1

A Decision Guidance Framework for Sustainability Performance Analysis of Manufacturing 

Processes

Duck Bong Kim, Seung-Jun Shin, Guodong Shao

Engineering Laboratory

National Institute of Standards and Technology 

Gaithersburg, MD, USA

Alexander Brodsky

Dept. of Computer Science

George Mason University

Fairfax, VA, USA

ABSTRACT

Life-Cycle Assessment (LCA) methods are widely used to assess the sustainability of manufacturing processes. 

Although it has several advantages such as systematic estimation and efficiency, LCA has limited functionality. For 

example, it does not support decision optimization and parameter calibration. Additionally, it does not provide a 

unified modeling environment in which to perform a sustainability analysis. In this paper, we present a decision 

guidance framework that addresses these deficiencies. That framework consists of six phases: goal and scope 

definition, data collection, model generation for process and analytics, sustainability performance analysis, 

interpretation, and decision support and guidance. To demonstrate the use of the framework, we include a case 

study of a turning process. We also discuss and evaluate the feasibility of the framework in terms of functionality, 

usability, flexibility, reusability, and interoperability. 

Keywords

Sustainable manufacturing, sustainability analysis, decision support and guidance, manufacturing processes, and 

machining process.

1. INTRODUCTION

Manufacturing industries are under intense social and competitive pressures to reduce the environmental impact 

of their products and processes. Sustainable Manufacturing (SM) addresses these pressures. Its goal is to achieve

a global competitive edge, while taking into account environmental, societal, and economic factors [1]. Research 

and development (R&D) efforts related to SM aim to increase the economic and societal benefits throughout the 

product life-cycle. These efforts cut across different organizational boundaries and technological issues [2, 3]. To 

date, however, most of these R&D efforts have focused on product sustainability; far fewer have focused on 

process sustainability [4]. A commonly used method in these product efforts is Life-Cycle Assessment (LCA).

LCA provides functions and procedures to assess accurately the environmental impacts of products. Currently, 

however, it does not support decision optimization and parameter calibration, which could reduce those impacts. 

LCA also does not provide a unified modeling environment across the life cycle. These additional capabilities have

been recognized as key enablers for sustainability improvements [5, 6]. Linking these capabilities to LCA will not 

be easy [7, 8]. Without them, however, manufacturers, particularly small and medium enterprises (SME), will 

continue to struggle to make informed decisions that improve sustainability.

Several international efforts such as the CO2PE! Initiative [9] and Ecoinvent [10] have recognized the need for 

these added capabilities. The CO2PE! Initiative is a coordinated, international effort to collect, document, and 

assess the environmental impacts of conventional and emerging manufacturing processes. Ecoinvent has 

developed a database, containing lifecycle inventory (LCI) data. It has also specified an LCI data exchange format 

for use with LCA software. Although these efforts extend the scope of LCA to include manufacturing processes, 

they still focus only on assessment. Therefore, a comprehensive and systematic framework that includes the 

aforementioned decision-making capabilities still does not exist. 

This paper proposes such a framework, which we call a decision guidance framework. That framework includes 

procedures and guidelines for different types of sustainability analyses and a unified modeling environment. It 

consists of six phases: goal and scope definition, data collection, model generation, sustainability performance 
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analysis, interpretation, and decision support and guidance. A case study of a turning process is performed to 

demonstrate the use of the framework. Energy consumption and nitrogen dioxide (NO2) are sustainability 

indicators in this case study. Optimal manufacturing parameters are derived through what-if analysis and decision 

optimization. 

The paper is organized as follows. Section 2 explains the related work and the requirements for the decision 

guidance framework. Section 3 presents our framework. Section 4 includes a case study of a turning process. This 

case study shows the feasibility and effectiveness of the framework and demonstrates how the framework can be 

applied to a real-world problem. Section 5 evaluates the framework qualitatively in terms of functionality, usability, 

flexibility/reusability, and interoperability. Section 6 provides a conclusion and future work. 

2. BACKGROUND

2.1 Related work

The idea of LCA was started by the international scientific society of environmental chemists, Society of 

Environmental Toxicology and Chemistry (SETAC). SETAC launched the Life-Cycle Initiative to provide life-cycle 

concepts, data, tools, and services. The International Standards Organization (ISO) subsequently initiated a global 

standardization process for LCA. Four standards were developed and issued in the ISO 14000 series of standards 

for Environmental Management, (ISO 14040-14043 [11-14]). The LCA framework in ISO 14040 consists of four 

modules: goal and scope definition, inventory analysis, impact assessment, and results interpretation. For more 

details, overview and survey papers can be found in [15].

Although LCA is a powerful methodology to assess the environmental impact, it has, as noted above, several 

limitations when it is used directly for decision support or process improvement. First, it lacks the mathematical 

foundations needed to support optimization. Second, it lacks the adaptability needed to deal with dynamic shop 

floor conditions. Third, it lacks the flexibility needed to incorporate different kinds of analysis tools. These 

limitations are well known, and several researchers have attempted to address them. Kellens et al. proposed [16] 

and demonstrated [17] a methodology for systematic analysis and improvement of sustainability. Jawahir et al. [18] 

proposed an innovation-based 6R - reduce, reuse, recycle, recover, redesign, and remanufacture - methodology to 

achieve optimized technological improvements and process planning. Pusavec and Kopac et al. [19, 20] presented 

methods and performed case studies to improve sustainability of machining processes. They concluded that such 

improvements can reduce total life-cycle costs. 

