NISTIR 7745

Modeling, Simulation and Analysis for Sustainable 

Product Development 

Mahesh ManiN , Bjorn JohanssonC,N

Kevin LyonsN , Ram D. SriramN, Gaurav AmetaW

NNational Institute of Standards and Technology

Manufacturing Systems Integration Division, Gaithersburg, MD

Email: {mahesh,klyons,sriram} @nist.gov

C

Chalmers University of Technology

Product and Production Development

SE-412 96 Gothenburg, Sweden

Email: bjorn.johansson@chalmers.se

WWashington State University

Department of Mechanical Engineering, Pullman, WA

Email: gameta@wsu.edu

Abstract

Simulation plays a critical role in the design of products, materials and manufacturing 

processes. Increasingly, simulation is replacing physical tests to ensure product reliability 

and quality thereby facilitating steady reductions in design cycles. However, with the 

current global environmental crisis, there are gaps in the simulation tools used by industry 

to provide reliable results from which effective and equitable decisions can be made 

regarding environmental impacts at different stages of product realization and life cycle. 

For sustainable manufacturing planning, enhanced modeling techniques are needed to 

understand and predict the sustainability aspects through design and manufacturing where 

technologies can be applied to transform materials with reduced energy consumption, 

reduced emissions, reduced generation of waste products, and reduced use of non 

renewable or toxic materials. In this paper, a holistic and systems approach to Sustainable 

Manufacturing Planning and Simulation (SMPS) is identified and presented in an effort to 

bridge the gaps in the simulation tools to incorporate aspects pertaining to sustainability

across the product lifecycle. For SMPS framework we consider building upon the 

previously developed Systems Integration of Manufacturing Applications (SIMA) project 

at NIST. Based on a case study and discussions we aim to address the usefulness of 

research towards having a simulation reference framework, and corresponding information 

models to facilitate effective decision making using simulation at different phases of 

product lifecycle. In the course of the paper we also identify and propose potential areas of 

future research in this direction. 

Keywords: sustainable manufacturing, sustainable design, planning, simulations, systems 

approach, framework

November 2010
NISTIR XXXX

1. Introduction

Simulation is all-pervading in the industry and has proven to be a very effective approach 

for problem solving and optimization. It plays a critical role in the design of materials, 

manufacturing processes, and products. Increasingly, simulation is reducing the physical 

testing required to ensure process and product reliability and quality. For products, this 

results in fewer physical prototypes and shorter design cycles. Steady reductions in design 

cycles are crucial to remain competitive in a world where the pace at which new consumer 

products are being developed is ever increasing day to day, especially at a time of 

economic and environmental crisis. Despite these promising predictions for simulation, 

current research illustrates significant shortcomings and challenges in different areas.

Overall, simulation in industry sees continuing growth while facing significant obstacles to 

meet its full potential. According to the Blue Ribbon Panel on Simulation-Based 

Engineering Science [1], simulation has yet to play a central role in important industrial 

and defense design applications. The reason is that large-scale simulation typically does 

not enter into the design cycle until its later phases. Model preparation, typically requires

substantial amount of time and labor and to achieve the appropriate fidelity and accuracy 

months are taken to prepare a model. This may be followed by calibrated tests if the design 

is substantially different from previous designs. 

Sustainable manufacturing [2, 3] has varying definitions depending on the context of 

discussion [See Appendix A]. It broadly implies the development of innovative 

manufacturing sciences and technologies that span the entire lifecycle of products and 

services to minimize negative environmental impacts, conserve energy and natural 

resources, are safe for employees, communities, and consumers, and are economically 

sound. In this paper we limit ourselves in exploring how modeling and simulation can 

augment sustainable manufacturing, by facilitating decision making across difference 

stages of product realization and lifecycle. 

Law and McComas [4] reported that one of the primary application areas for modeling and 

simulation is in the area of manufacturing systems. In the literature, detailed discussions on 

modeling and simulation can be found in numerous books, among them Banks et al. [5] and 

Law and Kelton [6] are well known. The technology of utilizing discrete event simulation 

(DES), in particular, has been popular for numerous purposes other than manufacturing, 

such as patient flows in healthcare, military strategies, logistics, call centers and 

restaurants. Today sustainability considerations are increasingly relevant and require 

greater attention as industry needs to access the resource use increases. However, analysis 

and optimization of multiple objectives is not common in manufacturing simulation. DES 

in combination with Lifecycle Assessment (LCA) data is one possible approach for 

analyzing the cause and effect of various scenarios where time, resources, place, and 

randomness determine the outcome and being sustainable is considered crucial. For such 

analysis only a few research publications exist: Solding and Petku [7] and Solding and 

Thollander [8] both describe how DES can be utilized to lessen the electricity consumption 

for foundries. Ostergren et al. [9] and Johansson et al. [10] describe how DES can be 
NISTIR XXXX

utilized in combination with LCA for quantifying environmental impacts during food 

production. Based on the literature review, Table 1 summarizes the roles and the 

corresponding gaps in simulation for sustainable manufacturing. Table 2 correspondingly 

summarizes the manufacturing information and tool related problems with respect to 

simulation for sustainability. 

Table 1 Simulation for sustainability: roles and gaps

Roles Gaps (resulting from a lack of)

• critical role in the design of products, 

materials and manufacturing processes

• metrics regarding sustainability

• integration between design and simulation 

tools

• facilitates steady reductions in design cycles • support for sustainability in the simulation 

tools 

- reliable results from which effective 

and equitable decisions can be made 

regarding environmental impacts

• enhanced modeling techniques

- ability to understand and predict the 

sustainability aspects through design 

and manufacturing where technologies 

can be applied to transform materials

Table 2 Sustainable manufacturing simulation: information and tool related problems

Information related problems result from a lack of:

• models that support simulation for sustainable product planning

• product-process-resource integration for simulation

• reference model for to identify and retrieve appropriate information 

• interfaces between requirements-design, manufacturing and execution planning

• reasoning and decision support

Simulation tool related problems result from a lack of:

• integrated manufacturing planning and simulation

• characterization of manufacturing process resources

• integration of sustainability indications between tools

• extension beyond discrete event simulation

• sustainability metrics and indicators

With reference to DES, one of the key challenges is to define concepts and tools to 

incorporate attributes and metrics for various sustainability aspects in manufacturing 

enterprises. Case studies that incorporate sustainable manufacturing parameters for 

simulation need to be identified, including the evaluation metrics that incorporate the 

scoring of sustainability factors. Research efforts are also required to determine additional 
NISTIR XXXX

functional capabilities, validated sustainability reference data, and new information models 

to cater to the new challenges. But in order to realize SM, industries need an reference 

integrated framework, quantifiable measurement techniques and appropriate performance 

metrics to objectively evaluate all aspects of sustainable manufacturing (including energy 

efficiency, emissions, key process technologies, modeling and simulation, and 

standards) necessary to meet expected regulatory requirements. Research and development 

of new measurement methodologies and assessment technologies of product and process 

performance are also crucial to ensure sustainability in manufacturing. 

