ontological approach to new product developmentdevelopment department of mechanical engineering,...

Journal of Industrial and Intelligent Information Vol. 2, No. 2, June 2014

2014 Engineering and Technology Publishing 98doi: 10.12720/jiii.2.2.98-107

Ontological Approach to New Product

Development

Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand

Email: [email protected], [email protected]

Abstract—Determining the feasibility of new product

development (NPD) projects early in their lives is difficult

due to the large uncertainties involved with the conceptual

design stages. A system engineering approach is applied to

this area, using ontologies. We show that it is possible to

anticipate the production implications, including layout, flow,

and line balancing, on the flimsiest of design and market

information. In turn –and we believe this is important- the

results can then be fed back to the NPD process to further

inform the design, market-research, and cash-flow

considerations. Repetition of this process has the potential to

progressively remove the uncertainties and optimize the

NPD process as a while. These inferences can be useful for

giving engineering designers potential valuable information

to determine the production implications in the early new

product development (NPD) process.

Index Terms—ontology, new product development (NPD),

feasibility study, systems engineering (SE), optimization

I. INTRODUCTION

At its most fundamental level, engineering is about

developing technological solutions to complex problems

[1]. Those solutions include new products, plant,

infrastructure, software and analytical models, i.e.

technology systems in general. The engineering

problem-solving process uses systematic methods of

analysis, creative design, structured cognitive approach to

synthesizing solutions, and methodical construction of the

solution. It has also the ability to assess variables in

product development process by using application of

mathematical and science-based analysis.

There is need for elaborating the integration in

engineering problem-solving applications which can be

achieved by using systems engineering (SE) and

ontological approaches. These methods seek to capture the

interactions within the production system, specifically

interactions between the hierarchies of the product

feature-tree, the layout of the production plant, and the

workflow of the assembly tasks. In contrast conventional

approaches to manufacturing tend to treat the part, plant,

and flow characteristics as independent variables.

Ontological approaches help identify the hidden

system-level interactions, and then permit their

optimization for a better overall flow of product families

through a plant. Specific tools are available to help build

these ontologies, for example the Onto-Edit

platform(Protégé)may be used to capture and represent

concepts, their relationship and instances in product

design.

Existing applications of the ontological approach have

been to mature products in the production environment.

Our research question is somewhat different: we are

interested in the application to new product development

(NPD).

Specifically, we are interested in being able to predict

the production requirements of a product, early in the

design life cycle. This is worth doing for the potential to

predict production economics before the design is

finalized. In this paper we show that it is indeed possible in

principle to use the ontological approach to achieve this.

For this purpose, we start with a simple case study –hand

saw- positioned at the early conceptual design stage, and

from this we extract several production consequences of

the design, including space requirements for a given

production volume and workflow for assembly.

Furthermore, we defined an optimization model for

estimating costs and gains of the venture capitalists as an

economic feasibility study.

II. ENGINEERING APPROACHES FOR PROBLEM SOLVING

The purpose of engineering is always to meet a need,

whether of a client, customer, or society, and to do so in an

ethical and socially and environmentally acceptable

manner. Engineering ventures that seek to create new

value typically have an element of risk, both opportunity

and threat, and these need to be captured and avoided

respectively. Engineering activities require resources,

which are invariably finite, and require coordination,

management, and decision-making. Engineering

approaches can be divided into some categories as

follows:

A. “Design”: The Way to Find Solution

One of the characteristic engineering methods is design.

This is the mechanism that engineering uses for synthesis

of solutions, i.e. the bringing together of components into

holistic response to the problem. Design also readily

accommodates creativity and intuition at the early stages,

which complement the mathematically-rich processes of

analysis. The latter stages of design are purposefully

directed towards the construction of the solution, and

through drawings and other documents set the detailed

attributes of the solution. Engineering design traditionally

focuses strongly on the hard technology parts of the Manuscript received October 15, 2013; revised December 15, 2013.

Mohsen Karimi Haghighi and Dirk J. Pons


2014 Engineering and Technology Publishing 99

problem. It is still common to see an emphasis on clear and

unambiguous prior definition of design specification,

followed by a methodical process of creating design

features to provide the requisite functionality [2]-[6].

However this is a very insular view of design. Successful

products need not only good design, but real paying

customers. Those customers need to see a proposition of

value in the product. The NPD perspectives tend to be

better at representing this perspective. Specifically, they

identify the necessity of consulting the prospective

customer before starting the design process,. This is

typically done through marketing activities of focus

groups and the collection of customer requirements.

Methods like house-of-quality are then used to convert

these soft requirements into design variables [7]-[9]. NPD

also emphasizes, in a way that plain design does not, the

need to have a route to market – and to be thinking about

that route early in the design process. The route to market

consists of not merely the distribution channel and the

elements of the marketing mix (4P’s: product, price,

placement, place) but also the strategic decisions about

partnerships/competition with other firms, complementary

products/service, the product life-cycle, project

management, and the cash flow requirements of the

venture.

