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Lean Management – from Waste to Perfection

“The most dangerous kind of waste is the waste we do not recognize.” – Shigeo Shingo, Engineer and Co-developer of the Toyota Production System

In the early 90s Toyota revolutionised the automotive industry. With the release of the Toyota Production System, Toyota laid the foundation for modern process and quality management. The goal was to completely tailor production to the needs of the customer, eliminating any waste along the value chain. Over the years, the concept of lean production has become more and more generalised and the concept of lean management has been implemented.

“There are so many men who can figure costs, and so few who can measure values.” – unknown author

Lean management has long ceased to focus on the manufacturing industry, in particular the automotive industry, but has developed into a leadership philosophy. Throughout all corporate processes, we want to avoid any form of waste, minimise the cost of unnecessary mistakes, and at the same time, strive for the best possible quality.

Lean management has long been understood as a permanent, consistent and integrated application of a whole set of principles, methods and measures for effective and efficient planning, design and control along the entire value chain of goods and services. First and foremost, Lean Management implements an organisational and leadership philosophy to which the entire company is geared.

Based on the idea that in a company, the only processes to be maintained are those necessary for the achievement of objectives and those that therefore create value, the quality of the services offered can be increased in the long term and costs strongly reduced. The streamlining of processes can be achieved, above all, by eliminating hierarchies in order to balance the disadvantages of existing organisational patterns such as the matrix organisation. Appropriate working philosophies must be developed for this purpose. Lean management can not be implemented by the mere application of individual tools and techniques, but only by the consistent orientation of all corporate processes to the principles of lean management.

Lean Six Sigma in the Financial Sector

In the early 1990s, a concept was developed under the title “The Next Revolution in the Automotive Industry”, which was intended to increase the efficiency and quality of development and production systems. The goal was to establish lean production processes in the automotive industry and, above all, to prioritise the principles of a lean organisation. But this focus soon shifted. The term “lean production” became “lean management”, which defined a leadership philosophy that has been adapted far beyond the boundaries of the automotive industry and its suppliers by managers in all industries. In 2002, modern lean management was combined with the Six Sigma concept.

Lean Six Sigma is considered one of the most efficient management methods today. A standardised improvement process based on the DMAIC cycle (Define – Measure – Analyse – Improve – Control), the avoidance of waste along a process chain, the statistical optimisation of processes to an error level close to zero and the targeted training of the project team according to the Lean Six Sigma method are just a few advantages that this management concept offers.

Lean Six Sigma has long extended past production processes, and can be used to streamline any form of process. Lean Six Sigma has become an important topic in the financial sector as well. Using the Lean method, three key inhibitory factors are systematically identified and reduced:

    1) inflexibility (inability to deliver to the customer exactly what he has ordered at the desired time and in the right amount)

    2) waste (use of resources for activities that do not add value for the customer and are not necessary, such as “gold plating”)

    3) variability (changes in quality and throughput time due to differences in various factors, e.g., human, processes)

Implementing Lean Six Sigma in the finance sector highlights four levers that include a variety of measures designed to address process design flaws.

1. Introduction of systematic team and performance management

In the area of team management, the focus is on the sharing of skills in order to achieve optimum capacity management (multi-skilling). The goal is the classification of very small and system-specialised teams within individual groups. To enable a constant recording of performance, a stringent, figure-based performance management system must also be introduced.

2. Strengthening of cooperation between accounting and IT

An elaborate and rigid budgeting process is based on steady requirements, but these requirements change frequently and are rarely stable over a longer period of time. An optimised management of the budget should lead to the improvement of a systematic management of the project portfolio. Introducing technical tools to enhance communication and collaboration among virtual teams is a key IT challenge. Working in separate areas often complicates cooperation, especially if no suitable technical aids are available. Furthermore, this lever provides for the introduction of an explicit project organisation in accounting and general capacity planning.

3. Establishing agile development methods

Above all, long decision-making processes relating to technical development issues can delay the start of the project. The introduction of agile best-practice development methods can counteract this problem. For example, the Scrum method can be widely adapted and integrated into an existing organisational structure. Implementing standard methods when estimating the complexity of projects can also prevent delays to the start of the project. For example, a mandatory estimation of resources can be commissioned very early on even if information is insufficient. However, it is necessary to be able to flexibly and quickly integrate significant changes over the course of the project, primarily due to regulatory requirements.