The subsequent paper is organized as follows: in section 2 we present an initial simplified 

framework for sustainable manufacturing planning and simulation. Section 3 then discusses

the scope of research primarily in two directions: a) integrated manufacturing planning and 

simulation and b) information models that support such integrated manufacturing planning 

and simulation for sustainability. In section 4, we discuss a case study to show how 

manufacturing simulation can help decision support across various tiers promoting 

sustainability. Finally in Section 5 we present our conclusions and the scope for future 

work.

2. Sustainable Manufacturing Planning Methodology

Our initial objective is to develop a holistic and systems approach to Sustainable 

Manufacturing Planning and Simulation (SMPS). Problem solving using a holistic, systems 

or concurrent engineering is not new and has been widely reported in the literature. What is

new is the focus on sustainability. Figure 1 presents a simplified illustration of the proposed 

approach to SMPS. We draw inspirations from the earlier Systems Integration of 

Manufacturing Applications (SIMA) project at NIST. To set the stage, we represent the 

different phases of product realization namely design product, engineer manufacture of the 

product, engineer production system and produce products as planning tiers. Visualizing 

then as tiers further helps us understand how simulation results can be useful within and 

across these tiers. From Figure 1, the first tier represents the design of the product, the 

second is engineering the manufacturability of the product, and the third is the engineering 

the production system or manufacturing planning. The last phase is the actual production 

execution. Figure 2 presents a detailed view of the tiers with corresponding high level 

product realization activities. The figure additionally shows sustainability related 

information flow across the tiers. Figure 3 shows the corresponding SIMA manufacturing 

activity model reproduced from [11]. For details on the corresponding activity models 

within each tier, please refer [11].The following sub-sections briefly discuss the individual 

tiers.
NISTIR XXXX

Tier 1

Tier 2

Tier 3

Tier 4

Design Product

Engineer Manufacture of Product

Produce Products

Engineer Production System

PLC/LCA Data

Figure 1. Typical stages in sustainable manufacturing planning and simulation

2.1 Design Product

When designing products, designers must cater for a multitude of technical, economic, 

social, environmental, and political requirements among others. This phase helps identify 

and conceptualize a marketable product, and create the complete description of it. With 

today’s growing concerns for green products and green manufacturing, generating the right 

product and manufacturing specifications poses significant challenge. The activities in this 

phase as described in [11] include:

A11: Plan Products

A12: Generate Product Specifications

A13: Perform Preliminary Design

A131: Develop Functional Decompositions

A132: Evaluate and Select Decomposition

A133: Develop Preliminary Configurations

A134: Consolidate Configurations

A135: Evaluate Alternative Designs

A136: Select Design

A14: Produce Detailed Designs

A141: Design System/Component

A142: Analyze System/Component

A143: Evaluate System/Component Design

A144: Optimize Designs

A145: Produce Assembly Drawings

A146: Finalize System/Component Design

In order to respond to the increased demand for sustainability in product realization one can

embrace total quality management (TQM) [13] as a means of understanding certain rules of 

thumb for good design analysis and planning in sustainable product and process

development. For example, while generating product or system specifications one can 

introduce design requirements, constraints and the notion of value-added design constraints
NISTIR XXXX

i.e., a constraint that favors improved resource utilization and reducing environmental 

impact. Value added constraints in general imply the addition of value to a product, process 

or service, with or without accompanying cost and price increases. Viewing them as value 

added design constraints provides the advantage of assigning weights for tradeoffs favoring 

specific constraints, sustainability constraints in this case.

Figure 2 Integrated manufacturing planning and simulation
NISTIR XXXX

Figure 3 SIMA Manufacturing Activity Model: Realize products [11]

2.2 Engineer Manufacture of Product

This phase involves engineering the manufacturability of the product, i.e., defining the 

process of making the product, including the elementary stock materials and components to 

be acquired, the equipment, tooling and skills to be used and the details of that usage. 

Details include the exact sequence of setups and operations to be performed, and the 

complete instructions for each operation, whether by human or automated resources. By 

extension, the process of making the product includes measurement and inspection 

activities performed during production for process control and quality assurance. The 

activities in this phase as described in [11] include:

A21: Determine Manufacturing Methods

A211: Derive manufacturing features

A212: Select stock materials

A213: Select processes

A214: Select Major Resources

A215: Develop Preliminary Cost Estimates

A22: Determine Manufacturing Sequences

A221: Specify Operations
NISTIR XXXX

A222: Sequence operations

A223: Specify Part Routing

A224: Optimize & Validate Plan

A23: Engineer New Processes

A24: Develop Tooling Packages

A241: Select Tooling and Assemblies

A242: Design Tooling Assemblies

A243: Design Special Tooling

A244: Estimate Tooling Cost

A25: Develop Equipment Instructions

A251: Derive in-process work piece configurations

A252: Specify tooling requirements

A253: Generate Operator instructions

A254: Generate Machine Programs

A255: Validate equipment instructions

A26: Finalize Manufacturing Data Package

A261: Develop Final Cost Estimates

A262: Release Resource Package

A263: Release Scheduling Package

A264: Update Plan library

2.3 Engineer Production System

This phase involves the design of new or modified production facilities for the manufacture 

of a particular collection of Parts. A facility may be a plant, a shop, a line, a manufacturing

cell, or a group of manufacturing cells. This activity encompasses both design-from-thewalls of such a facility and reengineering of all or part of such a facility to improve the 

production of certain products. It includes identification of the parts, products and 

processes for which the production system is to be tailored, identification of the equipment 

to be installed or replaced, (re)design of the floor layout, and development of an

implementation plan for the (re)designed production system. The activities in this phase as 

described in [11] include:

A31: Define Production Engineering Problem

A311: Identify Project Type

A312: Identify Part Mix

A313: Identify Related Parts

A314: Identify Critical Dates

A315: Identify Target Costs

A316: Identify Manufacturing Constraints

A32: Specify Production & Support Processes

A321: Specify Process Requirements

A322: Specify Process Flows

A323: Specify Materials Flow Requirements

A324: Specify Support Systems Requirements

A33: Design Production System
NISTIR XXXX

A331: Specify Production Equipment

A332: Specify Control & Automation Systems

A333: Develop Facility Layout

A334: Design Information System

A335: Integrate System Designs

A34: Model and Evaluate System

A35: Define Implementation Plan

2.4 Production Execution

This phase involves providing and maintaining the production facilities to produce the 

Parts according to the specifications in the process plans. This involves defining the 

production schedules and controlling the flow of materials into and out of the production 

facility, scheduling, controlling and executing the production processes themselves, 

providing and maintaining the production equipment and the human resources involved, 

developing and tracking the tooling and materials, etc. The activities in this phase as 

described in [11] include:

