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Integrated Part Classification for Product Cost and Complexity Reduction

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Abstract and Figures

The manufacturing industry is moving towards a truly global arena. Organizations are adopting the philosophy of “design anywhere, manufacture anywhere, and sell anywhere”. Global operations with local focus have become the core of an organization’s strategy. Organizations are trying to have a vast product portfolio with mass customization to meet the customers’ increasing demand for personalized products. While expanding the product portfolio and bringing new products to the market the aspect of product sustenance across its life cycle is often missed out. With regulatory standards becoming more stringent, product maintenance and retirement are becoming challenging and costly. The aspect of “circular economy” is extending the life of the product and individual parts beyond the traditional end of life with re-fabrication, reconditioning and recycling of parts. The part-level detailing is becoming very important at the design stage. This provides huge growth opportunities for organizations, but comes with challenges of increased complexity, variety and cost. One of the potential ways to address the challenges listed above is the availability and maintenance of part-level information and dynamic traceability across the lifecycle, enriched with cross functional inputs. This is important for business decision making during product portfolio planning and product design in both proactive and reactive scenarios. Based on the authors’ industry experience across multiple product development organizations, it is evident that there is limited awareness of the potential of classification and its impact beyond basic part search and reuse. In this paper, we discuss the need for an integrated, cross-functional model and a common database for part information management. We present an agent-based simulation to show the benefits of such an integrated modeling strategy. In the process, the approach has the potential to also bring configurability of the product till the end of life. Configurability is from the aspect of making a product that will perform to meet customer needs along with delivering profit for business and being compliant with various regulatory norms. Copyright © 2014 by ASME Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal
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1 Copyright © 2011 by ASME
Proceedings of the ASME 2014 International Design Engineering Technical Conferences & Computers and
Information in Engineering Conference
August 17-20, 2014, Buffalo, New York, USA
DETC2014- 34492
INTEGRATED PART CLASSIFICATION FOR PRODUCT COST
AND COMPLEXITY REDUCTION
Babu K Nagiligari
Tata Consultancy Services
Columbus, IN USA
Jimish Shah
Tata Consultancy Services
Columbus, IN USA
Zhenghui Sha
Mechanical Engineering, Purdue University
West Lafayette IN 47907, USA
Sathishkumar Thirugnanam
Tata Consultancy Services
Columbus, IN USA
Anurag Jain
Tata Consultancy Services
Mumbai, India
Jitesh Panchal
Mechanical Engineering, Purdue University
West Lafayette IN 47907 USA
ASTRACT
The manufacturing industry is moving towards a truly global
arena. Organizations are adopting the philosophy of design
anywhere, manufacture anywhere, and sell anywhere. Global
operations with local focus have become the core of an
organizations strategy. Organizations are trying to have a vast
product portfolio with mass customization to meet the
customers’ increasing demand for personalized products. While
expanding the product portfolio and bringing new products to
the market the aspect of product sustenance across its life cycle
is often missed out. With regulatory standards becoming more
stringent, product maintenance and retirement are becoming
challenging and costly. The aspect of “circular economy” is
extending the life of the product and individual parts beyond
the traditional end of life with re-fabrication, reconditioning
and recycling of parts. The part-level detailing is becoming
very important at the design stage. This provides huge growth
opportunities for organizations, but comes with challenges of
increased complexity, variety and cost.
One of the potential ways to address the challenges listed above
is the availability and maintenance of part-level information
and dynamic traceability across the lifecycle, enriched with
cross functional inputs. This is important for business decision
making during product portfolio planning and product design
in both proactive and reactive scenarios. Based on the authors’
industry experience across multiple product development
organizations, it is evident that there is limited awareness of the
potential of classification and its impact beyond basic part
search and reuse. In this paper, we discuss the need for an
integrated, cross-functional model and a common database for
part information management. We present an agent-based
simulation to show the benefits of such an integrated modeling
strategy. In the process, the approach has the potential to also
bring configurability of the product till the end of life.