2.2 Requirements analysis for the framework

We have divided high-level requirements into four categories ‘functionality’, ‘usability’, ‘flexibility\reusability’, and 

‘interoperability’. The following paragraphs briefly explain each of these categories. 

‘Functionality’ includes functions that 1) perform model formulation and composition for various sustainability 

performance analyses, 2) yield an application-independent, unified modeling environment, 3) provide reliable 

results and recommendations for improvements, and, 4) provide parameter calibration, modeling verification and 

validation, uncertainty quantification, sensitivity analysis, and integrity constraints. 

‘Usability’ includes 1) the ‘tradeoff between simplicity and sufficient expressiveness’ for modeling and 

representation, 2) a ‘graphical representation’ that allows stakeholders to perform modeling tasks intuitively using 

a graphical user interface, 3) ‘readability’ that allows the models to be understandable, interpretable for sharable 

among humans, and 4) ‘well-defined semantics’ that reduce misunderstandings between different stakeholders.

‘Flexibility/reusability’ helps handle the broadness and complexity of the problems and enables stakeholders to

reuse, modify, or exchange processes-related sustainability indicators and metrics. ‘Hierarchical representation’ 

and ‘extensibility’ are required to increase flexibility; ‘modularity’, ‘searchability’, ‘modifiability’, ‘storability’, and 

‘configurability’ are necessary for reusability. 
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‘Interoperability’ is required for various methodologies, standards, file formats, structures, and systems to work 

together. ‘Methodology independency’, ‘compatibility with standards’, ‘transformability’, ‘platform-independency’, 

and ‘machine readability’ are the five requirements for interoperability.

Based on an analysis of these requirements, we concluded that none of the currently used LCA approaches can 

satisfy all of them. Therefore, a new comprehensive and systematic framework is needed. 

3. THE PROPOSED DECISION GUIDANCE FRAMEWORK

Figure 1 shows the proposed framework that consists of six phases: a goal and scope definition, data collection, 

model generation, sustainability performance analysis, interpretation, and decision support and guidance. Similar 

to the LCA methodology [11], this framework supports incremental and iterative analysis; and, each phase can be 

revisited or reexamined several times. A brief description of each phase is given below and detailed explanations 

are provided in the following subsections. 

Goal and scope definition: defines the goal and scope of the target manufacturing processes, the types of 

performance analyses to be completed, and the characterization factors to be used in those analyses. 

Data collection: identifies and collects the sustainability data required to perform those analyses. 

Model generation: generates computer-interpretable information models to represent the data and performance 

models need to analyze that data. 

Sustainability performance analysis: uses the information and performance models to complete the performance 

analyses, including uncertainties, defined by the goal and scope. 

Interpretation: completes an uncertainty quantification to interpret the outputs from the performance analyses 

and provide those outputs to other stages. 

Decision support and guidance: uses interpreted outputs to make final recommendations based consensus of 

stakeholders (e.g., analysts, decision makers, and engineers). 

Figure 1. The proposed decision guidance framework.

3.1 Goal and scope definition phase

Figure 2 shows the procedures in the goal and scope definition phase as well as its interrelationship with other 

phases. This phase consists of five procedures: definition of the study goal, definition of the scope and system 

boundaries, determination of the sustainability indicators and metrics of interest, determination of the constraints 

and variables, and, generation of an abstract performance mathematical model. The information defined, 

determined, and generated in this phase will be inputs for the ensuing phases. The details of each procedure will 

be explained as follow. 
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Figure 2. Procedures in goal and scope definition phase and Interrelationship with other phases. 

Definition of the study goal: a study goal needs to be clearly understood to avoid incorrect results and wrong 

decisions. That goal specifies the reason for the study. Examples of goals include 1) assess the environmental 

impacts of a painting process, do a cost/benefit analysis to compare a conventional vs additive manufacturing. 

Definition of the scope and system boundaries: the scope and system boundaries determine what processes will 

be included in the sustainability assessment. Those processes can be characterized using Input-Process-Output 

(IPO) method [21] or the methodology by the CO2PE! [16]. In this paper, we have used the methodology by the 

CO2PE! to characterize all processes and sub-processes, as shown in Figure 3. Figure 3 shows that manufacturing 

processes can be analyzed and decomposed along two separate axes: physical- and manufacturing- cycle-based 

decomposition. The “three dots” (

...

) indicate that decomposition can result in several sub-processes between the 

two main processes. The physical-based decomposition is based on a spatial hierarchy. For example, Jiang et al. 

[21] analyzed and assessed a process plan that consists of turning, milling, drilling, and grinding. The turning 

process can be considered as sub-process I-I, while the grinding can be considered as sub-process IV-I. In addition, 

manufacturing processes can be decomposed using a temporal decomposition [22]. A machining process, for 

example, can be decomposed into four activities: setup, idle, active, and teardown. 

Determination of the sustainability indicators and metrics of interest: determination of indicators and metrics is 

closely related to a data collection plan. Sustainability indicators and metrics for machining processes have been 

reported in [16, 18, 22]. Examples provide by Lu et al. [23] include 1) economic factors such as labor cost, energy 

cost, and material cost; 2) environmental factors such as wastes, NO2 emissions, and carbon footprint; and 3) 

societal factors such as operational safety, human health, and ergonomics. 

