Diego Augusto de Jesus Pacheco
University Federal of Rio Grande do Sul, UFRGS, Brazil
E-mail: profdajp@gmail.com
Submission: 04/10/2013
Revision: 19/10/2013
Accept: 01/11/2013
ABSTRACT
The main objective of this study
is to analyse some points of convergence and divergence between the Theory of
Constraints and Six Sigma when used in an integrated manner for the continuous
improvement of operating manufacturing systems. This research also aims to
advance a better understanding of the fundamental principles of such
methodologies by performing a comparative analysis of critical issues. The
focus of the discussion of this study is to review the literature to identify the
mains similarities and differences between these two approaches when applied in
an integrated way in productive systems. To conduct this research, we carry out
a broad literature review of the state of the art on the topic of Operations Management.
The results of this study suggest that the Theory of Constraints and Six Sigma
have many complementary elements that overlap and that a vast field of research
must be explored on this issue. As a result, this study conducts a comparative
analysis of 28 critical criteria of the Theory of Constraints and Six Sigma to
understand these approaches.
Keywords: Theory of Constraints, Six
Sigma, Continuous Improvement.
The
main objective of this paper is to investigate the factors behind the
convergences and divergences between the Theory of Constraints (TOC) and Six
Sigma, when used as a combination for the continuous improvement of processes
in manufacturing environments. The importance of this study is based on the
extent of the use of approaches that focus on continuous improvement by
organisations; as a rule, such approaches have reached their limit of
performance given the current competitiveness and complexity of some markets.
Moreover, it is necessary to find elements of other approaches to make more
robust the current strategies that adopt continuous improvement in the face of
global competitiveness.
Authors
as Pirasteh and Fox (2011), Spector (2006), Montgomery (2010), Bendell (2006)
and Bañuelas and Antony (2004) have studied a combination of approaches to
provide integrated models of continuous improvement. However, from the
literature review of the databases searched, no comparative studies have thus
far discussed specific TOC and Six Sigma research focusing on the limits and
possibilities of integration aimed at improving continuous operating. The gap
evident from the lack of scientific articles discussing these two traditional
approaches was therefore one of the main reasons for the development of this
research. By carrying out a comparative analysis of the TOC and Six Sigma, this
research also sought to objectively present their main similarities and
differences in order to contribute to management decision making.
The
TOC was developed during the 1980s by a physicist who had an outstanding
knowledge of systems, Eliyahu M. Goldratt, and released in the form of the
business novel The Goal in 1984.
However, the origins of the TOC relate to the development of a software
production schedule during the 1970s, known as Optimised Production Technology,
also designed by Goldratt.
Nowadays,
according to Inman, Sale and Green Jr. (2009) and Gupta and Boyd (2008), the
TOC is defined as a management philosophy that provides a focus for continuous
improvement that results in enhanced organisational performance. Boyd and Gupta
(2004) defined the TOC as clearly identifying an “orientation to gain” along
with its three dimensions: mental models, measures and methodology. Recently,
research analysing the evolution of the TOC has been performed. Boyd and Gupta
(2004) investigated the extent of the TOC by analysing Operations Management
and obtained the following four findings. First, the TOC offers a new paradigm
in Operations Management that replaces an outdated consensus to seek to achieve
efficiency in the company, and thus the pursuit of the goal from a global
perspective is more consistent with this new paradigm in Operations Management.
Second, the TOC offers approaches to decision making in operations that can
optimise company activities. Third, the TOC provides a criteria framework for
Operations Management, but more empirical tests are needed to validate its
operational practicality. Finally, the TOC can provide a unified theme or
theory in Operations Management, thus offering new insights for researchers and
practitioners.
In
conclusion, owing to the improvement and development of the scope of the TOC
over time, it is now beginning to be discussed and analysed from the
perspective of becoming a valid theory in the field of Operations Management.
This finding implies the need for research linking it to other relevant and
related topics, such as the Six Sigma approach.
In
order to advance the current understanding about the TOC, Inman, Sale and Green
Jr. (2009) extended the model proposed by Boyd and Gupta (2004) and concluded
that when fully implemented (in terms of logistics, thought processes and
performance indicators), the TOC is an effective management philosophy.