Early design is characterized by great uncertainties. the

information is qualitative and the relationships of causality

between variables are subjective [10]. This makes for a

difficult environment for decision-support tools. Although

there are a number of systematic approaches to design,

including those of the European tradition [2]-[6], decision

support tools [11]-[13], project management (PM)[14],

and attempts to set design in the organizational context

[7]-[9], the focus is often design and product-centric. By

comparison, SE takes the broader view of the product

life-cycle [15] of which design is only a part.

B. Practical Construction of the Design into Tangible

Solution

A problem is only solved when the need is satisfied.

Thus another characteristic engineering method is the

practical construction of the design into tangible solution.

This involves manufacture (products), fabrication (plant),

construction (infrastructure), development (software), or

other such processes. Additional methods are used within

these processes, as relevant. For example, manufacturing

engineering can develop production management, quality,

PM, lean, and enterprise resource planning (ERP). These

are now substantial methodologies in their own rights, and

used outside of engineering, but all originated within

engineering. Such is the usefulness of the systematic

engineering approach to problem-solving that it has been

applied to many other disciplines.

C. Handling Problems by SE

The purpose of SE is to solve complex problems by

organizing the interconnections between elements. These

elements are typically organizational and include work

tasks as in PM, but extend also to design activities,

customer satisfaction, quality, life-cycle, modeling and

optimization, resources, support for decision-making and

interdisciplinary activities. SE seeks to apply a holistic

synthesis, as opposed to the hierarchical decomposition of

work of PM, but it is acknowledged that there are

similarities and overlaps. PM is a more established and

standardized process, as evidenced in the PMBOK [16],

whereas SE requires a thoughtful application to each

situation under examination.1

SE is a powerful method for handling complex

problems where the solution is not a linear collection of

tasks. It therefore accommodates the iterative processes of

design, and the complexity of a non-routine problem,

which is difficult for PM. However, the type of

information involved in coordinating these complex

projects which is often qualitative rather than quantitative

is an important challenge.

D. Engineering Feasibility Study

Not only is engineering itself a systematic approach to

problem-solving, but it also uses a systematic approach to

organize many individual problem-solving efforts. One of

the most important parts of any major engineering

undertaking is the engineering feasibility study. When

done correctly, this study addresses the relevant factors to

create a comprehensive cost vs. benefit picture, allowing

companies to assess the project accurately before deciding

whether or not to proceed. In situations of high uncertainty,

the feasibility study will suggest ways of minimizing

exposure to the threat part of the risk, while optimizing the

ability to capture the opportunity part of the risk [17]. The

feasibility study identifies the problem and opportunity

areas, and selects the focus area and potential solution. It

also explores the economical and technological solutions

to select a route to achieve the outcomes. The purpose of

feasibility study is not to solve the problem, but to

determine whether the problem is worth solving.

A staged approach is common, with the study

identifying the technological and other barriers to progress

of the venture, delimiting the uncertainties in knowledge,

and anticipating the research and development project

efforts required to overcome those barriers and resolve

those uncertainties. There may be several such stages as a

venture moves from early conceptual design through to

prototyping and onwards into production. Termination

decisions may be make at the end of each such stage,

thereby limiting the losses if the venture should become

non-viable for any reason.

Each feasibility study might be composed of many parts

[12]addressing the different aspects: intended product

functionality; the proposition of value to customers;

technology & production capability, and barriers thereto;

target markets and user needs; resource implications;

return on investment (ROI); competitors and how the

product is differentiated from competitors; distribution

1The primary purpose of PM (as it is commonly practiced) is to identify

tasks (hence work-breakdown-structure -WBS) and sequence them in a

timeline (hence Gantt chart or network diagram), for which it relies on the specification being well-defined at the outset, and the existence of a

well-defined solution path where the individual tasks can be anticipated

at the initial planning stage. With PM the main interaction between tasks is temporal: there is a strong precedence in time where one task needs to

precede another, in simple one-directional relationship of causality.



and promotion; milestones and timeframes for completion;

risks (threat and opportunities); intellectual property and

freedom to operate; product life cycle and environmental

implications.

In the specific situation under examination we are

interested in certain parts of the feasibility study, namely

the production capability and the production economics.

These have historically been among the most difficult

parameters to determine in early stage NPD projects, and

hence this is a particularly challenging problem.

III. ONTOLOGICAL APPROACHES

A. Principles

An ontology, as applied in the systems engineering

perspective, is a database that lists and categorizes

different concepts such as the resources available in an

organization, and shows the relationships between them. It

is primarily the representation of relationships between

concepts that is the point of difference compared to a

conventional database, and allows the connectedness

between concepts to be explored and optimized.

Ontologies are made by class (concepts) and relationships

or connections between concepts [18]. These visual

representations help the systems engineer to understand

what is going on in the system, which is otherwise difficult

to perceive in a conventional database. Semantic

web-based applications [19] such as Onto-Edit platform

(Protégé) [20]-[22] can be used for constructing

Ontologies. Protégé is a free, open source ontology editor

(platform) and a knowledge acquisition system [14]-[17].