Determination of the constraints and variables: to generate an abstract performance model input variables, 

output variables, and constraints must be identified. Identification will depend on the nature of that model [24]. 

Generation of abstract mathematical models: an abstract mathematical model is generated based on the outputs 

from the previous procedures. This model is chosen to conform to the stated goals, boundaries, and required 

information. The abstract mathematical model is used for different kinds of sustainability analyses. 

3.2 Data collection phase

The data collection phase provides the necessary data to run for performance models. As shown in Figure 4, the 

data collection phase includes four procedures: 1) data collection plan, 2) model existence check, 3) data collecting, 

and 4) determination of characterization factors. Each procedure is explained below. 
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Figure 3. System boundaries and the information flow of manufacturing processes based on the CO2PE!

Figure 4. Procedures in data collection phase and Interrelationship with other phases.
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Data collection plan: to minimize cost and effort, data collection must be well-planned. That data, which will come 

from multiple sources, can be categorized into quantitative and qualitative data [23]. The data collection plan not 

only determines the sources for collecting data, but also ensures that the quality and the accuracy meet the 

expectations of users.

Model existence check: a model checking determines whether the required data or models are available already. 

If not, the required data must be collected and a new model generated. 

Data collecting: the collected data provides inputs to the performance model. Quantitative data can be collected 

from 1) manufacturers’ repository systems, 2) measurements using software or sensors, 3) simulation systems, 4) 

direct empirical experiments, and 5) from analytical calculations. Qualitative data can be obtained by using the soft 

computing techniques - such as fuzzy-logic, neural networks, and genetic algorithms - that convert subjective 

knowledge/opinion into a mathematical formulation [26].

Determination of characterization factors: characterization factors (e.g., scaling, normalization, and weighting)

and aggregation methods should be determined. The collected data needs to be normalized and quantified in a 

scientific and objective manner, since each indicator is commonly measured in different unit systems (e.g., kiloJoule for energy and liter for coolant consumption) and some input data is qualitative and dimensionless (e.g., 

human safety and health). Weighting factors of each indicator at specific levels can affect the analysis results

significantly. They should be carefully defined. The normalization and weighting factors are mainly obtained based 

on the consultation (e.g., survey from experts and statistical methods) [27, 28]. For example, Jawahir et al. [8, 18, 

23] identified the manufacturing cost, personnel health, operational safety, waste management, energy 

consumption, and environmental impact as the six significant contributors to the SM processes and proposed an 

integrated approach for characterization factors. 

3.3 Model generation phase

The model generation phase takes the information collected from the selected process and the model selected 

from available performance models and integrates them to form a model that is in some generic, neutral format. 

Figure 5 shows the procedures in model generation phase and interrelationship with other phases. This phase 

consists of three procedures: manufacturing process modeling, analytics modeling, and an integrated SM process

model. The output of this phase is a generic model that incorporates process and analytics knowledge for multiple 

sustainability analyses.

Manufacturing Process modeling: creates a model that describes a specific manufacturing process, its inputs, and 

its outputs as shown in Figure 3. Inputs from technosphere I include information flows that describes the 

workpiece and raw materials to be processed. Inputs from technosphere II include information flows associated 

with the consumables used by the activities - setup, idle, and teardown - associated with the manufacturing 

process. Examples of consumables include electricity, compressed air, and coolants. The model of the physical 

manufacturing process can be represented in a number of ways including mathematical equations or dynamic 

simulations. Outputs to ecosphere are modeled as information flows that represent various environmental impacts 

from the physical manufacturing processes. Examples of these outputs include recyclable waste, CO2, and, NO2 to 

name a few. The outputs from process modeling include all of the aforementioned models. 

Analytics modeling: chooses from available predictive models for sustainability. Model types include analytical, 

numerical, empirical, and Artificial Intelligence (AI) [18]. Since SM contains various complexities and uncertainties, 

analytical models can provide an effective and efficient method for analytics [18]. A numerical analysis is widely 

used to analyze sustainability data [18], which includes a variety of specific techniques such as simple calculation, 

interpolation, regression, and finite element methods. Empirical methods can quantify subjective experiences and 

opinions into digitized values. The qualitative and quantitative sustainability data can be also modeled based on AIbased model, such as fuzzy-logic, neural network, and genetic algorithm [26]. In addition, we have included a 

sustainability impact model, which includes mathematical formula for calculating a sustainability single score, 
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should also be generated. The output from analytical modeling includes all of the models that will be used to 

calculate the sustainability metrics for this particular manufacturing process. 

Figure 5. Procedures in model generation phase and Interrelationship with other phases.

Integrated Modeling the SM processes: The selected analytics models require certain inputs. These inputs come 

from the outputs of the manufacturing process modeling. As noted above, these outputs come in two forms: 

information flow models and physical process models. Therefore, the formats of these outputs must match the 

input requirements of the analytics models. If they don’t, then numerous translations will be necessary. To 

facilitate these translations, we will use a recently develop modeling language called Sustainable Process Analytics 

Formalism (SPAF) [29]. We chose SPAF over other modeling languages such as BPMN [30], SysML [31], and PSL 

[32], because SPAF was designed specifically for SM applications. More importantly, SPAF provides a mechanism 

to represent information models, process models, and analytics models, and a variety of sustainability metrics in a 

common language. The resulting SPAF integrated model is the principal output from this function.

3.4 Sustainability performance analysis phase

This SPAF model proivdes the sole input into the sustainability performance analysis phase. As shown in Figure 6, 

this phase has three procedures i.e., translation, analysis tasks, and results extraction. Several analysis tasks such 

as query analysis, what-if analysis, and optimization analysis can be performed. The derived results need to be 

provided to the interpretation phase for accuracy and reliability check. 

Translation: the SPAF neutral models must be translated into application-specific models in certain formats. These 

formats are required to run the various analysis tasks. 

Analysis tasks: the applications-specific models can then be executed using different commercial analysis tools and 

solvers for query analysis, what-if analysis, and optimization analysis. Query analysis is denoted as “an analysis

process of finding the value of interest in manufacturing processes from the searching in database and delivering 

the queries dataset to the users”. What-if analysis is defined as “an analysis process of computing and comparing 

the different outputs of the model by changing the inputs such as process parametersand conditions to evaluate 

alternatives and decide prefered settings.”. Simulation and LCA are under this caterogy. Optimization analysis is
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explained as “an analysis process of searching optimal values for various process parameters that satisfy certain 

conditions and constraints with an objective function”. 

Results extraction: after performing the sustainability analysis tasks, several types of results can be extracted. Each 

result can be used to aid in the final decisions. 

Figure 6. Procedures in sustainability performance analysis phase and Interrelationship with other phases.

3.5 Interpretation phase

The extracted results contain uncertainties that can arise from a number of sources. These uncertainties are 

important because they can lead to discrepancies between the model results and reality. This, in turn, can lead to 

to incorrect decisions. It is imperative, therefore, to identify and characterize these sources and we need quantify 

the associated uncertainties. As shown in Figure 7, an interpretation phase consists of identification of significant 

issues, identification of types and sources of uncertainties, and investigation with interpreting modules. Such 

modules could include validation and verification (V&V), uncertainty quantification (UQ), sensitivity analsysis (SA), 

parameter calibration (PC), and integrity constraints (IC). 

Identification of significant issues: due to the amount of data collected, it is only feasible within reasonable time 

and resources to investigate the data elements that contribute significantly to the outcome of the results. 

Identifing the signficant issues to the results is the first step for interpretation.

Identification of types and sources of uncertainties: uncertainties can be categorized as either aleatory, epistemic, 

or a mixture of two [33]. Aleatory uncertainties come from the inherent variation or randomness in processes. This 

means that aleatory uncertainties are very difficult to reduce. On the other hand, epistemic uncertainty comes 

from uncertainties in the model or the data. In addition, there are four main sources of uncertainty: model inputs, 

model assumptions, model form, and numerical approximations [33]. As a result, epistemic uncertainties can be 

reduced with a more-accurate model, more detailed knowledge, or better data. If there a percieved problem, 

these are the first places to look for a solution.
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Figure 7. Example of interrelationship between Interpretation phase and other phases.

Investigation with interpreting modules: this procedure provides the understanding of the uncertainties and 

assumptions. In a V&V module, verification addresses the estimation of numerical errors (e.g., discretization error, 

iterative error, and round-off error), while the assumption in the model is usually addressed through model 

validation [33]. The UQ module is denoted as the science of quantitative characterization and reduction of 

uncertainties. The objective of SA is to evalute and identify the significant inputs. The PC module is to tune the 

parameters with respect to the dynamic shop floor conditions. The IC module is used to maintain accuracy and 

consistency of data, model, and metric unit system used throughout all phases. 

3.6 Decision support and guidance phase

In the decision support and guidance phase, stakeholders reach a final recommendation (decision) based on the 

results from interpretation phase. The process used to make this recommendation has, as shown in Figure 8, three 

procedures: reporting the results to stakeholders, critical review, and final recommendation.

Figure 8. Procedures in decision support and guidance phase. 

Reporting the results to stakeholders: once the study is completed, the results must be gathered into a 

comprehensive report for all stakeholders. The report provides the results, data, methods, assumptions, and 

limitations in detail to enable the stakeholders to clearly understand the context of the study. 

Critical review: Stakeholders will form are panel review the results before making a final recommendation. 
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Final recommendation: based on the discussion, consensus, and critical review by the stakeholders, the final 

actionable recommendations can be provided to decision makers. 

4. CASE STUDY

To demonstrate how to use the six steps in our approach, we present a case study based on a turning process. 

Section 4.1 defines the goal and scope. Section 4.2 builds the data collection plan and collects the data referred 

from [34]. In Section 4.3, an integrated model from the process and analytics models is generated using SPAF. It 

provides the inputs for assessment analysis (SimaPro) [35], and it is translated into Optimization Programming 

Language (OPL) format [36] for optimization analysis. The assessment results as well as the optimal manufacturing 

parameters are derived in Section 4.4. Section 4.5 interprets the derived results through validation and parameter 

calibration. Finally, the decision recommendations are provided in Section 4.6. 

4.1 Goal and scope definition phase

Definition of the goal of the turning process: the goal of this case study is to determine optimal process 

parameters, spindle speed, feedrate, and cutting depth, of a turning process, which imposes the minimal 

environmental burdens. This turning process inputs a cylindrical workpiece sized by 50 mm length and 50 mm 

diameter as shown in Figure 9 (a), and outputs a machined part as shown in Figure 9 (b). Only roughing operation 

is considered for the study, and Geometrical Dimension and Tolerance (GD&T) is ignored. 

 (a) (b) 

Figure 9. Shape and size of geometry workpiece (a) and its output product (b).

Definition of the scope and system boundary: as presented in Section 3.1, the turning process is decomposed into 

multiple sub-processes by using manufacturing cycle-based decomposition method. The turning process can be 

sub-divided into setup, idle, active, and teardown. Each sub-process has its own inputs from technosphere, and 

outputs to ecosphere and technosphere.

Figure 10. A flow of single score estimation of Eco-indicator 99 from the sustainability impact drivers.
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Determination of the sustainability indicators and metrics of interest: We used the impact assessment method 

described in Eco-indicator 99 [37] to estimate two main sustainability indicators: energy consumption and nitrogen 

dioxide (NO2) emission. Figure 10 shows the logical flow of a single scoring of Eco-indicator 99 starting from the 

sustainability impact drivers. The impact drivers indicate the process parameters (i.e., spindle speed, feedrate, and 

cutting depth), because the drivers affect the indicators. The indicators are classified into the eleven impact 

categories. Finally, a single score can be obtained from aggregation of values of the eleven categories.

Determination of the constraints and variables: the decision variables are cutting depth, spindle speed, and 

feedrate, since they significantly impact machining and environmental performances. The constraints are set to (2, 

3) mm for cutting depth, (955, 1590) rpm for spindle speed, and (0.23, 0.27) mm/rev for feedrate, respectively. The 

ranges are assigned from reviewing the allowances stated in the cutting tool catalogue.

Generation of the abstractive mathematical model: before planning a data collection, abstract mathematical 

models are prepared. The abstract models for energy consumption, NO2 emission, and a sustainability single score 

are shown as follows. 

Energy consumption: as defined in Section 4.1, the total energy consumption (Etotal) consists of energy 

consumption from the four sub-processes as below: 

 . (Eq. 1)

Each energy consumption for the setup (Esetup) and teardown (Eteardown) modes is a constant, whereas those of 

idle (Eidle) and active (Eactive) are dependent on machining performance. The energy consumption for setup and 

teardown will be measured in data collection phase in Section 4.2. The energy consumption for active mode is 

calculated as below:

 . (Eq. 2)

The power consumption for active mode (Pative) is calculated with extracting the relationship between power 

consumption and its manufacturing parameters (e.g., feed rate (f), spindle speed (S), and cutting depth (C)) as 

below:

 . (Eq. 3)

The active time is calculated [38] as below:

 

 

 

, (Eq. 4)

where L is length to be machined. Stnum denotes the number of stroke (one tool path for linear/circular 

interpolation), which can be estimated as below:

 

 

 

 , (Eq. 5)

where d is the total cutting depth and toffset denotes the tolerance offset that is required to reduce the radial 

run-out stroke.

Energy consumption for idle is calculated as below:

 . (Eq. 6)

The power consumption for idle (Pidle) can be assumed as a constant value and obtained by the direct 

measurement, which will be given in Section 4.2. The idle time (tidle) will be calculated as below:

 , (Eq. 7)
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where tattract, trapid, and tretract denote time for attract, rapid movement for a back path, and retract in seconds. 

Those values are given in data collection phase. 

NO2 emission: the NO2 emission is generated during active mode. The abstractive model for the total NO2 emission 

(EMNO2) is a function of NO2 emission rate f2( ) with three variables (f, S, C) and time for active mode (tactive) as 

below: 

 

 , (Eq. 8)

where the function ‘f2(f, S, C)’ will be shown in Section 4.3 and the tactive is known in Eq. 4.

Sustainability single score: the sustainability single score of Eco-indicator 99 (SSEI) can be estimated by summation 

of characterization factor for energy (CFEnergy) by total energy consumption (Etotal) and characterization factor for 

NO2 (CFNO2) by total NO2 emission (EMNO2) as below: 

SSEI = CFEnergy × Etotal + CFNO2 × EMNO2 . (Eq. 9)

From the assessment analysis, the sustainability single score is obtained. Assessment function is performed with 

respect to the decision variables as below: 

assess(SSEI) =assess(CFEnergy × Etotal + CFNO2 × EMNO2) . (Eq. 10)

For optimization, the objective function is to minimize the sustainability single score, which corresponds to the 

optimal manufacturing parameters. 

min(SSEI) = min(CFEnergy × Etotal + CFNO2 × EMNO2) . (Eq. 11) 

4.2 Data Collection phase

Data collection plan: To develop a data collection plan, we used Box-Behnken method [39] to establish the 

relationships between the decision variables and the output variables. We ran fifteen trials to derive the full 

quadratic regression models (see below) for predicting energy consumption and NO2 emission. The first 13 trials 

have different input values. Trials 14 and 15 are performed to check the repeatability of the experimental 

conditions. Wireless sensors are installed for measuring power and air emissions. 

Table 1. Measurement results with respect to feedrate, spindle speed, and cutting depth.