Moreover, the TOC results in positive outcomes such as increased profit,
reduced inventory levels and operating expenses, thereby improving
organisational performance. Further, contrary to the notion that orientation
improves the gain directly according to the study of organisational performance
by Boyd and Gupta (2004), Inman, Sale and Gree Jr. (2009) concluded that the
relation between the TOC and organisational performance is completely mediated
by the results of the TOC. That is, the implementation of the TOC does not
directly influence the financial performance of the firm and market as proposed
by Boyd and Gupta (2004). The conclusion is that the implementation of the TOC
improves outcomes, which in turn positively affects organisational performance.
Thus, the impact of the implementation of the TOC is felt primarily at the
operational level, indicating which metrics related to the success of the TOC
could focus on operational and organisational outcomes.
From
a statistical point of view, the sigma is a measure of the intrinsic
variability of a process, as defined by standard deviation (represented by the
Greek letter sigma (σ)). Under normal conditions, the measure Six Sigma is two
parts per billion. However, considering the fluctuation in a 1.5 sigma process
from a long-term perspective, the process tends to operate at a rate of 3.4
defects per million, which effectively equates to 4.5 sigmas against the
average (EHIE; SHEU, 2005). Thus, according to the concept arising from
Motorola, although the moving average is 1.5 sigmas, the nominal value is
expected to be 3.4 defects per million opportunities. Hence, if the standard
deviation value is low, the process will be more uniform and there will be less
variation in the results; in other words, the smaller the standard deviation,
the better the process is and the lower is the possibility of failure (TRAD; MAXIMIANO,
2009).
Initially,
Six Sigma focused on the manufacturing sector. However, with the maturity of
the approach over time, it has gained strength in services, health, food and so
on. According to Santos and Martins (2010), quality management has become
increasingly important in terms of measuring, quantitative methods, specialised
teams and clearly defined performance goals, Six Sigma thus is now used in a
wider context as a recognised strategy to improve business performance.
Currently,
by focusing on tangible opportunities for financial gain, organisations
approaching Six Sigma’s strategic issues define guidelines from a top-down
perspective. The study by Pinto and Carvalho (2006) showed that firms that
align Six Sigma projects with their corporate strategies have better
performance than those who do not. In addition, other factors may be added as
critical to the success of Six Sigma. The study of Trad and Maximiano (2009)
listed the following factors:
(i)
The
leadership and participation of senior management: must be active and with
clearly outlined and communicated,
(ii)
The
selection of projects: the right choice of projects aligned to the business
strategy from the perspective of the customer and
(iii)
Human
resources: beyond the technical field of quantitative approaches, skills such
as creativity, collaboration, dedication and communication are essential as is
choosing the correct team.
Since
the actual implementation of Six Sigma involves a series of steps focused on
continuous improvement, the models adopted are Define, Measure, Analyse,
Improve and Control (DMAIC) and Design For Six Sigma, which adopts the DMADV
model (Define, Measure, Analyse, Design and Verify). The DMAIC model is
designed and optimised for applications in existing processes and services in a
manufacturing environment, while the DMADV model is adopted when new
deployments of processes, products and services are made or when the current
sigma level is already high, around five sigmas (BENDELL, 2006).
This
research aims to bridge the gap in the Operations Management literature in
order to explain the similarities and differences between the TOC and Six Sigma
for continuous improvement. The results of this discussion will generate new
knowledge for the field of Industrial Engineering and Operations Management. Most
academic research requires understanding the state of the art on the research
problem. Thus, research is characterised as a theoretical review. For Khan et
al. (2003), the main advantage of using a systematic review is that it provides
information on the effectiveness of interventions to identify, evaluate and
summarise the results of an amount of data not otherwise treatable. This
research used as a basis the work of Silva (2009) and the study of Tranfield
and Palminder (2003) to develop a systematic literature review. The procedure
adopted for the literature review comprised the following six steps: (i) Extract
keywords from the search problem: the words chosen were “Six Sigma” and “Theory
of Constraints”, which were surveyed in the field “Abstract” in the databases;
(ii) Define the databases to search for publications, namely Emerald, Springer
Link, Scopus, Ebsco, Proquest and Scielo; (iii) Define the time horizon, namely
1995 and 2012; (iv) Review the titles and abstracts of publications. The study
included 33 articles. However, between these studies, only Jin et al. (2009),
Husby (2007) and Ehie and Sheu (2005) discussed the use of the TOC and Six
Sigma for continuous improvement. These studies are detailed in Section 5 of
this paper; (v) Decide on the inclusion criteria and; (vi) Carry out an
analysis, synthesis and inclusion of the information in the search: this thread
is consolidated in Section 5, which aligns the discussion of this research. A
systematic review showed that integration between the TOC and Six Sigma is a
recent theme in the literature and thus an opportunity for future research.