At a conceptual level, it helps ontology developers to have

an idea about domain models without knowing the syntax

of the language [13]. Browsing the classes and properties

can be possible through plug-ins (OntoViz, TGViz) and its

query tab allows searching [14]. It has many plug-ins

available for extending ontology construction, constraint

axiom, inferring and integration functions. Besides, there

are other types of semantic web-based

applications/languages which can help import or export

ontologies and develop them in their own format, such as

resource description framework (RDF), RDF schema

(RDFS), DARPA agent mark-up language + ontology

interchange language (DAML+OIL), extensible mark-up

language (XML), web ontology language (OWL) and

unified model language (UML)[18].

B. Applications

Typical applications of ontologies are to represent the

enterprise processes [23]. The main focus of the literature

is on the use of semantic methods to represent the

interconnectedness within systems, e.g. product design,

process planning, and manufacturing process. For

example, these methods have been useful in exploring the

operational and structural implications of engineering

products [24], and handling CAD data [19], [25].

Ontologies have also been applied specifically to

formalize knowledge sharing [26]-[24]. Developing

different ontologies to identify team profile, design and

production process is investigated in the literature [19],

[25], [27-28]. Existing ontologies have been applied

primarily to the physical sub-structure of the design

(product variants and model-tree relations), specific

products, decision support, enterprise processes

(corresponding to ERP), or general principles without

application.

In the production engineering area, the ontological

approach has the potential to organize modular systems for

use in NPD [29]. Platform-based (modular) product

developments seek to assemble components in different

ways to make product families [29], [30]. The benefits of

this strategy are increased diversity, decreased

time-to-market, and reduced design and manufacturing

costs [31]. They also have the potential to permit mass

customization. Modular (platform-based) product

development normally necessitates association of

geographically different workers from engineering design,

manufacturing, marketing, distribution, etc. These product

development efforts more generally modeled using

standard PM techniques relying on the WBS for assigning

specific members of the distributed team to certain

activities process [32], [33]. However, the PM methods

often struggle to represent the complexity of the

interactions [34], [35].There are also many problems in

traditional PM systems such as lack of reliable data storage

and consistent methods of reclaiming and reusing

information and knowledge. To be fair, the same

limitation also applies to the design methodologies.

However, there are some limitations in the use of

ontological approaches such as they need primary

preparation before being used [25]. There is also need to

develop reusable engineering models and provide the

system perspectives, e.g. to be able to take a holistic view

of the interactions between style design, engineering

design, analysis, manufacturing systems, and the CAD

activities [36]. Another serious limitation is that the

ontologies that have been fielded are abstract rather than

concrete. It is not immediately obvious how a design or

production manager might construct an ontological

approach, and the actual relevance to product design, PM,

production and marketing has not always been clear. So on

counts of applicability and relevance, ontologies have not

always scored highly.

TABLE I. CORRESPONDENCE BETWEEN ONTOLOGY CONCEPTS AND

UML NOTATIONS [39]

Ontology Concept UML Notations

Class Class, Property

Property Attributes, Association

Restriction Constraints defined by OCL

Data Type Data Type

Instance and Value Object Instance and Attribute Value

C. Context of Operational Implication sin Ontology

Unified model language (UML)is a general technique

for developing software engineering models as well as

high-quality object-oriented systems [35]-[36]. An

important part of UML is object constraint language (OCL)

that can be used for realizing the pre- and post-conditions

for operations as well as describing operation definitions

http://www.w3c.org/RDF

http://www.w3c.org/XML




[37], [35]. Using UML,ontology information can be

represented in the format of class (concepts in the domain)

diagrams as well as OCL [37], [38], [35], but with some

differences in their concepts and relationship notations

which are shown in Table I and Table II respectively[39].

Ontologies can be represented graphically using UML in

order to be clearer for users. However, there is some

plug-ins in protégé that can support UML but not accepted

as widely as UML. Some instance of these plug-ins are

Ontoviz, OWLVizand Jambalaya. Fig. 1 is an example for

these plug-ins. However, operational implications of

ontologies cannot be supported in protégé while can be

supported by OCL in UML [39]. Hence, it is necessary to

consider the power of OCL in the implementation of

operational implications in ontological approaches [35],

[37].

Figure 1. Main ontology for the HAND SAW case study. This was constructing by Protégé and its functional plug-ins

As shown in Table II, there are three types of

relationship in UML and ontology that can be used to

describe the relationship between concepts [35], [39].

