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XLRI Jamshedpur

Lean principles, learning, and

knowledge work: Evidence from a

software services provider

ORM2 Assignment Submission

Submitted by:

GROUP No. 2 Sec- C

Amitabh Vajpayee (B13131)

Anand Odedra (B13132)

Anirban Chakraborty (B13134)

Anup Joshi (B13136)

Archit Singh (B13137)

Anupam Maity (B13183)

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SECTION 1

Which topic/topics covered in ORM2B13-3 is discussed in the article? What is the primary

purpose for this research?

The topics discussed in the article which were covered in ORM2B13-3 are Lean Manufacturing&

Toyota Production System(TPS).

Lean manufacturing, or lean production, often simply, "lean", is a production practice that

considers the expenditure of resources for any goal other than the creation of value for the end

customer to be wasteful, and thus a target for elimination. Working from the perspective of the

customer who consumes a product or service, "value" is defined as any action or process that a

customer would be willing to pay for.

The primary purpose for this research is to test the applicability of the Lean Production System

to any Indian software services firm, through quantitative and qualitative research. The

quantitative analysis is primarily hypothesis-driven: problem definition, generation of candidate

solutions and evaluation/selection of a solution. It tries to identify as to what extent tasks be

specified within the context of knowledge work. The paper also tries to identify the significant

challenges to using ideas from lean production in a knowledge-based industry and to examine

how the challenges identified above are to be overcome.

Through empirical analysis, the comparison between lean and non-lean projects is made to

understand as to the degree of waste reduction induced in the software services firm by using

lean manufacturing system. Specific aspects of knowledge work- task uncertainty, process

invisibility, and architectural ambiguity- are discussed to question the relevance of leanproduction in this setting.

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SECTION 2

What was the research methodology used to carry out the research? You may explain

tool/technique/method/optimization model along with the results.

The series of methodologies used in carrying out the research are:

1.

SELECTION OF THE TARGET ORGANIZATION

The selection of the organization on which the research was to be conducted, was done on the

basis of 4 characteristics.

The organization must compete in a knowledge work industry. This condition is

important as this introduces the challenge that much of the work being completed must

be invisible to observers. The industry must have high task uncertainty, arising from number of sources, including

environmental change or customer involvement in production.

The architecture of the work should not be set.

The lean initiative progress should occur along with the research study. This way the

researchers will be able to view the process taking place, as opposed to just the

outcome.

Based on the above characteristics, Wipro was selected as the target organization.

2.

DATA COLLECTION & ANALYSIS

Data was collected using multiple methods, including interviews, meeting observation,

inspection of internal documents, and analysis of archival project data. A semi-structured

interview format was followed to elicit consistent information across respondents. The data

collected was then categorised based on the four basic principles identified in the lean

manufacturing.

Iteration between fieldwork and data analysis permitted the researchers to empirically ground

their observations and identify anomalies to advance the process. Also, quantitative project

data for both lean and non-lean projects was analysed. The database of projects containingdetailed information, permitted evaluation of performance of lean projects with respect to

contemporaneous non-lean projects.

3.

PERFORMANCE VARIABLES

In the research methodology, a number of performance variables have been used to quantify

and compare the performance between lean projects and non-lean projects.

Schedule DeviationBefore the beginning of a project, both the schedule and effort are

estimated and agreed by the customer. The revised estimates for both schedule and

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effort deviation are used in calculations, as these most accurately reflect a projects final

objectives.

Schedule Deviation = (Date Delivered Scheduled Date Due)(Scheduled Date Due

Start Date)

Effort DeviationIt captures total number of hours expended by a team on its project.

Effort Deviation = (Actual Effort Estimated Effort)/Estimated Effort

The measure is then normalized as effort deviation can be larger for large projects.

QualityThe number of defects in Customer Acceptance Testing (CAT) divided by KLOC,

has been used as the metric for quality measurement. Every project does not complete

CAT, so the sample data is reduced to 50 lean projects.

Lean ProjectAn indicator variable is used, coded as 1 for lean projects and 0 for non-

lean projects.

Total effort, duration, and complexityIncreasing effort, duration and complexity

decreases the operational performance, which are controlled for using estimated

number of project hours, calendar days required for the project, and actual kilolines of

manual code written for the project.

4.

PERFORMANCE EVALUATION

For evaluating the performance of projects, three samples are examined.

Comparison of lean to all non-lean projects- Two matched samples are decided for both lean

and non-lean projects, by matching on the basis of three traits: SBU, contract type and end year.

Then the Euclidean distance model is used, which requires minimizing the Euclidean distance

using the formula containing the variables, total effort, duration and KLOC.

A second sample is constructed by excluding KLOC and then following the same process as

above. This enables evaluation of all lean development projects among the data.