The
Six Sigma approach identifies projects that can reduce defects in the process
and make operational improvements. However, it does not fully involve operators
and lacks a systemic view to understand how these projects affect overall
system performance. According to Ehie and Sheu (2005), this aspect can lead to
project prioritisation, no financial impact for the company and the elimination
of positive impacts on other processes. Alternatively, Husby (2007) suggested
that the five focusing steps of the TOC can bridge this gap. However, the
author pointed out that the thought process of the TOC’s analysis and
troubleshooting uses language requiring complex intellectual driving by trained
experts and a different approach for management and operators.
From
the point of view of Jin et al. (2009), the focuses of Six Sigma and the TOC are
the customer and the company. Although they have different philosophies, both
have been used by various industries for process improvement because while Six
Sigma requires solutions in depth, the TOC can show bottlenecks and overcome
them. According to Nave (2002), the common form of integration between the TOC
and Six Sigma is to identify the constraints of the company and use Six Sigma
to reduce or solve this problem. According to Ehie and Sheu (2005), the main
advantages of the combination of the two approaches are threefold. First, the
restrictions are analysed, measured and controlled by using a set of
statistical tools, thereby increasing the understanding of the problem and
decisions. Second, the bottleneck is the first point to be analysed, thus
generating increased financial gain for the company. Finally, the Six Sigma
project is not chosen by a single business area, but rather based on the
overall view that the TOC will generate project outcomes throughout the system.
However,
according to Jin et al. (2009), the disadvantages are also threefold. First, it
does not always reduce variation, which will increase the capacity of the
restrictions. Second, when any reduction in variation elevates the production
rate of the bottleneck, downstream processes can generate higher rates of
rejection, with the focus placed on the bottleneck only. Finally, there is some
uncertainty when applying the principles of the TOC and Six Sigma. The model
integrating Six Sigma and the TOC proposed by Jin et al. (2009) assumes an
environment with a limited budget for improving and applying Six Sigma after
the bottleneck to ensure quality and efficiency. This model has been replicated
in a motor manufacturing company with satisfactory results.
For
Ehie and Sheu (2005), there are similarities between the improvement processes
of Six Sigma (DMAIC) and the TOC (five focusing steps). The authors proposed an
integrated model where the initial step is to identify the same restrictions in
both approaches. The next step follows the logic of the TOC by exploiting the
phase measures and analysing Six Sigma-like supports. Next, we use the Improve
phase of Six Sigma and its statistical tools to eliminate the problems and
causes indicated in the previous step. Step four uses the steps of the TOC and
Control of Six Sigma to ensure that all actions taken previously are applied in
the system. In step five, efforts are made to increase the capacity of the
constraints, while the last step evaluates the next constraint to avoid inertia
in the system. To refine the model, the authors suggested incorporating the TOC
Thinking Process to understand the cause/effect interactions in the system as
well as add other approaches aimed at continuous improvement. The study by Nave
(2002) compared some differences between the TOC and Six Sigma and concluded
that the two approaches are complementary. Similarly, Thompson (2005) contributed initially by
enumerating a set of strengths,
weaknesses and countermeasures to overcome any weaknesses, as shown in Table 1.
To
consolidate the results of the literature review in an objective manner, Table
2 presents a comparative analysis of the 28 criteria of the TOC and Six Sigma.
These criteria make it possible to understand the main similarities and
differences between them. It is clear that the criteria Production Control,
Production Planning, Inventory and Capacity Planning have no evidence in the
Six Sigma approach. Complementary elements of each approach include the following
criteria: Application Structure, Goal, Focus, Assumptions, Primary Effects,
Secondary Effects, Distribution of Knowledge, Dominant Culture, Leadership
Style, Information Technology and Management Performance Indicators. Likewise,
conflicting aspects that hinder the use of the two approaches included Focus,
Primary Effect, Deficiencies, Ease of Implementation, Hierarchical Level of
Application, Structure Implementation, Effect on Variability, Process Aspects
and Data for Application.
