Depletion of Natural Resources
Products consume natural resources throughout their lifecycle, beginning with the manufacturing phase after design finalization, where raw materials and energy are utilized Additionally, energy is expended during the product's use and at its end-of-life (EOL) for disposal, contributing to resource depletion Therefore, the product development process is crucial for exploring strategies to ensure the sustainability and durability of these natural resources The focus is on identifying ways to replenish the materials and energy consumed throughout the product's lifecycle, promoting sustainable design practices.
At the end of its lifespan, a product is typically discarded, but if the scrap can be reprocessed to recover materials for new products, it helps sustain raw material availability (Cagno et al 2000; Lambert and Gupta 2005) Additionally, replacing nonrenewable fossil fuels with renewable energy sources can prevent or reduce energy depletion, ultimately contributing to the sustainability of energy resources.
The Life Cycle Assessment (LCA) is a crucial framework that evaluates a product's journey from development to disposal, highlighting its impact on sustainability This comprehensive analysis, which includes both qualitative and quantitative studies, underscores the importance of a product's end-of-life phase, such as incineration, in preserving natural resources By examining the entire life cycle, LCA effectively determines the sustainability of raw materials and the resources involved, making it an indispensable tool for promoting sustainable practices.
Need for Sustainability
Sustainability refers to the capacity to endure and thrive without depleting resources The concept of "ecological balance" suggests that natural systems typically maintain a stable equilibrium, known as homeostasis Any alteration in one factor affecting this balance triggers corresponding changes in other factors, thereby preserving the original equilibrium of nature However, these reactive changes are often detrimental to creating a better living environment for humans and can disrupt the stability of ecological systems (Fussler and James 1996).
As production escalates, the risk of rapid depletion of Earth's resources increases due to the consumption of raw materials This process not only consumes energy, contributing to the depletion of energy sources, but also generates significant harmful waste that pollutes air, water, and soil Additionally, the lifecycle of products and their disposal further exacerbate environmental impacts through energy consumption and pollution.
The product life cycle stages—material consumption, manufacturing, operation, and disposal—can significantly disrupt the natural balance These phases can compel nature to adjust itself in an effort to restore equilibrium, which may result in adverse ecological conditions.
To address undesirable changes, it is essential to consume materials and energy at a sustainable yield, where consumption does not exceed the regeneration of resources This balance is crucial for achieving sustainability.
To attain sustainable yield, conducting a product life cycle assessment is essential for evaluating material and energy usage, along with the environmental impacts of manufacturing, product operation, and incineration These sustainability steps are vital in preventing negative consequences, ensuring a stable environment, and preserving ecological integrity.
Product Life Cycle
After selecting the product materials and defining manufacturing processes, the product enters the Pre-Production phase, preparing for production At this stage, tangible resources, primarily raw materials sourced from the environment, require processing to become suitable for manufacturing.
Once the material has been appropriately processed for production, it moves into the designated manufacturing phase, marking the Manufacturing stage of the product life cycle This phase encompasses the assembly of various components until the final product is fully prepared for use (Field and Ehrenfeld 1995).
The transportation and distribution of ready-to-use manufactured products from the shop floor to the market and their final operational locations is a critical aspect of supply chain management This phase, known as logistics, plays a vital role in ensuring efficient delivery and effective supply chain operations.
The product's service is integral to the Operation/Use phase of its life cycle Once it reaches the end of its intended lifetime, the customer discards the product, leading to the disposal phase, where recyclable materials are extracted and non-useful components are disposed of During this phase, certain parts are processed back into raw materials, which can be reused to create new products Other components are incinerated, generating energy and waste This cyclical process illustrates the Product Life Cycle (PLC), where the product transitions from material to material, emphasizing sustainability and resource recovery.
Impacts
The Product Life Cycle (PLC) stages are fundamentally influenced by the material used, which dictates the operations of the product Each operation consumes energy, sourced either from renewable options such as wind, hydropower, or solar power, or from nonrenewable sources like fossil fuels and nuclear energy Consequently, the PLC framework highlights the depletion of natural resources associated with nonrenewable energy consumption.
The environmental impact of product life cycles extends beyond tangible resource consumption, encompassing hazardous waste generation in solid, liquid, and gaseous forms Solid waste, including unusable raw materials and energy-depleted charcoal, contaminates landfills and disrupts local ecosystems Liquid waste, such as coolants and purifying chemicals, pollutes water bodies and degrades land arability Gaseous pollutants, primarily from fuel combustion, contribute to air pollution and acid rain, adversely affecting both human health and biodiversity Understanding the product life cycle is essential for identifying the causes and effects of these environmental impacts, enabling the implementation of necessary measures to promote sustainability in materials, resources, and the environment.
The impacts of product design can be categorized into five key types: carbon footprints, energy consumption, air acidification, water eutrophication, and water footprints To effectively integrate these impacts into the product design phase, it is essential to quantify them and provide designers with tools to assess sustainability These five types of impacts are referred to as impact metrics A software tool that incorporates these metrics would be instrumental for designers, enabling them to explore various "what if" scenarios and analyze the environmental implications of their designs (Murayama et al 1999).
Impact Metrics
Carbon Footprints
The carbon footprint measures the total greenhouse gases emitted throughout a product's life cycle, quantified in kilograms, pounds, or metric tons of CO2 equivalent Carbon dioxide is the primary greenhouse gas, but others such as water vapor, methane, ozone, and nitrous oxide also contribute to this footprint The impact of these gases is often expressed in CO2 equivalents to simplify understanding Any positive carbon footprint contributes to global warming, threatening species extinction due to temperature increases and causing ecological changes and ozone layer depletion, which lead to significant environmental instability.