A41: Develop Production Plan

A411: Create Master Schedule

A412: Define Capacity Requirements

A413: Create Production Orders

A414: Monitor Production Orders

A42: Define Production Jobs

A421: Define Jobs

A422: Generate Tool and Stock orders

A423: Release Jobs

A424: Monitor Job Completion

A43: Manage Tooling and Materials

A44: Schedule Jobs

A441: Generate Job Schedule

A442: Generate Operations Schedules

A443: Generate Delivery Schedules

A444: Track Jobs

A45: Control Production

A451: Direct Personnel and Machines

A452: Control and Monitor Jobs

A453: Coordinate Equipment Groups

A454: Control Equipment

A46: Manage Production Facilities

A47: Provide Production Facilities

During product realization, besides considering planning for the disposal (Reuse, 

Remanufacture and Recycle) of the part, it is equally important to consider the implications 

of sustainable manufacturing. Modeling and simulation of manufacturing processes can 

provide excellent opportunities in this regard. A well defined reference architecture can 
NISTIR XXXX

support the ability to model and simulate product realization and end of life forecasts based 

upon historical trends of similar products, marketing programs and what-if scenarios. Such 

an architecture with reference to simulation can create opportunities for engineers to 

distribute cost, reliability, and environmental impacts of product manufacturing, 

component reuse, remanufacture, and recycling in such a way that the ends result is higher 

customer satisfaction than designing one product for all customer groups. More 

importantly, the analysis and simulation data accumulated across the various phases will 

support for equitable decision making.

3. Envisioned reference architecture characteristics

The following are the envisioned system level model characteristics based on the on the 

SMPS framework: 

• Tiered approach: The four tiers, separated by naturally-defined boundaries, help to 

clarify specific engineering activities while also enabling local optimization. 

• Concurrent planning: the frame work supports concurrent planning and looping 

wherein data exchange takes places between the different tiers

• Sustainability: Key Performance Indicators are quantifiable measurements that are 

incorporated into a model supporting their consideration throughout the PLC and 

collectively reflecting sustainability conformance.

• Systems approach: to optimization: the framework, by its structure, provides a 

systems approach to problem solving, taking a holistic approach where all tiers 

work in concert

• Boundaries: the framework, between tiers and within tiers, allows for boundaries 

to be set for measurement, analysis, and improvement 

• Integrated life cycle assessment: LCA data can be dynamically linked for real 

time information management and assessment 

• Model congruency: SMPS congruence is the state achieved by the tiers interacting 

for a state of agreement resulting in either sustainable products, processes or 

procedures.

• Standards Integration: supports linking of relevant standards to framework to 

check on regulatory compliance 

4. Research Directions

We discuss the scope of research in two directions: 1) Integrated manufacturing planning 

and simulation and 2) information models that support such integrated manufacturing 

planning and simulation for sustainability.

4.1. Integrated Manufacturing Planning and Simulation

By developing a reference SMPS methodology and corresponding implementation one can 

expect the industry to simultaneously deal with trade-off factors such as manufacturability, 

cost, and environmental impact among others. With reference to Phase 3 in Figure 3, 
NISTIR XXXX

traditional production planning and control involves: forecasting, i.e. estimating the 

production demands using a horizon of a few months to a few years for long range 

planning, and production planning which is matching needed production to available 

resources. Today, with energy cost representing a considerable amount of the total 

production cost, energy savings has become a major concern for manufacturing companies 

[16-18]. For example, on a factory floor with large production lines many machines may be 

operating at the same time, and hence the peak energy consumptions will be generally high, 

incurring high cost. To minimize setup times and support automation, companies rely 

heavily on computer aided process planning (CAPP) [19-21] along with other 

manufacturing information systems to meet production queues. While cost, quality and lead 

time are the typical performance indices for manufacturability assessment in CAPP, 

introducing additional sustainability indices such as energy efficiency can result in

desirable alternative plans [22]. 

Through integrated production planning and simulation one can expect to expedite both the 

top-down and bottom-up approaches to facilitate better decision-making. Figure 5 presents 

an exploded view of different manufacturing processes, sub-processes, and further going 

into the details at the individual equipments. The idea behind Figure 5 is to show how 

sustainable indicators and corresponding values can be aggregated from the machine level 

to a factory flow, or further aggregated to the plant level. In the engineering production 

system phase (Phase 3), one must be able to answer questions like which production plan 

best utilizes the facility's capabilities? which plan is most energy efficient? which is an 

optimal scheduling plan? or which are the energy efficient machines? Note that the 

proposed approach allows for answering questions at both a macro and micro level (e.g. 

manufacturing process level versus machine level). The immediate challenge, in terms of 

research, is how companies do this implementation and integration with other systems and 

what other technical and business areas can benefit from understanding energy usage. In 

manufacturing scheduling, it is often known well in advance what has to be produced. This 

can often lead to a near optimal schedule for all jobs, ahead of time. But these methods at 

the present time are not well enough developed to cater for the new trends in economic and 

environmental crisis. 

Today, one of the important applications of simulation in manufacturing is discrete event 

analysis. Simulation is used to predict how complex systems will behave by incorporating 

functionality that incorporates random events, changing operating conditions and activities 

where many interactions are involved. Simulation typically involves developing a model 

that includes discrete stations and events that occur with some probability distribution. 

Simulation results are used to evaluate the modeled system based on machine utilization, 

lead time, down time, make-span, etc. Simulation is an effective tool when considering the 

effect of a change, comparing decision options, or refining a design. A well implemented 

framework would facilitate additional analysis and synthesis based on the overall 

improvement and asset management, in terms of environmentally friendly indices namely: 

manufacturing energy, cost vs. efficiency, time, material reduction, recyclability and 

reusability. As an example a discrete event simulation in Phase 3 could be useful to 

generate product specifications at Phase 1. The corresponding case study will be 

subsequently discussed in the paper.
NISTIR XXXX

To support sustainable manufacturing from a regulatory and compliance point of view, 

standards and inspection procedures must be studied and incorporated in the new 

simulation models for raw materials, manufactured part, production process parameters, 

including verification and calibration. ISO has established related standards addressing 

Environmental Management and specifically life cycle assessment [24-26]. Figure 6 

presents an initial mapping of relevant data required at various levels of simulation. For 

example in Phase 1, it involves the mapping of the engineering data, tools and appropriate 

standards for manufacturing planning. 

Figure 5 Manufacturing simulation (Phase 3)
NISTIR XXXX

Figure 6 Initial mapping of relevant data required at various levels of product realization

4.2. Models that support simulation for sustainable manufacturing

Information management problems affect many aspects of manufacturing operations, but 

they are in particular a hindrance to the creation and reuse of manufacturing simulations. 

