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Sustainable Evaluation of Production Programs Using a Fuzzy Inference Model – A Concept
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Procedia CIRP 00 (2018) 000–000
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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 73 (2018) 241–246 www.elsevier.com/locate/procedia
10th CIRP Conference on Industrial Product-Service Systems, IPS2 2018, 29-31 May 2018, Linköping, Sweden
28th CIRP Design Conference, May 2018, Nantes, France
Sustainable Evaluation of Production Programs Using a A new methodology to analyze the functional physical architecture of Fuzzy Inference Model – Aand Concept existing products for an assembly oriented product familyc identification a, b Maximilian Zarte *, Agnes Pechmann , Isabel L. Nunes Paul Universidade Stief *, Jean-Yves Dantan, Alain Etienne, Siadat Nova de Lisboa, Campus Caparica, 2829-516 Caparica,Ali Portugal a
University of Applied Sciences Emden/Leer, Constantiaplatz 4, 26723 Emden, Germany c École Nationale Supérieure d’Arts Nova et Métiers, Arts etUNIDEMI, Métiers ParisTech, LCFC EA 4495, 4 Rue Caparica, Augustin Fresnel, Universidade de Lisboa; Campus de Caparica, 2829-516 PortugalMetz 57078, France b
* Corresponding author. Tel.: +49-4921-807-1441; E-mail address: [email protected] * Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract Abstract Within their lifetime, products and processes pass through characteristic periods, which can be divided into distinct phases, such as design, In today’s business environment, the trend the towards more product variety and customization is unbroken. to this development, need of production, use, and disposal. Specially, production phase of products consumes a great amount Due of energy, non-renewablethe materials, agile and reconfigurable production emerged to cope with various products product families. To designand andgreenhouse optimize production renewable materials, ancillary inputs,systems and fossil fuels. Moreover, significant amounts and of emissions (wastes, effluents, gases) are systems as well as lead to choose the optimal product matches, analysis methods are needed. of the known methods aim of to generated, which to sustainable impacts, such as extraproduct costs, environmental damages, socialIndeed, issues. most Through a systematic overview analyze a product or one product family on the physical Different product families, however, differ largely in termsavoided. of the number and resources and emissions during the production planninglevel. process, potential sustainable impacts can may be identified and possibly The paper nature of acomponents. fact inference impedes an efficient comparison and choice of appropriate familyproduction combinations for theaccording production presents concept for This a fuzzy model to evaluate production programs for short- product and mid-term planning to system. A new methodology is proposed existing criteria products viewapplicable of their functional and physical architecture. The aim is to cluster sustainable indicators. For this approach,to theanalyze paper presents toin select measurements for sustainable production planning, three these products in new assembly oriented productproduction families forprograms, the optimization of existing assembly linesinference and the creation reconfigurable categories of sustainable indicators to evaluate a procedure to develop the fuzzy model, of andfuture possible actions for assembly systems. Basedprograms on Datum Chain, physical structure of the is analyzed. Functional subassemblies areimplemented identified, and optimizing production to Flow increase the the degree of sustainability. In products future works, the fuzzy inference model will be in aenterprises functionaltoanalysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the demonstrate the benefits of sustainable production planning. similarity between product familiesElsevier by providing design support to both, production system planners and product designers. An illustrative © © 2018 2018 The The Authors. Authors. Published Published by by Elsevier B.V. B.V. example of a under nail-clipper is used to the proposed methodology. industrial case on study on two Product-Service product familiesSystems. of steering columns of Peer-review of the committee of CIRP Conference Industrial Peer-review under responsibility responsibility ofexplain the scientific scientific committee of the the 10th 10thAn CIRP Conference on Industrial Product-Service Systems. thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. ©Keywords: 2017 TheSustainable Authors. Published by Elsevier B.V.Production Planning Manufacturing; Fuzzy Logic; Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Keywords: Assembly; Design method; Family identification
1. Introduction
The Brundtland Commission defined sustainability as 1.“development Introductionthat meets the needs of the present without compromising the ability of future generations to meet their Dueneeds” to [1]. the With fast the development in thepopulation, domain the of own increase in human communication and an ongoing trend of digitization and demand for products rises. Mass production of these products digitalization, manufacturing enterprises are facing important consumes a great amount of energy, non-renewable materials, challenges in today’s market environments: continuing renewable materials, ancillary inputs, and afossil fuels. tendency towards reductionamounts of product times and Additionally, significant of development waste, effluents, and shortened lifecycles. In addition, there is anproduct increasing emissionsproduct are generated during each stage of the life demand of Within customization, being at the same in a global cycle [2]. their lifetime, products andtime processes pass competition with competitors over the This trend, through characteristic phases,allwhich canworld. be divided into which is inducing the development from macro micro distinct phases, such as design, production, use, and to disposal. markets, in diminished lotonly sizes to augmenting However,results a product is sustainable if itdue is manufactured in product varieties (high-volume to low-volume production) [1]. a sustainable way [3]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 © system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. production is important a precise knowledge