Trial 

number

Cutting parameters Sustainability indicator of interest (SIOI)

Feedrate Spindle 

speed

Cutting 

depth

Total energy 

consumption

Air emission

(NO2)

Units mm/rev rpm mm kJ ppm mg

1 0.23 955 2.5 241.304 0.41 2.49

2 0.23 1590 2.5 204.181 0.49 2.98

3 0.27 955 2.5 206.729 0.42 2.55

4 0.27 1590 2.5 198.872 0.47 2.86

5 0.23 1273 2 231.656 0.39 2.37

6 0.23 1273 3 200.013 0.39 2.37

7 0.27 1273 2 212.249 0.38 2.31

8 0.27 1273 3 186.070 0.42 2.55

9 0.25 955 2 233.523 0.31 1.88

10 0.25 955 3 208.797 0.30 1.82

11 0.25 1590 2 213.740 0.42 2.55

12 0.25 1590 3 189.486 0.41 2.49

13 0.25 1273 2.5 209.810 0.38 2.31

14 0.25 1273 2.5 211.165 0.38 2.31

15 0.25 1273 2.5 209.067 0.38 2.31
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Data collecting: Table 1 shows the results of those 15 trials. As indicated, the inputs for the first 13 trials differ. 

Trials 14 and 15 repeat the inputs from trial 13. Note the variability in the outputs given those same inputs. Energy 

consumption is measured in unit kJ, and NO2 emission in unit ppm, respectively. In case of NO2 emission, the 

concentration unit of ppm needs to be converted to the mass unit of mg/mm3, which is required by Eco-indicator. 

So, 99. 1 ppm equals to 2.03 mg/m3in a temperature of 25 ̊C. Thus, the ppm unit can be converted to the mg unit, 

assuming that the machine room of 3 m

3

is sealed.

Table 2 shows the data relevant to times for setup, idle and teardown as well as powers for setup and teardown. 

Table 2. Measurement data for generating the mathematical model.

Machining mode Units Turning 

Time

Setup s 30

Idle 

Rapid movement s 3

Retract s 3

Attract s 9

Teardown s 30

Power

Setup kJ/s 0.4 

Idle kJ/s 1.3

Teardown kJ/s 0.4

Determination of characterization factors: the characterization factors are used to calculate the final sustainability 

single score, which is expressed in Points (Pt) or milli-Points (mPt). The characterization factors are adopted from 

Eco-indicator 99, and Ecoinvent v2.1 to replace the normalization and weighting factors. Table 3 shows the 

characterization factors for environmental impacts of energy consumption and NO2 emission. Total impact for 

each are specified for the eleven impact categories.

Table 3. Characterization factors for environmental impact of energy and NO2

Impact category Unit Energy [kJ] NO2[mg]

Fossil fuels Pt 3.83 × 10-8-

Minerals Pt - -

Land Use Pt - -

Acidification/Eutrophication Pt 3.71 × 10-94.6 × 10-9

Ecotoxicity Pt 3.2 × 10-10-

Ozone layer Pt 1.4 × 10-15-

Radiation Pt - -

Climate change Pt 2.37 × 10-8-

Resp. inorganics Pt 7.58 × 10-84.66 × 10-8

Resp. organics Pt 1.37 × 10-10-

Carcinogens Pt 1.02 × 10-9-

Total Pt 1.43 × 10-75.12 × 10-8

4.3 Model generation phase

Manufacturing process modeling: Figure 11 illustrates the information flows, inputs and outputs of a process 

model for the turning process. The setup process only consumes energy from technosphere II. The idle process 

consumes energy from technosphere II. The active process consumes energy from technosphere II and emits NO2

to ecosphere. The teardown process consumes energy from technosphere II and finishes the process.
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Figure 11. A turning process consisting of four sub-processes.

Analytics modeling: 

Estimation of active power and NO2 emission rate: Table 4 presents the power consumption for the four subprocesses and NO2 emission rate. The constant setup, idle, and teardown power are given in Table 2, and the idle 

time is obtained by Eq. 7. The active power can be calculating by dividing the active energy consumption by the 

active time using Eq. 2. The NO2 emission rate is the total NO2 emission divided by the active time.

Table 4. The power consumption for the four processes and air emission rate.