TABLE II. CORRESPONDENCE OF RELATIONSHIP BETWEEN ONTOLOGY

PROPERTIES AND UML [39]

Ontology UML Description

is_a Generalization Inheritance relation

between classes

has_attribute_of Association Relations between an

object and its attributes

has_part_of Composition Composition relation

between classes

TABLE III. A MAPPING BETWEEN DAML AND UML [41]

DAML concepts UML concepts

Ontology Package

Class Class

Hierarchy Class Generalization Relation

Properties Attributes

Restrictions Constraints

Cardinalities Multiplicities

Data Types Data Types

Instances Object Instances

Values Attributes Values

D. Context of Structural Implications in Ontology

Extensible mark-up language (XML) which is

developed by the World Wide Web Consortium (W3C),

allows information to be more accurately described. The

DARPA agent markup language (DAML) is developed as

an extension to XML since XML has a limited capability

to describe the relationships (schemas or

ontologies).DAML+OIL is the latest version of DAML

with the key ingredients such as XML, RDF, OIL, DAML

and Description Logics. It provides a rich set of constructs

in order to create ontologies and to mark up information,

thus it is machine readable and understandable. It has been

also submitted to W3C as a proposal for the basis of the

W3Cweb ontology language. It is designed to describe the

structure of a domain as it is an ontology language. It uses

an object oriented approach, with the structure of the

domain being described in terms of classes and properties

[40]. UML has been used directly as an ontology

representation, a modeling method for knowledge

representation languages and a graphical front-end for

DAML+OIL. Standard UML models can be transformed

into DAML+OIL ontologies [38], [41].DAML+OIL lack

representation possibilities such as a graphical front-end.

Generally, they do not have sufficient tool support, while

UML is a commonly accepted graphical notation that can

be used to represent DAML ontologies[41].To this end,

there is need for a mapping between DAML and UML as

shown in Table III. Fig. 2 shows a UML diagram for


http://www.w3c.org/



ontology structure of product which can illustrate

structural relationships in the concepts of production.

IV. CHALLENGING ISSUES AND OBJECTIVES

Anticipating production implications early in the design

process is important because production is a crucial

determinant of quality and cost. In particular, it is

important to be able to determine the engineering

economics as early as possible. This is valuable

information which can be used to inform the early design

stages where much of the production cost is committed,

and thereby provide for an early optimization of the design.

It also informs the marketing feasibility study, since price

and placement (distribution) are also volume-dependent.

This is a challenging problem because of the subjective

causality and qualitative information available at this stage.

This type of information would be of particular use to

venture capitalists and the decisions on capital rationing to

decrease production costs. At present there is a dearth of

methods available to assist this particular feedback loop.

Figure. 2. System model of the entrepreneurial process surrounding a new product development. This diagram is represented in IDEF0 systems

engineering notation [37].

V. METHODOLOGY

In this research we explore the application of an

ontological approach to assess operational implications in

NPD. The potential benefits are increase in product quality

and improvement in the work flow position of start-up

firm. Thus, as shown in Fig. 2, from our perspective, we

see the design of an innovative product as a set of activities

that are embedded within a start-up firm that is

concurrently researching the market, finding the

development funds, creating an organizational platform

(including production capability), and determining the

feasibility of its new product. The methodology we

followed in this research consists of a number of steps as

follow. First, by way of context we take a SE perspective

and create a model for the overall process of new product

entrepreneurship. This model incorporates the traditional

engineering design activities in product development and

is shown in Fig. 2. Second, we construct main ontology

(department: operational areas within the firm) using

Protégé as shown in Fig. 1. It is achieved from a number of

sub-ontologies for product(parts) and manufacturing

processes. Third, we extract the information from these

ontologies and further process it to extract the operational

inferences (work flow and facility layout planning). For

this stage we resort to manual methods, as these features

are not available in ontological systems. However the

principles are nonetheless valid. Forth, we integrate these

ontologies and operational inferences to study the

feasibility issue and optimization model. In particular we

use CORELAP to determine the layout. We illustrate the

application of these methods to a simple conceptual model

of a HAND SAW, comprising three parts each with

relatively simple features, and an assembly tree, see Fig. 3.

Each part feature is assumed to require a manufacturing

process, and those machine tools are structured into

production departments.

Figure. 3. Handsaw product used in case study

VI. RESULTS AND EVALUATION WITH CASE

STUDY(HAND SAW)

Parts product ontology

Manufacturing process ontology

Departmental ontology

Create work flow

model

For a given product e.g. HAND SAW

Production flow

Blade

Handle

Pins

Create CAD

models

Process plan for

design activity

Create Plant

LayoutDepartments

Facility areas

CAD,

SolidWorks

Out of scope

Cutting

Drilling

Grinding

Turning

etc. Plan

Communicator

Plant layout

CORELAP

Flow diagram

Machine

tools

ontology

Product

ontology

Feasibility and

Optimization

Figure. 4. Possible integrations and sequences between main

implications

It will be noted that the case study HAND SAW is not

specified in detail: this is consistent with the early stages

of design where the ideas are conceptual rather than

detailed. The results show that it is nonetheless possible to

extract useful production implications from this

ambiguous situation. We start with the reasonable

assumption that a production facility will be needed for the

mass production of the HAND SAW. We can also make

use of pre-existing production knowledge, namely

knowledge about production processes that may be used to

create different types of geometric features on parts. Also

known is type of plant & equipment that is required to

Create an organisational

platform(1: Om-5)

