Methoden zur Preisfindung von Neuprodukten (German Edition)
Jahrbuch Vertriebs- und Handelsmanagement Shopper Research — Kundenverhalten im Handel. Exzellenz in Markenmanagement und Vertrieb, Grundlagen und Erfahrungen 2. Netzwerke in Vertrieb und Handel. Case Studies zum Solution Selling. In press More details BibTeX. Best Practice des Solution Sellings. Modellierung von Netzwerkstrukturen und Erfolgswirkungen individueller Netzwerkpositionierung.
Vertrauensaufbau beim Markteintritt deutscher Dienstleister nach Osteuropa. The Concept of Solution Selling: Theoretical Considerations and Methods. Chancen und Barrieren deutscher Produkte und Dienstleistungen in Polen. Bedeutung der Ressourcenausstattung im Rahmen der Internationalisierung. Status Quo der Internationalisierung von Dienstleistungen: Towards a measurement of solution orientation: Construct and research directions. Antecedents and Performance Outcomes of Relationship Quality: The Case of Franchising.
What Schumpeter realized as well, was that most modernisations are only achieved by recombination of already existing products and ideas. So innovations not necessarily base on an invention. But in every case they are characterized by a discovery. First, the facilitation of innovations and the philosophical approach of a Problem Solving Method are crucial to its performance. Second, success of visions and innovations is dependent on how existing knowledge is used, i.
In this chapter Problem Solving Methods are presented by giving a short definition of each method, introducing their history, state-of-the-art usage, major tools and proceedings. A standard work is given as a reference for more detailed information. The presentation aims not at presenting the complete proceeding of the method but aims at establishing a basis for later example problem solving and method evaluation. The evaluation shall facilitate the choice of an appropriate method for a specific problem and product development phase.
Thereby methods can be applied where useful and with more success, for. Work should never be done for the sake of systematics or for pedantic reasons alone. In the following chapter the methods analysed and compared in this work shall be presented. Afterwards some more methods shall be suggested in short. Suh for product development. The method organizes the connection of the different domains, see Figure 3 , by the use of matrices and axioms.
Suh started to develop his ideas of Axiomatic Design in the late s; his main works were published in the s. His aim was to develop a theoretical framework for design. The framework should include principles that define good design. After a decade of work, he found two axioms: Those were extracted and abstracted from good design practice. Axiomatic Design should be supported by the use of software facilitating the development process. Axiomatic Design aims at avoiding common design mistakes like having more Design Parameters than Functional Requirements more than necessary , concentrating on symptoms missing concentration on the causes , too high information content lack of robustness , etc.
Therefore Axiomatic Design incorporates different domains and concepts. The Customer Domain describes customer and market requirements regarding the product, process or system. Functional Requirements are deduced from the Customer Domain. This domain can be compared to the product concept catalogue. In order to reach the next domain, technical solutions are developed, allowing the fulfilment of the functions, called Physical Domain, comparable to the requirement specification sheet.
These specifications finally have to be transformed to the Process Domain, containing a description of the necessary processes to produce a product or to deliver a service. Functional Requirements FR are described making use of a break-down structure, i. The same for the Design Parameters DP: A Functional Requirement is realized by a Design Parameter, consisting of more low-level parameters. Corresponding pairs have to be found before proceeding on to a lower level as there is interdependence between them and lower levels.
The ensemble of main Functional Requirements and their break-downs is called FR-set. The development of an optimum solution is supported by two axioms. The Independence Axiom states, that a solution where one Design Parameter fulfils independently one Functional Requirement is ideal. The Axiom is applied on a FR-Set to analyse and valuate found solutions. In the case of a non-square matrix A the design is coupled or redundant. Instead of a simple x or 0, the matrix can contain mathematical relationships to describe the relationship as well.
If the causality between FRs and DPs, i. Decoupled design and 3. Coupled design, see Table 3. The ideal solution is characterized by a matrix indicating affects only in the diagonal, i. Decoupled design is still acceptable, coupled design has to be avoided. The next step of deciding on the best design between different solutions is supported by the Information Axiom, which states that the best solution contains the least information, i. Or in other words: Simple solutions are the best. Information content is determined by applying formulae reverting to the probability that a Design Parameter will fulfil a certain Functional Requirement.
The most usual example to explain the Information Axiom shall be given, see Figure 5. The faucet in the design variant on the right is characterized by uncoupled design and less information content, allowing easier handling. Based on both axioms, general design laws theorems and general design rules corollaries were deduced assisting in design process, e.