4. Optimising cooperation with internal service providers

Oftentimes, internal logistics and IT services in particular are too slow, non-transparent, too complex and not flexible enough, resulting in unnecessary delays and waste. By implementing measurable quality standards and shorter response times, these vulnerabilities can be compensated for. By simplifying specifications, for example, through the use of checklists, processes can be made more efficient and, above all, more transparent.

Implementing Lean Six Sigma – The Next Steps

The introduction of Lean Six Sigma is only possible when management and the team communicate. A Lean Six Sigma core team should support and accompany all necessary steps. Within certain groups, the group leader should oversee the collection of basic information and, based on this, “mini” diagnoses of the capability of the process should be made. Moreover, data collected for the purposes of demand analysis contain valuable information about the claims that are made in a process. The plan for implementing scheduled actions should be communicated to all those involved. A kick-off meeting with the entire group and possibly with other stakeholders is essential.

(Statistic) Weighted Scoring Model

The “Weighted Scoring Model” is a measurement method for comparing different project or action alternatives with regard to various (weighted) measured variables.


    1) Definition of evaluation criteria (e.g. possible profitability, costs)

    2) Weighting of criteria according to their significance (sum of weightings = 100%)

    3) Determination of the degree of fulfilment of individual criteria (e.g. possible profitability = €15,000 or 20 “points”)

    4) Calculation of weighted ratings and addition for each project alternative

Result: The sum of all rated criteria equals the project’s significance, which then can be compared with the sum of the other projects.

Criticism and extension possibility:To develop actual relevance for real decisions, a static assessment of possible environmental conditions is often not enough. Influencing variables such as “possible profitability” depend on input factors, which in turn can be difficult to estimate. From past experience, however, one can make statements about an associated probability function according to which a particular measure has behaved in the past and could behave in the future. This can be used to extend a “static weighted scoring model” into a “multivariate stochastic-dynamic weighted scoring model”. A subsequent simulation with a high number of iterations thus makes it possible to obtain a much more accurate estimate (in some cases even significantly differing from the static perspective) about the benefits of different action alternatives.


A histogram is useful for graphically representing certain metrically-distributed characteristics of a frequency distribution and then analysing them using different methods. For normally-distributed random variables, this results in the known Gaussian bell curve. The area within a histogram represents 100% of all measured characteristic values. This makes it possible to define information on the frequency of occurrence of a characteristic through the use of lower and upper limits (LL and UL). Therefore, this form of representation may also be referred to as the estimated probability density of a random variable (expression).

Six Sigma College Düsseldorf - Histogramm

In practice, such simple methods can quickly provide information about the probability with which certain events have occurred or will occur. For example, the resulting scrap of a production batch can be calculated by specifying a quality characteristic to be fulfilled (target variable). The “width” of the histogram gives an indication of the standard deviation of the input data, i.e. how far the values spread around the desired target variable. This makes it possible for companies to statistically check their quality guarantees.

In reality, however, it will rarely lead to normally-distributed measurement results. For this, software solutions such as Minitab, Excel, or @Risk offer possibilities for quickly and effectively evaluating metrics on an automatic basis and for further use (e.g., for reports).


Failure Mode and Effects Analysis (FMEA) is a technique used to find potential weaknesses in a process. It follows the basic idea of precautionary error prevention instead of follow-up error detection. The goal is to sustainably reduce the costs of control and troubleshooting. The FMEA provides a systematic approach that can be applied in the early stages of product and process development within a product life cycle. Especially in the early stages of the product life cycle, the costs per error are still relatively low (see figure: Error cost trends).

Within a Six Sigma project, the FMEA is performed by both Black Belts and Green Belts and is designed to prevent or eliminate potential errors or errors that already exist in a product or process.

The FMEA includes the following measures:

    – Limitation and structuring of the system under consideration

    – Definition of the functions of the structural elements

    – Analysis of potential causes of errors, types of errors and consequences of errors

    – Risk assessment

    – Measures/solutions for prioritised risks

    – Monitoring of error avoidance and detection measures

Merging of Six Sigma and Professional Project Management

To remain competitive in the long term, companies must constantly improve and expand their services and production. Products and services should become cheaper, more readily available and must meet the quality requirements of customers. For the project portfolio, this means that projects are implemented with the goal of reducing costs and creating new revenues and profits.

The Project Management Institute (PMI), on the one hand, provides a comprehensive standard for designing, implementing, controlling, and ultimately (successfully) completing projects, but focuses on the characteristics and process of a project. A clear process structure enables a high degree of project control and thus forms the basis for the successful conclusion of a project.

