A Framework for an Intelligent Design-support System ...

PLM: Going beyond product development and delivery

A Framework for an Intelligent Design-support System Based on Product Lifecycle Data B. Song1, X. Li2 and W.K. Ng3 1

Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075 Fax: +65 67938223/+65 6791 6377 E-mail: [email protected] 2

Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075 Fax: +65 67936264/+65 6791 6377 E-mail: [email protected] 3

School of Computer Engineering, Nanyang Technological University, Block N4 Level B3C No. 14, Singapore Fax: +65 6790 6929/+65 6790 6929 E-mail: [email protected] Abstract: Over-capacity of mass-produced products leads to “cut-throat” competition in the marketplace. Customer-driven Mass-customized Product Development (CMPD) has emerged as a crucial capability for product manufacturers to gain a competitive advantage. The core of CMPD is the ability to create or change a product design fast and right-the-first-time according to customer requirements. The criteria for “right-the-first-time” decisions must be optimized over the product’s lifecycle processes. This paper proposes a framework for an Intelligent Design-Support System (IDSS) as an enabling technology for CMPD. The IDSS is to take advantage of integrated product lifecycle data from a PLM system and dynamically update a product knowledgebase through intelligent self-learning algorithms as product lifecycle data for a new product is created. When a set of customer requirements is given, IDSS is to derive a set of design configurations that can satisfy the requirements. An optimal design configuration is selected through optimization of design configurations with respect to the required cost and time-to-market. Keyword: customer-driven, mass-customization, product lifecycle, selflearning, intelligent design-support

1

Introduction

Product innovation and development has become a major growth driver for companies to move up the value-chain. More and more contract manufacturers seek competitive advantages in the global markets through strengthening product design and development capabilities. However, there has been a “cut-throat” competition and over-capacity for mass-produced products. Customer-driven Mass-customized Product Development (CMPD), which provides products to meet individual requirements at near mass-

A Framework for an Intelligent Design-support System Based on Product Lifecycle Data produced price and time-to-market, is emerging as a crucial capability for competitive advantages over other companies. CMPD aims to meet increasing demands by people for products that satisfy individual preferences, and hence provides high value-add. However, providing customized products at near mass-produced cost and time-to-market is a challenge. As majority of a product’s cost is determined at the product design stage [1], a crucial key to realize CMPD lies in the ability to rapidly develop a product design that is “rightthe-first-time” and that has accurate estimation of the cost and time-to-market. When “rapid” becomes a requirement, “right-the-first-time” design has to rely on the use of success cases. Since a product is typically developed by a number of companies forming a value-chain, accurate estimation of the cost and time-to-market must be derived from real-time and historical product life-cycle data covering a selected valuechain. To meet the requirements, we propose an Intelligent Design-Support System (IDSS) as the enabling technology for CMPD. The IDSS consists of two major elements. One element is a product knowledge self-learning engine (PKSE) that builds up a product knowledge database (PKD). The PKD has a three-layer relationship between customer requirements, design specifications and product configurations in a product family. When a set of customer requirements is given, the PKSE could derive the design specifications and thereafter all the possible product configurations that can satisfy the specifications. The other element is a product configuration optimization engine (PCOE) that builds product component database (PCD). The PCD establishes relationships between the product components and their procurement information or manufacturing process information. With the PCD, PCOE could compute an optimal product configuration from all the qualified ones based on cost, time-to-markets, and other criteria. The IDSS is aimed to act on top of a Product Lifecycle Management (PLM) system that manages processes and information over the product lifecycle in an extended enterprise. Such a system captures and stores an integrated product lifecycle data (PLD) starting from customer requirements, design specifications, product configurations, till downstream activities of procurement, production, and end-of-life. These data elements are interrelated through a specific product configuration. Taking advantage of the PLM environment, the PKSE would dynamically update the PKD through intelligent self-learning algorithms when the PLD for a new product is generated in the PLM system. Meanwhile, the PCOE would dynamically update PCD from the same PLD. In this approach, IDSS would continuously and timely develop its knowledge and relationships as the new designs are made, overcoming the rigidity and difficulties associated with the traditional expert system approaches [2, 3]. IDSS is meant to be a supporting facility rather than to automate the entire product design and configuration process. It is considered suitable for supporting product variant design based on a product family. The aim is to facilitate the manufacturer to build up computer-interpretable design knowledge for a product family, and to use the knowledge to develop design configurations and their associated feasibility metrics rapidly in response to customer requirements and design changes. This would enable IDSS to help companies to build up the CMPD capability. It is also promising in enabling companies to develop and accumulate their product design capability in a much faster pace.