After deciding on the DPs a mapping process to determine the PVs has to follow. The course is equivalent to the mapping of FRs into DPs. QFD dates back to the late s when it was developed by Yoji Akao. It was first implemented at the Japanese Mitsubishi Heavy Industries Kobe Shipyard in with some partial implementations beforehand. At that time it was usual practice to control quality during and after manufacturing. But Akao and his colleague Shigeru Mizuno wanted to develop a method that would integrate quality in the product already before it was manufactured. To grant this holistic idea of quality, by the time many tools were developed and composed to the comprehensive method of QFD.
The ideas of Value Analysis were integrated as well. QFD is both used for existing and new products and processes, which include a customer aspect. The latter is by mistake often seen as QFD. As QFD is going beyond problem solving till production, only the aspects related to problem solving are described in the following. Affinity diagrams are used to get insight and order into qualitative information like customer requirements.
Each aspect or statement is written on a small card, several cards are grouped under one associated headline according to their affinity. Thereby a hierarchy of requirements can be displayed. Relations diagrams are used to discover priorities, root causes of problems and unspoken customer requirements. Similar to the affinity diagram a hierarchy tree displays the structure of interrelationships between the different statements. However the approach is different: It is built top-down in an analytical manner. This allows to determine incompleteness and to make amendments.
In general the rectangular grid of cells allows a comparison of different items to display relationships. In a prioritisation matrix the relative importance of an item is weighted as well as the relationship with a second item. By multiplication of those numerical weightings a rating of the priority of the items can be gained.
In doing so, market segments can be differentiated. In a second table those statements are reworded and endorsed by the demanded quality, measurable quality characteristics, necessary functions, required reliability and possibly some comments. Pair wise comparisons on hierarchically organised elements or requirements contribute to achieve a set of priorities and to select from various alternatives.
Blueprinting is used as a tool to depict all processes that are carried out to provide a product or service. It unites customer requirements, market research and benchmarking data with measurable engineering targets. The general HOQ contains six elements, see Figure 6. Requirements derived from customer statements are listed. QFD takes up the idea of the Kano model, that there exist basic, functional and delight requirements. The planning matrix illustrates the customer perceptions gained in market surveys including the relative importance of the customer requirements and the performance of the company and its competitors to meet these.
This part of the HOQ displays the assessment of the degree of interrelationship of customer requirements and technical characteristics. Usually symbols are used to visualize the result of team discussions. The roof matrix is used to identify which technical requirements support or impede each other in the product design. Define goals and objectives 1. Deduce top level product requirements from customer needs possibly conduct survey 2. Develop product concepts to satisfy requirements 3.
Valuate product concepts and make choice 4. Partition into subsystems and transfer higher level requirements and characteristics 5. Derive lower-level requirements and specifications 6. Determine process steps of assembly or manufacturing 7. Set up quality and process controls to grant processes for critical parts 8. It is common to integrate further methods and tools in QFD. QFD is seen more as a framework to streamline the whole process of product or process development. The method strives for quality improvement and process robustness, i. Whereas DoE mostly aims at understanding the interactions of different factors, Robust Design aims at creating robustness.
Robust Design was developed by Genichi Taguchi starting in the s, Robust Design becoming popular in the s. His work was a reaction on traditional SPC and DoE including the perspective of losses when not meeting the target. Today Robust Design is quite often used in addition to Six Sigma, which tackles manufacturing and service-related processes by improving quality and saving costs.
Robust Design contributes the design-approach to Six Sigma to further improve quality and save costs. Robust Design is used in most different industries: Quality should be measured by the deviation from the target value, see Figure 8. This represents losses by low quality better than measuring the conformance with tolerance limits. This idea is contrary to the classical SPC.
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Quality has to be built in the product or process, respectively quality loss has to be avoided by proper design. Quality can not be achieved by inspections or rework. Robust Design aims at reducing the effect of factors causing variation and thereby avoids rework to defect parts. To pursuit this idea consequently, the design process is separated into three stages, see Figure 9. The goal of Robust Design is to support the decision on the settings of the control factors to grant a robust design, characterized by a low deviation from the target at low costs. The P-diagram is used to visualize and classify different parameters of a system and to understand their relevance.
The factors are classified into noise, control, signal, i. The noise factors are separated into inner, outer and between product noises. Control factors are those that can be easily controlled by the engineer, such as cycle time, material choice, temperature, number of units, etc. Noise factors are those, which can be controlled only with difficulty or cannot be controlled at all, such as outside temperature, humidity, etc. The P-diagram is visualized with a black box chart, see Figure The Quadratic Loss Function is used to quantify the loss that is caused by a certain deviation from the target value.