The Six Sigma methodology also provides tools and concepts for avoiding errors during continuous improvement projects, thus improving the quality of services over the long term. A structured approach (DMAIC) and statistical process control are at the heart of a Six Sigma project. The Six Sigma methodology originated in manufacturing, but is not limited to that field. In principle, any process that involves structured action, requires monitoring and is “in motion” can be improved through the Six Sigma approach.

The goal is therefore to combine both concepts and use their respective strengths. The improved control of the project management process according to PMI® and the strategy of statistical error reduction result in a consistent, predictable and controlled process of continuous improvement and systematic error prevention.

The Six Sigma concepts can be applied even in the project development stage. For example, Six Sigma tools can be used in the problem definition stage to minimise potential errors that may occur due to incorrect or conflicting ideas about the issue at hand. But also in the problem analysis stage, various tools (Gauge R&R, FMEA, Control Charts) can be applied. The way in which the Six Sigma and project management phases complement each other can be seen in the figure Integration of Six Sigma and Project Management.

Six Sigma College Düsseldorf - Six Sigma & project management

Abbildung: Integration of Six Sigma & project management

Quelle:, accessed on 13.09.2013

Quality Function Deployment

Quality Function Deployment is a comprehensive planning and communication method used to bring together and coordinate a company’s resources. The goal of the method is the development, production and marketing of products and/or services that have real customer benefits. Through application of this method, market and customer requirements are translated into in-house requirements and taken into account in every phase and every area of service provision.

The key challenge is to incorporate and transform customer voices (Voice of Customer) into in-house performance specifications. For this, an extensive survey of customer opinions must be undertaken, which consumes a great deal of time and money. If the customer’s performance requirements are known, his wishes can be translated into his own performance; in this case, the more detailed and precise the requirements perceived, the better they can be translated into one’s own performance.

To illustrate the relationship between customer requirements and performance characteristics, the House of Quality is used. This consists of six steps:

    1. Survey of customer requirements and weighting of requirements from the customer’s perspective.

    2. Assessment of the customer’s competitive performance for a comparison with the competition. In addition, priorities can be set by weighting the customer’s requirements.

    3. Implementation of the customer’s requirements into technical characteristics. For each customer requirement, measurable and controllable technical characteristics are determined as part of overall performance.

    4. Linking of quality performance characteristics with the resulting fulfilment levels for the customer requirement

    5. The technical significance of the quality characteristics is calculated from the product of the priorities, the customer requirement and the corresponding columns of quality characteristics in the relationship matrix.

    6. The ‘roof’ forms the correlation matrix and serves to clarify conflicting goals. These arise from positive and negative interactions of individual quality characteristics.

What is Lean?

First and foremost, let us define what the word “lean” actually means. Synonyms include sparse, thin or scarce, which give first indications of the significance in relation to production and process management. Whether “lean” has something to do with cutting production or rather thinning out processes is still unclear at this time.

To better clarify the actual goals of lean management, one has to look at the development of this process management system. The terms Lean Production, Lean Thinking and Lean Enterprise have their roots in the Toyota Production System. This system is not just a collection of tools, but offers approaches and methods that in the broadest sense involve a consistent avoidance of waste. The Toyota Production System was developed by Taiichi Ohno (1912-1990) in the aftermath of the Second World War at the Toyota Motor Corporation and helped secure the company steady growth and a very high level of quality in the decades that followed. As a basis for his work, Ohno used the system of mass production developed by Henry Ford and Frederick Taylor.

Now that we’ve looked at the history of Lean management, what is the purpose of this method? The most important target variables can be defined as follows: highest possible throughput; maximum utilisation of facilities; maximum utilisation of the workforce; lowest unit costs; large batch sizes and infrequent retrofitting of systems. Lean management offers numerous methods and gives instructions on how to achieve these goals. Many of these are based again on the theories of Henry Ford and Frederick Taylor.

However, this management system does not only have positive results. The conversion of processes can also lead to problems, such as high throughput times or long transport routes. Another problem can arise from the highest possible throughput: long waits at bottlenecks. Faulty units could be detected too late, because of time pressure in individual work processes, for example, through a “just-in-time” production process. With the introduction of Lean management, there is a need to pay more attention to possible implementation problems, since the optimisation of processes offers great potential for error.

