Domo offers a variety of simple, intuitive ways to get the right data to the right people, from live data dashboards with predictive alerts to internal reports delivered across the organization. Colgate-Palmolive’s Ann Tracy argued that collaboration is needed to study, analyze and apply data analysis. Data needs to be accessible across each function of the business—from senior leaders on down to more junior staff—and ultimately add value for the end user.
https://www.domo.com/blog/gartner-scc-diary-part-3-people-data-and-systems-arent-sustainable-without-each-other/

2013

In 2013 the Journal of Business Logistics published a white paper calling for   research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken.
https://www.forbes.com/sites/bernardmarr/2016/04/22/how-big-data-and-analytics-are-transforming-supply-chain-management/#cf32d3b39ad2

IWCR SAS Report on Supply Chain Analytics in 2010



Predicting demand accurately in volatile conditions requires sophisticated math based forecasting that can include downstream consumption data such as point-of-sales data, and model the impact of sales promotions, price, and other factors on demand. Analytics provides the capability.

SAS identified the follow levels of analytics.

8 LEVELS OF ANALYTICS


Level 1: Standard reports
Level 2: Ad hoc reports
Level 3: Query drilldown (or OLAP)
Level 4: Alerts
Level 5: Statistical analysis
Level 6: Forecasting
Level 7: Predictive modeling
Level 8: Optimization


When a company’s supply-chain management is fueled with data-driven insights, it is more effective at controlling costs, thereby protecting profits.

SAS emphasizes:
1. Efficiency and performance gains require predictive, data-driven insights.
2. Analytics are the wave of the future for next-generation supply-chains.


SAS Case Study STEEL MANUFACTURER IMPROVES PERFORMANCE, PROFITABILITY


A large, Asian manufacturer of steel (19,000 employees working to produce 28.5 million tons of steel annually), provided analytics support to  two of its process innovation (PI) programs using sas’s software.  the PI programs had a goal of updating 30-year-old business practices to improve efficiency and competitiveness. First, the company used sas to extract, transfer, and transform its ERP and legacy data into a data warehouse. secondly, the company combined sas’s analysis capabilities with its six sigma Project  tracking system. this combination allows managers to gather data on PI
projects, identify most-critical quality issues, and analyze them for root causes. By enabling daily and monthly monitoring, the company can resolve issues early on and improve overall manufacturing processes with the first PI phase, the company achieved a 50 percent reduction in lead times for standard hot coil production (from 30 days to 14 days), and a 60 percent reduction in inventory (from 1 million tons to 400,000 tons).

Further, by analyzing and then making necessary improvements to the manufacturing process, the company was able to reduce the scrap ratio on hot coil from 15 percent to 1.5 percent, leading to additional savings and  resulting in a total ROI of over $15.5 million in less than two years.

Source:  IW/SAS Supply-Chain Analytics Survey.  Email survey: Between June 8 and June 15, 2010,
Penton Research e-mailed invitations to participate in an online survey to 37,629 IndustryWeek print subscribers.  By June 30, 2010, Penton Research received 398 responses, a 1.1 percent response rate. Of those, 210 respondents that were involved in their companies’ supply-chain operations were considered qualified to answer the questions.






http://www.wipro.com/services/analytics/services/supply-chain-analytics/

https://www.forbes.com/sites/bernardmarr/2016/04/22/how-big-data-and-analytics-are-transforming-supply-chain-management/




Supply Chain Analytics - Google Books



Supply Chain Analytics and Modelling: Quantitative Tools and Applications

Nicoleta Tipi · 2021
Supply Chain Analytics and Modelling presents a range of business analytics models used within the supply chain to help readers develop knowledge on a variety of topics to overcome common issues.


Supply Chain Analytics: Using Data to Optimise Supply Chain Processes

Peter W. Robertson · 2021
By describing the key supply chain processes through worked examples, and the descriptive, predictive and prescriptive analytic methods that can be applied to bring about improvements to those processes, the book presents a more comprehensive learning experience for the reader than has been offered previously.



Essentials of Business Analytics: An Introduction to the
Bhimasankaram Pochiraju, ‎Sridhar Seshadri · 2019

This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners.



Logistics, Supply Chain and Financial Predictive Analytics: 
Kusum Deep, ‎Madhu Jain, ‎Said Salhi · 2018

This book addresses a broad range of problems commonly encountered in the fields of financial analysis, logistics and supply chain management, such as the use of big data analytics in the banking sector.

Supply Chain Management in the Big Data Era - 
Chan, Hing Kai, ‎Subramanian, Nachiappan, ‎Abdulrahman, Muhammad Dan-Asabe · 2016
 Highlighting emerging strategies from different industry perspectives, this book is ideally designed for managers, professionals, practitioners, and students interested in the most recent research on supply chain innovations.
https://books.google.co.in/books?id=aN52DQAAQBAJ


Big Data Driven Supply Chain Management: A Framework for ...books.google.co.in
Nada R. Sanders · 2014
Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains.