As an example, the emission of an automotive can be calculated using the following equation (http://sustainability.rice.edu/Content.aspx?id#97):
According to the US Environmental Protection Agency (EPA), a vehicle that consumes 11.76 gallons of gasoline weekly emits approximately 12,500 pounds of CO2 annually Additionally, the global average greenhouse gas emissions from household waste, including aluminum, steel cans, plastic, glass, and paper, is estimated at 1,027 pounds.
CO2 per year For more detailed information, visit www.epa.gov.
Energy Consumption
Energy consumption is a crucial element in the Product Life Cycle (PLC), impacting every phase from manufacturing to transportation and product usage It encompasses the depletion of nonrenewable natural resources, measured in megajoules A positive energy consumption value indicates a corresponding reduction in these finite energy sources, highlighting the importance of sustainable practices in production and consumption.
According to the US Energy Information Administration (EIA), the United States accounts for 20% of global energy consumption, utilizing various sources such as oil, gas, nuclear, and renewable energy This consumption is estimated at 98.6 quadrillion BTUs, which is approximately equivalent to 1.04 × 10^14 megajoules.
Air Acidification
Burning fossil fuels releases emissions like carbon dioxide (CO2), sulfur dioxide (SO2), and nitrogen oxides (NOx), which react with atmospheric water vapor to form acids such as carbonic acid (H2CO3), nitric acid (HNO3), and sulfuric acid (H2SO4) This process leads to air acidification and acid rain, negatively impacting tree growth and contaminating groundwater Air acidification is measured in kilograms of sulfur dioxide (SO2e) equivalent.
Air acidification is primarily driven by emissions from vehicles, power plants, and manufacturing, contributing significantly to sulfur dioxide (SO2) and nitrogen oxides (NOx) pollution Domestic activities, including the use of cooking gas and air conditioning, also contribute to this issue According to the EPA, street traffic accounts for about 50% of NOx and non-methane volatile organic compounds (NMVOCs) emissions In response to these environmental challenges, the United Nations Economic Commission for Europe introduced the Oslo Protocol in 1994, aimed at reducing sulfur emissions by lowering sulfur content in fuels and exploring alternative oil derivatives This protocol has demonstrated significant efficiency on a large scale For further information, visit [UNECE](http://www.unece.org/env/lrtap/fsulf_h1.html).
Water Eutrophication
Water eutrophication occurs due to the discharge of waste from material extraction, manufacturing processes, and agricultural fertilizers rich in phosphates and nitrates Industrial waste, including emissions from vehicles, significantly contributes to this issue Various industries, such as thermal power plants, pesticide production, and metal plating, generate toxic wastewater containing harmful substances like phosphates, nitrogen, and suspended solids, which, when released into water bodies, exacerbate eutrophication and harm aquatic ecosystems While most power plants and industries implement wastewater treatment systems before disposal, the environmental impact is often quantified in terms of kilograms of phosphates equivalent.
Water eutrophication can be computed using the following equation:
Water eutrophication is assessed using the Total Nutrient Status Index (TNI), which evaluates the nutrient levels in water bodies like lakes and rivers The TNI aggregates various nutrient parameters, where each parameter's contribution is represented by its proportion in the index Additionally, the relationship between chlorophyll and other nutrient parameters is considered For more detailed information, refer to the National Center for Biotechnology Information Journal at [NCBI](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2266883/).
Water Footprints
The various stages of the product life cycle (PLC) significantly deplete freshwater resources, leading to a shortage of this essential resource Given that humanity depends heavily on freshwater, measuring water consumption in cubic meters (m³) per year serves as a vital sustainability metric The water footprint acts as a clear indicator of the total volume of water utilized throughout the PLC.
According to the Water Footprint Network, the global average water footprint for nonrenewable energy sources is as follows: natural gas at 0.11 m³, coal at 0.16 m³, crude oil at 1.11 m³, and uranium at 0.09 m³ In contrast, renewable energy sources have varying water footprints, with wind energy at 0.00 m³, thermal energy at 0.27 m³, and hydropower significantly higher at 22 m³.
Life Cycle Assessment
Life Cycle Assessment (LCA) is a comprehensive method used to evaluate the environmental impacts throughout all stages of a product's life cycle By quantifying various impact metrics, LCA enables us to assess the overall environmental effects of a product, ensuring a thorough understanding of its sustainability.
◾ Identify all interactions between a given activity in the PLC and the environment.
◾ Quantify each of the interactions in terms of the negative impacts on the environment using the standard impact metrics.
◾ Evaluate the total load on the environment using the quantification that covers all interactions of the PLC with the environment.
Finding the interaction of each stage of the PLC with the environment can be represented as shown in Table 1.1.
Design for Sustainability
After evaluating the Product Life Cycle (PLC) through Life Cycle Assessment (LCA), we focus on the crucial connection between design and sustainability Increased emissions of CO2, SO2, and NOx contribute to a larger carbon footprint and greater air acidification Additionally, excessive by-products released into water bodies lead to heightened water eutrophication, while increased water consumption raises the water footprint Consequently, the overall undesirable impacts correlate with elevated impact metrics To attain sustainability and effective eco-design, it is essential to minimize all impact metrics.
Table 1.1 LCA Stages and Interactions
Step LCA Stage LCA—Interaction
1 Material consumption Material taken from the environment.
2 Material processing Consumes energy and gives out pollutants and effluents.
3 Transportation Material taken from environment For packaging and fuel consumption for logistics.
4 Usage Fuel may be consumed (natural resource consumed as energy) as per the product use.
5 Recycle/incineration Recycle may have a positive impact (recycle) and a negative impact (incinerate) metric.
The product design significantly impacts the entire Product Life Cycle (PLC), necessitating informed decisions throughout the design process to reduce environmental impact at each stage Therefore, design for sustainability focuses on minimizing a product's negative effects on the environment, leading to eco-design or green design practices.