Static ratios are differentiated from dynamic ones. Stable problems either have no or a fixed signal value. Dynamic problems in contrast are characterized by an ideal function of signal and response. At this stage the decision on control factors is crucial. When deciding on the wrong and less influential ones, the following results are worthless and invalid. The orthogonal arrays are the core of Robust Design. They contain the design of experiments and information gained through multiple runs of experiments.
Those tools are used in several steps to achieve a robust design, i. This step contributes to the understanding of the problem and its nature. The experiments can be either conducted in reality or with a simulation model. The latter can contribute to more economic experiments. The effects of the control factors are calculated statistically and the results are interpreted in order to select the optimum settings of the control factors. The performance of the product or process design for the optimum settings is calculated.
The calculation is approved by a further run of the experiment. It is a systematic approach, breaking the product down into functional modules. Each module can be designed following an outline for a systematic design process. Solutions are found by the help of rules and catalogues.
The decision for one alternative is taken based on use-value analysis. SAPB is just one method out of a broad range of systematic construction [vi] methods among which those of Roth , Koller , , , Rodenacker and Hansen The VDI guideline and the work of Pahl and Beitz form the basis for the proceeding during any design process. In Germany the use of this approach is custom, internationally the approach is highly appreciated and accepted. Originally developed for a paper-based approach, the integration of Software catalogues, CAX and prototyping systems is widespread.
The proceeding of design problem solving is divided into several phases; see Figure In this phase information on the product has to be collected, in particular constraints and requirements. They are finally leading to a design specification. An analysis of the requirements, identification of the problem and its abstract formulation found the basis for this phase. Components of abstract formulation are functions and sub-functions and the description of modules. The abstract formulation widens the horizon, enlarges the solution space to be analysed and avoids sticking to the first solution.
Solution finding is supported by creativity methods like brainstorming, analytical methods like patent and literature research and design catalogues containing physical and chemical effects and machine elements. To combine solution variants of different sub-problems, morphological matrices are used. The result is the description of the Working Interrelationship [vii]. With the help of use-value analysis the best variant is determined, which will be developed further. Use-value analysis is a very central part of SAPB as the right decision influences the remaining design process and related costs.
To give an idea of use-value analysis, the different steps shall be presented:. List the rating criteria 2. Assign weighting factors to the rating characteristic 3. Assign operational measures to each rating characteristic 4.
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Assign numerical rating values to the individual characteristic 5. Obtain an overall rating 6. Compare and contrast alternatives 7. Using Conceptual Design as starting point, layout and form of the product are developed. This phase is supported by rules, principles and guidelines.
Rules state conditions that have to be fulfilled to achieve good, i. Principles present abstract engineering knowledge Principle of the division of tasks, Principle of self-help, Principles of force and energy transmission, etc. In this last phase detail drawings and production documents are generated before the solution can be realized. TRIZ was developed by Genrich Altshuller starting in the s and is based on a huge patent research. The findings of this research, generic solution approaches used in thousands of patents, are made accessible through the methodology of TRIZ.
A systematic solution process and many tools lead to qualified and innovative solutions. This is why different inventors in different countries, working on the same technical problems independently, come up with the same answer. This means that certain regularities exist. If we can find these regularities, then we can use them to solve technical problems — by rule, with formulae, without wasting time on sorting out variants. His revolutionary ideas and the idealistic way of promoting it led to his imprisonment in a Soviet Gulag. So Altshuller was not able to publish the basics on his theory but in In the period from to Altshuller was banned from publishing.
However he continued developing his theory. From there it dropped to Europe. Today TRIZ is applied in most big companies worldwide. Essential elements of TRIZ are systematic problem description and problem solution based on principles extracted from thousands of patents. TRIZ comprises numerous tools which can be selected depending on the structure of the problem.
Due to this breadth, a short presentation of the method is difficult. In the following part the most basic ideas, procedures and tools shall be presented. The analysis of patents revealed that values of patents differ. Based on that finding Altshuller suggested five levels of innovation, indicating their value:. Existing products or processes are modified and improved to a small extend. The knowledge available within a trade is required. A minor technical contradiction is solved. Knowledge from different fields related to the problem is required.