Trial 

number

Energy consumption Air emission

Total Energy 

consumption

Setup Idle Active Teardown Active Active

Power Time Power Time Power Time Power Time Total NO2

emission

NO2 

rate

kJ kJ/s s kJ/s s kJ/s s kJ/s s mg μg/s

1 241.304 0.4 30 1.3 30 2.2018246 80.98 0.4 30 2.49 30.75

2 204.181 0.4 30 1.3 30 3.1963008 44.17 0.4 30 2.98 67.47

3 206.729 0.4 30 1.3 30 2.11553 67.94 0.4 30 2.55 37.53

4 198.872 0.4 30 1.3 30 3.8144987 35.62 0.4 30 2.86 80.29

5 231.656 0.4 30 1.3 33 2.542927 64.79 0.4 30 2.37 36.58

6 200.013 0.4 30 1.3 27 2.7711458 50.85 0.4 30 2.37 46.61

7 212.249 0.4 30 1.3 33 2.7270003 53.30 0.4 30 2.31 43.34

8 186.070 0.4 30 1.3 27 3.0426565 41.73 0.4 30 2.55 61.11

9 233.523 0.4 30 1.3 33 2.0174752 82.59 0.4 30 1.88 22.76

10 208.797 0.4 30 1.3 27 2.3273759 64.32 0.4 30 1.82 28.30

11 213.740 0.4 30 1.3 33 3.3289445 44.11 0.4 30 2.55 57.81

12 189.486 0.4 30 1.3 27 3.7338581 34.92 0.4 30 2.49 71.31

13 209.810 0.4 30 1.3 30 2.8011839 52.41 0.4 30 2.31 44.08

14 211.165 0.4 30 1.3 30 2.8367855 52.23 0.4 30 2.31 44.23

15 209.067 0.4 30 1.3 30 2.7827645 52.49 0.4 30 2.31 44.01

Regression models: a full quadratic regression method is used to create the predictive models for active power 

(Pactive, kJ/s) and NO2 emission rate (μg/s). The active power (Pactive) and NO2 emission rate are dependent variables 

(y), while feedrate, spindle speed, and cutting depth are independent variables (X1, X2 and X3). The X1, X2 and X3 are 

substituted to x1, x2 and x3 for normalizing the regression coefficients, where x1=(X1-0.25)/0.02, x2=(X2-1273)/317 

and x3=(X3-2.5)/0.5. The regression model is shown in Eq. 12. Table 5 shows the sets of coefficients of the two 

regression models.
15

 

   . (Eq. 12)

Table 5. Estimated regression coefficients for Pactive (kJ) and NO2 emission rate (μg/s). 

Function 

term 

Term Coefficient Pactive (kJ/s) NO2(μg /s)

Constant a 2.8081 44.1848

x1f b 0.1239 5.4860

x2 S c 0.6755 19.3304

x3 C d 0.1573 5.8579

 

 

f

2

e -0.0279 5.5092

 

 

S

2

f 0.0530 3.6377

 

 C

2

g -0.0081 -2.7058

x1 × x2f × S h 0.1760 2.2564

x1 × x3f × C i 0.0220 1.9350

x2 × x3 S × C j 0.0240 1.9868

SPAF modeling of the turning process: based on SPAF syntax and semantics [29], the SPAF model that consists of 

context, flow, flow aggregator and process is generated for sustainability analysis including assessment and 

optimization. The “context” is composed of overall information (e.g., the total cutting depth and length to be 

machined), which can be used by other model components. The “flow” is related to the inputs and outputs of the 

process. The workpiece is regarded as an input flow from technosphere I, and the machined part is an output flow 

to technosphere. The total energy consumption is an input flow from technosphere II and the NO2 emission is an 

output flow to ecosphere. The “flow aggregator” is used to summate the energy consumptions. The final SPAF 

model contains models for the main process and the sub-processes.

4.4 Sustainability performance analysis phase 

The generated SPAF model is translated into the specific tools for sustainability analysis. For what-if analysis, 

SimaPro software [35] is used for the sustainability assessment. For optimization analysis, IBM ILOG CPLEX [40] is 

used to derive the optimal manufacturing parameters. 

Figure 12. Results from the LCA software tool.
16

What-if analysis (Assessment): Figure 12 presents the bar graph of the assessment results from SimaPro model 

execution. Trial 1 is the worst case; it has the highest value (34.6 μPt). Trial 12 is the best case; it has the lowest 

value (27.2 μPt). The analysis shows that the sustainability impact (μPt) is significantly affected by the energy 

consumption rather than NO2 emissions. Statistically, the energy consumption dominates over average 99.83%, 

whereas the NO2 emission takes less than 0.2%.

Optimization analysis: the result from what-if analysis does not provide an optimal process parameter set. An 

optimization analysis is performed to provide the best parameter set. The SPAF model is translated into an 

optimization model in Optimization Programming Language (OPL) format, which consists of model and data files. 

The transformed model is executed using IBM ILOG CPLEX Optimization Studio. Specifically, Constraint 

Programming (CP) solver in the CPLEX optimizer is used for deriving the optimal parameter set. Figure 13 is a 

sample of the OPL model code for the constraint of cutting depth (line 68), the single sustainability score equation 

(line 112), and the objective ‘minimize’ function (line 118).

Figure 13. Example codes in an OPL model. 

Results extraction: Table 6 shows the optimal process parameter set and its corresponding result derived from the 

solver.

Table 6. Optimal manufacturing parameters.

Contents Sub-contents Unit Value

Optimal 

manufacturing 

parameters

Feedrate mm/rev 0.27

Spindle speed rpm 955

Cutting depth mm 2.6

Stoke Number - 6

Time

Setup s 30

Idle s 27

Active s 55.846

Teardown s 30

Energy

Setup kJ 12

Idle kJ 35.1

Active kJ 119.18

Teardown kJ 12

Total kJ 178.28

Power Active kJ/s 2.134

Sustainability Single score μPt 25.603

4.5 Interpretation phase

Identification of significant issues: We can identify three significant issues from these results: model accuracy for 

the active power and the NO2 emission rate, consistency between assessment results and optimization tools, and, 

adaptation capabilities to the dynamic manufacturing environment.

Identification of types and sources of uncertainties: for the first issue, model validations for the active power and 

the NO2 emission rate are necessary, since uncertainties can arise during the data fitting to the quadratic 

regression models. Specifically, the estimated data from the mathematical model is compared with the measured 

......