B. Song, X. Li and W. K. Ng

2

Technological Background

The concept of product mass-customization was first emerged in late 1980’s [4]. It is defined as to produce individually customized goods or services at the cost of standardized, mass-produced goods [5]. Further developments have let to more applicable concepts and methods that use information technology to enable the design, development, manufacturing, and delivery of products and services in a wide range [6-8]. While product mass-customization requires a total solution that encompassing the entire product lifecycle, the flexibility of design generation based on customer requirements is recognized as the top-level enabler [9]. In recent years, specific efforts have been made to develop methods and tools for design for mass-customization. Included are knowledgebased approaches to provide design advisors [10], case-based reasoning algorithms to construct a new BOM from previous design cases [11], and methodologies for product platform development [12-14]. These efforts had been focused on product design vision through either previous design data or design configuration modeling. Because masscustomization is a lifecycle issue, design knowledge for customer-driven design should be built using product lifecycle data (PLD). PLD integrates product configuration with the information of its components in the downstream processes. For example, when a component is procured, its data on pricing, lead-times, sourcing options, etc. are available; and when a component is made in-house, its manufacturing process, lead-time, and cost can be captured. The design configuration has to be optimized based on lifecycle cost and time-to-market. The application and implementation of product lifecycle management (PLM) systems in the industry has paved the way to overcome the complexity and difficulties of obtaining sufficient and timely LPD. This has in turn made it possible to establish a dynamically updated design knowledgebase from PLD through intelligent computational processes. The IDSS proposed here takes this novel approach with the aim to enable the customer-driven mass-customized product development in real world industrial applications.

3

A Learning from Product Lifecycle Data Approach

A product is developed through a series of stages, spanning from the customer requirements, design, manufacturing, sales and services, till its recycle/disposal. Correspondingly, the data about the product is enriched along its lifecycle stages (Figure 1). Consequently, each product has a lifecycle data that represent the entire semantic information about the product. As such, the product lifecycle data (PLD) is the knowledgebase of the product. When many PLDs, containing the variants and versions of products in a product family are captured in an integrated data schema, a knowledgebase of the product family is established.

A Framework for an Intelligent Design-support System Based on Product Lifecycle Data Figure 1

Information created by different disciplines along the product lifecycle stages

B. Song, X. Li and W. K. Ng Conventionally, the product lifecycle data are scattered in different departments and captured in drawing files and isolated databases. Such a situation makes it difficult, if not impossible, to employ computational methods to extract knowledge that can support design decision-making. Expert systems have to rely on manually extracted rules. This results in serious drawbacks on completeness and timeliness of the rules, limiting the systems from applications in industrial environment. With deployment of PLM systems in product manufacturers, PLD can be captured with rich semantics in real-time. For instance, the design team would develop the design specifications and generate the design configuration based on a set of customer requirements from sales/marketing and other channels. When the design is released for manufacturing, the components in the design are either procured or manufactured inhouse. When procured, a component’s data on pricing, lead-times, sourcing options, etc. would become available. If made in-house, information on the manufacturing process, lead-time, and cost could be captured. So are data on its sale volume (sale channels), failure rates (customer services), etc. after the product is launched to the market. A PLM system should be able to capture all these data and their associated semantics (relations, time, owners, etc.) into an integrated PLD. Utilizing the PLDs from a PLM system, appropriate self-learning algorithms could be developed to carefully harness the PLDs and represent them in a formal, semantically rich, and computable manner. This would enable the data to become latent knowledgebase that aid new product design. The core of the knowledgebase is the general relationships between customer requirements, design specifications, product configurations. Included are also the links between components of the product configurations, and the procurement, manufacturing, usage and other attributes of every component. The knowledgebase evolves as new design is generated. With the knowledgebase, the system would be able to suggest a set of design specifications and corresponding product configurations for a given set of customer requirements. It would also allow the assessment of product configurations based on cost and time-to-market considerations. We consider these capabilities essential for CMPD.

4

System Framework

In taking a learning approach based on PLM systems, we envision an intelligent design decision-support system (IDSS) that accepts customer requirements and product lifecycle data, and produce a variety of product configuration options together with the associated cost metrics. The IDSS would have the following necessary framework and working environment as shown in Figure 2.