To be able to traverse across the different product creation phases information models that 

can support integrated planning and simulation (See Figure 6) are needed. The SMPS 

framework provides the opportunities to introduce sustainability factors to minimize 

Product Realization Methods/ Tools Engineering Data

Tier 1 Tier 2 Tier 

3

Design Space

Concurrent

Concurrent

Design Product Engineer Manufacturing Engineer Production System

TQM, QFD, AHP, Statistical 

and Quality tools

Tier 

4

ExecutionProduction 

Optimized scheduling 

and planning 

Energy data

Concurrent

Asset Management

AP240

Machine data

Standards

CAD-geometry, dimensions and 

tolerances, design features, textual 

part/assembly specifications

Process parameters

Machine capability

Machinability

Tooling

Standard time library

Manufacturability 

Analysis

Enterprise level tools

Enterprise Resource 

Planning

Procurement

Suppliers

Service 

Structural/Thermal properties

Customer requirements –

System/Product 

specifications

System design 

decomposition and 

supporting process models

Design documentation (CADgeometry, dimensions and 

tolerances, design features, 

textual part/assembly 

specifications)

CAD analysis – interference 

& kinematics, NC (AP238), 

Design for Assembly, design 

for energy efficiency, design 

for disassembly, design for 

recycle, and design for 

refurbishment

Design tolerances

Material, Geometry, 

performance characteristics

Engineering Simulation

Design Analysis

Performance, FEM

System level decision support 

tools

Bill of Materials (Engineering)

Cost accounting

Manufacturing Simulation

Engineering CAD/LCM

Materials

Site (non-product specific) EHS

Lighting

HVAC – air quality

Maintenance

Safety

Material handling systems

Data on Process and 

Machines

AP233

AP203

AP224

AP214

STEP NC (AP 238)

ASME B5/TC56 

CMSD

ISO 14040:2006

ISO 14044:2006

ISO 14064

ISO 14065

LCA 

ISO 14040:

ISO 14040:2006

ASTM Material 

Standards

WEEE and RoHS

ISO 50001

A11: Plan Products

A12: Generate Product 

Specifications

A13: Perform Preliminary Design

A14: Produce Detailed Designs

A21: Determine Manufacturing 

Methods

A22: Determine Manufacturing 

Sequences

A23: Engineer New Processes

A24: Develop Tooling Packages

A25: Develop Equipment 

Instructions

A26: Finalize Manufacturing 

Data Package

A31: Define Production 

Engineering Problem

A32: Specify Production & 

Support Processes

A33: Design Production 

System

A34: Model and Evaluate 

System

A35: Define Implementation 

Plan

A41: Develop Production 

Plan

A42: Define Production 

Jobs

A43: Manage Tooling 

and Materials

A44: Schedule Jobs

A45: Control Production

A46: Manage Production 

Facilities

A47: Provide Production 

Facilities
NISTIR XXXX

environmental impacts at various stages of the product lifecycle. To support the SMPS 

framework we envision a corresponding well connected information model to support 

efficient management of data from characterization to simulation for sustainable 

manufacturing and planning. We call the information model as a Reference Information 

Model. Figure 7 illustrates the concept of a reference information model. The idea behind 

such an information model is to provide not only the adequate points of reference for 

information across the different phases but also to provide a mapping and interface between 

them. 

 

Figure 7 An information model that supports product-process-resource integration for Simulation

We envision that the new reference information model will facilitate the product-processresource integration and facilitate tradeoffs and multi-criteria decision support for 

sustainable manufacturing. Figure 8 presents a simple landscape of relevant data and 

standards for enabling information exchange. As shown in Figure 8, the idea is to explore 

how the reference information model will provide the appropriate interfaces and links 

between the various data models and standards across the different planning stages. Table 

5 in the Appendix presents some relevant manufacturing data related standards.

The idea behind the connecting lines in figure 8 is to show that different information 

models can be used at different stages of product realization yet a coherent mapping or 

reference information is vital for successful information exchange across simulation 

applications. The Y. axis shows the product, process and resource information models 

mapped across the different product realization stages on the X. axis.

Tier 1

Tier 2

Tier 3

Tier 4

Design Product

Engineer Manufacture of Product

Produce Products

Engineer Production System

PLC/LCA Data

Reference

Information 

Model

Product

Process
NISTIR XXXX

Figure 8 Landscape for enabling information exchange

There are number of useful implications one could envisage through sustainable 

manufacturing planning and simulation. Figure 9 presents the opportunities for 

manufacturing simulation, as means for analysis and optimization of machines and 

manufacturing processes to better implement sustainability strategies in an automotive 

manufacturing facility [27]. For example, energy efficiency trends of machines and 

manufacturing processes can be mapped and correlated.

Figure 9 Implications of simulation models

Patterns of Performance 

Energy Efficiency trends

Machine Monitoring

Preventive Maintenance 

Asset Management 

Efficient planning 

Green 

Manufacturing 

Benchmarking 

Process 

Optimization 

Decision Support 

Die Making Casting

Body Shop Stamping

Power Train Assembly

Painting Welding

Machines Molding

HVAC Lighting

Simulation models

Energy Templates

Product

Resource

Process

Tier 1 Tier 2 Tier 3 Tier 4

CMSD

AP238 STEP NC

AP219

AP223

AP233

AP203

AP224

AP214

CPM

OAM

AP214

CPM

CMSD AP238

AP238

ISO 14040

LCA

ISO 14040

ISO 50001

ISO 14064

ISO 14065

AP240

ASTM 

Others
NISTIR XXXX

5. Case Discussion

6.1. Industrial Scenario

To set the stage, consider simulating the energy utilization of a typical automotive 

manufacturing facility. Figure 10 presents the energy composite in selected automotive 

manufacturing operations [28]. The boundaries are marked in red to signal the energy 

losses in the energy infrastructure of the auto industry. Marked in yellow are the energy 

intensive manufacturing processes. 

Figure 10 Energy composite for automotive manufacturing operations

Figure 11 below presents the corresponding breakdown of energy [42]. This is a rough 

estimate based on the on-site manufacturing processes while other subsystems and 

component suppliers are not accounted. Die-making and casting have been excluded 

because they are assumed to be captive operations (on-site) or out sourced and not included 

in the energy composite. There are multiple tests and inspection points that occur at various 

levels in between these operations. Further abatement occurs within all operations at some 

level. 

Dies assembled from casting, 

inserts, binders and other parts

Body Structure including 

closures are produced (body in 

white)

Finished parts are pressed out 

of coiled sheet metal

Die-Making

Processes: assembly, 

machining, turning

Transmission

Stamping

Casting

Power Train 

Painting General Assembly

Body Shop

Engine

Processes: feed, blank, draw, 

form, re-strike, trim, wash

Processes: parts assembly, two 

stag-stage spot welds (initial 

and final structure), robotintensive assembly

Casting of Metal Parts

Processes: pattern making, sand 

handling/processing, core 

manufacturing, mold lines, metal 

melting, poring, machining

Finished body structure is painted

Processes: pre-treatment, seal, 

prime, top coat, repair; cure and 

drying

Assembly produces a retail 

ready Vehicle

Processes: trim, fit and finish, 

final assembly; power train and 

chassis assembly; exterior and 

interior components/ 

subsystems, electrical systems 

(HVAC)

Transmissions are assembled 

(clutch, gear sets, case 

controls, converters, shift)

Processes: machining, heat 

treatment and assembly)

Engines are produced by 

castings/ forgings (pistons, 

heads, block)

Processes: feed, blank, 

draw, form, re-strike, trim, 

wash

Energy Composite 

for Automotive 

Manufacturing 

Operations

Sub-system 

Suppliers

Component 

Suppliers
NISTIR XXXX

Figure 11 In-plant process energy distribution

From Figure 11, the paint shop consumes more energy than any other operation in the 

automotive industry. Within the paint shop, the conditioning of the air in the spray booths 

is the most energy-intensive process. Precise control of temperature and relative humidity 

in the paint booths are critical because it affects paint viscosity, or thickness which, in turn, 

affects the quality of the paint finish. Earlier, Bhattacharya from Honda [29], proposed a 

new control system that sets a tolerance window, provided by the paint supplier, rather than 

a fixed target. The new control system (knowledge based system), finds the temperature 

and relative humidity within that window that will require the least amount of heating, 

cooling or mist for humidity. It achieves energy savings by always directing the air to the 

least energy-intensive point within that tolerance window specified for production 

conditions. Figure 12 showing a snap shot of a paint shop simulation [30].