In general, conventional production planning systems consider resources such as material, labor, production capacity, and its respective costs, but neglect the role of sustainable of the product range anda characteristics manufactured and/or aspects [4]. Through systematic overview of required assembled in this system. In this context, the main challenge in resources and caused emissions already during the production modelling and analysis is now not only to cope with single planning process, sustainable impacts can be identified and products, a limitedThe product range and or existing product families, possibly avoided. evaluation optimization of products but also to be able to analyze and to compare products to define and processes according to sustainable criteria is a complicated new product families. It can be observed that classical existing task which involves the consideration of many economic, product families are regrouped in function clientsare or features. social, and environmental aspects. Existingofstudies focused However, assembly oriented product families are hardly find. only on single aspects of sustainability, such as to energy On the product family level, products differ mainly in two planning, waste management, and/or its respective costs, and main characteristics: (i) the number of components and (ii) the environmental impacts. The social dimension of sustainability type of components (e.g. mechanical, electrical, electronical). in particular has been neglected in previous studies [5–7]. Classical methodologies considering singlea products While conventional production planningmainly is already complex or solitary, already existing product families analyze task, the following questions need to be answered for the the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 10th CIRP Conference on Industrial Product-Service Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 10thDesign CIRP Conference Conference2018. on Industrial Product-Service Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.04.012
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Maximilian Zarte et al. / Procedia CIRP 73 (2018) 241–246 Author name / Procedia CIRP 00 (2018) 000–000
integration of sustainable aspects in production planning: What are the degree of freedoms which can be affected through production planning to increase sustainability? What are the potentials in increasing sustainability through sustainable production planning? How can knowledge of possible potentials be used to optimize the production program? And how does this change the procedure for conventional production planning? Assessing the degree of sustainability is a very complex task which needs expert knowledge and experience. Therefore, the application of fuzzy set theory to the assessment of sustainability seems appropriate, since the knowledge regarding sustainability is highly subjective. Fuzzy logic provides the appropriate logical and mathematical tools to represent knowledge and data as linguistic variables, which supports the understanding of the results for non-experience users in sustainable manufacturing [8]. It seems to be a general agreement that a single indicator for the evaluation of sustainability cannot be defined and a set of indicators is necessary to capture all important aspects for sustainable manufacturing [9]. With the aid of fuzzy logic, it is possible to normalize quantitative and qualitative sustainable indicators and to evaluate the indicators with soft thresholds [8]. The paper presents a concept for a fuzzy inference model (FIM) to evaluate production programs for short- and mid-term production planning according to sustainable aspects. The concept shows the criteria for the selection of measurements for sustainable production planning, sustainable indicators to evaluate production programs, a procedure to develop the FIM, and possible actions optimizing production programs to increase the degree of sustainability. The sustainable evaluation of processes and products with fuzzy logic has been demonstrated in previous studies. Inoue et al. (2012) [3] developed a decision-making support method for sustainable product creation in the early phases of the design process considering various design uncertainties and using fuzzy theory for the assessment. Ghadimi et al. (2012) [10] developed a weighted assessment method for product sustainability using the fuzzy analytical hierarchy method. Hemdi et al. (2013) [2] proposed a FIM to determine a comprehensive indicator to assess product and process sustainability as basis for strategic enterprise decision making. Piluso et al. (2010) [11] introduces a fuzzy theory based approach for the effective mid- and long-term assessment of industrial sustainability under the restrictions of data uncertainties. The current literature states that sustainable aspects play an important role for the product and process design, and evaluation considering single phases of product and process life-cycle. Nevertheless, current models for sustainable design of products cannot be adopted for the evaluation of other lifecycle phases, such as production planning and controlling. The problem is that the selected sustainable indicator for product and process design and evaluation targets one aspects of sustainability, such as caused costs, impacts on environment, or on stakeholders. For sustainable production planning, the indicator must show e.g. possible cost savings, potentials for avoiding social and environmental impacts, and the degree of achieved sustainable production goals. The knowledge on
potentials for improvements can be used to optimize production programs. Another problem is, that the defined membership functions are applicable for specific products and processes only. Production planning needs membership functions which can easily be adopted for different production scenarios to evaluate production programs. After this introduction, Section 2 presents methods and a methodology, which were used to develop the concept for the evaluation of production programs according sustainable aspects. Section 3 presents the development of the concept considering the selection of sustainable measurements and indicators, the fuzzification process, fuzzy inference process, and defuzzification process. Section 4 offers the conclusions and future works of the concept, followed by references. 2. Methods and Methodology 2.1. System boundaries for sustainable production planning Figure 1 presents the system boundaries (red dash line) of a production system for sustainable planning. The terms and definitions for the processes, input flows, and output flows are adapted from the standard DIN EN ISO 14040 for life cycle assessment [12]. The input flows are structured in three supply categories (raw material supply, ancillary inputs supply, energy supply), and elementary flows (input flows, output flows). In general, the evaluation model considers the impacts caused by the consumption and use of raw materials, ancillary inputs, electrical, and thermal energy. The sustainable impacts caused by external services, such as raw material acquisitions, disposal, ancillary input acquisitions, and energy generations are not considered. Internal processes, such as local recycling of materials, local energy generations (e.g. renewable energy plants, heating boilers, and energy recovery systems),
Fig. 1. System boundaries of the production system.