Configurability is from the aspect of making a product that will
perform to meet customer needs along with delivering profit for
business and being compliant with various regulatory norms.
Keywords: Classification, Product Development, Product
Cost, Complexity Reduction
1. INDUSTRY TRENDS AND MOTIVATION
Manufacturing industry is now global and has a clear focus on
simultaneous product development with design teams spread
across the world. Products are launched globally targeting
multiple markets to meet the market needs. Organizations have
to strive to attain the status of “Global Best”. Meeting diverse
customer needs, taking advantage of local supply chains to be
cost effective, increased quality and effective cost performance
are key aspects to be competent. This demands enterprises to
have matured operating models with focus on standardization,
reuse and smart decision making in a dynamic environment.
In a globally distributed product development and
manufacturing environment, collaboration and effective and
efficient part information availability for decision making
throughout the product lifecycle are some of the primary
challenges during product design and development.
2 Copyright © 2014 by ASME
Products and parts are defined by design engineers during the
initial phases of development and the part information is
recorded and catalogued in engineering systems based on the
dominant function view with focus on creation activities and
process with the purpose of forward engineering. This
information gets transferred to the sourcing database where
information is enriched and commodity, supplier and region
based analysis for decision making. Similarly manufacturers
and service providers use their individual databases and
information. Each function utilizes similar information from
different databases using different models creating information
silos. Frequently, there is information loss or misalignment.
This inhibits collaboration and hence organization loses on
possible profit and cost advantages arising due to reduced cycle
time (refer to Figure 1), consolidation and volume negotiations
for common requirements. Also, for effective collaboration a
common working language is required which requires standard
method of representing product data across organizational
functions.
Figure 1 - Increased cycle time due to lack of collaboration
The motivation for this paper is the need for an integrated,
cross-functional model and a common database for part
information management which would shift quality to the left
in the design engineering phase; thereby achieving a forward
looking design with byproducts of reuse, better reliability, etc.
Part classification is a foundation approach to enable this
integrated information management model. Through part
classification, part data can be harmonized and made visible
across the value chain and enriched throughout the product
lifecycle to support proactive and predictive decision making.
2. PRODUCT COST AND COMPLEXITY
2.1. Product Cost
To understand the product cost we need take a lifecycle view of
product development and how cost derivation occurs at
different phases of product development lifecycle (planning,
concept, designing, testing, sourcing, manufacturing, launch
and service). There are two different components of cost,
namely, project cost and product cost (incurred and
commitment cost), managing project cost is to control the
product development cost whereas managing product cost is to
come up with profitable products. For effective cost
management, both need to be considered in conjunction.
Figure 2 shows the cost curve in a product development
process. Approximately 80% of product’s target cost is
committed in the product planning, concept and detailed design
stage during which product designers determine the product’s
design and production process. Majority of the committed costs
are incurred during sourcing and manufacturing stage. The
committed costs that are already locked in at the design stage
are very difficult to change. This leaves manufacturing and
operations with approximately 20% opportunity for cost
reduction, even though the majority of the cost is incurred
during this phase and it is very difficult to significantly alter
costs after they have been committed. Manufacturing and
operations (along with other downstream functions) desire to
have increased flexibility for better operations. Hence, there is a
need to challenge the current trends of cost commitment to
provide downstream functions more liberty by postponing the
committed cost. A reduction of committed cost from 80% to
65% in the design phase will allow purchasing, manufacturing,
operations and service to have increased leverage to manage
and contain costs in the later stages.
Figure 2 - Product Development Cost Curve
The major contributor in product cost, material cost, decisions
on material type and material movement, are finalized in the
design phase. The design phase is the most commitment cost
centric phase and an ideal target to implement predictive and
proactive cost control strategy. Downstream functions like
manufacturing and operations will have less influence on the
material cost decisions and are less cost centric. Hence, part
lifecycle information, when made available at the design phase,
will enable design function to come up with lower commitment
costs without compromising design completion. Life cycle part
information consists of very well thought-of geometric, process
and behavioral attributes from engineering, manufacturing
process, reliability, service life phases and even from customer
experience.