Table 1: TOC vs. Six Sigma
Approach |
Fundamental Elements |
Strengths |
Weaknesses |
Countermeasures |
Six Sigma |
The cause of poor performance is the change in process and product quality. Random variation results in inefficient
operations, causing customer dissatisfaction because of unstable products and services. |
1. The rigour and discipline of the statistical approach solves complex problems that cannot be solved by simple intuition or trial and error. |
1. Statistical methods are not well suited to analysing problems of integrated systems. One can calculate the sigma level for a product specification but not for the failure of processes and interactions. |
1. Force 2 of the
TOC |
Increased competitive advantage comes from stable predictable processes, allowing raising yields, improved forecasts and reliable performance by the product or service. |
2. Data collection comes to managing strong subsidy support for decision making. |
2. The heavy reliance on statistical methods by its very nature is reactive, since it requires the repetition of the process to develop trends and confidence levels. |
|
|
|
3. The focus on reducing variation reduces risk and improves predictability. |
3. The strong focus on stable processes can lead to a complete aversion to risk and may penalise innovative practices that are inherently unstable and variable. |
|
|
TOC |
The cause of poor performance is failed management techniques. Logical systems are used to identify constraints and focus resources on restrictions. The constraint becomes the mainstay of management. |
1. The TOC provides simplified resource management through a narrow focus on restrictions to manage a process as well as efforts to improve (exploration). |
1. The emphasis on restrictions can lead actors to accept or tolerate excessive losses in cases of both no restrictions and a
restricted environment. |
1. Force 2 of Six Sigma |
|
2. It can see through all the processes in a system context to ensure that limited resources are not used to improve restrictions. |
|
|
|
|
3. The TOC differentiates physical from political
constraints. 4. It provides direction based on the appropriate indicators (G, I, DO) |
2. The TOC does not forward directly the need for cultural change. The change process of the TOC is technically oriented and fully recognises the need for other methods of improvement. |
|
Source: adapted from Thompson
(2005).
Table 2 shows that the main relevance to
managerial practice of this paper is that it presents to decision makers in the
industrial context important points about the convergence and divergence
between the TOC and Six Sigma when used in an integrated way for the continuous
improvement of manufacturing systems. The survey results
also allow managers and organisations to understand the limits
and possibilities of each individual approach as
well as their points of synergy
for practical implementation in manufacturing systems as well as to increase productivity when
exploring bottlenecks. Companies that already have elements
of the TOC or Six Sigma implemented, or
those that wish to apply them, may use
this article as a reference to analyse the production system and prioritise integrated application based on the complementary strengths of each approach.
Table 2: Summary of the comparative
analysis between the TOC and Six Sigma
Criterion |
TOC |
Six Sigma |
1. Origin |
Goldratt (1980s) |
Motorola and General Electric (1980s) |
2. Theory |
Constraint management and increase gain |
Reduce variability |
3. Application Structure |
1. Identify the constraint 2. Explore the constraint 3. Subordinate 4. Raise the constraint 5. Return to step 1 |
1. Define 2. Measure 3. Analyse 4. Improve 5. Control |
4. Focus |
On constraints |
On problem |
5. Goal |
Continuous increase in profits |
Maximise business results |
6. Strategic Objective |
Synchronise |
Stabilise |
7. Assumptions |
- Emphasis on velocity and volume - Analyse existing systems - Interdependencies between processes |
- There is a problem - Statistical tools are used - Improve the output rate of the system by reducing
variation in processes |
8. Primary Effect |
Increase gain rapidly |
Rate uniform process output |
9. Secondary Effect |
- Reduce inventories and losses - Gain is the benchmark of system performance - Improve quality |
- Reduce losses. - Reduce inventories - Variability is the benchmark of managerial
performance - Improve quality - Instigate cultural change |
10. Deficiencies |
Ignore parts of the organisation to focus on
manufacturing and the constraint |
- Interdependencies within the system - Improvements in processes made independently - Create elite employees |
11. Ease of Implementation |
Greater difficulty |
Medium difficulty |
12. Hierarchical Level of Application |
Top management |
Technical level and middle management |
13. Structure Implantation |
Jonah |
Belts and Champions |
14. Effect on Variability |
Absorb variation |