IDSS Frame Work and Environment

IDSS Input/Output

IDSS Framework

Customer Requirement Specifications

Qualified Product Configurations

Product Knowledge Self-learning Engine (PKSE)

Optimal Product Configurations

Product Configuration Optimization Engine (PCOE)

Integrated Product Lifecycle Data Model

Product Life-Cycle Management

PLM System

Product Definition

Production & Testing

Prototyping &Tooling

Design

Sales & Distribution

Services & Support

Recycle & Disposal

4.1 Integrated Product Lifecycle Data In the IDSS framework, integrated Product Lifecycle Data Model (PLDM) provides a neutral representation of the required lifecycle data for design-support computations. Through this layer, IDSS can be integrated into different PLM platforms as well as the diverse PLM solutions deployed. Figure 3

The concept of an Integrated Product Lifecycle Data Model Product Family

Product A

Product B

Model Series A

Model Series B

Model a

Model b

Assmbly A

Assembly B

Other Documents Sub-assembly Specifications Drawing/Model

Component a

Component b

Manufacturing Data Material Database

Procurement Data Supplier Database

Component N Service Data

Manufacturing Plan

Service Plan Agent Database Recycle Data

Procurement Plan

Recycle Plan Recycler Database

B. Song, X. Li and W. K. Ng PLDM essentially contains the components and their relationships in Bill-Of-Materials (BOMs) of product models in a product family. The BOMs are integrated with make (manufacturing), procurement, services, recycle, and other information required for the IDSS computation. Here, the component is defined as the basic unit that requires actions at downstream processes such as manufacturing, procurement, service, or recycle. Figure 3 illustrates the concept of such an integrated product lifecycle data model. The schema is to be substantiated as the relevant data are created and modified along the product lifecycle processes.

4.2 Product Knowledge Self-learning Engine (PKSE) The primary role of PKSE is to extract relevant data from PLDs and to build domainspecific relationships between customer requirements, design specifications, and product configurations. The integration of the customer in the product design configuration is a distinctive feature of the mass customization process. In facilitating customers to articulate their preferences, there is a need to construct a standard model to represent the requirements. The standardization is also essential for repeated use of the requirement definitions in product description so that their associations with the design specifications can be accumulated and analyzed. Design specifications are defined to satisfy the customer requirements. Their relationships are best captured by a house of quality matrix in Quality Function Deployment (QFD) method [18]. Similar to customer requirements, the construction of a standard design specification model is also necessary for accumulating and analyzing the corresponding partners with customer requirements. The design of a product is made to satisfy the design specifications. The output from design is in the form of a product configuration with product parts as its basic elements. In a well managed deign environment, the parts are taken from a master part library that standardizes and regulates the use of parts in products. This approach also provides the desired accumulation of historical usage of each part in product configurations. The standardized definitions and regulated use of customer requirements, design specifications, and product configurations provides the base for design knowledge capturing. The addition of new definitions in standard sets along with new product and design innovations expands and updates the corresponding standard sets and allow them to be stay relevant. The PKSE is tasked to develop the non-product specific relationships between the customer requirements, design specifications, and product configurations as a design is released, and updates relevant relationships based on feedbacks from manufacturing, sales, services and end-of-life processes. Each completed design is a historical case. Due to complexity, ever changing requirements and emergence of cheaper and better components, a fixed model of the relationships is difficult to achieve and would not last. The PKSE is to dynamically learn the relationships from the case as a product is designed. Specifically, the objective of “self-learning” is to build up the knowledge of correlations between each pair of the relationships. With the knowledge, the system would be able to related a given set of customer requirements to a specific set of product specifications (1:1 relationship), and to further identify the viable product configurations that can satisfy the product specification (1:m relationship). The issue is to explore and develop computational intelligence algorithms to dynamically build up the PKSE.