Design Considerations for Sustainability
Efficient Energy Consumption
Designers must prioritize energy optimization throughout every stage of the product life cycle (PLC) by creating energy-efficient products For instance, electric and hybrid vehicles demonstrate greater energy efficiency and lower energy consumption compared to traditional gasoline-powered cars, while also contributing to reduced air pollution.
Renewable Energy Sources
The key distinction between renewable and nonrenewable energy lies in their regeneration times; renewable sources like wind, solar, and hydropower are sustainable and replenish naturally, while nonrenewable sources such as fossil fuels require millions of years to form Developing new products that harness renewable energy is crucial for conserving fossil fuels and reducing environmental impact, thereby promoting a sustainable future Additionally, renewable energy sources positively affect the environment by minimizing pollution and reducing dependence on finite resources.
It conserves the use of natural resources (fossil fuel reserve) and is less harmful to the environment.
Appropriate Materials
Material selection is crucial in product manufacturing, as it influences the entire production process It is essential to choose materials that are readily available, environmentally safe, and recyclable to minimize the use of natural resources Incorporating recycled materials in product development is vital for sustainability For instance, automotive manufacturers prioritize lightweight materials to enhance fuel efficiency and reduce environmental impact during operation Similarly, in construction, the choice of pipe materials is significant; replacing harmful PVC pipes with biodegradable options like Polylactic Acid (PLA) can mitigate environmental damage.
Efficient Manufacturing Processes
To enhance energy efficiency in manufacturing, it is crucial to select appropriate processes and machinery Designers must possess a strong understanding of materials and techniques to make informed choices For instance, opting for rapid prototyping instead of casting can yield the same part while promoting greater energy efficiency.
Quality Manufacturing
Quality plays a crucial role in product development, influencing both costs and sustainability By prioritizing higher quality standards, companies can minimize scrap and waste, conserving materials and reducing environmental impact Implementing methodologies like Six Sigma and rigorous quality control measures is essential to decrease rework and waste.
Quality Product and Longer Product Life
Durable and long-lasting products significantly minimize environmental impact by reducing the need for servicing and repairs Higher product quality leads to fewer damages, such as those from falls, which in turn lowers energy consumption associated with repairs Ultimately, prioritizing durability not only enhances product longevity but also contributes to a more sustainable environment.
Reuse and Recycle
All manufactured products should be designed for recycling and reuse after their intended service (Zussman et al 1994) It is essential for manufacturers to create products that can be maximally recycled at the end of their lifecycle Mining processes for materials like steel and aluminum significantly harm the environment by depleting resources and causing pollution Recycling helps mitigate these impacts For instance, IBM has launched a program to recycle silicon wafers and chips, collecting silicon waste from various industries to produce new silicon wafers, which, while not suitable for production, can be utilized for testing and experimentation.
Efficient Transportation
Transportation predominantly relies on nonrenewable resources, but there is a growing shift towards alternative sources powered by renewable energy or efficient electric systems Many automotive manufacturers traditionally assemble complete vehicles for distribution; however, a more efficient approach could involve transporting key components to strategic locations for assembly near major distribution centers.
Material Management
Effective material management during the product design phase significantly reduces environmental impact (Ginley and Cahen 2011) For instance, girders are often designed with an 'I' cross-section, optimizing strength while using less material Additionally, modern construction techniques have introduced hollow bricks, which conserve raw materials while maintaining structural integrity Another example is the design of plastic chairs with holes, which enhances material efficiency and lowers manufacturing costs Furthermore, the use of carbon nanotubes has become prevalent in enhancing the strength of lightweight materials.
Sustainability Assessment
The Material Effect
The final design of a product significantly influences its entire life cycle, from manufacturing to usage and eventual retirement Specifically, the choice of materials plays a crucial role in determining subsequent phases, including pre-production, manufacturing, and end-of-life processing Evaluating the environmental impact of materials during the design phase is essential (Warhurst 2002) Comparing different materials and their environmental effects is fundamental to sustainable design practices (Rosy et al 1993).
Assessment of Sustainability
After selecting a product material, the Life Cycle Assessment (LCA) quantifies the impact metrics Section 6 details these metrics and their calculation methods The LCA process can be summarized as a series of sequential steps.
1 Define product geometry and shape.
4 Select the place of manufacture.
5 Estimate the logistics overhead to the environment.
6 Estimate the product lifetime, and service time impacts.
7 Estimate the incineration, and recycle impacts.
8 Compute the cumulative impact, and document the results.
The LCA "tree" consists of various "nodes" where environmental impact optimization can occur Sustainability assessment focuses on determining the most environmentally optimal value at each node, aiming to minimize the overall impact of the PLC This approach promotes greener and more eco-friendly design practices (Pennington et al 2000).
In a sustainability assessment, the Life Cycle Assessment (LCA) of a chosen material is evaluated, establishing baseline impact metrics for the analysis Additional assessments can explore various materials, allowing for a comparative analysis of impact metrics across all evaluated options The material demonstrating the lowest impact metrics is identified as the optimal design, leading to a green and sustainable solution.
Sustainability Software
To effectively integrate sustainability and Life Cycle Assessment (LCA) into design, it is essential to quantify impact metrics, making them suitable for incorporation into CAD software Designers can utilize available tools like Dassault Systems SolidWorks’ Sustainability Xpress, PTC’s Windchill Product Analytics, and PE International’s GaBi LCA Software Platform to conduct sustainability assessments By following the outlined steps in Section 10, designers can successfully implement sustainable design practices within their CAD systems.
1 Create the part CAD model.
2 Assign material to the model.
3 Select a manufacturing process CAD systems aid in this selection based on the material.
4 Select the manufacturing region for the part.
5 Select the use region of the part.
7 Set the baseline This baseline is the impact metrics values.
8 Iterate steps 2 to 6 with different materials and processes.
9 Select the best design based on the impact metrics values.
Case Studies
Case 1: Sprocket Sustainable Design
We assess a 14-tooth sprocket that meets the American National Standards Institute (ANSI) standard, primarily utilized in chain-driven applications like automotive systems and pumps This sprocket effectively transmits rotary motion between shafts, serving as an ideal alternative when gears are not suitable.