Reduce variation |
15. Major Contribution |
Systemic view of constraints |
Organisational structure with experts in
improvements, projects and guided quantification of cost reductions |
16. Process Aspects |
- Metric-specific accounting - Focus on systematic constraints |
- Specific statistical tools - Specific terminologies - Specific structure experts |
17. Batch Size |
- Larger batches for constraints and lower for
non-bottlenecks |
Not applicable |
18. Production Control |
The algorithm Drum-Buffer-Rope is used to release
stock |
Not applicable |
19. Production Planning |
- Detailed planning for constraints and less
detailed for non-bottlenecks - Drum-Buffer-Rope |
Not applicable |
20. Distribution of Knowledge |
Knowledge is centred and focused on constraints |
Knowledge centred on belts and training is highly
focused |
21. Dominant Culture |
- Requires a change in approach - Extends across all parts of the business |
- Empowerment of employees - Change philosophy - Focus on customers |
22. Leadership Style |
Leader of driver profile |
Leader of driver profile |
23. Need for Data and Information |
Amount and accuracy of data is less critical
compared with traditional production methods |
Requires a large quantity and a high accuracy of
data for decision making |
24. Inventory |
- Inventory is needed to facilitate production, but
the goal is to minimise inventory - Buffers are placed in front of the constraint and
the intersection between the paths of non-bottlenecks and the path of a
constraint to their production orders |
Not applicable |
25. Capacity Planning |
- Consider finite capacity - Is planned by computer simulation |
Not applicable |
26. Information Technology |
Computational resources are needed for deployment |
Computational resources used mainly for statistical
analysis |
27. Stability Requirements for Deployment |
Indifferent, but performs best in environments of
medium or low stability |
Indifferent |
28. Management Performance Indicators |
- Global Indicators: Net Profit (NP), Return on
investment (ROI), Cash Flow (CF) - Local Indicators: Gain (G), Inventory (I),
Operational Expenses (OE) |
Defects Per Million Opportunities |
Source: the author based on the literature review
(2013).
This
study analysed important factors about TOC and Six Sigma when used for the
continuous improvement of processes in manufacturing systems. The discussion
also contributed to a better understanding of the fundamental principles of
such methodologies by performing a comparative analysis of aspects considered
to be critical. After the analyses, it was found that examining the points of
convergence and exclusion between the two approaches contributes to a better
understanding of its fundamental principles. It was found in general that there
are more points than aspects of overlap between the two approaches and that it
is viable to think about constructing an integrated continuous improvement
process that enhances competitiveness.
However,
critical factors must be considered in constructing models integrating the two
approaches, without which the development of a integrated model becomes weak.
Among the main critical factors is that the literature still does not have a
clear definition on such aspects as well as areas for opportunities for
researchers:
·
How to choose the correct
elements of each approach according to the real needs of the organisation?
·
How can the company
precisely define its priority? To reduce variability? Or to reduce losses and
improve flow? Or to remove constraints?
·
The correct diagnosis on
the culture, goals, strengths and weaknesses of the organisation should also be
considered to be an aspect of the integration of the approaches.
·
Another
critical factor to be considered is the breaking of useful mental models, but
how driving this change? For example, the non-effective engagement of operators
is a characteristic of the cultural implementation of the TOC and Six Sigma;
·
The
principles of the construction of a model incorporating such approaches must
necessarily be aligned with the company's strategy and goals. Top down or
bottom up? In this sense, a starting point for future research on these topics
can found in the discussion held in Pacheco et al. (2013) and Pacheco (2014).
In a
general context, especially from the summary in Table 2, the results of this
study showed that the TOC and Six Sigma have complementary aspects that overlap
the points of exclusion and that there is a wide open field for research on the
topic. Therefore, in order to advance the discussion and provide an in-depth
understanding of the interrelationships between approaches or evaluate the
contributions of other approaches, the following research topics also emerge
for future research agenda:
·
What
indicators should be used to measure a performance model integrating the two
approaches and how should we structure them (which levels) in the organisation?