Also, since some projects might not have gone through acceptance testing, not all matches

would have had quality values. So, restricting the pool of data so that all matches have qualityvalues will be the way to go.

5.

TESTING HYPOTHESIS

Non-parametric tests have been used to test the hypotheses, as there is no reason to assume

normality, and the sample size being small for some tests. Wilcoxon rank-sum tests the

hypothesis with respect to schedule deviation to test the average values between lean and non-

lean projects. For the matched smaples, Wilcoxon matched-pairs signed-ranks test is used to

test significance of the sample without KLOC.

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relatively automatically, but this took many years of experience making incremental

innovations (Fujimoto, 1999).

To apply lean production in a sufficiently novel setting, one would expect similarly

significant effort to be necessary to learn how to identify problems in the new context.

The costs of not being able to identify a problem early can be huge. As in the case ofmanufacturing, rectifying a problem becomes more difficult and costlier as the point of

identification moves down the value stream. Similarly, in the case of knowledge work,

the costs of rework and scrap increase exponentially as we proceed in the project

timeline.

3)

Problems and solutions form a strict pair with respect to time, space, and person. The

Toyota production system advocates for continuous identification and resolution of

problems. Toyota has structured the problem solving process to minimize chances of

ambiguity and variability and maximize the chances of success. Lean production system

highlights the importance of using the scientific method of problem identification and

solving.

This article finds that in order to make the problem solving exercise successful, it is

essential to keep problems and solutions together in person, time and space. This is an

extension of the common logic that knowledge about a task comes from actually doing

and completing the task; therefore pairing of information and action at the same time

enables more effective learning and improvement. In the case of software code reviews

at Wipro, the article has found that keeping the problem and solution bound together in

a single person can also prevent an engineer from repeating the mistake.

Along with the person dimension, space i.e. location also plays an important role in

problem solving, due to the contextual and essential knowledge that is usually

embedded there. For example, some problems cannot be solved unless the

development engineer actually travels to the clients site.

Finally, the temporal dimension is also equally important. By creating opportunities for

individuals to solve problems immediately after the occurrence, the likelihood of

successful resolution increases. This draws analogy from the use of Andon cords at a

Toyota assembly line, where the worker stops the line to correct the problem as soon as

it is detected. If there is a time lag between identification and solving, some of the

information relating to the problem might be lost or modified, which will affect the

solvability of the problem.

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IMPLICATIONS:The implications of the research findings can potentially be huge. The

software services industry is huge. According to market researcher Data Monitor, the

size of the worldwide software industry in 2008 was US$303.8 billion, an increase of

6.5% compared to 2007. So, if successfully implemented on a large scale, the savings and

efficiency increase due to implementation of lean systems in the software industry canbe huge. This case presents a classic example where the best practices in a particular

industry (automotive) can be adapted and furthered by another (software services).

Also, the findings of the merits of lean systems in knowledge work can be extended to

outside the software services industry eg. Medical services, art productions etc.

The above described findings of the research article are a new learningbecause it is for

the first time that the postulates and processes of the Toyota Production system has

been studied and analysed in the context of knowledge work. The TPS originated in a

routine manufacturing context, where the work is characterized by high analysability

and low task variability. Knowledge work, in contrast, is characterized by high variability

and low analysability. Hence, the applicability of lean production methods in a

completely different concept is worth studying. The research found both similarities and

differences between the implementation of a lean system in manufacturing industry and

software services industry. The major findings have been listed and described above.

SECTION 4

What are the limitations of this research as per the article?

The article says that one should be cautious in applying its results. This study examines the

applicability and implementation of lean production in knowledge work, by investigating the

experience at Wipro. It is possible that we cannot generalize the findings to other settings. It

also says that while Wipro has reaped the benefits of lean production, it still has a long way to

go to go realize the complete benefits of lean. This limitation is a necessary but unattractive

consequence of the detail and lack of recall bias that the real time nature of the study permits.

Finally, the basic premise that what Wipro has done is truly lean is questionable. Leanproduction derives from a manufacturing context and analogies to knowledge work do not exist.

Since no definition of lean in software is accepted, it has been assumed that Wipro is

consciously trying to create a system for software services that imitates some of the unique

ways of working at Toyota.

What are the limitations of this research beyond what is already mentioned in the article?

In addition to the limitations listed by the authors, we feel that there are a few more that can be

noted.

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5)

We feel that in addition to the factors considered by the author, other factors could

have been considered and factored into the calculations while assessing the effects of

lean production on performance.

Here, the authors have drawn highly upon the findings of Spear and Bowen as published

in the article Decoding the DNA of the Toyota Production System. However, inaddition to the four major characteristics of a lean system, there can be others like

degree of system flexibility, and responsiveness to change.

The applicability of concepts like Kanban and the mapping of the wastes into the

seven categories as pointed out in Taiichi Ohno, within a software services context could

have also been explored.