2.2. Product Complexity
In an attempt to serve global customers and demanding
customer requirements, organizations tend to create varying
Current Situation
Engineering
Manufacturing
CAE
CAD
CAT
CAM
Plant Layout
Simulation
3 Copyright © 2014 by ASME
products and end up with problems of part proliferation.
Fulfillment of unique requirements needs high end
customization. Product variety results in complexity because
engineering units prefer to create new designs rather than
reusing and/or expanding existing modules and designs. This is
a result of the difficulties in searching a closest design due to
non-standard and improperly organized product structures.
Creation of new designs and parts not only results in higher
engineering costs, inventory cost, costs for downstream
functions but also greatly increases overall product
development cycle times. We understand that variety in
products impacts per-unit costs, revenue and profitability.
Hence, there is an immediate need to encourage harmonized
and standard integrated part classification across an
organization. Integrated part classification will aid in achieving
product variety with very strong internal standardization. Also,
integrated part classification combined with product schema
and good configuration management has the potential to enrich
decision making and product portfolio management.
With current mandates for shorter product life cycle times,
reduced product complexity a different strategy has to be
implemented during early stages and as a long term
organizational strategy. To overcome the product complexity
and cost challenges, organizations can think of adapting a
number of methods such as component commonality, platform
based product development and portfolio management,
modularization and design for value (Design for X). However
enabling these requires detailed and integrated views of product
information.
3. INDUSTRY ANALYSIS
Current industry scenario has been analyzed based on the study
of multiple product development projects in the discrete-
manufacturing industry. Information management processes
along with information systems, classification and Master Data
Management projects were analyzed to provide a complete
assessment of the industry. In most companies, part and product
classification is treated as low priority and limited management
attention is given to the topic. The use of classification and part
information is managed individually by functional teams within
different information databases. In many cases classification of
parts is limited to the design and engineering departments. In
recent years, there have been significant technology advances
supporting classification, including social media tools,
geometry based search and grouping etc. This has improved the
effectiveness of search and reuse within the design and
engineering functions. Despite that, it was evident during the
assessment that there is limited awareness of the potential of
classification and its impact beyond basic part search and reuse.
Also there are many standards (eCL@SS, GS1, USSPSC,
ETIM etc.) that help in classifying part families based on the
industry and region (US, Europe, Germany, France and etc.).
These standards drive harmonization in the nomenclature for
most standard parts across an industry. Many companies have
adopted these standards while defining their classification
hierarchy.
Existing literature on classification addresses different
classification models such as dynamic classification modeling,
geometry based modeling, etc. Although multiple models are
available and technology has enhanced to support these models,
the primary focus of classification is on the prevailing needs of
engineering functional unit with the motivation to enhance
search and reuse. The possibility of extension of classification
to other functions and its role as a foundational capability to
drive other higher capabilities beyond reuse, such as in design
for X (assembly and manufacturing), compliance, cost
management, complexity reduction, standardization, etc. is
explored [1]. The authors mention that classification is a
foundation which drives many other capabilities (refer to
Figure 3). When used in conjunction with other core
capabilities it will deliver continuous benefits. The authors
provide the potential of possibilities but are not able to
demonstrate the benefits which can be used for business
planning and ROI justification.
4. INTEGRATED DECISION MAKING IN PRODUCT
DEVELOPMENT
Product development starts with the product requirements and
the target costs which are the initial inputs to the planning
phase. There are a number of decisions and activities that are
Figure 3 - Product Development and Integrated Information View
4 Copyright © 2014 by ASME
taken during the entire product development cycle from
concept to retirement phases (see Figure 3). As we have seen
earlier, the decisions taken at the initial stages in product
development have the maximum impact. During the concept
design phase, the product requirements are evaluated and
decisions on the products structure are taken which require
intelligent insights. Instead of focusing on the dominant
function’s needs alone, focus should be on the needs of all
stakeholders in the value chain. The needs of downstream
usage in operations, sourcing, warranty, etc. are given the
required focus. Classification provides integrated views of part
information from various perspectives for decision making.
Design reuse has the maximum impact on cost management,
variety reduction, and downstream complexity reduction, along
with ensuring high degree of compliance. Figure 4 shows the
structural aspect and information coverage of an ideal
classification structure which forms the foundation for an
integrated decision making model for product development.
Standardization is an important capability driven by
classification. Increased standardization is one of the keys to
multifold savings from the entire value chain. Classification is a
continuous process and should involve all stakeholders for
regular and methodical updates.
Figure 4 - Classification Structure and Coverage
5. CLASSIFICATION MATURITY, SCOPE AND
POTENTIAL
Integrated Part Structure or Classification is defined as
“systematic arrangement in groups or categories of objects
under consideration, based on certain parameters at various
levels.” While this definition is fundamental, potential benefits
are still evading most organizations [1].
The most important aspect of classification is to see it from the
value chain perspective, the perspective where classification is
not only linked to the static definition of parts or products, but
is coupled with the value chain processes and reverse processes
as well. Here, classification assumes living existence, and
hence it is vital to design its origin and usage with provision for
regular refinements and revisits, this is why classification
structure should consist of attributes from different business
functions, namely, engineering, change management, quality
control, testing, supply, manufacturing, service, warranty,
logistics etc.
Figure 5 depicts the framework of benefits from classification
as the foundational layer. Organizations can derive many
benefits leveraging classification, beginning with material &
process standardization to part reuse, capacity planning, and
design reuse. Advanced benefits from classification include
process design, inventory planning and cost analysis.
Classification serves as a foundation for several capabilities and
when combined with other key cross functional capabilities can
deliver perennial benefits. Benefits realized will vary from
foundational capabilities to a strong analytics based decisions
support system.
Figure 5 - Classification as Foundation [1]
A building block approach is proposed for deployment. Each of
the cubes, in the Figure 6 given below, represents the enablers
for classification at that level. The framework represents a
matrix structure with different levels of benefit realization
cutting across the organizational objectives. To meet the
classification requirements, an organization should go along the
desired path of benefit realization with respect to the objectives.
Figure 6 - Implementation Framework [1]
Figures 7 and 8 also include two different approaches by which
the framework can be deployed. But on both the approaches the
focus is aligned towards benefit realization. First approach
Part Name,
Numbering
Part
Specifications
Design
Parameters
Process
Variance
Supply
Preferences
Compliance
Grouping
Product
Specification
Service
Selections
Inventory &
Logistics
Basic Advanced Matured
Stage 1 Stage 2 Stage 3
Part
Geometry
Test
Specification
Manufact’g
parameters
Quality
Criteria
Product
Usage
Design
Functionality
External
Influencers
Part Costing
Objective
Benefits Realization
Warranty
qualifiers
Part
Reuse
First
Time
Right
Report &
analysis
Part
Reuse
Design
Reuse
First
Time
Right
Shifting
Quality
to Left
Tracea-
bility
Process
Reuse
Relational
Analysis
Reduce
Part
Cost
Reporting
Approaches
5 Copyright © 2014 by ASME
(Figure 7) shows the benefit-centric approach where the
classification is defined to meet a specific objective of the
organization and build upon the benefits. Second approach
(Figure 8) addresses a linear pattern for classification
deployment to meet multiple objectives of the organization
without compromising on the benefits. There may also be an
objective centric approach (hybrid approach) where the
deployment takes a zig-zag pattern to suit the needs of the
organization. Whatever the approach may be, the basic
principle of deployment should be to grow with the respective
foundational blocks and to make it a process continuum. Based
on the current maturity level of the organization, one can
choose one of the defined classification implementation
approaches and achieve benefits in phases.