Initial seed funds

Find development funds

(Ent-2)

Determine Feasibility

(Ent-3)

Product design or engineering

science feasibility

Design innovative product (service/ system/process)

(Nv)

Organisational entity (external structure, to

interact with the external environment)

Internal structure of organisation (to coordinate

internal effort in the production of value)

new design of product (service)

Research market (1: Om-4-2-1)

customer value preferences (voice)

competitor's capability

degree to which products (own or

competitor's) are rare, inimitable, and

valuable

Production feasibility

Economic feasibility

Organisational financial capital



provide those production processes. Thus a cylindrical

part feature might require turning, which is achieved by a

lathe. We also know the type of production rates of these

various machines, their typical physical sizes, and their

compatibility with other processes (e.g. hot-work

processes will need to be separated from fine assembly

tasks). The point we make is that much of this knowledge

is already known by production engineers – it is just that it

is not easy to integrate it together and make it available to

the inventor who is working early in the NPD cycle. This

is where the ontology comes in. The ontology permits

relationships to be established between part features,

manufacturing processes, machines, departments, and

assembly times. The overview of the system is shown in

Fig. 4. It allows us to explore how part features cause

production implications in production department. The

logic of the resulting ontology structure is shown in Fig. 5.

-+productID : char

-+part : Part

-+assembly : Assembly

-+description : char

-+productDefinitionType : ProductDefinitionType

-+manufacturing : Manufacturing

Product

*

-part

*

-+assemblyID : char

-+part : Part

-+featureType : FeatureType

-+assemblyFeature : AssemblyFeature

Assembly

-+assyFeatureID : char

-+part : Part

-+jointFeature : JointFeature

-+material : char

AssemblyFeature

-+featureTypeID : char

-+assemblyFeature : AssemblyFeature

-+jointFeature : JointFeature

-+geometricFeature : GeometricFeature

FeatureType

-+partID : char



Part

-+assyFeatureID : char

-+jointType : char


-+joiningMethod : char

JointFeature

-+geometricFeatureID : char

GeometricFeature

-+manufacturingID : char

-+manufacturingProcess : ManufacturingProcess

-+assembly : Assembly

Manufacturing

-+manufProcessID : char


ManufacturingProcess

-+productDefinitionTypeID : char


ProductDefinitionType-+productSegmentTypeID : char

-+productDefinitionType : ProductDefinitionType

ProductSegmentType

*

-Assembly

*

-part *

*

*

-productionDefinitionType *

-productDefinitionType

**

*

-manufacturingProcess *

-geometricFeature*

*

-geometricFeature

*

*

-jointFeature

*

*

-jointFeature*

*

*

-part*

-geometricFeature

*

*

*

-featureType

*

*

-assemblyFeature

*

*

-manufacturing

*

-assembly Feature

*

*

*

-geometricFeature*

-assembly

*

*

Figure.5. Product ontology structure in UML

At this stage there is no automatic integration with part

features with CAD, nor with the CAD assembly tree,

instead, we process that information manually in this case

study, to prove the notion.

A. Layout Planning

For defining the layout of our firm in order to produce

HAND SAWS, we used the CORELAP method as a

technique of facility layout planning. This is based on the

calculation of the relationships between activities in each

department with other departments. The algorithm of this

method acts as follows: the sum of each activity's closeness

relationships will be compared with all other activities and

the activity with the highest total closeness relationship

(TCR) will be selected and located first at the centre of the

layout matrix and will be designated the “winner”. Then,

the activity with the highest closeness relationship with the

winner will be selected and placed as adjacent as possible

to the winner in the best location neighboring to the

previously placed departments. This activity will be called

“A” (closeness absolutely necessary) and named as

“victor”. A search of winner's remaining relationships for

more A-related victors will then be made and placed as

close to each other as possible. If no more “A” can be

found, the victors become potential winners and their

relationships will be searched to find “A”. If an “A” is

found, the victor becomes the new winner, and the

procedure will be repeated. When no “A “is found, the

same procedure will be repeated for “E” (closeness

especially important), “I” (closeness important), and “0”

(Ordinary closeness o.k.) until all activities have been

placed in the layout. CORELAP also puts a value on the

“U” (closeness Unimportant) and “X” (closeness not

desirable) relationships. The major steps of this algorithm

are: a) defining basic data, b) determination of placement

order, c) placement of departments in the layout, d) finding



the total score of the layout. Its input requirements also are:

a) number of departments, b) closeness relationship as

given by REL-Chart, c) weighted ratings for REL-Chart

entries. See Fig. 6.

Figure. 6. REL-Chart

In this method, the final layout will not have a regular

rectangular shape. Therefore, we had to modify it slightly

to suit the situation. Fig. 7. shows the final layout after

modification.