The sustainability analysis of the sprocket requires us to establish the environ- mental baseline first The SolidWorks workflow to establish the baseline is
◾ Select Sustainability or Tools (menu).
The detailed steps are as follows:
Step 1: Select material, based on Class (Steel) and Name (Alloy Steel) criteria in the tool (Figure 1.2).
Step 2: Select the appropriate Manufacturing Process (CNC Milling) and
Manufacturing Region (China, Asia) (Figure 1.3).
Step 3: To complete the baseline setting process, SolidWorks Sustainability tool requires the transportation and use region of the product to be specified North America was selected as the “Use Region,” considering it is one of the largest consumers of automobiles (Figure 1.4; as per CNBC’s recent global survey for World’s Largest Auto Markets - http://www.cnbc.com/ id/44481705/World_s_10_Largest_Auto_Markets?slide).
Step 4: Baseline can be set using this icon, located at the bottom of the tool.
The Sustainability tool calculates and reports metrics based on the impact factors derived from all stages of a product's life cycle, ensuring comprehensive assessment through every input.
The consolidated reports are generated in a pie chart format (Figure 1.5).
The ANSI standard 14-teeth sprocket has a total environmental impact of 0.51 kg CO2 emissions, 5.72 MJ of energy consumption, 2.92 × 10 −3 kg SO2 (equivalent) of sulfate, and 3.92 × 10 −4 kg PO4 (equivalent) of phosphate SolidWorks allows for the assessment of impact metrics at each stage of the product's life cycle The carbon footprint, primarily consisting of carbon dioxide and greenhouse gases, is illustrated in Figure 1.6(i) Figure 1.6(ii) depicts the energy used in procuring steel, manufacturing the sprocket, and its disposal, encompassing all nonrenewable energy resources and energy conversion processes Additionally, Figure 1.6(iii) highlights the air acidification throughout the sprocket's life cycle.
The CAD model and engineering drawing of a sprocket are illustrated in Figure 1.1 Additionally, Figure 1.6(iv) highlights the water eutrophication effects throughout the entire life cycle of the sprocket By aggregating the impact metrics at each stage, we can determine the total impact metrics for the product's life cycle.
A sustainability assessment of the sprocket was conducted using the SolidWorks® sustainability tool, evaluating various design alternatives The results, detailed in Tables 1.2 to 1.4, indicate that switching the sprocket material from iron to aluminum does not improve sustainability, as iron proves to be the more sustainable option despite similar water eutrophication levels for both materials Furthermore, the analysis in Table 1.3 reveals that milling is significantly more environmentally friendly than sand casting Lastly, Table 1.4 highlights that manufacturing in India is more sustainable compared to other locations.
Figure 1.2 Step 1 of Establishing the Baseline.
Figure 1.3 Step 2 of Establishing the Baseline. parts of Asia including China This is due to the transportation distance India is closer to the United States than is Asia.
The evaluation of six design alternatives is illustrated in Figure 1.7, which displays the designs (cases) along the X-axis and their corresponding impact metrics along the Y-axis.
Figure 1.4 Step 3 of Establishing the Baseline.
Figure 1.7 illustrates the impacts of material changes (A & B), manufacturing process alterations (C & D), and shifts in manufacturing regions (E & F) The Life Cycle Assessment (LCA) approach is essential for evaluating a product's environmental effects throughout its entire lifecycle, from material selection to product usage Each scenario is represented by four impact metrics displayed on the Y-axis, providing a comprehensive overview of sustainability considerations.
Assessment case D demonstrates the highest environmental impact, encompassing factors such as carbon footprint, energy consumption, air acidification, and water eutrophication Therefore, the materials and processes used in case D should be avoided A comparison between cases D and C, which share many life cycle stages, highlights that the choice of manufacturing processes is a critical factor in mitigating environmental effects.
(i) Carbon footprint (ii) Energy consumption
(iii) Air acidification (iv) Water eutrophication
Figure 1.6 Impact metrics of a sprocket.
In a case study focusing on ductile iron produced through sand casting in Asia for use in North America, key environmental metrics were assessed The carbon footprint was measured at a significant level, contributing to greenhouse gas emissions Energy consumption during the manufacturing process was quantified in megajoules, indicating the resource intensity of production Additionally, the potential for air acidification was evaluated, expressed in terms of sulfur dioxide emissions, while the impact on water eutrophication was represented through phosphate levels These criteria highlight the environmental implications of material selection and manufacturing processes in the context of sustainability.
0.42 3.77 2.01 2.16 B Material: Aluminum (1060 Aluminum Allo y) Manuf acturing Process: Sand Casting Manuf acturing Region: Asia Use Region: Nor th America
Table 1.3Manufacturing Process Effect Case Design Cr it er ia
The manufacturing of alloy steel through CNC milling in Asia results in a carbon footprint of X kg CO2, consumes Y MJ of energy, and contributes to air acidification at a rate of Z × 10−3 kg SO2 Additionally, this process leads to water eutrophication quantified at A × 10−4 kg PO4, with the final product being utilized in North America.
0.51 5.72 2.92 3.92 D Material: Steel (Allo y Steel) Manuf acturing Process: Machined Sand Casting Manuf acturing Region: Asia Use Region: Nor th America
The manufacturing region significantly impacts the environmental metrics of products, as illustrated in Table 1.4, which outlines the case design criteria for ANSI 4130 steel The carbon footprint is measured in kilograms of CO2, while energy consumption is quantified in megajoules (MJ) Additionally, the air acidification potential is expressed in kilograms of SO2, and water eutrophication is indicated in kilograms of PO4 This analysis focuses on CNC milling as the manufacturing process in Asia, with the final product intended for use in North America.