Figure 7 - Implementation Framework [1]
Figure 8 - Implementation Framework [1]
6. CONSTRUCTING AN INTEGRATED PART
CLASSIFICATION BASED PRODUCT
DEVELOPMENT MODEL
6.1. Model overview
We considered three aspects: reuse, standardization and
procurement, which can be utilized to build the model related to
product cost and complexity. Increasing reuse helps in
reduction of the part and product design, development,
verification and tooling costs. It also ensures quality of the
parts.
Increasing the use of standard parts in the products leads to
reduction of product complexity. The cost of the end products is
lowered due to lower specialized parts in the products. By
pursuing standardization, reuse and utilizing the material
information we can reduce the product material and purchasing
cost by capitalizing economies of scale, reduced suppliers and
overhead costs.
We built a simulation model based on decision factors and
value chain elements to simulate product development and
effectively demonstrate the benefits of integrated part
classification. While the model has all other full-fledged
features to simulate end to end capabilities of classification to
support proactive and predictive decision making, in this paper,
we have only studied and detailed the classification-based
search module (see Figure 9).
Figure 9 - Classification Based Part Search Model
Based on the model we believe that there will be areas in the
product development process where classification will have an
immediate cost impact and some areas where the decisions
made will impact future product cost and complexity. Table 1
highlights impacts to the sub-processes in the product
development process.
6 Copyright © 2014 by ASME
6.2. Model description
We have two product structures, e.g. product 1 and product 2.
We assume that the organization already has product 1, and
designers have complete knowledge on how to design and
assemble product 1. Due to the new market needs, the company
needs to develop a new product, i.e., product 2, which has
almost the same structure as product 1. However, the
requirements for product 2 are somewhat different from product
1 and need new components in the structure. Since the
designers already have the design knowledge of product 2,
what they need to do is just to find the new parts that satisfy the
new requirements. We have two data classification techniques,
and we will simulate how the new data classification improves
the design efficiency and reduces the development cost and
product complexity.
The steps implemented in the model are as follows:
1. Identify the new components based on the product structure:
The new components are all the single components in
product 2 but not in product 1. In terms of the product tree,
the single components are all nodes with degree equal to 1.
2. Part search with old classification structure: In this scenario,
we assume that the old classification has part description as
an attribute. The designers search a specific part just based on
its description. Once they get all the parts list with the same
description, they manually identify the part they are looking
for by going through each one of them individually. If the
search output part list is large, then they will either spend
time to identify the specific part or chose to create a new part.
3. Part search with improved classification structure: In this
scenario, it is assumed that the designers have improved
classification structure. Besides the description of parts, more
attributes of parts are added into the classification and thus
available for designers to search for. Designers can get a
specific part with more accurate search criteria. To simulate
this process, we randomly generate the search criteria. This
reason is that we do not know which designer would perform
the search. Different designers may use different attributes of
the part to perform the search. To quantify this uncertainty,
we randomly pick N attributes to form the search criteria.
Here, N quantifies the maturity of the classification
technique. It means the more attributes the designer can use
to perform the search, the better the classification is. With the
randomly generated requirement, the search is performed and
candidate parts can be identified. Once the designer gets
candidate parts, he/she manually selects the part accordingly.