The Three-layer Network Structure of PKSE r1

R1

S1

r2

L1 L2

S2 r3 R2

r4

C11

C12

C21

C22

C31

C32

CI1

CI2

L3 S3

r5

L4 L5

RN rJ

SM

LJ

The issue can be abstracted into a three-layer network structure (Figure 4) to represent the relationships between customer requirements, product specifications and product configurations. Here, the customer requirements are defined as {R} = {R1, R2, …, RN}; the design specifications as {S} = {S1, S2, …, SM }; the product configurations as [C] = [Clx] where l = 1, 2, …, L, and x = 1, 2, …, x, and the x is a variable number because the product configurations may not have the same number of elements. We define the relationships between {R} and {S} as {r} = {r1, r2, …, rJ }. For each given set of {R}, there should be a most desirable {S} that can satisfy each and all the elements of {R}, i.e., a 1:1 relationship. As the {r} is derived from historical cases, each element of {r} would have qualifiers denoted in probability (%) of the chances that the relationship between a pair of Rn and Sm would likely happen. The links between the {S} and [C] are defined as {L} = {L1, L2, …, LN} and {L} is 1 to many. This means that a number of product configurations might be available to satisfy a set of design specifications. The variations can be in component selection, subassembly, manufacturing processes, and other aspects. For instance, a number of parts from different suppliers might be able to satisfy a given functional requirement. In this case, each part would make a different product configuration option. Based on the above abstraction and definitions, we propose an extendable rule-based system with association-rule mining and self-learning algorithms as the approach of PKSE for deriving the relationships {r} and {L}. The PKSE should have a learning capability to fine-tune these relationships according to customer requirements changes. A case of instantaneous water heater product family is given in Figure 5. The heaters have a series of product models, each model has a designated or a range of power consumption, power voltage, available color, and designed markets, etc. These form the customer requirements. For a given set of customer requirements, there is a corresponding product specification, e.g., WH-SP2-3.0-BL3. For each product specification, there are a number of product configurations, e.g. the cases on subassembly involving various possible heating tanks, power units, and flow valves.

B. Song, X. Li and W. K. Ng Figure 5 An Illustration of the relations and configuration elements Product Specifications

Customer/Order Requirements Model: Sp1, Sp2, Mp1, Mp2, Fantasy, Zen…. POWER : 3KW -> 9kw Voltage : 220v, 230V, 240V Color : Blue, Gray, Pink, Matt Silver…… Country : Singapore, Indonesia, China, UK……. ……

Product Configuration ASSY-SP2-3.0-WH3 COVER-SP2-BL TEST-SP2-3.0-SIN PCK-SP2-3.0-SIN ……

WH-SP2-3.0-BL3 ……

ASSY-SP2-3.0-WH3 ASSY-SP1-6.0-WH2 ASSY-SP2-8.5-WH2 ASSY-MP2-4.5-GY3 …… Heating Tank TANK-SP-3.0-220 TANK-MP-4.5-230 TANK-SP-6.5-CHA TANK-SP-9.5-240 ……

Power Unit

Flow Valve

POWER-16A-16A POWER-16A-22A POWER-16A-25A POWER-25A-25A POWER-25A-25A-UK ……

FLOW-S-16A FLOW-S-25A FLOW-D-16A-16A FLOW-D-16A-25A FLOW-DSTOP-25A-25A ……

In this case, the {C} nodes are the input customer requirements that are linguistic statement qks, such as, for instance of a IWH’s case shown in Figure 5, ‘Power is 3.0KW’, ‘Model is SP1’, ‘Voltage is 220v’, etc. The task of layer one is to translate these linguistic statements to binary format. The uis are their input numeric values. The {S} notes are the product specifications, such as ‘WH-SP2-3.0-BL3’, meaning WH series, specification set 2, variation 3.0, and blue color type 3. Each of the specifications is associated with certain customer requirements. The {r} contains the rule nodes with the rule values rjs. The relationships here define the preconditions (customer requirement) of the rule nodes, while the node outputs define their consequences (product specification). The nodes at this layer perform the heuristic AND operations. Sometimes, they also represent ‘IF-AND-THEN’ rules. The rule value could be fine-tuned with a supervised learning algorithm. The outputs are the percentage of supports s% to the rule. For example, an association rule can be as follows: Model (X, “Sp1…Sp5)∧Power(X, ‘3Kw’) ∧Voltage(X, ‘220v’) ∧Product Spec(X, ‘WH-SP2-3.0-BL3’) ∧…. ⇒ Product Config(X, “ASSY-SP2-3.0-WH3”) ⇒Heating Tank(X, ‘TANK-SP3.0-220’)∧Power Unit(X, “POWER-16A-16A”) ∧Flow Valve(X, “FLOW-S-16A”) ∧… [rule support = 46.7%] where X is a variable representing the choice of a customer. Rule support describes the mapping between customer requirements and product specifications. Generally, the higher rule support denotes the more accurate the relationships. The system’s self-learning ability could update the rule value with new customer requirements.