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the reuse and local generation of ancillary inputs (e.g. air pressure, water), and impacts on employees are considered as part of the production. The elementary flows include the use of elementary resources and releases to air, water and land associated with the production system and internal processes. The considered elementary input flows are climate information (radiation, wind speed, moisture, air pressure), water, air, and fossil fuels (naturel gas, oil). The considered elementary output flows are emissions to water (e.g. sewage), ground (e.g. solid waste), and air (e.g. greenhouse gases). 2.2. Theoretical background on fuzzy logic Fuzzy set theory was developed by Zadeh (1965) [8] to take into account the inherent uncertainty and complexity involved in the process of modelling a real world problem. Fuzzy theory provides the appropriate logical and mathematical tools to represent knowledge and data as linguistic variables, which are complex, imprecise, vague, incomplete, and subjective. Linguistic variables are defined by membership functions, which show the grade of membership of an element to a linguistic term (µA(x)): If an element ‘x’ fully belongs to a linguistic variable (µA(x) = 1), and if an element ‘x’ does not belong to the linguistic variable under consideration (µA(x) = 0). Equation 1 and Figure 2 shows a linear membership function, which describes the grade of membership of an element ‘x’ to the linguistic variable ‘A’ with the minimum value ‘xa’, and the maximum variable ‘xb’ [13]: 0, if x x xa , if x x x μ x ba 1, if x x
(1)
2.3. Methodology to develop the fuzzy inference model A FIM consists of three processes: fuzzification process; fuzzy inference process; and defuzzification process (see Figure 3). With the aid of membership functions, the fuzzification process transforms the input variables to the fuzzy input. The fuzzy inference process combines fuzzy values to create the fuzzy output using ‘If–Then’ rules, logical connectivity operations (e.g. ‘AND’, ‘OR’, ‘NOT’), and aggregation operators (e.g. ‘MIN’, ‘MAX’). The defuzzification process converts the fuzzy output back to a crisp output, which can used for further operations and analysis.
Fig. 3. General structure of the fuzzy inference model.
The methodology to develop the FIM consists of four steps: 1. 2. 3. 4.
3. Development of the Fuzzy Inference Model 3.1. Definition of sustainable indicator for the evaluation of production programs Ranganathan (1998) [14] clearly pointed out that without any agreement on the fundamentals of what and how to measure, the production management will all be awash in a sea of confusing, contradictory, incomplete, and incomparable information. Hence, the selection of the right indicator is relatively important toward the sustainable evaluation of production programs. However, there are several sets, frameworks, and methodologies to select indicators for the evaluation of sustainable manufacturing [14–17], but there is no set of indicator for the sustainable evaluation of production programs. SI can be selected to monitor causes, effects, and impacts on financial status, social well-being, and environment [16]. In general, an indicator is calculated based on measurements from a system. For the evaluation of production programs, a set of criteria is defined to identify applicable measurements for a specific use case. The measurement must be:
Fig. 2. Example of the linear membership µA(x).
Definition of sustainable indicator (SIi) for the evaluation of production programs; Definition of linguistic input and output variables (Aj; Bj; Cj; and Dj) as membership functions (µj(SIi)) for the fuzzification process; Definition of ‘If–Then’ rules, and selection of fuzzy set aggregation operations for the inference process; Defuzzification of the fuzzy outputs (µ(D)) to a crisp value (D) and interpretation of the results to optimize the production program.
Relevant regarding sustainable manufacturing, this includes affected stakeholder groups (e.g. well-being of employees, satisfaction of customers); Understandable and easy to interpret by decision maker (e.g. production scheduler);
Maximilian Zarte et al. / Procedia CIRP 73 (2018) 241–246 Author name / Procedia CIRP 00 (2018) 000–000
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Quantitative predictable by a production planning system and; Assignable to one or more supply categories or elementary flows (see system boundaries).
According to these criteria, three SI categories have been defined evaluating the production inputs, production outputs and the status of sustainable goals: 1. 2. 3.
Degree of used sustainable resources (SIA); Degree of production efficiency (SIB); Degree of achieved sustainable goals (SIC).
The SI “Degree of used sustainable resources” (SIA) evaluate the input flows of the production system. Depending on the purpose of the considered measurements, the SI can control the use of recycled materials (satisfaction of customers) or hazard materials (well-being of employees). The indicator is calculated through the ratio of available sustainable resources (Mi), and the total required resources (MTotal Consumption,i) to produce the products (see Equation 2).
,
(2)
The SI “Degree of production efficiency” (SIB) evaluate the output flows of a production system. Depending on the purpose of the considered measurements, the SI can control the discharge of effluents (well-being of local communities) or emission of greenhouse gases (climate protection). The indicator is calculated through the ratio of caused production outputs (Mi) and the total potential of savings these outputs (MTotal Savings,i) through e.g. reuse of effluents or using renewable energy (see Equation 3).
,
(3)
The SI “Degree of achieved sustainable goals” (SIC) evaluate requirements by stakeholders on sustainable manufacturing. The goals can be set by the enterprise; costumers; suppliers; through benchmarking with local, national, and international competitor; and according to national and international political regulations for e.g. resource efficiency [18]. The indicator is calculated through the ratio of the considered impact (Mi) and the set goal for the impact (MGoal,i), (see Equation 4).
,
Table 1. Definition of the linguistic input variables for the fuzzification. Indices
Linguistic Variables
Linguistic Terms
A
Degree of used sustainable resources
A1: Low Availability A2: Balanced Availability A3: High Availability
B
Degree of production efficiency
B1: Low used potentials B2: Medium used potentials B3: High used potentials
C
Degree of achieved sustainable goals
C1: Goal not achieved C2: Goal partial achieved C3: Goal achieved
The linear membership function for A1; B1; and C1 (red line in Figure 4) is given by Equation 5. 1, , μ,, 0,
The triangular membership function for A2; B2; and C2 (purple line in Figure 4) is given by Equation 6. 0, , μ,, , 0,
(6)
The linear membership function for A3; B3; and C3 (blue line in Figure 4) is given by Equation 7. 0, , μ,, 1,
(7)
Table 2 presents possible values for SPa, SPb, and SPc related to the linguistic input variables. Through small adjustments of the min and max parameters, the presented membership functions can be easily adapted to other uses cases. Future analysis´ are required to find the most applicable min. and max. values for the evaluation of production programs. These values are subjectively defined by the author and can differ depending on the considered enterprises and production scenario.