Table 1 - Product Development Costs
Proposal
Design & Development
Design Verification
Production Validation
ID
Process
ID
Process
ID
Process
ID
Process
ID
Process
1
Engineering
1.1
BOM
Development
1.4
Product Definition
1.11
Design Verification Updates
1.14
Production Validation Design
Updates
1.2
Initial Layout
1.5
CAD Model Definition
1.12
Requirements Verification
(DVP&R and DFMEA)
1.15
Supplier Compliance
1.3
Requirements
1.6
Analysis
1.13
Compliance Verification
1.16
Production Validation Updates
1.7
DFMEA
1.17
Requirements Feasibility
(PVP&R)
1.8
Eng Design Drawings
and Models
1.9
Requirements
Feasibility Verification
1.10
Layout Check
2
Manufacturin
g
2.1
Quote
Assumptions
2.2
PFMEA
2.6
Production Manufacturing
Requirements
2.10
Release Production Equipment
2.3
Manufacturing
Requirements
2.7
Release Design Verification
Equipment
2.11
Build and Install Equipment
2.4
Plant Layout
2.8
Build and Install Equipment
2.12
Equipment Quality
2.5
Design Verification
Manufacturing
Requirements
2.9
Equipment Quality
3
Purchasing
3.1
Make/Buy Decision
3.6
Purchase Orders for Design
Verification Tools etc
3.7
Purchase Orders for Production
Validation Tools etc
3.2
Sourcing Tracking
Matrix
3.3
Supplier SOWs
3.4
Award Contract
3.5
Prototype Supplier
Quotes
4
Quality
4.1
Past Problem
Roadmap
4.2
Engineering Design
MSA Plan
4.6
Design Verification
Measurement Capability
4.9
Supplier Part Approval
4.3
Engineering Design
Control Plan
4.7
Production Validation MSA
Plan
4.10
Production Control Plan
4.4
Design Verification
MSA Plan
4.8
Production Validation
Control Plan
4.11
Customer Part Approval
Immediate impact with Classification
Immediate and future decision making impact with Classification
7 Copyright © 2014 by ASME
4. Probability calculation: In the old classification scenario, the
probability of finding a specific part at the first time is
1
1/n
where
1
n
is the number of parts with the same description.
Similarly, we can get the probability of finding a specific part
in certain time,
T
. In the improved classification scenario,
the probability of finding a specific part at the first time is
2
1/n
, where
2
n
is the number of parts in the resulting
candidate list. Similarly, we can get the probability of finding
a specific part in certain time,
T
. This is based on all other
factors being constant.
5. Post analysis:
a) Suppose the probability of finding a specific part is
p
,
which is inversely proportional to the cost,
( ) ( )C s f p
,
where
()fx
can be a monotone decreasing function.
b) The improved classification would increase the probability of
finding a specific part in certain time,
T
, which increases the
probability of reusing a part, and increases the probability of
using standard parts. The results quantify this improvement.
The reusability and standardization will significantly reduce
the development cost and time, and at the same time reduce
the product complexity because of modularization and life
cycle information available in the classification structure. We
can specify
()gx
, which is dependent on reusability and
degree of standardization. On similar lines, a function to
calculate the cost of complexity can also be modeled.
c) Analysis of the effect of maturity of classification: By
increasing the maturity of classification quantified by
N
, we
can investigate how the maturity will increase the probability
of finding a part. As a summary, the simulation gives us
functional relationships between classification maturity and
the probability of finding a part.
7. MODEL ANALYSIS AND RESULTS
The model was run to simulate two different scenarios:
Scenario 1: Part search and reuse with traditional (old)
classification structure
Scenario 2: Part search and reuse with improved
classification structure
The results showed that the probability of finding a part
with improved classification structure is greater than the
probability of finding a part with the traditional
classification structure, while keeping all other search
criteria and design function considerations the same.
Figure 10 shows the comparison on the probability of finding
the specific parts (i.e., the new parts needed for developing
product 2) with old classification technique and improved
classification, respectively. The results indicate that the
probability of finding all the new components with the old
classification technique is always less than or equal to 0.1.
Figure 10 - Part Search probability plot
For the improved classification, there are two attributes
available for the designer. Since different designers may use
different attributes to search, to quantify this uncertainty, two
attributes are randomly generated. The search part of the
simulation runs 10 times and the average value is reported. The
results indicate that the probabilities are greatly improved with
the improved classification. For example, with the improved
classification technique, the probability of finding the part
A195 is 1, which means it can be directly identified and reused.