The current state of process management in companies offers room for improvement. Most processes are not “lean”, i.e. the efficiency of the process cycle is less than 10% (value added time/total throughput time). The primary goal is to reduce the processing time, but also the number of units in the processes. According to the Pareto principle, 20% of the causes result in 80% of the delays, so apparently small changes offer an extremely large potential for improvement. All processes must be designed based on data, since invisible performance components can not be improved. To reduce scattering and throughput time, processes should be pulled and not pushed. In almost every branch of production, or management, there are these significant opportunities for improvement, which in the long term have an extremely positive effect on the competitiveness of companies.

Causal Relationships and Correlations

It’s certainly interesting to know how big the machines are that produce cars and how the relative distribution of each brand looks like. Even more interesting, however, are causal relationships. In our example, this would mean that one relates the size of the machines to the quality of the car and checks for a relationship.

In statistics, this type of a relationship is called a correlation. The mathematical relationship between two variables can be positive or negative; if the correlation is positive, the value of one variable increases as the other increases. In our example, a positive correlation would be that brands with larger machines produce higher-quality cars. A negative correlation would be that brands with large machines produce lower-quality cars. The third possibility is that there is no correlation between the size of the machines and the quality of the cars. In our example, the most likely variant.

Suppose now that a statistical analysis shows a very strong correlation between two variables. Does this mean that these two variables actually influence each other? This would be an appropriate conclusion, but it is wrong. For example, the actual cause may be a third variable that affects both variables. Let’s come back to our example: Suppose there is a positive correlation between the size of the machines and the quality of the car, i.e. the bigger the machine, the better the car. One could assume that all cars made by a big machine are better than those made by a small machine. This is quite possible, but another reason is much more likely. The average price of cars made by large machines is significantly higher than the price of cars made with small machines. If a car costs more money, more money can be invested in the machines. Yet it is impossible to truly prove a causality between the price of the car and size of the machine; the information collected is not suitable. You can only provide a stronger indication.

Pareto Analysis

The Pareto analysis is an elementary Six Sigma tool and is based on the theories of Italian economist Vilfredo Pareto (1848-1923). A Pareto analysis helps distinguish the essential from the inessential. It helps separate the few important influencing factors from the many unimportant ones with regard to the overall impact and paves the way for a targeted initiation of troubleshooting measures in the company. In Six Sigma, the Pareto analysis is used to define error types and then to determine the error frequency as a percentage. Based on these percentages, a ranking is created that shows the significance of each error in relation to the total cost of troubleshooting.

The Pareto principle, which says that most of the effects of a problem (80%) are often due to a small number of causes (20%), is the basis of the analysis. In Six Sigma, it is important to determine this 20% and to specifically attempt to tackle the “major sources of the error”. The results of the analysis also clarify what causes are already in good condition and require little or no troubleshooting. Once these areas are determined, you can quickly and effectively reduce the overall cost of troubleshooting.

The diagram shows an example of error types and the associated costs for repairing bicycles. At first glance, it becomes clear which sources of error are significant and which ones can be ignored. As shown theoretically in the Pareto principle, it can be seen in the example that a small part of the error types (circuit and bottom bracket) represent a large percentage (73%) of the total error cost. The focus of a bike manufacturer should therefore be more on managing the quality of circuits and bottom brackets.

Six Sigma College Düsseldorf - Pareto Analyse

The Six Sigma Success Story

The basics of Six Sigma can be traced back to the theories of German mathematician and astronomer Carl Friedrich Gauss (1777-1855). The theory of normal distribution established by Gauss is an important type of continuous probability distribution and forms the basis for Six Sigma. The unit of measure for determining the deviation from the mean is called sigma. The higher the sigma value, the more probabilities that are covered and the lower the probability of error. A market standard in quality management is 3 sigma, which means a probability of error of about 0.27%. This value doesn’t seem significant, but for companies like Deutsche Post, which sends around 72 million letters every day, even that is too high.

Motorola was the first to use this theory to introduce enterprise-wide quality management called Six Sigma in the 1980s. To reduce the likelihood of errors, Motorola has drastically reduced the variance of its individual production processes. This standardisation has made processes more transparent and measurable. Motorola has made teamwork, transparency and measurability as the foundation for a company-wide, customer-focused approach. Motorola introduced revolutionary quality management with these key objectives and globally-consistent standards.

The global breakthrough of Six Sigma required an even more influential group. American management icon Jack Welch took advantage of Motorola’s patented process and radically transformed General Electrics. With comprehensive training and promotions exclusively through successful Six Sigma cases, he paved a path of nearly perfect quality management for the company. Welch took advantage of the already existing theories and refined them precisely to the needs of GE, resulting in 1997 in a $700 million profit increase after only two years. This successful model was the final breakthrough for Six Sigma. Nowadays, the risk of losing the high initial investment is more than offset by the high potential for savings.