(4)
3.2. Definition of linguistic input and output variables and construction of membership functions For the fuzzification of the defined SI, three linguistic variables are defined in Table 1 and presented in Figure 4.
(5)
Fig. 4. Functions of the linguistic terms.
Maximilian Zarte et al. / Procedia CIRP 73 (2018) 241–246 Author name / Procedia CIRP 00 (2018) 000–000
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Table 2. Definition of the linguistic input variable min. and max. values. Linguistic Variable
SPa
SPb
SPc
Degree of used sustainable resources
0.5
1
1.5
Degree of production efficiency
0.5
0.75
1
Degree of achieved sustainable goals
0.5
0.75
1
For the defuzzification, a linguistic output variable with triangular membership functions is defined to evaluate the degree of sustainability of the considered production program (see Table 3 and Figure 5). A production system has the degree ‘High Sustainability’, if to the same time: the availability of resources is balanced, all potential for savings are used, and all set production goals achieved. Table 3. Definition of the linguistic output variables for the defuzzification. Indices
Linguistic Variables
Linguistic Terms
D
Degree of sustainability
D1: Low Sustainability D2: Low-Medium Sustainability D3: Medium Sustainability D4: Medium-High Sustainability D5: High Sustainability
3.3. Definition of ‘If–Then’ rules and selection of fuzzy set aggregation operators Fuzzy inference is a two-step process: the implication process and the aggregation process [19]. According to the fuzzy input, the implication process defines a fuzzy conclusion for each ‘If–Then’ rule. The aggregation process then defines an overall fuzzy output for the entire fuzzy rule base. Equation 8 calculates the total number of required ‘If–Then’ (RTotal) which depends on the number of defined SI (n) and the number degrees of the input variables (v) [19].
(8)
In this concept, three degrees are used for the fuzzification of the SI (v = 3).
Fig. 5. Triangular membership for the defuzzification
The number of SI depends on how many SI are a defined by the production planner to evaluate the production program. With higher number of SI, the number of required rules exponentially increases. Figure 6 shows an example for the implication and aggregation process. The example illustrates three possible rules for three defined SI (SI1, SI2, SI3). For SI2 and SI3, the linguistic degrees are the same in all three rules. For SI1, all three possible degrees are used. The implication process (horizontal arrow) defines for each rule the fuzzy output (µj(SIi)). The ‘If–Then’ consists of the three defined SI, which are logical connected by the operator ‘AND’ to express the relationship to the degree of sustainability. The aggregation process (vertical arrow) combine the fuzzy output from each rule using a ‘MIN’ operator (µRi(D)). Through the ‘MIN’ operator, rules which contain fuzzy outputs equal zero, are not considered for determination of the degree of sustainability (see rule 3 in Figure 6). 3.4. Defuzzification of the fuzzy outputs and interpretation of the results Figure 7 presents the overall fuzzy output, which is used for the defuzzification. The defuzzification converts the fuzzy outputs from an area under the curve to a crisp value. The method used most often is the center of gravity method, which defines the fuzzy output as a value that divides the area under the curve into two equal subareas (see red line in Figure 7) [2].
Fig. 6. Implication and aggregation process to evaluate production programs.
Maximilian Zarte et al. / Procedia CIRP 73 (2018) 241–246 Author name / Procedia CIRP 00 (2018) 000–000
246 6
Fig. 7. Example defuzzification to determine the degree of sustainability.
The degree of sustainability (D) is calculated by Equation 9. D
μ μ
References (9)
To increase the degree of sustainability, calculated scores for alternative production programs can be compared and the relation between day time-dependent resources and the degree of sustainability can be evaluated. The following presented actions to create alternative production programs are adopted and extended from a previous study for renewable energy production planning [4]:
optimize the production program increasing the degree of sustainability, flexible processes are required, which can be interrupted or shifted to continue in other times. The main user of the model are production schedulers. To use the model, experience in conventional PPC, and knowledge about the economic, social and environmental consequences of actions to improve the sustainability of the production program are required. In future work, the concept must be implemented and tested in enterprises to determine the benefits of sustainable production planning.
Using machines with lower resource consumption (if applicable); Scheduling of production steps in times of high sustainable resource availability; Start machines in times of high sustainable resource availability and hold in idle mode and; Interruption of processes with continuation later in times of higher sustainable resource availability.