Supply Chain Optimization through Segmentation and Analytics books.google.co.in 
Gerhard J. Plenert · 2014
Then which planning and scheduling solution do you utilize? This book introduces the concept of segmentation as the planning and scheduling tool that facilitates the optimization of the supply chain.

Supply Chain Metrics that Matter books.google.co.in 
Lora M. Cecere · 2014
Mistakes are hard to overcome. Supply Chain Metrics that Matter tells this story. The book links corporate financials to supply chain maturity. In the book, the author analyzes which metrics matter.

Encyclopedia of Business Analytics and Optimization books.google.co.in › books
Wang, John · 2014
Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.


Applied Predictive Analytics: Principles and Techniques for ...books.google.co.in › books
Dean Abbott · 2014
The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to ...

The Applied Business Analytics Casebook: Applications in ...books.google.co.in › books
Matthew J. Drake · 2013
The first collection of cases on “big data” analytics for supply chain, operations research, and operations management, this reference puts readers in the position of the analytics professional and decision-maker.

Networks Against Time: Supply Chain Analytics for Perishable ...books.google.co.in › books
Anna Nagurney, ‎Min Yu, ‎Amir H. Masoumi · 2013
This book can also be used by advanced undergraduate students and graduate students in the disciplines noted above to familiarize themselves with methodologies and supply chain network models and applications.​​

Supply Chain Network Design: Applying Optimization and ...books.google.co.in › books
Michael Watson, ‎Sara Lewis, ‎Peter Cacioppi · 2012
Using strategic supply chain network design, companies can achieve dramatic savings from their supply chains.



















Global Business Analytics Models: Concepts and Applications ...books.google.com › books
Hokey Min · 2016
THE COMPLETE GUIDE TO USING ANALYTICS TO MANAGE RISK AND UNCERTAINTY IN COMPLEX GLOBAL BUSINESS ENVIRONMENTS Practical techniques for developing reliable, actionable intelligence–and using it to craft strategy Analytical opportunities to ...


Business Analytics: Progress On Applications In Asia Pacificbooks.google.co.in › books
Jorge L C Sanz · 2016

Business Analytics: Progress on Applications in Asia Pacific provides a useful picture of the maturity of the Business Analytics domain in Asia Pacific.


Big Data Analytics in Supply Chain Management: Theory and ...books.google.com › books
Iman Rahimi, ‎Amir H. Gandomi, ‎Simon James Fong · 2020
Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain ...

Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era 

David Simchi-Levi, ‎S. David Wu, ‎Zuo-Jun (Max) Shen · 2004
The Handbook is a comprehensive research reference that is essential for anyone interested in conducting research in supply chain.


Towards Supply Chain Risk Analytics: Fundamentals, ... - Page ibooks.google.co.in › books
Iris Heckmann · 2016
 – PAGE I
In this thesis, Iris Heckmann develops a profound conceptual basis of supply chain risk analytics.




Data Science for Business: What You Need to Know about Data ...books.google.co.in › books
Foster Provost, ‎Tom Fawcett · 2013
This guide also helps you understand the many data-mining techniques in use today.




Modeling the Supply Chain books.google.co.in › books
Jeremy F. Shapiro · 2007 · ‎No preview
The book also shows how competitive advantage in supply chain management can be most fully realized by developing and applying optimization modeling systems.




Logistics Management: An Analytics-Based Approachbooks.google.co.in › books
Tan Miller, ‎Matthew J. Liberatore · 2020 · ‎No preview


Data Strategy: How to Profit from a World of Big Data, ...books.google.co.in › books
Bernard Marr · 2017

Data is revolutionizing the way we work and it is the companies that view data as a strategic asset that will survive and thrive. Data Strategy is a must-have guide to creating a robust data strategy.




Big Data Analytics in Supply Chain Management: Theory and ...books.google.com › books
Iman Rahimi, ‎Amir H. Gandomi, ‎Simon James Fong · 2020
Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain ...


Industry 4.0 Data Analyticsbooks.google.co.in › books
Rajesh Agnihotri, ‎Samuel New · 2016 · ‎No preview
This book's comprehensive, one-of-a-kind coverage, journeys from the birth of digital data during Industries 3.0, to the latest practices, trends, and emerging developments in Industries 4.0.


Our Quick Notes On Supply Chain Analyticsbooks.google.com › books
Vivek Sood · 2020
Do not be deceived by their short nature - these notes are only 22 pages or so. But these are 22 pages of potent dynamite that will supercharge your thinking in the right direction.


Data Science for Supply Chain Forecasting - Page ibooks.google.co.in › books
Nicolas Vandeput · 2021
 – PAGE I
This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework.