Figure. 7. Final layout

B. Flow Determination

By focusing on the flow of material in our layout, we

investigated the sequence of material movement. The

analysis of material movement (flow) involves

determining the most effective sequence(s) of moving

materials through the necessary steps of the involved

process(s) and the intensity or magnitude of these moves.

An effective flow means that materials will move

progressively through the process, always advancing

toward completion and without excessive detours or

back-tracking (counter flow). Flow of materials analysis is

the heart of layout planning, wherever movement of

materials is a major portion of the process especially when

materials are large or heavy or many in quantity, or when

transport or handling costs are high compared with costs of

operation, storage, or inspection.

C. Determining the Method for Flow Analysis

There are various methods of analyzing flow of

materials. Actually, a part of the problem is to know which

method should be used for any given project. The various

methods which are based on the volume and varieties of

materials are as follow:

Using Operation Process Chart (OPC) for one or a

few standardized products or items

Using Multi-Product Process Chart for several

products or items.

In case of having many products or items, it is

better to

o Combine them into logical groups and

analyze as 1 or 2 above, or

o Select sample products or items and apply

1 or 2 above.

Using From-To Chart for so many diversified

products or items.

Different methods of flow analysis should be used for

various conditions of product volume and variety, and it

can guide the type of analysis which is needed to make.

However, as shown in Fig. 8 and Fig. 9, we have

investigated the OPC in our research as we have only one

product as a case study. The other methods are suitable for

larger number of case studies.

Inward

Inspection

Store accepted sheets,

timbers & wires in the

Inward stores

Cutting sheets

with Inspection

Cutting

timbers with

Inspection

Part wires

with

Inspection

Saw-toothing, facing,

grinding & drilling with

Inspection

Wood-turning,

grinding & drilling

with Inspection

Chamfering with

Inspection

Buffer & Delay Buffer & DelayBuffer & Delay

Assemble

with

Inspection

Figure. 8. Operation process chart (OPC)

Figure. 9. Process flow chart

D. Line Balancing

The method also lends itself to being able to predict the

line balancing requirements. For this we need to anticipate



a production volume –which of itself is information that is

important for and derived in conjunction with market

analyses. To illustrate, we make the following assumptions:

Demand = 10 SAW/Day; Shift hours = 8 hours; Number of

shifts =2. The results of the calculations, shown below,

may then be overlaid on the layout, see Fig. 10.

Inward

Store

Cutting

Blade

Drilling

Wood

Turning

Cutting

Handle

Saw-

toothingGrinding

Assemble

Chamfering

Facing

Part

Wires

10

30 30

40+40=8070 10+10=20

30

670

6 12

30

50120200

242354

324312

318 212

364

Figure. 10. Line balancing model

( )

( )

( )

( )

( )

( )

E. Economic Feasibility Study (Optimization)

Economic Feasibility study can be used to a priori

estimation and evaluation of the costs and the gains of the

venture capitalists based on their investments in the firm.

In our case study, we defined it as an optimization model

that can use variables such as production level, quality

level and capital investments. The strategic goal is

expected to reach Production target (p*), Quality target (q*)

and Capital investments (C).p*is the sum of firm

production volume and can be taken after answering the

question on how scalable is a particular production firm

regarding production volume? -q* is the minimum quality

of product based on an as effective as possible innovation,

quality performance, etc. which are very important

variables in the NPD. There are some variables which

should be considered in the C such as longitudinal cash

flow (return on sale and investment, Gross Margin, etc.),

timing of income streams (increasing cash flow from a

variety of different sources), time value of money (product

cost and price % reduction per year), etc. These variables

are important for estimating cost of product that can be

calculated from product costs (total operating cost, total

engineering cost, general and administrative cost,

development cost, etc.) at introduction to market. However,

we considered the three mentioned variables in our current

optimization model and will investigate the other variables

in our future work for more complex case studies. The total

investment is denoted by A that is needed by each firm to

reach the production and the quality target. It is the sum of

the investment for the process innovation in term of

production increase (Ap) and the investment for the

product innovation in term of quality increase (Aq).

A = Ap+ Aq (4)

where p is the process innovation in term of production

increase and q is the product innovation in term of quality

increase. They can be considered linearly dependent to the

investment Ap and Aq respectively.

p = μAp q = σ Aq (5)

The total amount of resources of income streams for this

model is represented by the common fund (CF), that is

formed by the contributions provided by venture capitalists

for firm, and is denoted by ∑nAn°

CF = ∑nAn° (6)

The goal is to maximize the Gain of the firm as objective

function:

G = αP + βQ – C (7)

Subject to constraints on Production level, Quality level

and Capital investment as follows:

P=p ≥ p* (8)

Q=Min(q )≥ q*

C=A ≤ C

This model is a typical LP problem, where α and β are

the measuring of the improvements in financial terms.