0.48 5.50 2.93 3.97 F Material: Steel (ANSI 4130 Steel) Manuf acturing Process: CNC Milling Manuf acturing Region: India Use Region: Nor th America
The analysis reveals that machined sand casting should be avoided for improved environmental benefits, particularly for a specific PLC Case A demonstrates the best overall performance and optimization for developing products like sprockets In contrast, Case B, which involves a different material, leads to increased energy consumption, air acidification, and a larger carbon footprint Cases E and F show similar outcomes, with variations attributed to differences in manufacturing regions.
Case 2
In Case 2, a standard assembly featuring hex bolts and nuts was assessed, characterized by a length of 1.992 inches, a head diameter of 0.709 inches, a head thickness of 0.272 inches, and a pitch diameter of 0.21 inches, adhering to a class 3A thread specification The assembly is visually represented in Figure 1.8, showcasing the CAD model.
This case study examines an assembly process under two distinct scenarios: the first involves manufacturing bolts and nuts in separate regions and transporting them directly to the assembly location, while the second scenario entails transporting hex bolts to the nut manufacturing region before shipping both components together to the assembly site The results of this analysis are illustrated in Figure 1.9.
The initial design, illustrated in Figure 1.9, reveals a total environmental impact comprising a carbon footprint of 1.68 kg CO2, energy consumption of 19.67 MJ, air acidification measured at 10.34 × 10 −3 kg SO2, and water eutrophication quantified at 6.1 × 10 −4 kg PO4 for both the blot and the nut.
Carbon footprint (in kg CO 2 ) Energy (in Mega Joules) Air acidification ×10 –3 (in kg SO 2 ) Water eutrophication ×10 –4 (in kg PO 4 )
Figure 1.7 Impact metrics for the six sprocket designs.
(a) CAD model of a standard hex bolt and nut, created in SolidWorks 2011
Figure 1.8 CAD model of a bolt-nut assembly.
(i) Hex bolt (ii) Hex nut
Figure 1.9 Impact metrics of a bolt-nut assembly.
The second design demonstrates improved sustainability, with environmental impact metrics showing a carbon footprint of 1.242 kg of CO2, energy consumption of 14.61 MJ, air acidification measured at 7.69 × 10 −3 kg of SO2, and water eutrophication at 2.75 × 10 −4 kg of PO4.
Conclusion
This chapter explores the principles and practices of sustainable design philosophy, emphasizing the need for user-friendly design tools to make sustainability mainstream Designers require quantitative analysis to evaluate design alternatives effectively Understanding the fundamentals of the product life cycle and life cycle assessment (LCA) is essential for designers to interpret the results from sustainability tools like SolidWorks Sustainability Xpress.
Pratheep Ayyamperumal holds a “Bachelor of Engineering” degree in Mechanical
An alumnus of the College of Engineering Guindy at Anna University in Chennai, India, he possesses six years of software engineering experience focused on CAD data interoperability and PLM tools at Triad Software Private Limited Additionally, he earned a Master of Science degree in Computer Systems Engineering from Northeastern University in Boston, MA, USA His professional interests encompass CAD/PLM and software product development.
Ranjit Vinu is a graduate student at Northeastern University pursuing MS in
Computer Systems Engineering professional with an undergraduate degree in Mechanical Engineering from Visvesvaraya Technological University Possesses extensive experience in CAD/CAM tools, CAD Data Management, and Product Lifecycle Management, having spent three years with PTC® Areas of interest include CAD Mathematics, Data Structures, and Parallel Programming.
Abe Zeid is a Professor with the Department of Mechanical and Industrial
Dr Zeid, an esteemed faculty member at Northeastern University, specializes in engineering research focused on mobile agents for enhanced information access in manufacturing, XML-based algorithms for mass customization, and a Java/Web-based system for disassembly analysis This innovative system enables users to disassemble PC components and assess the associated disassembly costs Additionally, Dr Zeid has authored textbooks on CAD/CAM and Internet technologies and is recognized as a Fellow of the American Society of Mechanical Engineers (ASME).
Sagar Kamarthi received his PhD in Industrial Engineering from the Pennsylvania
Since 1994, he has been associated with State University, where his research interests encompass prognostics and health management, as well as mass customization Recently, he has concentrated his efforts on scalable nanomanufacturing and the customization of healthcare solutions.
He worked on several NSF-funded research projects and published his research contributions in reputed journals.
Tucker J Marion is assistant professor in Northeastern University’s College of
Dr Marion, a faculty member at the School of Technological Entrepreneurship, specializes in product development, innovation, and entrepreneurship With extensive experience in product development and manufacturing at major companies, he has also co-founded multiple start-ups He earned a mechanical engineering degree from Bucknell University, a Master's in technology management from the University of Pennsylvania and Wharton School, and a PhD in industrial engineering from Penn State His research has been published in the Journal of Product Innovation.
Management, Design Studies, Research-Technology Management, and International
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Research and Development Engineer, Powell Industries, Inc., Houston, Texas
The Cellular Manufacturing System (CMS) has evolved significantly, reflecting a shift in manufacturing and production systems over time Its historical development is closely tied to the principles of Group Technology, which emphasizes the grouping of similar products to enhance efficiency While CMS offers numerous advantages, such as improved flexibility and reduced lead times, it also presents certain disadvantages that must be considered Key assumptions underpinning CMS include the need for effective organization and resource allocation Major topics within CMS encompass various flexibilities—routing, machine, process, product, volume, and product-part mix—that contribute to its adaptability Additionally, production issues, constraints, cell formation objectives, layouts, and data structures play crucial roles in optimizing CMS performance Understanding these elements is essential for leveraging the full potential of Cellular Manufacturing Systems in modern production environments.