4. Conclusion and Future Work The paper presents a concept for a fuzzy inference model (FIM) to evaluate production programs for short- and mid-term production planning according sustainable indicator (SI). The concept provides a set of criteria to identify applicable measurements for production planning. Based on the measurements, three SI categories are defined to evaluate production programs. With the aid of a FIM, the defined SI are normalized to determine the degree of sustainability of the considered production program. To optimize the production program, the concept provides possible actions to increase the degree of sustainability. To implement this concept, a running production planning system is required. To generate an overview of resources and emissions during the production planning process, the planning system must contain all required production steps, which are necessary for producing desired outputs. Additional to material consumptions, and machine time, the resource data (consumption of e.g. energy, ancillary inputs, elementary flows), and emissions (effluents, waste, greenhouse gases) by specific processes must be available. For sustainable production planning, it is required to extend the planning system with new resources, e.g. those needed for infrastructure (e.g. light, air conditioning) and for maintaining operational capacity including (e.g. renewable plants, processes for the acquisition of ancillary inputs, storages for resources). To
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ScienceDirect
Available online atonline www.sciencedirect.com Available at www.sciencedirect.com
ScienceDirect ScienceDirect
Procedia CIRP 00 (2018) 000–000
www.elsevier.com/locate/procedia
Procedia CIRP 00 (2017) 000–000 Procedia CIRP 73 (2018) 241–246 www.elsevier.com/locate/procedia
10th CIRP Conference on Industrial Product-Service Systems, IPS2 2018, 29-31 May 2018, Linköping, Sweden
28th CIRP Design Conference, May 2018, Nantes, France
Sustainable Evaluation of Production Programs Using a A new methodology to analyze the functional physical architecture of Fuzzy Inference Model – Aand Concept existing products for an assembly oriented product familyc identification a, b Maximilian Zarte *, Agnes Pechmann , Isabel L. Nunes Paul Universidade Stief *, Jean-Yves Dantan, Alain Etienne, Siadat Nova de Lisboa, Campus Caparica, 2829-516 Caparica,Ali Portugal a
University of Applied Sciences Emden/Leer, Constantiaplatz 4, 26723 Emden, Germany c École Nationale Supérieure d’Arts Nova et Métiers, Arts etUNIDEMI, Métiers ParisTech, LCFC EA 4495, 4 Rue Caparica, Augustin Fresnel, Universidade de Lisboa; Campus de Caparica, 2829-516 PortugalMetz 57078, France b
* Corresponding author. Tel.: +49-4921-807-1441; E-mail address: [email protected] * Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract Abstract Within their lifetime, products and processes pass through characteristic periods, which can be divided into distinct phases, such as design, In today’s business environment, the trend the towards more product variety and customization is unbroken. to this development, need of production, use, and disposal. Specially, production phase of products consumes a great amount Due of energy, non-renewablethe materials, agile and reconfigurable production emerged to cope with various products product families. To designand andgreenhouse optimize production renewable materials, ancillary inputs,systems and fossil fuels. Moreover, significant amounts and of emissions (wastes, effluents, gases) are systems as well as lead to choose the optimal product matches, analysis methods are needed. of the known methods aim of to generated, which to sustainable impacts, such as extraproduct costs, environmental damages, socialIndeed, issues. most Through a systematic overview analyze a product or one product family on the physical Different product families, however, differ largely in termsavoided. of the number and resources and emissions during the production planninglevel. process, potential sustainable impacts can may be identified and possibly The paper nature of acomponents. fact inference impedes an efficient comparison and choice of appropriate familyproduction combinations for theaccording production presents concept for This a fuzzy model to evaluate production programs for short- product and mid-term planning to system. A new methodology is proposed existing criteria products viewapplicable of their functional and physical architecture. The aim is to cluster sustainable indicators. For this approach,to theanalyze paper presents toin select measurements for sustainable production planning, three these products in new assembly oriented productproduction families forprograms, the optimization of existing assembly linesinference and the creation reconfigurable categories of sustainable indicators to evaluate a procedure to develop the fuzzy model, of andfuture possible actions for assembly systems. Basedprograms on Datum Chain, physical structure of the is analyzed. Functional subassemblies areimplemented identified, and optimizing production to Flow increase the the degree of sustainability. In products future works, the fuzzy inference model will be in aenterprises functionaltoanalysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the demonstrate the benefits of sustainable production planning. similarity between product familiesElsevier by providing design support to both, production system planners and product designers. An illustrative © © 2018 2018 The The Authors. Authors. Published Published by by Elsevier B.V. B.V. example of a under nail-clipper is used to the proposed methodology. industrial case on study on two Product-Service product familiesSystems. of steering columns of Peer-review of the committee of CIRP Conference Industrial Peer-review under responsibility responsibility ofexplain the scientific scientific committee of the the 10th 10thAn CIRP Conference on Industrial Product-Service Systems. thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. ©Keywords: 2017 TheSustainable Authors. Published by Elsevier B.V.Production Planning Manufacturing; Fuzzy Logic; Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Keywords: Assembly; Design method; Family identification
1. Introduction
The Brundtland Commission defined sustainability as 1.“development Introductionthat meets the needs of the present without compromising the ability of future generations to meet their Dueneeds” to [1]. the With fast the development in thepopulation, domain the of own increase in human communication and an ongoing trend of digitization and demand for products rises. Mass production of these products digitalization, manufacturing enterprises are facing important consumes a great amount of energy, non-renewable materials, challenges in today’s market environments: continuing renewable materials, ancillary inputs, and afossil fuels. tendency towards reductionamounts of product times and Additionally, significant of development waste, effluents, and shortened lifecycles. In addition, there is anproduct increasing emissionsproduct are generated during each stage of the life demand of Within customization, being at the same in a global cycle [2]. their lifetime, products andtime processes pass competition with competitors over the This trend, through characteristic phases,allwhich canworld. be divided into which is inducing the development from macro micro distinct phases, such as design, production, use, and to disposal. markets, in diminished lotonly sizes to augmenting However,results a product is sustainable if itdue is manufactured in product varieties (high-volume to low-volume production) [1]. a sustainable way [3]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 © system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. production is important a precise knowledge