Supply Chain Optimization, Design, and Management: Advances ...books.google.co.in › books
Minis, Ioannis, ‎Zeimpekis, Vasileios, ‎Dounias, Georgios · 2010
Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues.
No image available

Supply chain analyticsbooks.google.com › books
3G E-learning LLC, USA · 2017 · ‎No preview


Behind Every Good Decision: How Anyone Can Use Business ...books.google.co.in › books
Piyanka Jain, ‎Puneet Sharma · 2014
Readers will learn how to:• Clarify the business question• Lay out a hypothesis-driven plan• Pull relevant data• Convert it to insights• Make decisions that make an impactPacked with examples and exercises, this refreshingly ...




Inventory Optimization: Models and Simulationsbooks.google.co.in › books
Nicolas Vandeput · 2020
In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . .




Creating Value with Big Data Analytics: Making Smarter ...books.google.co.in › books
Peter C. Verhoef, ‎Edwin Kooge, ‎Natasha Walk · 2016

This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics.







Supply Chain Analytics - Simple Steps to Win, Insights and ...books.google.com › books
Gerard Blokdijk · 2015 · ‎No preview
The job can be accomplished by having a roadmap and experiences from previous Supply Chain Analytics changes. This is where this book is your guide and roadmap.


Supply Chain Analytics Data a Complete Guide - 2020 Editionbooks.google.com › books
Gerardus Blokdyk · 2019 · ‎No preview
You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in.




Analysis and Algorithms for Service Parts Supply Chainsbooks.google.co.in › books
John A. Muckstadt · 2004
This book provides a broad overview of modeling approaches and solution methodologies for addressing service parts inventory problems found in high-powered technology and aerospace applications.


Supply Chain Planning and Analytics: The Right Product in ...books.google.com › books
Gerald Feigin · 2011 · ‎No preview
This book smartly focuses on the three interlinked processes that compose effective supply chain planning: demand planning, sales and operations planning, and inventory and supply planning.




Supply Chain 4.0: Improving Supply Chains with Analytics and ...books.google.com › books
Emel Aktas, ‎Vasileios Zeimpekis, ‎Michael Bourlakis · 2021 · ‎No preview
This edited book considers the latest technologies and operational research methods available to support smart, integrated, and sustainable logistics practices focusing on automation, big data, Internet of Things, autonomous vehicles and ...
No image available

The Effect of Supply Chain Analytics on Business Decision-makingbooks.google.com › books
2020 · ‎No preview
Supply chain analytics -- Decision-making -- Data-driven decisions -- Supply chain management -- Big data -- Big data analytics.


Inventory Analytics: Prescriptive Analytics in Supply Chainsbooks.google.com › books
Horst Tempelmeier · 2020
The book has been written for students of Supply Chain Management and Operations Management as well as for practitioners who are confronted with inventory management in their daily work.


Supply Chain Analytics Complete Self-Assessment Guidebooks.google.com › books
Gerardus Blokdyk · 2017 · ‎No preview
This book is for managers, advisors, consultants, specialists, professionals and anyone interested in Supply Chain Analytics assessment.



Supply Chain Analyticsbooks.google.com › books
Aegli Marathefti · 2011 · ‎No preview


Supply Chain Analytics Complete Self-Assessment Guidebooks.google.com › books
Gerardus Blokdyk · 2017 · ‎No preview
This book is for managers, advisors, consultants, specialists, professionals and anyone interested in Supply Chain Analytics assessment.



Managing Supply Chain Analytics - Guiding Organizations to ...books.google.com › books
Tino T. Herden · 2020 · ‎No preview





Advanced Analytics for Green and Sustainable Economic ...books.google.co.in › books
Zongwei Luo · 2012 · ‎No preview
"This book focuses on the development of innovative techniques and tools that answer urgent questions in the global trend of sustainable economic development, providing researchers and business managers with a valuable resource for the ...


Übungsbuch Supply Chain Analytics: Operations Management und ...books.google.com › books· Translate this page
Hans-Otto Günther, ‎Horst Tempelmeier · 2020
Dieses Übungsbuch ergänzt das von denselben Autoren verfaßte Lehrbuch "Supply Chain Analytics - Operations Management und Logistik".






Operations Rules: Delivering Customer Value through Flexible ...books.google.co.in › books
David Simchi-Levi · 2010
In Operations Rules, David Simchi-Levi identifies the crucial element in a company's success: the link between the value it provides its customers and its operations strategies.






Artificial Intelligence. An International Perspective: An ...books.google.co.in › books
Max Bramer · 2009

Artificial Intelligence (AI) is a rapidly growing inter-disciplinary field with a long and distinguished history that involves many countries and considerably pre-dates the development of computers.


Data Science for Executives: Leveraging Machine Intelligence ...books.google.co.in › books
Nir Kaldero · 2018 · ‎No preview
If you're ready to meet the challenges of this new revolution, this essential guide will help you take your business to the next level.


Supply Chain Analytics with SAP NetWeaver Business Warehousebooks.google.com › books
Amol Palekar · 2012 · ‎No preview








Updated on8.8.2023,  20 July 2021,  9 September 2019, 14 April 2017.