Figure 12 Paint shop simulation

The simulation tools used by industry must provide reliable results to cater for effective 

decisions towards sustainability and create opportunities to distribute the cost, reliability, 

and environmental impacts of energy use, emissions, waste, component reuse, 

remanufacture, and recycling in such a way that the end result is higher customer 

satisfaction. Since real-world phenomena are not deterministic, statistical methods that can 

quantify uncertainty will be needed. Considering design optimization, the constraints on the 

optimization of a product design relate to manufacturability, robustness, and a variety of 

other factors in addition to environmental sustainability. To be effective for engineering 

design, optimization methods must be closely coupled with simulation techniques. 

In-Plant Energy Distribution*

12%

10%

19%

13%

36%

10%

Stamping

Body Shop

Transmission

Engine

Paint

General Assembly
NISTIR XXXX

6.2. Case Study

To demonstrate a manufacturing planning scenario with an emphasis on sustainability, a 

simulation model has been built based on the work flow schematic as shown in Figure 13.

The work flow schematic was adopted and modified from [31]. This scenario presents a 

paint shop with six painting steps to set the scene for requirements in an automotive paint 

shop. In terms of the simulation integration architecture the case study is also intends to 

demonstrate how simulation in one phase can help with decisions in another. Here we show 

how DES in phase 3 can be utilized to generate design requirements or specifications for

manufacturing systems in the early design stage (Phase 1) or at the manufacturing planning 

stage [32]. Requirement specification denotes the description of the behavior of the system 

to be developed. The case study develops a prototype paint shop model which incorporates 

the use of LCA data in combination with DES and supports alternate decisions on energy 

use, choice of machines, and environmental bottleneck detection.

Figure 13 Example paint shop processes (adopted from Leng, and Yingchao 2005)

In Figure 3, the spray booth shows six steps (Body Preparation, Tag Rag, Base Coat, Clear 

Coat, Oven and Polishing) incorporated in the simulation model. The model was created 

based on some earlier work [33-35] as seen in Figure 14. 

Figure 14 The 3D Simulation model of the paint shop

ED Line Sealing

Line

Body

Prep Tag Rag BaseCoat

Clear

Coat Oven Polishing

Spray Booth

Body 

Preparation

Tag Rag

Base Coat

Clear Coat

Oven

Polish
NISTIR XXXX

6.2.1. Input data to the Simulation model

Each production step has a setting for the resource to be down, idle, or busy. Down means 

disconnected from the power provider, i.e., no electricity is used. Idle means that the 

resource is on standby, i.e., some electricity is used. Busy means doing the work cycle as 

such, i.e., electricity is used. Table 3 shows the input data specifying the energy use from 

the default settings in the paint shop model, as well as other data needed for setting 

parameters at the resources of the model such as cycle times, MTTF (Mean Time To 

Failure), MTTR (Mean Time To Repair), etc. The data herein presented are for the 

purposes of demonstration of our scenario and do not necessarily imply an actual paint 

shop data.

Table 3 Default settings for resources in the paint shop1

6.2.2. Case study Problem and Goal description

When designing a new manufacturing system certain production goals and economic 

measures need to be fulfilled. For example, the production capacity is specified to be at 

least a certain level, the cost of the manufacturing system needs to be within the budget, 

and the environmental impact is expected to be below a certain guideline value. In this case 

study, the goals of the sustainable manufacturing system are assumed as follows:

• to reach a production capacity of at least 50000 cars per year, 

• there will be no more than metric 500 tons of CO2 emission per year, and 

• no new investment in equipment for the existing factory. 

 1 The data herein presented are for the purposes of demonstration of our scenario and do not necessarily imply 

an actual paint shop data.

Resource Body 

Prep

Tag 

Rag

Base 

Coat

Clear 

Coat

Oven Polish

Processing Times

Cycle time (Normal distribution) n n n n n n

mean (Seconds) 120 130 140 130 240 125

Standard deviation 2 4 1 3 2 1

Energy (kW)

Down 1 1 1 1 1 1

Idle 5 4 50 50 1800 50

Busy 20 18 500 500 1800 200

Failures

MTTF (Uniform distribution) u u u u u u

Min (Seconds) 1000 1200 1000 900 1000 900

Max (Seconds) 5000 5200 11000 10900 15000 4900

MTTR (Normal distribution) n n n n n n

Mean (Seconds) 240 260 600 590 1000 240

Standard deviation 2 3 2 3 2 3
NISTIR XXXX

The current factory is represented by the input data in Table 3, as well as the output data 

from Trial run 1 in Table 4.

6.2.3. Simulation Experiments

In this case study, the number of input variables are simplified to only a few choices as 

shown in Table 4. In a real world application however, a variety of designed operating 

parameters are considered based on the required system throughput. In the experiments, the 

number of input data parameters can be varied more extensively and practically anything 

feasible for a real world change could be varied if necessary to bring forth sound 

requirements specification for the considered manufacturing system.

From the initial settings (Trial run 1 in Table 4), the oven had been identified to be the 

bottleneck in terms of utilization as well as energy consumption. Some trial runs were 

performed based on different parameter settings. The settings included the energy source, 

oven cycle time, and energy consumption as well as a single or two ovens in parallel. The 

energy sources in the parameter setting included wind, water, or a mix of energy sources 

depending on the country where the factory is located. 

The primary purpose of this simulation is to provide requirements specification support 

data, and hence also provide support towards designing a sustainable paint shop. In line 

with this effort, some examples of measures are provided in terms of energy, throughput 

and CO2 based on the simulation runs. In Table 4, from the twelve trial runs one can 

identify the bottlenecks, energy consumption and CO2 emissions due to energy type used in 

the paint shop. The results presented in Table 6 are calculated by running the simulation 

model. The model incorporates lifecycle assessment data from an European Union LCA 

database as described in [48]. 

Following are examples of conclusions arrived from looking at Table 4:

• The initial setting gives the lowest energy consumption per produced car, as well as 

trial 3 and 5

• The Oven is the throughput bottleneck initially (trial 1)

• Decreasing cycle time for the oven with 60 seconds does increase output of cars; 

however Oven is still the bottleneck.