In general, conventional production planning systems consider resources such as material, labor, production capacity, and its respective costs, but neglect the role of sustainable of the product range anda characteristics manufactured and/or aspects [4]. Through systematic overview of required assembled in this system. In this context, the main challenge in resources and caused emissions already during the production modelling and analysis is now not only to cope with single planning process, sustainable impacts can be identified and products, a limitedThe product range and or existing product families, possibly avoided. evaluation optimization of products but also to be able to analyze and to compare products to define and processes according to sustainable criteria is a complicated new product families. It can be observed that classical existing task which involves the consideration of many economic, product families are regrouped in function clientsare or features. social, and environmental aspects. Existingofstudies focused However, assembly oriented product families are hardly find. only on single aspects of sustainability, such as to energy On the product family level, products differ mainly in two planning, waste management, and/or its respective costs, and main characteristics: (i) the number of components and (ii) the environmental impacts. The social dimension of sustainability type of components (e.g. mechanical, electrical, electronical). in particular has been neglected in previous studies [5–7]. Classical methodologies considering singlea products While conventional production planningmainly is already complex or solitary, already existing product families analyze task, the following questions need to be answered for the the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 10th CIRP Conference on Industrial Product-Service Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 10thDesign CIRP Conference Conference2018. on Industrial Product-Service Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.04.012
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integration of sustainable aspects in production planning: What are the degree of freedoms which can be affected through production planning to increase sustainability? What are the potentials in increasing sustainability through sustainable production planning? How can knowledge of possible potentials be used to optimize the production program? And how does this change the procedure for conventional production planning? Assessing the degree of sustainability is a very complex task which needs expert knowledge and experience. Therefore, the application of fuzzy set theory to the assessment of sustainability seems appropriate, since the knowledge regarding sustainability is highly subjective. Fuzzy logic provides the appropriate logical and mathematical tools to represent knowledge and data as linguistic variables, which supports the understanding of the results for non-experience users in sustainable manufacturing [8]. It seems to be a general agreement that a single indicator for the evaluation of sustainability cannot be defined and a set of indicators is necessary to capture all important aspects for sustainable manufacturing [9]. With the aid of fuzzy logic, it is possible to normalize quantitative and qualitative sustainable indicators and to evaluate the indicators with soft thresholds [8]. The paper presents a concept for a fuzzy inference model (FIM) to evaluate production programs for short- and mid-term production planning according to sustainable aspects. The concept shows the criteria for the selection of measurements for sustainable production planning, sustainable indicators to evaluate production programs, a procedure to develop the FIM, and possible actions optimizing production programs to increase the degree of sustainability. The sustainable evaluation of processes and products with fuzzy logic has been demonstrated in previous studies. Inoue et al. (2012) [3] developed a decision-making support method for sustainable product creation in the early phases of the design process considering various design uncertainties and using fuzzy theory for the assessment. Ghadimi et al. (2012) [10] developed a weighted assessment method for product sustainability using the fuzzy analytical hierarchy method. Hemdi et al. (2013) [2] proposed a FIM to determine a comprehensive indicator to assess product and process sustainability as basis for strategic enterprise decision making. Piluso et al. (2010) [11] introduces a fuzzy theory based approach for the effective mid- and long-term assessment of industrial sustainability under the restrictions of data uncertainties. The current literature states that sustainable aspects play an important role for the product and process design, and evaluation considering single phases of product and process life-cycle. Nevertheless, current models for sustainable design of products cannot be adopted for the evaluation of other lifecycle phases, such as production planning and controlling. The problem is that the selected sustainable indicator for product and process design and evaluation targets one aspects of sustainability, such as caused costs, impacts on environment, or on stakeholders. For sustainable production planning, the indicator must show e.g. possible cost savings, potentials for avoiding social and environmental impacts, and the degree of achieved sustainable production goals. The knowledge on
potentials for improvements can be used to optimize production programs. Another problem is, that the defined membership functions are applicable for specific products and processes only. Production planning needs membership functions which can easily be adopted for different production scenarios to evaluate production programs. After this introduction, Section 2 presents methods and a methodology, which were used to develop the concept for the evaluation of production programs according sustainable aspects. Section 3 presents the development of the concept considering the selection of sustainable measurements and indicators, the fuzzification process, fuzzy inference process, and defuzzification process. Section 4 offers the conclusions and future works of the concept, followed by references. 2. Methods and Methodology 2.1. System boundaries for sustainable production planning Figure 1 presents the system boundaries (red dash line) of a production system for sustainable planning. The terms and definitions for the processes, input flows, and output flows are adapted from the standard DIN EN ISO 14040 for life cycle assessment [12]. The input flows are structured in three supply categories (raw material supply, ancillary inputs supply, energy supply), and elementary flows (input flows, output flows). In general, the evaluation model considers the impacts caused by the consumption and use of raw materials, ancillary inputs, electrical, and thermal energy. The sustainable impacts caused by external services, such as raw material acquisitions, disposal, ancillary input acquisitions, and energy generations are not considered. Internal processes, such as local recycling of materials, local energy generations (e.g. renewable energy plants, heating boilers, and energy recovery systems),
Fig. 1. System boundaries of the production system.