VII. CONCLUSIONS AND FUTURE WORK

We have examined the existing mechanisms in SE and

ontological engineering models, and applied these to the

early stages of the NPD process. We have shown it is

possible to anticipate the production implications,

including layout, flow, and line balancing, on the flimsiest

of design and market information. In turn –and we believe

this is important- the results can then be fed back to the

NPD process to further inform the design, market-research,

and cash-flow considerations. Repetition of this process

has the potential to progressively remove the uncertainties

and optimize the NPD process as a while. As a future work

of our ongoing research, we plan to investigate other

variables for more complex case studies. Another future

line of research is to add the people dimension to the

solution. We therefore also plan to provide a wider and

richer treatment of resources than is possible from the PM

methodology with its instrumentalist focus on resources as

units of work. There is a need to enhance integrative

management of resources, represent and find value in the

degrees of association between people, and increase the

quality of relationships, and the systems engineering and

ontological approaches have potential here. Specifically,

the ontological approach has the potential to show deeper

and more nuanced, specific interactions between people

and resources.This issue along with the main objective of

the paper will be particularly useful in knowledge-sharing

achievements, asynchronous interactions and



cross-organizational relationships between concepts in

new systems in order to achieve high integrity and

productivity in feasibility of new engineering projects.

REFERENCES

[1] IEM. Graduate Attributes and Professional Competencies. (23 Nov

2009). Version 2. [Online]. Available:

http://www.washingtonaccord.org/IEA-Grad-Attr-Prof-Competencies-v2.pdf

[2] G. Pahl and W. Beitz, Engineering Design: A systematic Approach,

K. E. Wallace, Ed., Springer-Verlag (Berlin) and The Design Council, London,1988.

[3] V. Hubka, Principles of Engineering design WDK1, Zurich:

Heurista, 1987. [4] V. Hubkaand and W. E. Eder, Theory of Technical Systems,

Springer-Verlag, 1988.

[5] V. Hubka and W. E. Eder, Design Science, Berlin: Springer-Verlag, 1996.

[6] S. Pugh, Total Design, Integrated Methods for Successful Product

Engineering, Addison Wesley, 1991. [7] C. Hales, Managing Engineering Design, Longman Scientific &

Technical, 1994.

[8] D. J. Pons and J. Raine, "Design mechanisms and constraints," Research in Engineering Design, 2005, pp. 1-13.

[9] D. J. Pons, "Ventures of coordinated effort," International Journal

of Project Organisation and Management, vol. 4, no. 3, pp. 231-255, 2012.

[10] D. J. Pons and J. Raine, "Design with uncertain qualitative variables

under imperfect knowledge," Journal of Engineering Manufacture, vol. 218, no. 8, pp. 977-986, 2004.

[11] D. G. Ullman, "Robust decision-making for engineering design,"

Journal of Engineering Design, vol. 12, no. 1, pp. 3-13, 2001. [12] D. G. Ullman, "Toward the Ideal mechanical engineering design

support system," Research in Engineering Design, vol. 13, no. 2, pp.

55-64, 2002. [13] P. Cronemyr, A. Ronnback, and S. Eppinger, "A decision support

tool for predicting the impact of development process

improvements," Journal of Engineering Design, vol. 12, no. 3, pp. 177-199, 2001.

[14] D. J. Pons, "Project management for new product development," Project Management Journal, vol. 39, no. 2, pp. 82-97, 2008.

[15] INCOSE. (2013). International Council on Systems Engineering.

[Online]. Available: http://www.incose.org/practice/whatissystemseng.aspx

[16] PMI, A Guide to the Project Management Body of Knowledge

(PMBOK Guide), 14 Campus Blvd, Newtown Square, Pennsylvania, USA: Project Management Institute, 2008.

[17] ISO 31000, Risk Management-Principles and Guidelines,

International Organization for Standardization, 2009. [18] N. F. Noy and D. L. McGuinness, Ontology Development 101: A

Guide to Creating Your First Ontology, 2000.

[19] D. Ruikar, et al., "Using the semantic web for project information management," Facilities, vol. 25, no. 13/14, pp. 507-524, 2007.

[20] K.-W. Huang, et al., "Design and implementation of an

ontology-based intelligent project management system,” in Proc. 26th International Conference on Advanced Information

Networking and Applications Workshops, 2012, pp. 339-343.

[21] K. Wang and S. Tong, "An ontology of manufacturing knowledge for design decision support," in Proc. 4th International Conference

on Wireless Communications, Networking and Mobile Computing,

2008, pp. 1-5.

[22] E. Sachs, Getting Started with Protege-Frames, 2006.

[23] H. K. Lin, J. A. Harding, and M. Shahbaz, "Manufacturing system

engineering ontology for semantic interoperability across extended project teams," International Journal of Production Research, vol.

42, no. 24, pp. 5099-5118, 2004.

[24] M. E. Ginn and A. H. Rubenstein, "The R&D/production interface: A case study of new product commercialization," Journal of Product Innovation Management, vol. 3, no. 3, pp. 158-170, 1986.