A significant physical contradiction is solved. Knowledge of related industries is required. A long-standing contradiction is solved by a new and breakthrough technology. Interdisciplinary knowledge is required. A new principle or phenomena is discovered what allows a higher technological solution level. TRIZ aims at assisting inventors to elevate their solutions to levels 3 and 4. As allready mentioned contradictions form the reason for problems. A contradiction occurs, when solving one parameter relevant to a product or process a second one deteriorates, hindering the achievement of an ideal result.
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Those problems solved, formerly containing a contradiction, tend to bring up innovative solutions. Therefore TRIZ primarily aims at solving contradictions. In usual problem solving, contradictions are handled by tradeoffs. For an administrative contradiction AC it is obvious and well-known that the contradiction has to be tackled.
The contradiction is allready analysed but still the final goal and the mean to get there have to be decided on. The technical contradiction TC refers to the inconsistency of the optimization of two parameters. To give an example:. When augmenting the maximum velocity of an airplane by installing a stronger engine, this might require bigger wings what could reduce velocity due to more drag. To describe technical contradictions Altshuller proposed 39 technical parameters, see below. Finally there are physical contradictions PC: They demand two contradictory properties from one single element.
To give an example as well:. An airplane should on the one hand have wheels to allow debarking and on the other hand it should not have wheels for they increase drag. The ideal object is perfect: The description of an Ideal Final Result IFR , though not possible to reach, is meant to give some guidance on the way to develop a solution. The degree of idealty can be determined by the equation. The Law of Idealty testifies that any technical system tends to become more ideal during lifetime, i.
All systems tend to more idealty during lifetime. One is life cycle using S-curves to position a product and predict its future. A second is dynamisation, suggesting a transition from a rigid to a dynamic and flexible system. Starting with a concrete problem, TRIZ first analysis the problem in detail. The Main Useful Function MUF is identified, positive and negative relationships of the system described, contradictions discovered, ressources summed up and parameters described. With this abstract formulation of the problem, acess to solution creating tools like the contradiction matrix is given.
The gained abstract solution still has to be concretised to be able to realize the solution. ARIZ gives a step-by-step instruction to find a solution. The first ARIZ was developed in , many consequent versions precised the algorithm. State the problem 2. Imagine the Ideal Final Result 3. Find the contradiction 4. Find the reason for the contradiction 5. Find conditions during which the contradiction is removed. Explore the possibility of making changes in the object itself 2. Explore the possibility of dividing an object into independent parts 3.
Explore the possibility of altering the outside environment 4. Explore the possibility of making changes in neighbouring objects 5. Study prototypes from other industries 6. Change the shape of a given object 2. Change other objects that interact with the one under consideration 3. The contradiction matrix to solve technical contradictions described by technical paramenters is the central tool of TRIZ. Its componenents and usage shall be described in the following paragraph. Altshuller collected 39 technical parameters, see Table 4 , that satisfy describing most common technical problems.
By the means of these, the problem has to be described. There might be one contradiction i.
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In his patent research Altshuller found out that most of the patents contain recurring solution principles for perfoming a certain action and solve certain contradictions. He discovered and described 40 Inventive Principles, for an enumeration see Table 5. To give access to those solution principles Altshuller combined Technical Parameters and Inventive Principles to a 39 by 39 matrix. An extract of this matrix is presented below, in Table 6. Psychology - Social Psychology. Business economics - Business Management, Corporate Governance.
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Table of contents 1. Innovation and invention 2. Positioning of the methods in the problem solving process or innovation process making use of the W-model 3. Criteria and characteristic to describe the problem 4.
Proceeding to read the matrix statement 5. Summary List of literature Appendix A Recommendations for the fast reader: List of abbreviations illustration not visible in this excerpt List of figures Figure 1: Current research directions concerning improvement of Problem Solving Methods Figure 3: A simple S-field, composed of a field and two substances Figure Thinking in time and space: Solution level of example problems: P-model of the convey glass disc problem Figure Possible conveyor for hot material Figure Black Box depicting of convey glass disc problem Figure P-model of the disperse varnish problem Figure Black Box depicting of disperse varnish problem Figure Su-field model of varnish dispersion Figure P-model of the glass polishing problem Figure Functional diagram of polishing glass lenses Figure Black Box depicting of polish glass lens problem Figure PIM process Figure P-model of the PIM problem Figure Black box depicting of PIM problem Figure P-model of tea brewing with tea ball Figure Different design solutions for irons Figure Composition of the AC-matrix: W-part, C-part and P-part Figure Presentation of the overall AC-matrix Figure Steps to read the AC-matrix statement Figure Extract of the Altshuller Matrix Table 7: Funtional Analysis Table Table 8: