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Strategic Planning of Future Products with Product Scenarios

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S0219877010002100

International Journal of Innovation and Technology Management
Vol. 7, No. 3 (2010) 237–246 c World Scientific Publishing Company
DOI: 10.1142/S0219877010002100

Int. J. Innovation Technol. Management 2010.07:237-246. Downloaded from www.worldscientific.com by PORTLAND STATE UNIVERSITY on 07/25/15. For personal use only.

STRATEGIC PLANNING OF FUTURE PRODUCTS
WITH PRODUCT SCENARIOS

VOLKER GRIENITZ∗ and VOLKER BLUME
Industrial Engineering, University of Siegen
Paul-Bonatz-Str. 9-11
Siegen, 57068, Germany
∗volker.grienitz@uni-siegen.de
Received 29 January 2010
Revised 4 March 2010
Accepted 15 April 2010

Manufacturing based corporations often find themselves confronted with complexities of increased pressures to innovate in order to ensure their comparative market positions. In order to react to various exogenous changes corporations need to develop strategies that match their manufacturing resources as well as products with the markets requirements.
Product scenarios represent a holistic approach for managing innovation processes and technologies efficiently. The analysis through evolutionary algorithms for compatibility between and amongst the product structure segments provides the necessary information about their suitability. The resulting scenarios, roadmaps and regular monitoring processes are prerequisite for the managerial decision making process and the implementation of product and technology strategies.
Keywords: Product scenarios; scenarios; evolutionary algorithms; monitoring; scenario planning. 1. Introduction
The early recognition and visionary anticipation of technological potentials as well as the combination of technologies plays a vital role in a globalizing world that is characterized by the dynamics of increasing competitive pressure.
Various empirical studies have shown a correlation between the success of corporations and their efforts to enhance innovation [Berth (2003)]; [Harhoff et al.
(2001)]. It has further been recognized that technological advancement significantly influences the innovation of processes and products. However, these opportunities also bear risks that decisively impact the subsistence of corporations. In order to strengthen and augment profitability and competitiveness of corporations it is important to creatively coordinate the dynamic interaction of corporate strategy, products and technologies [Itami and Numagami (1992)].
Coping with present and future challenges requires targeted coordination of numerous present technologies as well as the search for new technologies through the process of strategic product management. In this sense product management
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systematically analyses these potentials. However, in order to determine the success of particular technologies the analysis has to focus upon the interacting forces and effects of the technological and the social environment. Naturally, this enquiry is often based upon contemporary expertise. With the lack of knowledge about developments that lie ahead it should be our primary aim to minimize the factor of uncertainty within the product development process [Zahn and Braun (1992)].
The Scenario-Method can thereby make a valuable contribution to the strategic planning of technologies [Geschka (1994)].
This paper is primarily focusing on products in the manufacturing and assembly industry. 2. Specification and Purpose of Product Scenarios
Present literature outlines various definitions for the term Product Scenario [Paul
(1996)]. For the remainder of this paper the following definition of the term will be used:
Generated Product Scenarios comprise coherent combinations of technologies, whilst simultaneously satisfying all conditional constraints (requirements and rules). Product Scenarios thus represent product structures that allow the compliance of specific market demands. Fig. 1 illustrates the structure of a Product Scenario.
The framework of a Product Scenario (for e.g. a camera) incorporates Product
Structure Segments (PSS) of technologies that represent the overarching structure of the Product. The specific attributes (e.g.: power supply, interfaces etc.) are referred to as a Product-Structure-Segment-Attribute (PSSA).
Future products in specific market segments (known as Product-Market Combinations) should be implemented via Scenarios. The sophisticated application of
Product Scenarios has become increasingly more company specific with regards to the corporation’s requirements. On one hand, particular know-how with respect to
Digital
Photograph

Product Scenario (PS)
Product Structure Segement (PSS)

Power Supply

Lenses

Battery

Accumulator

Fuel Cell

Product Structure Segment Attribute (PSSA)

Interfaces

Memory

Fig. 1. Structure of product scenarios like a morphologic box.

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technologies may be considered, on the other strategic constraints and the ambition of technological market leadership may need to be factored in the Scenario.
Moreover it is to conclude that the fundamental tenor for the development of
Scenarios is systems thinking that equally focus on socio-economic and political factors as well as technological components. This holistic interdependence is vital.