2.1 Background of Cellular Manufacturing System
In today's modern landscape, manufacturing industries remain crucial to the wealth-generating activities of both developed and developing nations As global competition in manufacturing intensifies and consumer expectations evolve, traditional mass and job shop production methods are increasingly inadequate for meeting the demand for diverse, small-lot-size products Companies must adapt to rapid market changes characterized by a need for variety, specialty, and flexibility, as well as shorter product life cycles This shift towards multi-product, small-lot-size production, also known as batch-type production, presents challenges such as increased production item variety, complex processes, and difficulties in planning and scheduling To address these challenges, it is essential to develop effective theories and systems, leading to the adoption of various manufacturing techniques and philosophies, including Production Batch Control (PBC), Just-In-Time (JIT), Materials Resource Planning II (MRP II), and Flexible Manufacturing Systems (FMS).
Cell formation techniques play a crucial role in optimizing manufacturing processes, with various methods such as visual inspection, part classification, and product flow analysis Advanced approaches include mathematical programming, p-median formulation, heuristic techniques, graph partitioning, and artificial intelligence methods Additionally, generic algorithms, simulated annealing, simulation, and similarity coefficient methods contribute to effective cell design and formation strategies Performance measurements are essential for evaluating these techniques, underscoring the significance of Group Technology (GT) in addressing contemporary manufacturing challenges Manufacturers aiming to remain competitive in today's market should prioritize the adoption of GT principles.
Group Technology (GT) is a manufacturing philosophy that focuses on identifying and grouping similar parts and processes to enhance production efficiency By leveraging similarities in design and manufacturing, GT promotes effective design rationalization, data retrieval, and standardization, leading to improved operational efficiency (Ham 1985) Traditionally, batch-type manufacturing treats each part as unique, but GT enables the formation of part families based on design attributes or operational processes This approach offers numerous benefits, including mass production effects, streamlined process routes, reduced setup times and costs, simplified material handling, and standardized production processes (Ham 1985).
Cellular Manufacturing Systems (CMS) is a vital application of Group Technology (GT) in production systems, focusing on processing part families with similarities using dedicated machine cells These machine cells consist of functionally independent, dissimilar machines clustered together on the production floor Unlike traditional flow shop environments that emphasize high productivity and low flexibility, or job shop environments that offer high flexibility but low productivity, CMS presents a unique manufacturing approach that balances efficiency and adaptability.
As in the overlapping of flow shop and job shop, CMS seeks to deliver both high productivity and high flexibility.
2.2 Evolution of Manufacturing/Production Systems
Manufacturing systems focus on transforming raw materials into unique products or multiple copies, aiming to produce high-quality items at low costs and within tight timelines To achieve this, manufacturers have consistently sought technological innovations, upgrades in machine tools, enhancements in computing technologies, and new management strategies Researchers and practitioners primarily emphasize technological advancements to overcome these production challenges.
As early as the 6th century B.C., the Phoenicians were known for their large-scale brick production, which relied heavily on extensive labor Their manufacturing methods were distinctly different from contemporary production practices, as they did not adhere to a single production principle recognized today.
Manufacturing can be traced back to the early 1900s, driven by new inventions and the rise of mechanized methodologies (Wild 1972) The evolution of manufacturing systems relied heavily on product design and power sources, with water and steam power playing significant roles This led to the emergence of flow shop (mass) production, characterized by large-scale production, cost efficiency, and high throughput rates, but lacking flexibility and requiring substantial initial investment The Industrial Revolution's innovations, particularly in the textile industry, facilitated mass production through advanced tools like lathes and drilling machines (Wild 1972) Mass production features quantity production, involving the semicontinuous use of specialized equipment for large quantities, and flow production, which addresses the complexity of manufacturing composite items Flow production relies on continuous product movement through various facilities, with a focus on achieving part interchangeability and high accuracy for complex items (Wild 1972) In summary, mass production encompasses two technologies: variety production and flow production, with variety production serving as a precursor to flow production.
Design for Variety
Design for Variety (DFV) is an innovative methodology aimed at assessing the impact of product variety on manufacturing systems, ensuring customer needs are met efficiently and cost-effectively Unlike traditional methods that prioritize minimum cost by reducing variety, DFV recognizes the necessity for diverse product offerings in response to global market changes and competition Companies are increasingly pressured to shorten time-to-market, exemplified by Toyota's reduction of automobile development time from 24 to 18 months The DFV approach focuses on identifying standard models and implementing strategies that streamline product design and processes, ultimately leading to reduced process time, inventory, and logistics costs By employing cost indices, DFV provides insights into the true costs of introducing new product variants, enabling better decision-making for managers and engineers.
◾ Differentiate as late as possible
◾ Shorten the time between the processes
◾ Delay the addition of value to the product to later in the process flow.
Design for Value (DFV) integrates concurrent engineering and a holistic approach to design and manufacturing, emphasizing the importance of considering marketing, service, and other product life cycle factors that influence cost and profitability It highlights the necessity of managing product variety and variants in alignment with customer demand Most existing literature on product variety has concentrated on the foundational work by Da Silveira in 1998.
◾ Its importance within the competitive strategy
◾ Its impact on operations performance
◾ The use of flexibility for dealing with product and their variety in operations strategy
Product variety management is crucial for businesses, focusing on either the importance or performance of product offerings To address importance, companies may choose to limit product variety or redefine their objectives through strategies like priority setting, focused manufacturing, or mass customization On the performance side, enhancing variety performance can be achieved by increasing flexibility through methods such as setup reduction, cellular manufacturing, design for manufacturability, and the adoption of flexible technologies Understanding the distinction between product variety importance and performance is essential for effective management.
◾ The strategic importance of product variety
◾ The impact of increasing variety in performance
◾ The management of product and part variety through adaptive flexibility strategies
◾ The flexibility types related to flexibility strategies
Costs of Variety
Increasing product variety leads to more features per part, resulting in greater complexity in both design and manufacturing processes This complexity drives up costs, necessitating thorough analysis to determine the optimal level of variety that balances the benefits of both volume and diversity Product architecture is evaluated based on the number of functions per component; for instance, modular architecture features fewer functions per component, while integral architecture incorporates a higher number of functions.