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the reuse and local generation of ancillary inputs (e.g. air pressure, water), and impacts on employees are considered as part of the production. The elementary flows include the use of elementary resources and releases to air, water and land associated with the production system and internal processes. The considered elementary input flows are climate information (radiation, wind speed, moisture, air pressure), water, air, and fossil fuels (naturel gas, oil). The considered elementary output flows are emissions to water (e.g. sewage), ground (e.g. solid waste), and air (e.g. greenhouse gases). 2.2. Theoretical background on fuzzy logic Fuzzy set theory was developed by Zadeh (1965) [8] to take into account the inherent uncertainty and complexity involved in the process of modelling a real world problem. Fuzzy theory provides the appropriate logical and mathematical tools to represent knowledge and data as linguistic variables, which are complex, imprecise, vague, incomplete, and subjective. Linguistic variables are defined by membership functions, which show the grade of membership of an element to a linguistic term (µA(x)): If an element ‘x’ fully belongs to a linguistic variable (µA(x) = 1), and if an element ‘x’ does not belong to the linguistic variable under consideration (µA(x) = 0). Equation 1 and Figure 2 shows a linear membership function, which describes the grade of membership of an element ‘x’ to the linguistic variable ‘A’ with the minimum value ‘xa’, and the maximum variable ‘xb’ [13]: 0, if x x xa , if x x x μ x ba 1, if x x
(1)
2.3. Methodology to develop the fuzzy inference model A FIM consists of three processes: fuzzification process; fuzzy inference process; and defuzzification process (see Figure 3). With the aid of membership functions, the fuzzification process transforms the input variables to the fuzzy input. The fuzzy inference process combines fuzzy values to create the fuzzy output using ‘If–Then’ rules, logical connectivity operations (e.g. ‘AND’, ‘OR’, ‘NOT’), and aggregation operators (e.g. ‘MIN’, ‘MAX’). The defuzzification process converts the fuzzy output back to a crisp output, which can used for further operations and analysis.
Fig. 3. General structure of the fuzzy inference model.
The methodology to develop the FIM consists of four steps: 1. 2. 3. 4.
3. Development of the Fuzzy Inference Model 3.1. Definition of sustainable indicator for the evaluation of production programs Ranganathan (1998) [14] clearly pointed out that without any agreement on the fundamentals of what and how to measure, the production management will all be awash in a sea of confusing, contradictory, incomplete, and incomparable information. Hence, the selection of the right indicator is relatively important toward the sustainable evaluation of production programs. However, there are several sets, frameworks, and methodologies to select indicators for the evaluation of sustainable manufacturing [14–17], but there is no set of indicator for the sustainable evaluation of production programs. SI can be selected to monitor causes, effects, and impacts on financial status, social well-being, and environment [16]. In general, an indicator is calculated based on measurements from a system. For the evaluation of production programs, a set of criteria is defined to identify applicable measurements for a specific use case. The measurement must be:
Fig. 2. Example of the linear membership µA(x).
Definition of sustainable indicator (SIi) for the evaluation of production programs; Definition of linguistic input and output variables (Aj; Bj; Cj; and Dj) as membership functions (µj(SIi)) for the fuzzification process; Definition of ‘If–Then’ rules, and selection of fuzzy set aggregation operations for the inference process; Defuzzification of the fuzzy outputs (µ(D)) to a crisp value (D) and interpretation of the results to optimize the production program.
Relevant regarding sustainable manufacturing, this includes affected stakeholder groups (e.g. well-being of employees, satisfaction of customers); Understandable and easy to interpret by decision maker (e.g. production scheduler);
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Quantitative predictable by a production planning system and; Assignable to one or more supply categories or elementary flows (see system boundaries).
According to these criteria, three SI categories have been defined evaluating the production inputs, production outputs and the status of sustainable goals: 1. 2. 3.
Degree of used sustainable resources (SIA); Degree of production efficiency (SIB); Degree of achieved sustainable goals (SIC).
The SI “Degree of used sustainable resources” (SIA) evaluate the input flows of the production system. Depending on the purpose of the considered measurements, the SI can control the use of recycled materials (satisfaction of customers) or hazard materials (well-being of employees). The indicator is calculated through the ratio of available sustainable resources (Mi), and the total required resources (MTotal Consumption,i) to produce the products (see Equation 2).
,
(2)
The SI “Degree of production efficiency” (SIB) evaluate the output flows of a production system. Depending on the purpose of the considered measurements, the SI can control the discharge of effluents (well-being of local communities) or emission of greenhouse gases (climate protection). The indicator is calculated through the ratio of caused production outputs (Mi) and the total potential of savings these outputs (MTotal Savings,i) through e.g. reuse of effluents or using renewable energy (see Equation 3).
,
(3)
The SI “Degree of achieved sustainable goals” (SIC) evaluate requirements by stakeholders on sustainable manufacturing. The goals can be set by the enterprise; costumers; suppliers; through benchmarking with local, national, and international competitor; and according to national and international political regulations for e.g. resource efficiency [18]. The indicator is calculated through the ratio of the considered impact (Mi) and the set goal for the impact (MGoal,i), (see Equation 4).
,
Table 1. Definition of the linguistic input variables for the fuzzification. Indices
Linguistic Variables
Linguistic Terms
A
Degree of used sustainable resources
A1: Low Availability A2: Balanced Availability A3: High Availability
B
Degree of production efficiency
B1: Low used potentials B2: Medium used potentials B3: High used potentials
C
Degree of achieved sustainable goals
C1: Goal not achieved C2: Goal partial achieved C3: Goal achieved
The linear membership function for A1; B1; and C1 (red line in Figure 4) is given by Equation 5. 1, , μ,, 0,
The triangular membership function for A2; B2; and C2 (purple line in Figure 4) is given by Equation 6. 0, , μ,, , 0,
(6)
The linear membership function for A3; B3; and C3 (blue line in Figure 4) is given by Equation 7. 0, , μ,, 1,
(7)
Table 2 presents possible values for SPa, SPb, and SPc related to the linguistic input variables. Through small adjustments of the min and max parameters, the presented membership functions can be easily adapted to other uses cases. Future analysis´ are required to find the most applicable min. and max. values for the evaluation of production programs. These values are subjectively defined by the author and can differ depending on the considered enterprises and production scenario.