A Framework for an Intelligent Design-support System Based on Product Lifecycle Data The associated rule-based approach is also applicable the links {L}. Here, the input is the design specification {S}, and the output is a vector of product configurations that are above a set level of support, e.g, 45%.

4.3 Product Configuration Optimization Engine (PCOE) Taking advantage of the PLDM, the value of lifecycle cost (LCC) for each qualified product configuration could be calculated based on the activity-based costing [15] approach. The cost elements are categorized into different cost types, i.e., material, manpower, equipment, buying price, transportation, etc. The PCOE analyses each product configuration elements in accordance with related procurement data, manufacturing data, service data, and other types based on a set of rules. The cost of a product is the sum of the associated cost elements of all its components calculated based on historical data available from the PLDM. At the current stage, we confine LCC of a product configuration as the total cost of developing and manufacturing. Other lifecycle cost aspects, such as delivering, servicing or maintaining, and recycling or disposing, can be expanded later. As such, the calculation of LCC and its corresponding Time-To-Market (TTM) for each available product configuration can be made by the sum of all time and cost activities based on the historical data. We take such results as the estimates. The following formulas are applicable to the historical-data based approach: n

LCC = ∑ Ci i =1

(1)

m

TTM = ∑ Ti C i =1

(2) where n is the total number of cost categories, Ci is the dollar value of cost category i, m is the total number of time elements on the critical path, and TiC is the ith time element on the critical path of the product development and manufacturing plan. The cost categories can include manpower, equipment, materials, procurements, and transportation. For instance, n

C manpower = ∑ Ti RiManpower

(3)

i =1

R Manpower is the hour rate of the where Ti is the time (hours) spent by the manpower i, and i manpower i. The PCOE calculates the LCCi and TTMi for each product configuration Ci. As there are more than one product configurations available, an optimization process can be carried out to choose the best configuration among the available ones. At present, we only take LCC and TTM as the key factors for optimization. They must be equal or less than the time-to-market (TTMR) and cost (LCCR) required by the customer or by the product’s business plan, i.e., PC

o

= ( LCC

o

, TTM

o

) = min{( LCC 1 , TTM

1

), ( LCC

2

, TTM

2

),......( LCC

n

, TTM

n

)}

B. Song, X. Li and W. K. Ng (4) where

LCC

o

≤ LCC

R

and

TTM

o

≤ TTM

R

.

In case the condition cannot be met by any of the product configurations, PCOE would prompt for a re-evaluation of the historical data and processes used.

5

Conclusion Remarks

Studies have shown that Information Technology Infrastructure facilitates mass customization [16, 17]. Nonetheless, it still remains an open issue as to how information systems should be designed and implemented for the provision of CMPD products and services in the intra-company and inter-company level. Resulting from the accelerated implementation of PLM solutions in the industry, the integrated product lifecycle data (PLD) become available. Leveraging on the data and its unprecedented richness of schematics, the intelligent design-support system that facilitates CMPD becomes achievable. We therefore propose a self-learning from PLD approach in an effort to develop an intelligent design decision support (IDSS) system for rapid response to customer requirements and design changes in a CMPD environment. The core of the IDSS consists of a PKSE and a PCOE. The PKSE captures and derives relationships between the customer requirements, design specifications, and product configurations. The users can use PKSE to compute and suggest the possible product configurations for a given set of customer requirements. The PCOE calculates the LCC and TTM values for each possible configuration and suggest a optimal one for the given cost and time-to-market limits. The IDSS is still at a preliminary stage of development. Both the PKSE and PCOE are subject to deeper investigation and analysis for alternative and better algorithms. They also need to be validated in the real industrial environments. Its ultimate objective, and challenge, lies in the development of a dynamic product design capability that (1) learns from the design and development history of past mature products and (2) makes use of this knowledge to create a variety of product options and variation that take into account the associated costs and constraints over the product lifecycle phases. This capability can be used support the design decisions that optimize the benefits throughout the product’s lifecycle spanning from design and development, manufacturing and supply chain, sales and services, till end-of-life stages.

A Framework for an Intelligent Design-support System Based on Product Lifecycle Data

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