Product Variety Performance Flexibility Strategies
Figure 7.1 The role of adaptive and flexibility strategies in product variety management.
Expanding a product line with lower volumes for each item can lead to increased unit costs due to higher overhead expenses While opting for a modular architecture may compromise some performance, it offers significant advantages in maintainability, manufacturability, and serviceability Various methods for achieving modularity have been discussed in earlier sections Additionally, designers need to assess both the direct and indirect costs associated with introducing product variety, as outlined by Martin and Ishii (1996).
◾ Engineering time to make new drawings, analyze the new design, run qualification tests, etc.
The indirect costs (overhead) of adding variety are (Martin and Ishii 1996)
◾ Work-in-process (WIP) inventory
◾ Reduction in capacity due to setups
◾ Increased logistics of managing variety
The indirect costs are always difficult to determine and, in many cases, estimated values are used for analysis.
The cost of variety is influenced by the number of functions and features in a part Incorporating fewer features leads to lower assembly costs, while adding numerous functions increases design complexity, resulting in higher part costs despite a smaller production volume.
A proposed measure of cost of variety is (Prasad 1997; Martin and Ishii 1996):
Cost of variety = min cost of manufacturing an assembly * (1 – C v) + max cost of manufacturing an assembly * (C v) where
0 ≤ αi ≤ 1 i = 1,2,3 α1 = Number of variation in the product α2 = Time measured to the finish stage α3 = Changeover effort
These parameters are user defined The impact of design variety on the total cost is shown in Figure 7.2 (Christiansen 2003).
Manufacturing costs are influenced by product variety and production volume, with costs decreasing as total volume increases through mass production However, as product variety rises, complexity increases, leading to higher costs due to additional setup, material handling, inventory, and overhead expenses The goal is to identify the optimal balance between volume and variety to minimize overall costs Cellular and flexible manufacturing systems effectively enhance product variety while controlling production costs A framework illustrating the direct and indirect impacts of product variety on manufacturing is depicted in Figure 7.3 (Yeb and Chu 1991).
Qualitative Methods for Managing Product Variety
Product Structure Graph
The Product Structure Graph (PSG) provides a hierarchical visualization of product variety, enabling engineers to concentrate on essential features and streamline offerings by eliminating unnecessary variants It aids in distinguishing standardized components from those that can be modularized Inputs for the PSG typically encompass the scope of components and features within the product line, which may derive from Quality Function Deployment (QFD) analysis or involve product permutations and their combinations that yield various product variants.
The product structure graph, which includes various product variants, serves as a valuable tool for designers aiming to optimize design combinations and enhance manufacturing processes while minimizing investment costs Although it primarily offers a qualitative overview, this graph, along with complexity measures, has been effectively applied to diverse challenges such as automotive window regulators, heat trace cable connectors, and hard disk drives By clarifying the overall product structure and identifying cost drivers, the graph provides a visual guide for potential redesign opportunities.
Process Sequence Graph
The process sequence graph depicts the flow of a manufacturing process and highlights key differentiation points By delaying product differentiation until later in the assembly phase, companies can lower inventory costs and simplify their manufacturing systems (Lee and Billington, 1993) The effectiveness of these strategies is influenced by the manufacturing time relative to the required lead time (Martin and Ishii, 1997).
Commonality Graph Method
Another method used to manage the variety and its cost is the application of the commonality graph method In this method, a series of charts are developed
The impact of product variety on manufacturing systems is significant, as illustrated in Figure 7.3 from Yeb and Chu (1991) This research highlights the correlation between component commonality and efficiency in production, aligning with the findings of Martin and Ishii (1997) Understanding these relationships is crucial for optimizing manufacturing processes in selected industries.
◾ Amount of variety desired by customer
The relationships between the commonality of features and the process sequence are presented using this graph A commonality index for each component (CIcomp) is calculated as
U = number of unique part numbers
V n = final number of varieties offered
If only one component is considered sufficient for all the required varieties, the CIcomp is set to 1 This is considered the desired options.
Standardization may be unnecessary when components exhibit low commonality and short lead times However, when lead times are extended, achieving 100% commonality becomes crucial to reduce inventory costs linked to safety stock levels and to implement standardized components effectively.
Variety voice of the customer (V2OC) serves as a key metric for understanding the diverse component preferences of customers, highlighting both their significance and the market's heterogeneity A prominent method for assessing this attribute is conjoint analysis, which effectively captures customer priorities and preferences.
7.3.3.3.1 High Variety Low Volume (HVLV)
The principle of lean manufacturing (LM) is modified to match the high variety and low volume (HVLV) conditions (Jina et al 1997) This framework is illustrated as shown in Figure 7.4.
7.3.3.4 Design for Logistics and Manufacture (DFLM)
DFLM is critical for HVLV since it will reduce the cost and complexity associated with adding variety through
Organizing for lean processes minimizes variation in material flow by effectively managing high-level demand for assembly and subassembly, while also integrating customer demand with the order release stage.
This method proposes the use of generic raw material design, part, and subassem- blies from single source rather than multiple vendors.
Monitoring operational progress involves key metrics such as batch sizes, space utilization, setup times, the rationale behind unplanned engineering changes, supplier delivery frequency, customer satisfaction ratings, and delivery times.
Figure 7.4 Adapting LM principles to HVLV.
An empirical approach has been utilized to assess the impact of product variety and identify necessary variations, as demonstrated in five case studies conducted in Britain and Brazil by Da Silveira (1998) The main objective was to create a framework for product variety management The findings from these case studies, combined with literature reviews, led to the development of the proposed framework, as depicted in Figure 7.5.