A comparison of current approaches suggests a particular need for a new methodological focus in the strategic planning process of products and scenarios.
The approaches in present literature seem unsatisfactory when they are examined for their adaptability to complex change and dynamics as well as their flexibility to react to the naturally occurring changes of the external environment.
The following section develops the methodology for the strategic planning of products through a structured life cycle approach illustrated by the means of an example from the photography industry. This approach rests upon an integrated and intuitive discourse that is supported through a software based application.
The process of developing Product Scenarios is structured into the following three phases (Fig. 2).
Scenario field analysis: The scenario field represents the product structure mentioned in Fig. 1. All Product Structure Segments (PSS) impact differently on the task at hand and are to be ranked by their behavior. For a reduction of complexity
Scenario-Software is used and the evaluation is done by an influence and weight matrix. The following system analysis gives an overview about which factors are drivers and which factors are indicators (system determinant — driver or driven). Two parameters are enough to describe the entire system — the ‘activity’ and the ‘passivity’ of the factors. The activity is calculated by accumulating the values listed in the lines of the impact matrix and the passivity is given by the sum of the values over the columns.
Scenario prognosis: This is the most creative phase of scenario planning. Possible future attributes have to be found for every Product Structure Segment. Depending on the time horizon two to ten future attributes, so called future projections, should

Scenario procedure

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3. New Methodology for the Creation of Product Scenarios

Scenario field analysis
1

Scenario prognostic
2

Scenario creation
3

Network of Product
Structure Segm ents
Possible and thinkable developments Product-StructureSegment-Attribute
Product Scenarios

Fig. 2. Process steps of product scenario creation.

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Prod. Str. Segm. (PSS)
Lenses
Memory

Power Supply

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S0219877010002100

PSS Attribute (PSSA) Sc1 Sc2 Sc3
2A
2B
2C
3A
3B
3C
3D
4A
4B
4C
4D

Single lenses
Optical Zoom
Digital Zoom
Hard Disk
Flash Memory
USB-Stick
Floppy Disk
Accumulator
Batteries
Manual Mode Dynamo
Fuel Cell

100
0
0
63
37
0
0
85
5
15
0

0
0
100
0
55
45
0
0
6
94
0

0
82
18
0
100
0
0
37
0
0
100

Fig. 3. Attribute allocation like DNA of each scenario.

be found. Future projections are not only probable but also thinkable or imaginable ones. For this scenario creation all future projections have got the same probability!
All projections can be listed like a morphologic box. Already at this point future product scenarios could derive. But those solutions would not be consistent ones.
Scenario creation: After the look into the future, the projections will be evaluated against each other. The question is: Does a future segment attribute fit together with the other projections or not? This will be done by the consistency matrix.
The consistent combinations will be evaluated by evolutionary algorithms explained some chapter later. The result is a huge number of consistent combinations — also called rough scenarios.
Also at this stage Scenario-Software helps to get the results. All combinations will be clustered and the result is a small number of scenarios. The results of these calculations are tables too, which show the framework of scenarios like a DNA
(Fig. 3).
Another way to present the scenario is the landscape of the future (Fig. 5). This diagram shows the scenarios grouped by similarity. The closer the scenarios the more similar they are. This diagram is generated by multi dimensional scaling —
MDS.
The last step is the naming of the scenarios. Every scenario should get a unique name. This step could be supported by the content of the scenarios. Another way is to look for interesting names, like cinema films or book titles, which everyone knows.
The following chapter will describe the detailed development of the scenario.
4. Scenario Creation with Evolutionary Strategies
There are several rules within the creation process:
• The product structure represents the construction and sequence of the Product
Scenarios.
• The requirement profile (specification catalogue) defines key attributes and specifications that determine the ideal-profile. With this in place, direct comparisons of compatibility can be made between the technologies and the Ideal Profile.
• Further rules emerge from the occurrence of substituting technologies and their consequential requirements and conditions.

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It should however be kept in mind that with the exception to the rules that particular cases offer or require the expansion as well as the possibility of not applying or substituting one or more of the remaining rules. Generally speaking, leading on from Occam’s razor, the principle that should apply when determining the rule framework, is to maximize the benefit of the Product Scenarios with a minimum amount of rules in order to allow sufficient and efficient scope for the scale of innovation [Schwefel (1977)].
Product scenarios are created through the combinations of technologies. In this step ancillary conditions laid out in the requirement profile have to be considered.
One will soon realize that the divergence of objectives does not always make it easy to directly derive an optimal solution.
This section outlines the process of reasoning for the selection of the optimization process. Since the amount of auxiliary conditions can be numerous and since the optimization of local goals of optimization can not necessarily be accounted for as the total maximum or minimum it proves difficult to define an overall global optima’s (solutions that meet all predefined requirements). Thus it is more important to have a solution driven strategy that focuses on the local optima from which specific Product Scenarios can be defined and selected [Rasenack (1998)].
The search for a solution is determined through a complete calculation of all aggregated combinations. This means that only six Product Structure Segments
(PSS) with 10 Product Structure Segment Attribute (PSSA) result in 60.466.176 batches that have to be evaluated and audited. The level of complexity and the associated effort for the evaluations does not make this approach very practical.
The commonly applied Consistency-Analysis or Cross Impact Analysis can also not be drawn upon for the creation of Product Scenarios as these methods only consider future projections pair wise [Gausemeier et al. (1996)].
The paired results are insufficient, since they do not satisfy auxiliary conditions [Schwefel (1977); Nissen (1994); Nissen (1997); Nissen (1998)] who introduce a wide ranging catalogue of solution strategies based on several optimization tasks.
Having compared all present strategies with respect to their likelihood of convergence (referring to the local optima) the selection points towards the Evolutionary
Strategies (ES). Other processes of optimization show advantages with respect to the initial research time, however their likelihood of convergence is rather weak
[Grienitz (2004a)].
With the application of ES it is possible to overcome numerous auxiliary conditions whilst being able to deliver a product-technology-bundle in the optimization process. These requirements are best met through natural analogical processes.
Evolution has shown that the living species of today, that emerged from a “long term experiment” (3 Billion years) [Rechenberg (1973)] have optimally adapted to the given environmental factors whereby less adaptable ones have been eliminated.
Evolutionary theory has shown that the constant struggle and competition of species for resources and space has been the driving force for the changes and improvements of species and the populations. The results and knowledge derived from evolution, is the main focus of Bionics [Rechenberg (1973)].
Alongside the research field of bionics is that of evolutionary techniques or respectively — Evolutionary Algorithms (EA’s). EA’s are used in particular for

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the identification and optimization of particular processes [Nissen (1994)]. However both fields of research follow a common goal. They attempt to simulate the principles of evolution and apply its logic in an abstract way to complex problems of optimization.
Evolutionary algorithms imitate the primary principles of evolution: Multiplication (replication), alteration (variation) and choice (selection) on an abstract level
[Nissen (1994)]. Evolutionary algorithms (EA’s) differentiate themselves from conventional search and optimization methods through their close adaptation of natural terminologies. Thus they focus on the natural evolution process that is particularly advantageous for optimizations. The difference of deterministic and evolutionary algorithms is highlighted by the characteristics that the latter is simply based on chance and can be applied without any kind of constraints. In theory it is possible to use EA’s universally [Weicker (2002)].
Rechenberg [Paul (1996)] defines evolutionary strategies as multi-applicable processes of optimization. They simulate strategies on an abstract scale, whereby the level of relative adaptation to the given environment determines their survival. The process and function is illustrated in a rather abstract way in Fig. 4.
Nissen [Nissen (1998)] describes the process as followed:
“One starts off with a frequent stochastically generated initial population of possible alternative solutions that are ranked to their ‘level of fitness’ accordingly. An interactive cycle generates repeatedly modified new suggestions for the old solutions.
[. . .] In the process of creating offspring the information (solutions) from the parent generation is copied (replicated) and applied to several other outcomes such as mutations or crossovers that could determine solutions. The resulting offspring from this process is then given values and selected so that a new population is generated
(selection for survival). It is thereby possible, that the parent generation could rival with their progeny for survival.”

Fig. 4. Exemplary sequence of evolutionary generation of solutions [Grienitz (2004)].

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Table 1. Terminology of evolutionary strategy basic principles.
Meaning

Relevance of evolutionary strategies Product scenarios

Structure (entails the relevant that constitute the solution)

Combination of Product Structure
Segments (PSS)

Population (of individuals) Number of Structures (Solution
Alternatives)

Number of product scenarios (PS)

Parents (µ)

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Individual

Selected Individuals for the reproduction For the selected reproduction PS

Parent

One reproductive individual

One reproduction PS

Fitness

Quality of solutions with regards to the target criteria

Value of the fitness function determined by the set of rules

Offspring, children (λ)

Individual that stem from parent

PS generated from the parents

Generation

Process iteration

Mutation

Search operator that modifies an individual Operator, modifying the characteristic of Product
Structure Segment Attribute
(PSSA)

Recombination/
Crossover

Search operator that mixes all elements of the individuals

Operator that mixes several with one another

Following the Evolutionary Strategy, the creation of product component and technology bundles “select and drop” (whilst considering the sequence) are comparable. According to the evolutionary strategy and the notation from Table 1, parents and children represent the outcome of the optimization cycle (generations). Children and parents represent intermediate solutions — so called product component and technology bundles.
The first step involves the identification of universal populations of technologies whereby first bundles are created following the scenario structure. This process is repeated until at least two technology-combinations (starting population) are generated. The whole solution space is derived through the potential amount of possible combinations of technologies. It is assumed that all technologies are compatible with one another. The reality however shows that it is difficult to combine fundamental technologies. Additionally it should be kept in mind that previous parents or children narrow the space even though the production of children indirectly contains the universal population of technologies. This means that no child can be identical to one particular parent or other child.
Once the starting population is identified, the repetition of the three fundamental steps is carried out as follows:
• Replication, Recombination and Mutation: The parents are copied. Through the process of recombination or mutation new children are created [Nissen (1998)].
• Ranking of a bundle (offspring): Parents and children each represent bundles.
These are evaluated and ranked according to their level of fitness [Grienitz (2007)].

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Scenario 1: High-Tech Single Lens Reflex
Camera – SLR with Biometric
Functions
Scenario 2: Low-Budget Camera with Manual Mode
Dynamo

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Scenario 3:

High-Tech Camera
Modules with Open
Interfaces

Szenario 4:

Szenario 5:

Fig. 5. Multi dimensional scaling — landscape of products.

• Selection: The level of fitness is the key indicator for the selection of parents and children. The selection determines which bundles will be kept for the next population (survival of the fittest). Parents and children are equitable (hold the same level of importance) when comes to the selection process. The new population then triggers the next step of iteration [Nissen (1998)].
Iterations, the creation of solutions (generations of parents and children) are carried out until the changes in the target function are minimized and the target function itself will be maximized. The target function is measured by the value of fitness which determines how good the derived solutions match the respective requirements. The value of the target function is iterated until the threshold of a particular amount of generations (50 to 200) has been generated.
The adapted variations of the evolutionary operators should indicate whether or not the solution is based on a local maximum. Once the two indicators, one being the value of the target function and the other being a large number of generations, the search for a solution is completed. For the further processing or interpretation the children that were generated in the final round are to be used for visualization and
Roadmapping.
The number (100–500) of solutions has to be merged, that there are a useful quantity of scenarios. This will be done by the statistical method — cluster analysis. Along the cluster process the comparable solutions (children of the evaluation process) will be grouped. Every group represents one scenario. At this point every
PSSA will be counted, how often it is a part of this evaluate scenario. That calculation generates a table like Fig. 3.
In order to communicate and present the Product Scenarios, especially to people that were not involved at the stage of the development process, a map (visualization) of the future as a result of the Multi-Dimensional Scaling (MDS) illustrates the separation of the scenarios from one another.

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Each bubble represents one possible product-structure-attribute-bundle whereby each cluster outlines one product scenario. Through the list of characteristics and attributes additional key aspects can be highlighted and added to the map.
From the overarching road map for all Product Scenarios, generally applicable development paths can be highlighted. Moreover, it is possible to identify stages of development for specific technologies. This means that for example two Product
Scenarios use the same technology. Consequentially it is the maturity of the technologies that highlights possibilities for technological advancement.

5. Conclusion
This work defines Product Scenarios as coherent combinations of Product Structure
Segment Attributes.
Our initial analysis has shown that there is yet no coherent methodology that allows a complete integration of the cycle of strategic product development nor do any of the existing methods provide a holistic judgment of technologies from a socio-economic, technical and ecological perspective.
The results of the evaluation and identification of constraints are coined in coherent Technology — Combinations (Product Scenarios) that are perfectly suited for the implementation of market requirements.
The process of strategic product planning is not a one-off analysis but should be undertaken regularly. The continuous observation of the environment will significantly support the results derived from the ranking (so called technology characterizations). This characterization represents an important tool for the early
‘technological reconnaissance’.
Future research in the field should concentrate on the optimization of the Algorithm that is used for the creation of the product scenarios. Application of self adjusting strategy parameters could hereby accelerate the identification of the solution algorithm.
A process of self optimization could incorporate the changes that have emerged from the operators (recombination and mutation) in the evolution process automatically. The adjustments made for qualitatively high individuals are thereby stored so they can be used for further adaptations. The Strategy-Parameters are hereby subject to changes in the evolutionary operator. Such a procedure would give the opportunity for flexible and direct adjustments; however in certain cases control can be lost due to outside influences. [Weicker (2002)].
Fundamentally it is to say that this approach offers several opportunities for abstraction that can be integrated in existing approaches well beyond the management of products.