For the improved classification, we assumed that there were
only two attributes available for the designers. However, what
if there are more attributes which can be used? We have termed
the number of attributes which can be used for the search as the
“level of classification”. Figure 11 shows how the level of
classification affects the probability of finding a specific part.
Figure 12 shows an overall growing trend as the level of
classification increases, average probabilities are plotted.
Figure 11 - Probability vs. Level of Classification
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
A196
A195
A194
A193
A192
A191
A190
A179
A178
A177
A176
A175
A203
A202
A189
A201
A182
A180
Probability of finding the part
Parts/ Component
Old Classification
Improved Classification
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3
Probability of finding a part
Level of Classification
A196 A195
A194 A193
A179 A177
A189 A201
A180
8 Copyright © 2014 by ASME
Figure 12 - Average probability vs. Level of Classification
To investigate how the probability can be improved by
increasing the level of classification, we calculated the
improvement, i.e.,
21
1
PP
P
, as the level of classification is
increased. The results are shown in the Figure 13. It is found
that the addition of attribute into the classification significantly
improves the probability that the part can be found on first
search. As shown in the figure, with one attribute, the
probability can be improved by 7.68 times as compared to the
one by using old classification. The overall improvement by
using different level of classification is shown in a cumulative
way in Figure 13.
Figure 13 - Part search probability plot
8. CLOSING REMARKS AND FUTURE RESEARCH
Through this research it is evident that integrated part
classification, when implemented, has a potential to reduce part
proliferation and increase part reuse. Prevention of each
duplicate part creation can result in lot of benefits, namely,
reduced engineering and design costs in addition to
downstream costs associated with testing, manufacturing,
purchasing, inventory, logistics support and service. In addition
to cost savings, part proliferation when controlled, can result in
rapid product development and shorter product development
times.
Integrated part classification has tremendous potential in
realizing benefits and when these foundational benefits are
achieved will free organizational resources and enable
organization to be more innovative in this competitive
environment.
In this paper, the benefits of reuse, cost savings and
classification maturity are demonstrated. However the model
built for this study has greater capabilities to articulate all
potential benefits possible through classification, and also
potential benefits in conjunction with other initiatives. In future
studies, the aspects of larger integrated data structure will be
considered and inputs from cross functional value elements will
be demonstrated. Attributes from sourcing, operations,
manufacturing and service will be incorporated into the
classification structure to study the product development
process with visibility of complete part life cycle information
for forward engineering. A lot more is possible with
classification and current simulation demonstrates foundational
steps for modeling its impacts on product design.
ACKNOWLEDGEMENTS
Jain, Nagiligari, Shah and Thirugnanam would like to thank the
leadership at Tata Consultancy Services (TCS) for providing
this opportunity to put forward our point of views through this
paper. They are also thankful to TCS leadership for their
encouragement and support in this effort.
REFERENCES
1. Jain, A., Gadgil, S., Sathishkumar T., Pandey, S., and Shah,
J., Classification Redefined - A foundation to organizational
efficiency with minimal investments, NPI/PLM, Tata
Consultancy Services, Dec 03, 2012, PDMA.
2. Fredendall, L. D., and Gabriel, T. J., Manufacturing
Complexity: A Quantitative Measure, POMS Conference,
April 4 April 7, 2003, Savannah, GA.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3
Average Probability of
finding a part
Level of classification
7.2
7.4
7.6
7.8
8
8.2
8.4
8.6
8.8
0 to 1 1 to 2 2 to 3
Cumulative improvement of the
searching probability
Increase of level of classification
... Actually, in the extant literature, the most diffused barrier identified for the adoption of circular economy in manufacturing and thus, the appropriate management of product along their life cycles, regards data and information management [10]. Nevertheless, data and information are fundamentals to support an appropriate decision-making process [11], and in line with that are considered one of the most important resources in circular economy [9]. At the best of the authors' knowledge more narrowed researches have been performed to streamline information management, such as the ontological framework for Industrial Symbiosis [12] or for product life cycle management but without including circular economy considerations [13]. ...