Quantitative Methods for Managing Product Variety (Martin and
Designers can utilize developed measures to assess the cost implications of adding variety to their products One key metric is the Commonality Index (CI), which quantifies the use of standardized parts across different product models The CI indicates the percentage of components that are shared among multiple models in the manufacturing process, providing valuable insights into efficiency and cost-effectiveness.
U = number of unique part numbers
Sources of product variety needs:
Impact of product variety in
(3) Flexibility Strategies (Strategic/Operational Flexibility)
Figure 7.5 A framework of product variety management.
P j = number of parts in model j
V n = final number of variants offered
A low index number indicates a high level of standardization If CI is 1, there are no two parts alike A revised CI n is also defined based on the number of unique parts.
The Component Index (CI n) is a metric that evaluates the effectiveness of design in utilizing standardized components A higher CI n value indicates a lower reliance on unique parts across various designs, suggesting a more uniform approach The updated CI n is expressed as follows.
P j = number of parts in model j
V n = final number of varieties offered
The differential point index (DI) is a key measure that evaluates the stages of a process contributing to variety, factoring in the time from differentiation to the final stage A lower DI indicates that differentiation occurs later in the process.
V i = number of different products exiting process i n = number of processes
V n = final number of varieties offered d i = average throughput time from process i to sale d 1 = average throughput time from beginning of production to sale a i = value added to process i
The setup cost index (SI) is another measure that is used This measure defines the percentage of setups as a function of the total cost SI is measured as
V i = number of different products in exiting process i
C i = cost of setup at process i
C j = total (material, labor, overhead) at the jth product
The cost of variety is estimated using a regression model as Ψ = β 0 + β 1 CI + β 2 DI + β 3 SI (7.6) where Ψ = indirect cost of providing variety β0, β1, β2, and β3 are regression coefficients.
Manufacturing Complexity
Reasons of Complexity
Increased levels of manufacturing complexity could be attributed to (Wiendah and Scholtissek 1994 and Calinescu et al 1997, 2002):
◾ Market globalization: delivery time, quality, time to market, and customer satisfaction have become more important than price.
◾ The coordination between order-related and customer-independent manu- facturing.
◾ The lack of the integration among the different segments of the system.
◾ Adding flexibility to the production system without proper control.
◾ Adding flexibility to the factory floor increases the scheduling alternatives and the decision-making complexity.
Product Variety and Manufacturing Complexity
The variety of products a manufacturing firm can manage is largely influenced by its production volume Job shop production, which caters to low-quantity production, is highly agile and effectively handles high product variety In contrast, medium-quantity production utilizes batch production to manage hard product variety, where materials are processed in fixed batches with designated changeover times Soft product variety in medium production is addressed through cellular manufacturing, where specialized cells produce specific sets of similar parts Mass production, characterized by high volume, focuses on specific products with significant demand and can be divided into quantity production, which mass-produces single parts, and flow line production, where multiple workstations sequentially assemble products A prime example of flow line manufacturing is found in automobile assembly lines, while mixed-model assembly lines are designed to accommodate soft variety in vehicle assembly operations.
Manufacturing complexity can be divided into two categories: structural (static) complexity and operational (dynamic) complexity, as defined by Frizelle and Woodcock in 1995 Structural complexity refers to the anticipated information required to describe a system's state, while the production schedule supplies the necessary data to assess this static complexity The measurement of static complexity is accomplished through the entropy equation.
(7.7) where m is the number of resources. s is the number of scheduled states. p ij is the probability of resource i being in scheduled state j.
Operational complexity refers to the information needed to describe a system's deviation from its scheduled performance due to uncertainty It is calculated by measuring the disparity between the system's actual performance and the expected figures outlined in the schedule.
P is the probability of the system being in control.
The probability of a system being out of control is represented by (1−p), where 'm' denotes the number of resources and 'n s' indicates the number of nonscheduled states Additionally, 'p ij' signifies the probability of resource 'i' being in a nonscheduled state 'j'.
Estimating the cost of increased product variety can be challenging due to numerous indirect costs that are often overlooked These costs include raw material inventory, work in process inventory, finished goods inventory, postsales service inventory, and the reduction in production capacity caused by frequent setups Additionally, the logistics costs associated with managing a wider range of products further complicate the cost assessment Figure 7.8 illustrates the financial implications arising from heightened complexity in product offerings (Martin and Ishii, 1996).
The cost of variety in manufacturing is primarily influenced by setup time and batch size, as mass production relies on specific machinery that lacks flexibility for product variants Large setups associated with mass production encourage higher lot sizes to minimize downtime, leading to increased work-in-process (WIP) inventory, larger floor space requirements, and lower machinery utilization This push system of production results in higher internal transportation costs and quality costs due to repeated errors and manufacturing defects Conversely, smaller lot sizes reduce part rejection and lower quality costs While larger lot sizes enhance machine utilization due to economies of scale, they hinder flexibility in accommodating product variety Frequent setups for diverse products further escalate setup and labor costs, contributing to operational inefficiencies Overall, mass production struggles with flexibility in managing product variety, resulting in increased costs and inefficiencies.
Excess Plant & Equipment Excess Storage Additional IT
Materials Labor Utilization Transportation Overheads
Excess Labor Interest Charges Rectification Costs Revenue Loss Warranty Payments Excess Overheads
Figure 7.8 Costs generated due to complexity.
Ali K Kamrani is an Associate Professor of Industrial Engineering He is also
The Founding Director of the Design and Free Form Fabrication Laboratory at the University of Houston, USA, holds multiple degrees from the University of Louisville, including a BS and Master’s in Electrical Engineering, a Master’s in Computer Science and Engineering Mathematics, and a PhD in Industrial Engineering His research focuses on the application of systems engineering in the advanced design and development of complex systems He also serves as the Editor-in-Chief for the International journal.
Journal of Collaborative Enterprise and the International Journal of Rapid
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