[25] C. J. Anumba, et al., "Collaborative project information

management in a semantic web environment," Engineering, Construction and Architectural Management, vol. 15, no. 1, pp.

78-94, 2008.

[26] B. Dippelreiter and M. Pöttler, "Scenarios for evaluating a semantic

project management approach," Publication in a Scientific Journal

of the Series of Scientific Journal of RTU, vol. 46, pp. 65-71, 2011..

[27] C. Bock, et al., "Ontological product modeling for collaborative design," Advanced Engineering Informatics, vol. 24, no. 4, pp.

510-524, 2010.

[28] Y. C. Lai, et al., "Semantic-Web supported knowledge management system: An approach to enhance collaborative building design," in

Proc. IT in Civil Engineering, Nashville, Tennessee, 2003.

[29] N. Khilwani, J. A. Harding, and M. K. Tiwari, Ontology Mining for Platform Extraction in Product Development, 2007.

[30] M. Dong, D. Yang, and L. Su, "Ontology-based service product

configuration system modeling and development," Expert Systems with Applications, vol. 38, no. 9, pp. 11770-11786, 2011.

[31] E. Thal, A. Badiru, and R. Sawhney, "Distributed project

management for new product development," International Journal of Electronic Business Management, vol. 5, no. 2, pp. 93-104, 2007.

[32] V. Popa, D. Tanasescu, and C. Dinca, "Project management on new

product development and launch in the automotive industry," in Manufacturing Engineering, Quality and Production Systems,

2008.

[33] D. Pons, "Project management for new product development," Project Management, vol. 39, no. 2, pp. 82-97, 2008.

[34] F. Chen, et al., A Collaborative Project Management Architecture.

in System Sciences, Hawaii, 2003. [35] C.-H. Chen, S. F. Ling, and W. Chen, "Project scheduling for

collaborative product development using DSM," International

Journal of Project Management, vol. 21, no. 4, pp. 291-299, 2003. [36] S. K. Chandrasegaran, et al., "The evolution, challenges, and future

of knowledge representation in product design systems,"

Computer-Aided Design, vol. 45, no. 2, pp. 204-228, 2013. [37] FIPS. (1993). Integration Definition for Function Modeling

(IDEF0). [Online]. Available:

http://www.itl.nist.gov/fipspubs/idef02.doc [38] W. V. Siricharoen, “Ontologies and object models in object

oriented software engineering,” IAENG International Journal of

Computer Science, vol. 33, no. 1, 2007. [39] L. F. Lin, W. Y. Zhang, Y. C. Lou, C. Y. Chu, and M. Cai,

“Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment,” International Journal of

Production Research, vol. 49, no. 2, 2011.

[40] M. Gogolla, “Model Development in the UML-based speccation

environment (USE)),” Methods for Modelling Software Systems,

2007.

[41] P. Kogut, S. C ranefield, L. Hart, M. Dutra, K. Nethbaclawski, M. Kokar, and J. Smith, “UML for ontology development,” The

Knowledge Engineering Review, vol. 17, no. 1, pp. 61–64, 2002.

[42] L. Zhu, U. Jayaram, S. Jayaram, and O. Kim, “Ontology-driven integration of CAD/CAE applications: Strategies and comparisons,”

in Proc. ASME International Design Engineering Technical

Conferences & Computers and Information in Engineering Conference, 2009.

[43] I. Horrocks, P. F. Patel-Schneider, and F. V. Harmelen, “Reviewing

the design of DAML+OIL: An ontology language for the semantic Web,” in AAAI, 2002.

[44] E. I. Neaga, J. A. Harding, and H-K. Lin, “Towards a meaningful

manufacturing enterprisemetamodel: A semantic driven framework,” Journal of Engineering Manufacture, vol. 221, no. 499, Part B,

2007.

Mohsen Karimi Haghighi is a PhD student in

Mechanical Engineering, University of Canterbury,

New Zealand. His research interest includes New Product Development, Lean Manufacturing,

Flexible Manufacturing Systems, Design of

Manufacturing Systems, Virtual Manufacturing, CIM, CAD/CAM, Decision Making and

Optimization, Operation Research and its

Application in Engineering and Management Systems, ERP, CMMS, Facility Layout Planning, Assembly Line

Balancing, Planning, Project Control, Project Management and

Scheduling, Simulation and Modeling, Statistics and Probability.



Dirk Pons (PhD) has a research agenda that is

focused at the intersection of engineering and

organisations. He is interested in Engineering design

and new product development (NPD) (including management thereof), Systems engineering and

modeling of enterprise processes, Engineering

management (including knowledge management, innovation, entrepreneurship, governance, strategic

risk management), Production management (including Lean inventory,

JIT systems, process optimisation, high-value manufacturing), Project management practice and application to novel areas, Risk management

and reliability engineering, and Human error in technology situations.

ontological approach to new product developmentdevelopment department of mechanical engineering,...

Documents