• By adding another parallel oven, the Base Coat will be the bottleneck.

• Wind powered paint shop gives the lowest CO2 emissions (from energy) per car 

produced.

Note that the above conclusions are not the only items to consider, however they provide

additional information and support a better decision space than a normal non-discrete event 

simulation analysis. Such information related to sustainability can be very useful for 

decision making across the different phases.
NISTIR XXXX

Table 4 Simulation runs and their input data vs. output data changes 

The left side of Table 4 shows the input data which is varied for the twelve runs. Column 

one on “Input parameter changed” can be set to either 1 for normal conditions or 2 for 180 

sec cycle time and 2400kWh. Column two shows which type of energy is used, 1 for an 

average country energy (i.e. mixed sources), 2 for wind power, 3 for water power. Column 

three shows the number of parallel ovens used in the model.

6.2.4. Discussions

The study results and output data are shown in Table 4. Constraints from the stated goals of 

the study have to be considered while analyzing the study results. To satisfy the goal to 

produce at least 50000 cars per year, Table 4 output data shows that trial runs 7-12 are 

feasible, however an investment in another oven will need to be added to the process. The 

next goal is to decrease the CO2 emissions to less than 500 metric tons per year. To reach 

this goal, standard fossil fuel energy cannot be used. Alternatively wind or water powered 

energy will need to be used. Table 4 shows trial runs 9-12 as feasible solutions with the 

use of “green” energy alternatives. In order to minimize the investment goal, the cycle time 

and energy consumption of the oven does not need to be changed. This means trial run 9 or 

11 will be the preferred choice, depending on the energy cost from the power provider. It 

may be worthwhile to notice that the wind power could be a better choice than the water 

powered energy alternative in terms of CO2 emissions. 

The study demonstrated that using the environmental measures from a LCA database and

traditional input data with cycle time, disturbance data, etc. for discrete event simulation,

new output measures from the model can be used to identify and analyze sustainable 

manufacturing system design and measures such as energy consumption at the aggregated 

shop floor level, resource level, and production throughput. Such analysis can also be 

useful in identifying the bottlenecks on any environmental measure; in this case the energy 

consumption and carbon footprint in relation to energy source used.
NISTIR XXXX

Based on the described case study it would be desirable to be able to represent 

sustainability related data in a neutral format. One possible solution to store and use 

sustainability and other related data for discrete event simulation is to utilize the Core 

Manufacturing Simulation Data Information (CMSD) [36] specification, which allows us to 

maintain neutral and accessible measures. 

6. Conclusions and Future work

In this paper, a holistic approach to Sustainable Manufacturing Planning and Simulation

was identified necessary and presented in an effort to bridge the gaps in the simulation 

tools to incorporate aspects pertaining to sustainability. To augment the approach and 

methodology we present a case where simulation can be utilized to generate design 

requirements or specifications for manufacturing systems in the early requirements or 

design planning stage or at the manufacturing planning stage. 

The proposed SMPS approach can help industry achieve benefits such as:

• minimize energy use and improve productivity through improved engineering of 

product and process through planning and simulation

• promote environmentally responsible and economically competitive businesses

• implement a comprehensive monitoring and preventive maintenance program that 

takes into consideration energy usage through simulation 

• deploying energy indicators into any plans that include asset acquisition, allocation, 

or replacement

• awareness of environmental impact when taking decisions / holistic system 

viewpoint for increased cause vs. effect understanding

Potential future work will include all or any of the following directions: a good simulation 

reference architecture, investigate models that support various stages of planning , dynamic 

scheduling with integrated manufacturing resources, sustainable material flow analysis, 

characterization of manufacturing cells for sustainable engineering planning and 

simulation, mapping of relevant data required at various levels of simulation, develop a

core manufacturing ontology for simulation purposes, integration and interoperability of 

information at various levels, case studies, science based engineering simulation for 

characterizing different manufacturing processes in terms of the energy (source, use and 

efficiency, key controlling parameters), identify the scope and boundaries, e.g., enterprise 

level, shop floor level, etc, integrated or extended simulation with design optimization 

processes to simultaneously deal with factors such as manufacturability, cost, and 

environmental impact, new models and representations that support simulation for 

sustainable manufacturing planning and the integration of simulation applications with 

other relevant engineering/ manufacturing applications.

7. DISCLAIMER

No approval or endorsement of any commercial product by the National Institute of 

Standards and Technology is intended or implied.
NISTIR XXXX

8. References

[1] Report of the National Science Foundation Blue Ribbon Panel on Simulation-Based Engineering 

Science, Revolutionizing Engineering Science through Simulation, May 2006.

[2] http://www.ita.doc.gov/competitiveness/sustainablemanufacturing/how_doc_defines_SM.asp

[3] http://www.nacfam.org/PolicyInitiatives/SustainableManufacturing/tabid/64/Default.aspx/.

[4] Law, A.M., McComas, M.G. 1999. Simulation of Manufacturing Systems. In Proceedings of the 

1999 Winter Simulation Conference, ed. P. A. Farrington, H. B. Nembhard, D. T. Sturrock, and G. 

W. Evans, Pheonix, AZ. pp. 56–59.

[5] Banks, J., Carson, J. S., Nelson, B. L., Nicol, D. M. 2000. Discrete-event system simulation. 3rd ed. 

Upper Saddle River, New Jersey: Prentice-Hall, Inc.

[6] Law, A. M., Kelton, W. D. 2000. Simulation modeling & analysis. 3rd ed. New York: McGrawHill, Inc.

[7] Solding, P., Petku, D. 2005. Applying Energy Aspects on Simulation of Energy-Intensive 

Production Systems, in Proceedings of the 2005 Winter Simulation Conference, ed. Kuhl, M. E., 

Steiger, N.M., Armstrong, F. B., Joines, J. A., Orlando, FL, USA.

[8] Solding, P., and Thollander, P. 2006. Increased Energy Efficiency in a Swedish Iron Foundry 

Through Use of Discrete Event Simulation, in Proceedings of the 2006 Winter Simulation 

Conference, ed. Perrone, L. F., Wieland, F. P., Liu, J., Lawson, B. G., Nicol, D. M., Fujimoto, R. 

M. Monterey, CA, USA.

[9] Ostergren, K., Berlin, J., Johansson, B., Stahre, J., Tillman, A-M., 2007. A tool for productive and 

environmentally efficient food production management. European conference of Chemical 

Engineering, Copenhagen 16-20 September 2007.

[10] Johansson, B., Stahre, J., Berlin, J., Östergren, K., Sundstrom, B., Tillman, A.M., 2008, Discrete 

Event Simulation with Lifecycle Assessment data at a Juice Manufacturing System, in proceedings 

of the 5th FOODSIM.

[11] Barkmeyer J. E., (Ed)SIMA Reference Architecture. Part 1: Activity Models, NISTIR 5939.

[12] Masanet, E., Horvath, A., California Air Resources Board's Chair's Air Pollution Seminar, 

http://industrial-energy.lbl.gov/node/402/.