·
How
can we choose what will be the dominant culture of the company and how should
we build it, assuming that the approaches coexist?
BAÑUELAS,
R.; ANTONY, J. (2004) Six sigma or design for six sigma? The TQM Magazine, v. 16, n. 4, p. 250-263.
BOYD, L.; GUPTA, M. (2004) Constraints management: what is the theory? International Journal of Operations &
Production Management, v. 24, n. 4, p. 370–371.
GUPTA, M.; BOYD, L. (2008) Theory of Constraints: A Theory for
Operations Management. International
Journal of Operations and Production Management, v. 28, n. 9/10.
EHIE, I.; SHEUS, J. (2005) Integrating six sigma and theory of
constraints for continuous improvement: a case study. Journal of Manufacturing Technology Management, v. 16, n. 5, p.
542–553.
GOLDRATT,
E. M.; COX, J. F. (1984) A Meta.
1.ed. São Paulo. Nobel.
HUSBY, P. (2007) Competition or Complement: Six Sigma and TOC. Material Handling Management, p. 51–55.
INMAN, R. A.; SALE, M. L.; GREEN Jr., K. W. (2009) Analysis of the relationships among TOC use,
TOC outcomes, and organizational performance. International Journal of Operations e Production Management, v. 29
n. 4, p. 341–356.
JIN, K.; HYDER, A.R.; EIKASSABGI, Y.; ZHOU, H.; HERRERA, A. (2009)
Integrating the Theory of Constraints and Six Sigma in Manufacturing Process
Improvement. Proceedings of world
academy of science, engineering and technology, v. 37.
KHAN, K. S.; KUNZ, R.; KLEIJNEN, J.; ANTES, G. (2003) Systematic reviews to support
evidence-based medicine. London: Royal Society of Medicine Press Ltd.
NAVE, D. (2002) How to compare Six Sigma, Lean and the Theory of
Constraints. Quality Progress, p.
73–79.
MONTGOMERY D. C. (2010) A modern framework for achieving enterprise
excellence International. Journal of
Lean Six Sigma, v. 1, n. 1, p. 56-65.
PACHECO, D. A. J. et al. (2013) Balanceamento de
fluxo ou balanceamento de capacidade: análise e proposições sistêmicas. Ahead
to print in in Gestão & Produção.
PACHECO,
D. A. J. (2014) Teoria das Restrições, lean manufacturing e seis sigma: limites
e possibilidades de integração. Ahead to
print in Revista Produção.
PINTO,
S. B.; CARVALHO, M. M.; HO, L. L. (2006) Implementação de programas de
qualidade: uma survey em empresas de grande porte no Brasil. Gestão & Produção, v. 13, n. 2, p.
191–203.
PIRASTEH, R.
M.; FOX, R. E. (2011) Profitability with
no boundaries. Quality press.
SANTOS,
A. B.; MARTINS, M. F. (2010) Contribuições do Seis Sigma: estudos de caso em
multinacionais. Produção,
v. 20, n. 1, p. 42–53.
SILVA,
É. R. P. (2009) Método para revisão e
mapeamento sistemático da literatura (DEI-POLI/UFRJ). Trabalho de conclusão
de curso em Engenharia de Produção.
SPECTOR, R. E. (2006) How constraints management enhances Lean and Six
sigma. Supply chain management review,
v. 10, n. 1, Jan.-Feb., p. 42-47.
TRAD,
S.; MAXIMIANO, A. C. (2009) Seis Sigma: Fatores Críticos de Sucesso para sua
Implantação. RAC, Curitiba, v. 13, n. 4, art. 7, pp. 647–662, Out./Dez.
TRANFIELD, D.; PALMINDER, D. (2003)
Towards a methodology for developing evidence-informed management knowledge by
means of systematic review. British
Journal of Management, 14, p. 207–222.
BENDELL, T. (2006) A review and comparison of six sigma and the lean
organization. The TQM Magazine, v.
18, n. 3, p. 255–262.
THOMPSON, S. W. (2005) Lean, TOC
or Six Sigma: Which Tune Should a Company Dance To? Society of
Manufacturing Engineers. January 15, 2005. Disponível
em:<http://www.sme.org/cgi-bin/get-newsletter.pl?LEAN&20030811&1&>.