Voice of Customer

The “Voice of Customer” (VoC) concept aims to convert customer requirements into target variables and thus bridges the gap between a frequently-verbalised customer problem and concrete measures to solve the problem. For example, if customers have the problem that the product is “difficult to use,” a resulting target variable may be to “increase the size of the labels on the product.”

This tool is particularly important in the Define phase of a Six Sigma improvement project. A poor quality of results is often caused by the fact that the result deviates from the customer’s expectations. The VoC method can be used to ensure that the customer’s expectations match the project’s results.

The method originates from market research and gives insight into the expectations, needs and aversions of the customer. There are a variety of ways to collect the information. These include focus groups and expert interviews.

The advantages of the VoC method at a glance:

    – forms a detailed understanding of the customer’s requirements

    – serves as a common ground and language for the project team

    – provides important input for designing concrete product/project specifications

    – can serve as a starting point for project, product and process innovations

House of Quality as a tool of Quality Function Deployment can be used to translate customer requirements into concrete performance specifications and characteristics.


The DMAIC process is the core Six Sigma improvement process and is used to improve existing products and processes. It is an extension, for example, of the Definition (Define) of customer needs through corresponding Data Acquisition (Measure) and the Analysis (Analysis) of the data, e.g. with regard to possible reasons for deviations from defined performance targets, the implementation of Improvements (Improve) and the Control (Control) of the implemented measures.

The DMAIC process combines different approaches and methods into one systematic approach. Compared to the PDCA (Plan-Do-Check-Act) process, in which the work steps are more closely interweaved, the DMAIC process offers a somewhat clearer, but above all, purely sequential project flow chart. This means that PDCA provides a fundamental orientation for process improvement, while DMAIC offers a concrete process model for optimising existing processes according to the Six Sigma methodology.


Brainstorming is an advanced way of generating ideas. Developed by Alex F. Osborn in 1939, this method was originally used primarily in advertising and as a creative method, but has long since found its way into many other areas, including product development and the construction of new technical devices.

Although the term is used interchangeably today for a variety of different techniques, the process has pretty much remained the same. As part of a small group (ideally 5-7 people), the participants are expected to spontaneously generate ideas to solve a problem. The participants’ flow of thoughts should not be interrupted, so that each thought is first perceived in its pure form. Ideally, the participants will inspire each other and let the ideas of others flow into their own approach. The ideas and approaches are recorded and logged so that they can be analysed and evaluated in the second step.

It is important that the group is put into a productive and inventive mood. The following rules apply as a first step:

    No criticism of other contributions: Creative ideas can also evolve from seemingly nonsensical suggestions.

    No assessments or judgements: There should be no obstacles to disturb the participants’ free flow of thoughts. The second phase is designed to assess the ideas.

    Everyone should be free to express their thoughts.

    No knockout arguments.

    The bolder and more imaginative the idea, the better: The goal is to broaden horizons and to break away from old ways of thinking.

As part of a Six Sigma project, for example, brainstorming can be used to capture all aspects of a process and fill a SIPOC diagram with content.


SIPOC is a widely-used method for visualising processes. It represents a sort of “snapshot” of the processes and serves as a starting point for making improvements.

Supplier – Inputs – Process – Outputs – Customers

Above all, the purpose of the SIPOC method is to gain a relatively quick overview of all elements and influencing variables of the considered process. For each process, all necessary inputs and associated outputs should be broken down. Moreover, the method envisages listing all internal and/or external input suppliers (suppliers) as well as all internal and external customers (customers) of one or more processes.

The method of filling each column of an SIPOC chart with content can be quite different. It may make sense to be strictly methodical and to fill the columns with content from left to right. On the other hand, a brainstorming approach can be chosen, which does not restrict the methodological procedure and allows the participants’ thoughts to run wild.

SIPOC is a very simple but also extremely effective procedure. It not only provides a clear overview of processes but can also be used as a starting point for more detailed analyses. Above all, however, SIPOC can be used to form a consistent basis for discussion when it comes to analysing and improving processes in a team as part of a Six Sigma project.

ISO Certifications in International Comparison

Process management deals with the analysis of processes. In this regard, ISO certification represents a unified standard that should match processes in quality management.

ISO certifications vary widely in international comparison:

ISO-Zertifizierung im internationalen Vergleich

Source: The ASQ Global State Of Quality Discoveries 2013