(4)
3.2. Definition of linguistic input and output variables and construction of membership functions For the fuzzification of the defined SI, three linguistic variables are defined in Table 1 and presented in Figure 4.
(5)
Fig. 4. Functions of the linguistic terms.
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Table 2. Definition of the linguistic input variable min. and max. values. Linguistic Variable
SPa
SPb
SPc
Degree of used sustainable resources
0.5
1
1.5
Degree of production efficiency
0.5
0.75
1
Degree of achieved sustainable goals
0.5
0.75
1
For the defuzzification, a linguistic output variable with triangular membership functions is defined to evaluate the degree of sustainability of the considered production program (see Table 3 and Figure 5). A production system has the degree ‘High Sustainability’, if to the same time: the availability of resources is balanced, all potential for savings are used, and all set production goals achieved. Table 3. Definition of the linguistic output variables for the defuzzification. Indices
Linguistic Variables
Linguistic Terms
D
Degree of sustainability
D1: Low Sustainability D2: Low-Medium Sustainability D3: Medium Sustainability D4: Medium-High Sustainability D5: High Sustainability
3.3. Definition of ‘If–Then’ rules and selection of fuzzy set aggregation operators Fuzzy inference is a two-step process: the implication process and the aggregation process [19]. According to the fuzzy input, the implication process defines a fuzzy conclusion for each ‘If–Then’ rule. The aggregation process then defines an overall fuzzy output for the entire fuzzy rule base. Equation 8 calculates the total number of required ‘If–Then’ (RTotal) which depends on the number of defined SI (n) and the number degrees of the input variables (v) [19].
(8)
In this concept, three degrees are used for the fuzzification of the SI (v = 3).
Fig. 5. Triangular membership for the defuzzification
The number of SI depends on how many SI are a defined by the production planner to evaluate the production program. With higher number of SI, the number of required rules exponentially increases. Figure 6 shows an example for the implication and aggregation process. The example illustrates three possible rules for three defined SI (SI1, SI2, SI3). For SI2 and SI3, the linguistic degrees are the same in all three rules. For SI1, all three possible degrees are used. The implication process (horizontal arrow) defines for each rule the fuzzy output (µj(SIi)). The ‘If–Then’ consists of the three defined SI, which are logical connected by the operator ‘AND’ to express the relationship to the degree of sustainability. The aggregation process (vertical arrow) combine the fuzzy output from each rule using a ‘MIN’ operator (µRi(D)). Through the ‘MIN’ operator, rules which contain fuzzy outputs equal zero, are not considered for determination of the degree of sustainability (see rule 3 in Figure 6). 3.4. Defuzzification of the fuzzy outputs and interpretation of the results Figure 7 presents the overall fuzzy output, which is used for the defuzzification. The defuzzification converts the fuzzy outputs from an area under the curve to a crisp value. The method used most often is the center of gravity method, which defines the fuzzy output as a value that divides the area under the curve into two equal subareas (see red line in Figure 7) [2].
Fig. 6. Implication and aggregation process to evaluate production programs.
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Fig. 7. Example defuzzification to determine the degree of sustainability.
The degree of sustainability (D) is calculated by Equation 9. D
μ μ
References (9)
To increase the degree of sustainability, calculated scores for alternative production programs can be compared and the relation between day time-dependent resources and the degree of sustainability can be evaluated. The following presented actions to create alternative production programs are adopted and extended from a previous study for renewable energy production planning [4]:
optimize the production program increasing the degree of sustainability, flexible processes are required, which can be interrupted or shifted to continue in other times. The main user of the model are production schedulers. To use the model, experience in conventional PPC, and knowledge about the economic, social and environmental consequences of actions to improve the sustainability of the production program are required. In future work, the concept must be implemented and tested in enterprises to determine the benefits of sustainable production planning.
Using machines with lower resource consumption (if applicable); Scheduling of production steps in times of high sustainable resource availability; Start machines in times of high sustainable resource availability and hold in idle mode and; Interruption of processes with continuation later in times of higher sustainable resource availability.
4. Conclusion and Future Work The paper presents a concept for a fuzzy inference model (FIM) to evaluate production programs for short- and mid-term production planning according sustainable indicator (SI). The concept provides a set of criteria to identify applicable measurements for production planning. Based on the measurements, three SI categories are defined to evaluate production programs. With the aid of a FIM, the defined SI are normalized to determine the degree of sustainability of the considered production program. To optimize the production program, the concept provides possible actions to increase the degree of sustainability. To implement this concept, a running production planning system is required. To generate an overview of resources and emissions during the production planning process, the planning system must contain all required production steps, which are necessary for producing desired outputs. Additional to material consumptions, and machine time, the resource data (consumption of e.g. energy, ancillary inputs, elementary flows), and emissions (effluents, waste, greenhouse gases) by specific processes must be available. For sustainable production planning, it is required to extend the planning system with new resources, e.g. those needed for infrastructure (e.g. light, air conditioning) and for maintaining operational capacity including (e.g. renewable plants, processes for the acquisition of ancillary inputs, storages for resources). To
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