[13] Oakland J. S. Total Quality Management, Butterworth-Heinemann Publishers, 2003

[14] Saaty T.L. Decision making with the analytic hierarchy process, International Journal of Services 

Sciences, Volume 1, Number 1 , pp 83 – 98.

[15] Akao, Y. 2004. Quality function deployment: integrating customer requirements into product 

design, Productivity Press.

[16] Energy Efficiency, Water and Waste-Reduction Guidebook for Manufacturers: Proven Ways to 

Reduce Your Costs and Improve Operations, http://www.energysavings.gov/.

[17] Energy Efficiency, Water and Waste-Reduction Guidebook for Manufacturers: Proven Ways to 

Reduce Your Costs and Improve Operations, 

http://www.nam.org/s_nam/bin.asp?TrackID=&SID=1&DID=233434&CID=89&VID=2/.

[18] Partners in Practice, Guidelines for Energy Management, 

http://www.energystar.gov/index.cfm?fuseaction=partners_in_practice.showHome/.

[19] Dong, J.J., Parsaei, H.R., Leep, H.R., Manufacturing process planning in a concurrent design and 

manufacturing environment, Computers & Industrial Engineering Volume 30 (1996), pp 83-93.

[20] Li, L., Fuh, J. Y.H., Zhang, Y. F. and Nee, A. Y.C. (2005) Application of genetic algorithm to 

computer-aided process planning in distributed manufacturing environments. Robot. Comput.-

Integ. Mfg, 21, pp. 568-578.
NISTIR XXXX

[21] Mahesh, M., Ong, S.K., Nee, A.Y.C., Fuh, J.Y.H., Zhang, Y.F., Towards A Generic Distributed and 

Collaborative Digital Manufacturing, Robotics and Computer Integrated Manufacturing, Vol. 23, 

No 3 (2007), pp 267-275. 

[22] Mahesh M, Lyons, K., Sudarsan R, Feng S.C, Sriram R.D, Ameta, G., “Introducing Sustainability 

indices early into Manufacturing Process Planning and Scheduling”, Proceedings of the 14th 

International Conference on Manufacturing Science and Engineering, ASME, October 07-10, 2008.

[23] Ameta G, Mahesh M, Sudarsan R, Feng S.C, Sriram R.D, Lyons K., 'Carbon weight analysis for 

machining operation and allocation for redesign' , International Journal of Sustainable Engineering, 

Volume , Issue 4 December 2009 , pages 241 - 251.

[24] ISO. 1997. Environmental Management – Life Cycle Assessment – Principles and Framework. ISO 

14040:1997. European Committee for Standardization CEN, Brussels, Belgium.

[25] ISO. 1998. Environmental Management – Life Cycle Assessment – Goal and Scope Definition and 

Inventory Analysis. ISO 14041:1998. European Committee for Standardization CEN, Brussels, 

Belgium.

[26] ISO. 2000. Environmental Management – Life Cycle assessment – Life Cycle Interpretation. ISO 

14043:2000. European Committee for Standardization CEN, Brussels, Belgium.

[27] Mahesh M, Lyons K, Ameta G, “Introducing Sustainability indices early into Manufacturing 

Process Planning and Scheduling”, Proceedings of the 14th International Conference on 

Manufacturing Science and Engineering, ASME, October 07-10, 2008.

[28] Technology Roadmap for Energy Reduction in Automotive Manufacturing, Workshop Report, U.S. 

Council for Automotive Research, 2008.

[29] http://www.autonews.com/article/20080811/ANA03/808110336/.

[30] Johansson, B., Fasth, Å. Stahre, J., Heilala, J., Leong, S., Lee, Y. T., Riddick, F. 2009. Enabling 

Flexible Manufacturing Systems by Using Level of Automation as Design Parameter. In 

Proceedings of the 2009 Winter Simulation Conference, eds. M. D. Rossetti, R. R. Hill, B. 

Johansson, A. Dunkin and R. G. Ingalls, Piscataway, New Jersey: Institute of Electrical and 

Electronics Engineers, Inc.

[31] Leng, and Yingchao 2005

[32] Johansson, B., Mahesh, M., Skoogh, A., Leong, S. Discrete Event Simulation as Requirements 

Specification for Sustainable Design of Manufacturing Systems. Proceedings of the PERMIS 

Workshop. NIST 2009.

[33] Heilala, J., Saija, V., Tonteri, H., Montonen, J., Johansson, B., Stahre, J., Lind, S. 2008. SimulationBased Sustainable Manufacturing System Design, In Proceedings of the 2008 Winter Simulation 

Conference, eds. Mason, S. J., Hill, R. R., Mönch, L., Rose, O., Jefferson, T., Fowler, J. W. 1922–

1930, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

[34] Lind, S., Krassi, B., Viitaniemi, J., Kiviranta, S., Heilala, J., Berlin, C. 2008. Linking Ergonomics 

Simulation to Production Process Development, In Proceedings of the 2008 Winter Simulation 

Conference, eds. S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, and J. W. Fowler, 1968–

1973, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

[35] Lind, S., Krassi, B., Johansson, J., Viitaniemi, J., Heilala, J., Stahre, S., Vatanen, S., Fasth, Å., 

Berlin C., 2008. SIMTER: A Production Simulation Tool for Joint Assessment of Ergonomics, 

Level of Automation and Environmental Impacts. The 18th International Conference on Flexible 

Automation and Intelligent Manufacturing (FAIM 2008), June 30 – July 2, 2008.

[36] SISO 2009. Standard for Core Manufacturing Simulation Data Information Model: UML model 

(Draft) CMSD Product Development Group, Simulation Interoperability Standards Organization. 

http://www.sisostds.org/index.php?tg=fileman&idx=get&id=49&gr=Y&path=Papers+and+Present

ations&file=06F-SIW-028_final.pdf/. 

[37] http://www.steptools.com/support/stdev_docs/express/ap203/.

[38] http://www.autofieldguide.com/articles/109902.html/.

[39] http://www.steptools.com/support/stdev_docs/express/ap219/.
NISTIR XXXX

[40] http://www.sfsa.org/sfsa/news/2005/april05.pdf/.

[41] http://www.steptools.com/support/stdev_docs/express/ap224/.

[42] http://www.ap233.org/.

[43] http://www.steptools.com/support/stdev_docs/express/ap238/.

[44] http://www.steptools.com/support/stdev_docs/express/ap240/.

[45] http://www.igi-global.com/reference/details.asp?ID=9684&v=preface/.

[46] http://www.sisostds.org/index.php?tg=fileman&idx=get&id=49&gr=Y&path=Papers+and+Present

ations&file=06F-SIW-028_final.pdf/.

[47] http://www.iso.org/iso/catalogue_detail?csnumber=37456/.

[48] http://www.iso.org/iso/catalogue_detail?csnumber=38381/.

[49] http://www.iso.org/iso/catalogue_detail?csnumber=40685/.

[50] http://www.iso.org/iso/pressrelease?refid=Ref1157/.

[51] http://www.steptools.com/library/standard/step_4.html/.