68 dvar int Depth_Cut[Orders] in Min_Depth_Cut .. Max_Depth_Cut;

 ......

112 dexpr float Sustainability [o in Orders] 

 = C_E_Total*totalEnergy[o] + C_N_Total*totalNO2[o];

......

118 minimize sum(o in Orders) Sustainability[o];
17

data. For the second issue, each result from the assessment and the optimization tools needs to compared for 

result consistency, since errors can be generated from the different usages of unit system (e.g., kJ/s and kW) and 

factors. For the third issue, adaptation capabilities are required to tune parameters in manufacturing processes, 

since the dynamic manufacturing environment usually generates the different manufacturing conditions that lead 

to uncertainties.

Investigation with interpreting modules:

Model validation for active power and NO2 emission rate: the Normal Root Mean Square Error (NRMSE) is used to 

determine data accuracy. The coefficient of determination, denoted R

2

, is used for quantifying how well measured 

data fits the quadratic regression model. Eq. 13 is the NRMSE function to calculate an error term of measured data 

(Xmeasured, i) and the predicted data (Xpredicted, i), where a lower value indicates a better accuracy. Eq. 14 is the 

coefficient of determination, where a higher value indicates a better fitting. Table 7 shows the results of NRMSE 

and R

2

calculation. From the results, it is interpreted that the predictive models are generated accurately.

n

X X

X

NRMSE

n

i measuredi predicted i

measured

 



1

2

, , 1 ( ) , (Eq. 13)













  n

i measuredi measured i

n

i measuredi predicted i

X X

X X

R

1

2

, ,

1

2

2 , ,

( )

( )

1

 , (Eq. 14)

Table 7. Validation of the predictive models for active power and NO2 emission rate.

Error metrics Pactive NO2 emission rate

NRMSE 0.7104 % 2.1968 %

R

2

99.74 % 99.86 %

Validation between two applications: consistency is validated by checking whether the sustainability single score 

from IBM CPLEX is the same with that of SimaPro. In the case of the optimal process parameter set (feedrate = 

0.27 mm/rev, spindle speed = 955 rpm, and cutting depth = 2.6 mm), the score from IBM CPLEX is given to 25.603 

μPt, and the score from SimaPro is assigned to 25.6 μPt. Based on this validation result, we believe that the two 

applications are consistent with respect to the metrics, indicators, models, data, and the results.

Table 8. Sustainability single score and its optimal manufacturing parameters according to the different 

manufacturing inputs.

Manufacturing inputs Sustainability Optimal manufacturing parameters

Total cutting 

depth (mm) Length cut (mm) Eco-indicator 99(μPt)

Cutting depth 

(mm)

Spindle speed 

(rpm)

Feed rate 

(mm/rev)

15 mm 40 mm 25.603 2.6 955 0.27

10 mm 30 mm 15.912 2.6 955 0.27

10 mm 40 mm 18.770 2.6 955 0.27

10 mm 60 mm 24.487 2.6 955 0.27

15 mm 30 mm 21.315 2.6 955 0.27

15 mm 50 mm 29.891 2.6 955 0.27

20 mm 30mm 24.638 2.9 955 0.27

20 mm 40 mm 29.848 2.9 955 0.27

20 mm 50 mm 35.506 2.9 955 0.27

19 mm 40 mm 29.578 2.8 955 0.27

22 mm 40 mm 33.075 2.8 955 0.27
18

Parameter calibration: Table 8 shows the result of optimal process parameter sets and their single scores 

corresponding to the changes of geometric properties of the workpiece. Within the optimal process parameter set, 

only cutting depth is changed, while the other two parameters stay the same. The result implies that cutting depth 

is a dominant factor that affects the geometric changes.

4.6 Decision support and guidance phase

Table 6 shows the results of sustainability single score and its optimal manufacturing parameters based on the 

given manufacturing condition. The results from the sustainability performance analysis phase are interpreted by 

performing the two validations and parameter calibration. Decision support, guidance, and actionable 

recommendations are given in Table 8.

5. CONCLUSION AND FUTURE WORK

In this paper, we proposed a decision guidance framework to address the limitations of LCA methods. It consists of 

six phases: goal and scope definition, data collection, model generation, sustainability performance analysis, 

interpretation, and decision support and guidance. The decision support and guidance are conducted in an 

incremental and iterative manner; and, each phase can be revisited or reexamined several times to reduce the 

uncertainties. In addition, the framework provides a unified modeling and analysis environment that help resolve 

the interoperability issues among different analysis applications. The framework also supports modular, reusable, 

extensible modeling and analysis, which leads to the reduction of time and effort for sustainability analysis.

We also demonstrated the feasibility of the framework based on a case study using a turning process. We believe 

such a framework can facilitate more the use of more accurate assessments which can lead to improve 

sustainability for manufacturing processes.

In the near future, we plan to develop a reference architecture and a prototype system based on the proposed 

framework. In addition, a translator will be developed to automatically transform the generated models into a 

standard or specific model format for sustainability analysis. 

Disclaimer

Certain company names or commercial products may have been identified in this paper. Such identification was 

used only for demonstration purposes. This use does not imply approval nor endorsement by NIST. Furthermore, it 

does not imply that such company names and products are necessarily the best for the purpose.

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