Case Study Overview

The case study aims at comprehensively identifying and developing the metrics needed for evaluating sustainability performance of manufactured products and their manufacturing processes. The objectives of the case study are summarized as follows:

Manufacturer

• Evaluation of sustainability rating for selected component and process
• Identification of areas with potential for sustainability improvements

Academic Team

• Validation of the initial set of metrics for product and process sustainability
• Identifying areas where current metrics are lacking and expanding to a comprehensive set of metrics for sustainability assessment

The product under investigation is an automobile power train component. The manufacturing process flow chart is shown in Fig. 8.

Not all the data were provided by manufacturer, either due to the lack of measurement or due to confidentiality, and were estimated based on information available in public sources like automotive industry reports (Dreher et al. 2009; Environmental Affairs Co-ordination Office 2002; Schmidt and Taylor 2006; Toyota North America 2010), government agencies, and scientific literature.

Case Study Results

Product Sustainability Assessment

This section presents the results from applying the total life-cycle-based product sustainability assessment methodology to the automobile component and the resulting ProdSI. Due to lack of information, most data collected for the automobile component relates to its manufacturing stage. Information for other stages are extracted from public resources as mentioned in the previous section and then adapted to the component.
The results of sustainability measurements and the corresponding scores are presented and discussed next based on each cluster of the metric elements: economy, environment, and society. Then, a summary of product sustainability assessment is presented.

Economy: Cost

Because cost data were withheld by the manufacturer, values estimated based on public sources or estimation are used in this category.
Therefore, the costs related to labor and to training are based on average wage estimation. The information on labor hours consumed for each of the tasks and public data of average automobile worker pay rates are known. The energy cost is estimated by multiplying the amount of energy consumed and price per unit of energy. The costs for materials, transportation, and warehousing are based on estimates as well. Costs for operation and maintenance/repair are based on published data about a medium-size sedan (IntelliChoice 2011).
All cost measurements are in dollars spent per unit of product manufactured and are aggregated to the subcluster level without normalization. The normalized scores are given at the subcluster level and equal weights are assigned to these scores. Metrics with no data, namely, capital and legal cost, were not included in the calculation. A final score of 7.53 has been calculated for the cluster Cost based on equal weight.

Economy: Innovation

In this cluster, the material consumption efficiency is based on in-line measurement. The other two individual metrics – R&D cost and average disassembly cost – are left out due to lack of data. Linear normalization is applied to the material consumption efficiency. Since the material consumption efficiency is 79.1 % in general, a final score of 7.91 is given to the cluster Innovation.

Economy: Profitability

In this cluster, the profit is based on published data for a medium-size sedan. The rest of the metrics are not considered, as data for the reuse, remanufacturing, or recycling of the component is not available.

Economy: Product Quality

In this cluster, 12 years is considered as a benchmark for the average life span of a vehicle provided by the U.S. Department of Transportation. Reliability score of 6.09 is given according to market ranking. Data for the other metrics within this cluster were not provided.

Environment: Material Use and Efficiency

In this cluster, the scores of both recycled material use ratio and recycled packaging material use ratio are rated on a scale from 0 to 10 for 0 % to 100 %, respectively. A score of 10 is given based on the use of 100 % recycled material, according to the supplier. Also, there are no restricted materials used to make the component; thus, a full score is given to this metric. The mass of material used measured was the average mass of the raw workpiece.
Since there are no plastic materials integrated into the product, no data are given to the associated individual metric. It should be noted that the mass of packaging material is estimated and the mass of transportation plastic tray is unknown.

Environment: Energy Use and Efficiency

In this cluster, the data for manufacturing energy use is from a direct measurement, in kilowatt hour per unit (kWh/unit) of product manufactured. It should be noted that only the manufacturing stage is considered for this metric. Data for the product energy use (e.g., use stage of life-cycle) is allocated based on the total energy use across the four life-cycle stages of the automobile and the weight for the component as a ratio of the overall weight of the automobile. The energy reduction ratio is from published reports, considering overall manufacturing operations. Again, product recycling, reuse, and remanufacturing are not properly traced by this case company, and the associated information is unknown.
The score of 6.64 is given to manufacturing energy use and product energy use as a reference value. The final score is aggregated and generated from those three individual metrics.

Environment: Water Use and Efficiency

In this cluster, all the data are for a medium-size sedan based on published reports. Linear normalization is applied to the metric of recycled water use. The three metrics equally share the weight leading to a final score of 7.30.

Environment: Residues

In this cluster, full scores (10 in score column) are given to the metrics solid waste stream and liquid waste stream as there is no waste disposed (0 in measurement column). Furthermore, both landfill reduction and environmental regulation compliance receive full credit because the research team was informed that no waste is sent to landfill and no environmental regulations have been violated, respectively. The measurement for the gaseous emissions during the use stage is calculated from the total gaseous emission for a medium-size sedan provided by the UK Department for Transport (United Kingdom Department for Transport 2013).
Since the post-use data are unknown, the metrics for product recycling, reuse, and remanufacturing are not aggregated to the final score. A final score of 9.33 is calculated without considering the end-of-life (EOL) metrics, which are left blank. To have a more sound and comprehensive product sustainability assessment, considering product EOL is a necessity.

Environment: Product End-of-Life Management

All measurements in this cluster come from published reports. It must be emphasized that low scores are given due to no cosideration of the product EOL. 

Society: Education and Customer Satisfaction

Data related to customer satisfaction and product repairs and returns are not available. Therefore, the final score of 7.70 is based on the metric of employee training and development.