... In the extant scientific literature, one of the most diffused barriers, encountered by manufacturers in exploiting the potentialities of circular economy, concerns data and information management [10]. This is reflected in the creation, exchange, and exploitation of data and information [14], within and outside firms' boundaries, to support the manufacturers' decision-making process [11]. For years, end of life (EoL) strategies to enable resource loops have been addressed in the extant literature, since EoL it has been always considered the joint stage to ensure to close the loop. ...
... The reusability, re-manufacturability or recyclability of products might be affected by technical issues but also by consumers' demand for refurbished products [27], for this reason is required to be able to stimulate the market and thus, to create the demand of refurbished products through adequate marketing plans. All the choices taken at the EoL are also influenced by economic considerations, which are used to define whether a strategy could be adoptable or not and how the product could be designed in the future to ease its circularity [11], [28]. ...
Chapter
Nowadays, due to the limited availability of resources, the adoption of sustainable practices is gaining importance, especially while dealing with the manufacturing sector that is considered one of the most resource greedy sectors. To cope with this issue, a new sustainable and industrial economy, called “circular economy” arisen. The diffusion of this economy can be eased by the advance management of data and information. Nevertheless, from the extant literature emerged some criticalities regarding information management flows while dealing with circular economy strategies adoption. Indeed, the present work aims to first identify the main criticalities in information management while adopting circular economy principles, and second to investigate the decisions, and the related data and information required to make these decisions, that manufacturers have to undertake to enable the circular product life cycle management. To achieve this goal, the present work relies on the scientific literature. This choice enables to grasp the widespread knowledge developed by scholars about these concepts, by individualizing the main decisions that should be taken by the company internal stakeholders, to manage circular products, being affected by external stakeholders’ behaviours and decisions along product life cycle. Therefore, this work aims to support circular product life cycles management and, this objective has been achieved through the development of a data classification model.
... This can be done through maintenance or repairing activities that, if performed on internal physical assets, would foster the availability and the reliability of machinery and equipment, by also increasing the levels of safety [45] and product quality. Last, information costs regarding the planning (e.g., product requirements, finalization, materials), concept (e.g., reuse possibility), design (e.g., standardization, reusable parts), source (e.g., make or buy decisions, material procurement, supplier selection), manufacturing (e.g., process tooling, operational planning), launch (e.g., warranty analysis, predictive maintenance services), service (e.g., recycling, refurbishments) need to be estimated [46]. ...
Article
Full-text available
In the extant literature, circular economy (CE) is considered a driver for sustainable development of the manufacturing sector, being it an industrial paradigm aiming at regenerating resources. CE is transferred to manufacturing companies through the adoption of different Circular Manufacturing (CM) strategies (e.g., recycling, remanufacturing, etc.). Nowadays, manufacturers are struggling to implement these strategies to limit their resource consumption and pollution generation. To enable their adoption, the extant literature unveiled the importance to control along the entire value chain different types of resource flows (i.e., material, energy, and information). Nevertheless, while for material and energy management some advancements were achieved, information management and sharing remains one of the major barriers in adopting these strategies. The present work, through a systematic literature review, aims to identify the relevant information and data required to support the manufacturer’s decision process in adopting and managing the different CM strategies to pursue the transition towards CM. Furthermore, based on the results obtained, this research proposes a theoretical framework. It elucidates the four main areas to be managed by manufacturers in adopting CM strategies and it provides to the manufacturer an overview of what should be updated and upgraded inside the company to embrace CM strategies.
Classification Redefined -A foundation to organizational efficiency with minimal investments
  • A Jain
  • S Gadgil
  • T Sathishkumar
  • S Pandey
  • J Shah
Jain, A., Gadgil, S., Sathishkumar T., Pandey, S., and Shah, J., Classification Redefined -A foundation to organizational efficiency with minimal investments, NPI/PLM, Tata Consultancy Services, Dec 03, 2012, PDMA.