[52] http://ts.nist.gov/standards/iges/.

Appendix A:

Department of Commerce (DOC)

For the purposes of Commerce's Sustainable Manufacturing Initiative, sustainable manufacturing is 

defined as the creation of manufactured products that use processes that minimize negative 

environmental impacts, conserve energy and natural resources, are safe for employees, 

communities, and consumers and are economically sound [2]. 

National Council for Advanced Manufacturing (NACFAM) 

Sustainable manufacturing includes the manufacturing of “sustainable” products and the sustainable 

manufacturing of all products. The former includes manufacturing of renewable energy, energy 

efficiency, green building, and other “green” & social equity-related products. NACFAM aims to 

address both of these topic areas and emphasize the sustainable manufacturing of all products 

taking into account the full sustainability life cycle issues related to the products manufactured [3].

NACFAM uses the definitions above and build off the concepts of the triple bottom line (developed 

by John Elkington [52]) and sustainable enterprise. The triple bottom line emphasizes financial 

profitability, environmental integrity, and social equity. This concept attempts to incorporate 

financial, environmental, and social capital into a comprehensive framework that can help 

companies analyze their current processes, innovate, and identify new sources of revenue and cost 

reduction.

Table 5 Relevant manufacturing data related standards

Information 

Model 

Description

AP203 Application Protocol for Configuration Controlled Design [37]. 

AP203 defines the geometry, topology, and configuration management data of solid 

models for mechanical parts and assemblies. AP-203 Edition 2 is a new version of the AP203 standard for exchanging 3D geometry between CAD systems. It includes Geometric 

Dimensions & Tolerances (GD&T) data. The new data has been designed to meet the 

requirements of design and manufacturing so the same model has also been incorporated 

into AP-238. 

AP214 Core Data for Automotive Mechanical Design Processes [38]. 

AP214, designed for the automotive industry, defines the core data for the automotive 

mechanical design process, specifically the car body, power train, chassis, and the interior 
NISTIR XXXX

parts of a car. AP214 goes well beyond AP203, providing a far more comprehensive model 

for automotive applications and covering the lifecycle of a design from engineering 

through manufacturing. It also covers such design issues such as colors and layers, 

geometric dimensioning and tolerancing, and design intent. 

AP219 Application Protocol for Dimensional inspection information exchange [39].

AP223 Application protocol for casting [40].

STEP AP 223 is part of a large standards effort to allow permanent storage and retrieval of 

technical and process information for castings.

AP224 Application Protocol for Mechanical Product Definition for Process Planning Using 

Machining Features [41].

AP224 is a vendor-independent digital product data specification that includes all 

information needed to manufacture and assemble a machined part. In addition to part 

geometry (dimension and tolerance) information, AP224 captures part features, material, 

surface finish and other notational information needed in manufacturing. 

AP233 Application protocol for Systems Engineering and Design [42].

AP233 is an information model designed as a neutral data exchange capability for data 

created by Systems Engineering computer applications.

AP238 (ISO 

14649)

Application Protocol for Computerized Numerical Controllers [43].

AP-238 is the official ISO STEP number for the STEP-NC standard. In AP-238 the 

information required to control a machine is linked to the information created by CAD and 

CAM systems to create a truly independent, fully documented CNC control file for the first 

time. ISO 14649 is the machining data model underlying AP-238. The authors of ISO 

14649 assessed the state of the art in CNC, and developed an object-oriented data model 

based on the concepts of features and working steps that reflected today's highperformance machining needs. AP-238 takes this data model and brings it into the suite of 

STEP standards, so that CNCs can be fully integrated with CAD, CAM, CAPP and other 

CA- applications. AP-238 defines a new interface for existing systems.

AP240 Application Protocol For Process Plans For Machined Products [44, 45]. 

STEP AP240 can support macro process planning by connecting CAPP with CAM. This is 

because AP240 defines such a high-level process plan for a machined part, and contains 

data about manufacture of a single piece or assembly of single piece parts. It serves as an 

interface for capturing technical data out of the upstream application protocols, and issuing 

work instructions for the tasks required to manufacture a part and the information required 

to support NC programming of processes specified in the process plan.

CMSD Data specification for efficient exchange of manufacturing life-cycle data in a simulation 

environment [46]. The objective leads to: foster the development and use of simulations in 

manufacturing operations, facilitate data exchange between simulation and other 

manufacturing software applications, enable and facilitate better testing and evaluation of 

manufacturing software, increase manufacturing application interoperability.

ISO14040 ISO 14040:2006 [47] describes the principles and framework for life cycle assessment 

(LCA) including: definition of the goal and scope of the LCA, the life cycle inventory 

analysis (LCI) phase, the life cycle impact assessment (LCIA) phase, the life cycle 

interpretation phase, reporting and critical review of the LCA, limitations of the LCA, the 

relationship between the LCA phases, and conditions for use of value choices and optional 

elements.

ISO14064 ISO 14064-1:2006 [48] specifies principles and requirements at the organization level for 

quantification and reporting of greenhouse gas (GHG) emissions and removals. It includes 

requirements for the design, development, management, reporting and verification of an 

organization's GHG inventory.

ISO14065 ISO 14065:2007 [49] specifies principles and requirements for bodies that undertake 

validation or verification of greenhouse gas (GHG) assertions. It is GHG programme 

neutral. If a GHG programme is applicable, the requirements of that GHG programme are 

additional to the requirements of ISO 14065:2007.

ISO50001 The future ISO 50001 [50] will establish a framework for industrial plants, commercial 

facilities or entire organizations to manage energy. Targeting broad applicability across 
NISTIR XXXX

national economic sectors, the standard could influence up to 60 % of the world’s energy 

use.

RS274D 

(ISO6983)

RS274D is the data standard currently used to tell CNC systems how to make a part [51]. 

An RS274D file contains a list of instructions called G-codes. Each code tells the CNC 

machine where to move the cutting tool next. If the CNC executes all of the instructions 

correctly, then the part is made. Each code is very primitive and an RS27D file needs to 

contain hundreds of thousands of codes to make a part. The CNC is not given any 

information about what it is making or why the instructions have to be executed in the 

given order. Therefore, no optimizations can be made on the control. The RS274D standard 

is also limited in functionality, and CNC vendors have invariably extended it to include 

newer features in their controls such as spline interpolation. These "dialects" are not 

standardized, so programs written for one vendor's CNC are unlikely run on another 

vendor's CNC. 

IGES IGES is a standard for transferring drawing information between CAD systems and 

between customers and suppliers [52]. When a customer wants a job shop to make a part, it 

sends a description of the part as a drawing in an IGES file. The job shop then reads that 

IGES file into a Computer Aided Manufacturing (CAM) system and an operator uses it to 

generate the RS274D code necessary to make the part on a specific machine tool. It can 

take quite a long time for the operator to convert the drawing into RS274D codes and if 

anything changes the whole process has to start again. The people who developed IGES 

used the lessons learned to develop an international standard for the complete life cycle of 

a product and called it STEP. There will be no more versions of IGES after the current one.