Society: Product End-of-Life Management and Product Safety and Societal Well-Being

Most data are unavailable as they relate to the product EOL stage, for which the company did not have information readily available. There are no product processing injuries for the manufacturing line, and a full score is given to the corresponding metric and the perfect score of 10 is generated solely based on that.

ProdSI

Economic subindex sustainability performance is visually presented by the color-coded table shown in Table 10.
As most data are extracted from public sources, there is an opportunity to improve the accuracy of the ProdSI by using actual data related to the component and the automobiles bearing the component.
Environmental sub-index sustainability performance is visually presented by the color-coded table shown in Table 11.
Results indicate that the product EOL management is the weakest subcluster that needs further consideration and improvement.
Societal sub-index sustainability performance is visually presented by the color-coded table shown in Table 12.
The three sub-indices, combined with equal weighting, give a final score for the ProdSI that is 7.59 (out of a full score of 10). The final product sustainability index (ProdSI) in Table 13 once again reveals that the element of product end-of-life management is the area that requires the most efforts for improvement.
For better visualization, the results can also be represented in the form of spider charts, as shown in Figs. 9 and 10 below. The discontinuity in the charts is due to nonavailability of data for those subclusters/clusters.

Process Sustainability Assessment

Machining Cost

The research team did not have access to most of the cost data related to the component studied. The labor cost and training cost are estimated based on the number of labor hours (known) consumed for each of the tasks and public data on average automobile worker pay rate. The operation energy cost and coolant-related costs are estimated by multiplying the amount consumed and the unit price of purchasing. Scrap loss is calculated based on number of scrap parts made and raw material price.
All cost measurements are in dollars spent per unit of product manufactured. Thus, they are aggregated without any weighting. The normalization takes place at the subcluster level. Since financial data were not available, a score of 7 was assumed for the machining cost cluster. Benchmark data can be applied for more accurate normalization.

Energy Consumption

Most of the energy consumption data are directly measured in kilowatt hours per unit (kWh/unit) of product manufactured. The data are not normalized until the cluster level. A reference energy consumption amount was set for manufacturing one piece of the component (in $), based on the total amount of energy consumed to manufacture a car as described in published reports. Based on the reference points selected, the energy consumption data are normalized and then aggregated to generate the final score of this cluster.

Waste Management

The major solid waste streams of the manufacturing line are the machining chips and scrapped parts. While a significant coolant losses were present the exact coolant loss streams were not identified because the coolant system was shared with other manufacturing lines.
The company adopts a “zero landfill” policy which makes for a good recovery and recycling practice. Therefore, full scores are given for the corresponding individual metrics. Benchmarks are needed for a more objective scoring of reuse and waste generation metrics.

Environmental Impact

GHG emissions are tracked in terms of the carbon content for the electricity consumed. The only stream of restricted material use related to the coolant loss. Solid waste is tracked, but the liquid waste and gaseous waste streams are not identified.
“Zero landfill” policy results in full scores for all the corresponding individual metrics. A reference for the water usage is set by value allocation based on the total amount of water consumed to manufacture a car as reported in published reports. The result shows that manufacturing the component is not a heavy waterconsuming process. The major problem in this cluster is the high greenhouse gas emission (GHG), due to the fact that the major source of electrical power in the local electricity grid being coal. The plant does not utilize any form of renewable energy. The reference point is given by monetary value allocation based on the total amount of GHG emission during the manufacturing of a car as stated in published reports.

Personal Health

Measurements concerning the working conditions are tracked, based on the benchmarks set by safety regulations from agencies or organizations including EPA, OSHA, and NIOSH (NIOSH 1992, 1998a, b). The records of operator absenteeism were referred to and no health-related absenteeism was found.
Noise at the manufacturing site is close to the threshold limit of 85 dB, the personal protective equipment (PPE) is not considered; thus, a poor score is given. Aside from that, it was unexpected to see from the physical load index (PLI) questionnaire results that a highly automated manufacturing process still involves considerable physical stress to the operators. The operators often had to bend their bodies to lift heavy workpieces. It is likely that the bodyguards on the equipment are restrictive and limit the ability of operators to access workpieces and tools.

Operator Safety

All exposures to hazards are well shielded. According to the safety records of the line, no injuries occurred during the period of investigation. As a result, full scores are given to all the measurements within this cluster. 

ProcSI Results

The aggregated scores for the ProcSI clusters are shown in Table 14.
The results are also represented in the form of spider chart, as shown in Fig. 11 below.
The final score for the ProcSI is 7.61 out of 10.

Concluding Remarks for the Case Study

The component under investigation and its manufacturing processes received a fairly good score, considering that a score of 8 is given indicating industry-leading performance, based on the value creation evaluation, market behavior and customer impressions. However, there are some concerns that must be addressed.
First, the uncertainty and difficulty of the assessment should be considered. Data collection was not easy, and many measurements lack detailed data from the manufacturer. Estimating from other sources and ignoring the influences of the metrics due to missing data can reduce the reliability of the evaluation made. This emphasizes the significance of data collection and management effort needed for a realistic evaluation.
On the other hand, the scope of this objective sustainable value creation assessment has not been taken by current manufacturers. one indication is that, in this study the data provided focus on the manufacturing stage while the data in other life-cycle stages are inadequate. Here is an example of the resulting effects. The influence of energy consumption on the economic value creation might be relatively small due to the inexpensive local price of electricity. Under this situation, the manufacturer showed inferior performance in energy related metrics, especially when compared with their performance in other metrics. It implies that even the market-leading manufacturers have difficulties taking the ideas of sustainable value creation and sustainable manufacturing concepts into their applications, thus the full potential of sustainable value is not retained.