References
Berth, R. (2003). Auf Nummer sicher. Harvard Business Manager, Vol. 6, pp. 16–19.
Geschka, H. (1994). Technologieszenarien — ein Analyse- und Planungsinstrument des
Technologiemanagements. In: Zahn, E. (Hrsg.): Technologiemanagement und Technologien f¨r das Management. Stuttgart: Sch¨ffer-Poeschel. u a

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Grienitz, V. (2004). Erschließen von technologischen Erfolgspotenzialen — Technologieszenarien in der Strategischen Technologieplanung. ZWF — Zeitschrift f¨r den u wirtschaftlichen Fabrikbetrieb, Nr. 9, 421–426.
Grienitz, V. (2004a). Methodik zur Erstellung von Technologieszenarien f¨r die strategische u Technologieplanung. Paderborn, Universit¨t, Dissertation, HNI-Verlagsschriftenreihe, a Bd. 154.
Grienitz, V. (2007). Development of mechatronic a/c cabin systems, 1st international Workshop on Aircraft system technologies, Hamburg, conference proceedings pp. 179–187.
Gausemeier, J. et al. (1996). Szenario-Management Planen und F¨hren mit Szenarien. u M¨nchen: Carl Hanser. u Harhoff, D. et al. (2001). Innovationswege im Maschinenbau — Ergebnisse einer Befragung mittelst¨ndischer Unternehmen. Stuttgart: Stiftung Impuls. a Itami, H. and Numagami, T. (1992). Dynamic Interaction between Strategy and Technology. Strategic Management Journal, Vol. 13, pp. 119–135.
Nissen, V. (1994). Evolution¨re Algorithmen — Darstellung, Beispiele, betriebswirta schaftliche Anwendungsm¨glichkeiten. G¨ttingen, Georg-August-Universit¨t, Grado o a uiertenkolleg, Dissertation.
Nissen, V. (1997). Einf¨hrung in Evolution¨re Algorithmen — Optimierung nach dem u a
Vorbild der Evolution. Braunschweig: Vieweg.
Nissen, V. (1998). Einige Grundlagen Evolution¨rer Algorithmen. In: Biethahn, J. (Hrsg.), a H¨nerloh, A., Kuhl, J., Leisewitz, M.-C., Nissen, V., Tietze, M.: Betriebswirtschaftliche o Anwendungen des Soft Computings — Neuronale Netze, Fuzzy Systeme und Evolution¨re Algorithmen. Braunschweig: Vieweg. a Paul, M. (1996). Szenariobasiertes Konzipieren neuer Produkte des Maschinenbaus auf
Grundlage m¨glicher zuk¨nftiger Technologieentwicklungen. Paderborn, Universit¨t, o u a Dissertation, 1996, In: Gausemeier, J. (Hrsg.): HNI-Verlagsschriftenreihe, Bd. 11.
Rasenack, W. (1998). Parametervariation als Hilfsmittel bei der Entwicklung des FahrzeugPackage. Berlin, Technische Universit¨t, Institut f¨r Straßen und Schienenverkehr — a u
Fahrzeugtechnik, Dissertation.
Rechenberg, I. (1973). Evolutionsstrategie. Stuttgart: Friedrich Frommann.
Schwefel, H.-P. (1977). Numerische Optimierung von Computer-Modellen mittels der
Evolutionsstrategie. Berlin, Technische Universit¨t, Fachbereich Verfahrenstechnik, a Dissertation.
Weicker, K. (2002). Evolution¨re Algorithmen. Stuttgart: Teubner. a Zahn, E. and Braun, F. (1992). Identifikation und Bewertung zuk¨nftiger Techniktrends — u Erkenntnisstand im Rahmen der strategischen Unternehmensf¨hrung. In: VDI Techu nologiezentrum (Hrsg.): Technologiefr¨haufkl¨rung. Stuttgart: Sch¨ffer-Poeschel. u a a

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