A Product/Service System Design Schema: Application to Big Data Analytics
Abstract
:1. Introduction
- What relationships exist between PSS design and BDA?
- What are the achievements thus far and development opportunities in PSS design through applying BDA?
- What does the literature report on the environmental implications of applying BDA in PSS design?
2. Theoretical Background
2.1. PSS Design
2.2. BDA (Big Data Analytics)
2.3. Systematic Literature Review and Literature Synthesis
3. Research Method
3.1. Overview
3.2. Intra-Disciplinary Meta-Synthesis and Consolidation of Prescriptive PSS Design Insights
- Abstract to identify the essential problems;
- Establish function structures: overall function—subfunctions;
- Search for working principles that fulfil the sub-functions;
- Combine working principles into working structures;
- Select suitable combinations;
- Firm up into principle solution variants;
- Evaluate variants against technical and economic criteria.
3.3. Systematic Literature Review and Inter-Disciplinary Meta Synthesis of BDA Literature with the PSS Design Schema
4. Results
4.1. Intra-Disciplinary Meta-Synthesis of the PSS Design Insights
4.1.1. Meta-Analysis and Synthesis
4.1.2. Consolidation of Synthesized PSS Design Insights
4.1.3. PSS Design Benefiting from BDA
4.2. Systematic Review and Inter-Disciplinary Meta-Synthesis of BDA Literature with PSS Design Schema
4.2.1. Overview of the Results of the Literature Search
4.2.2. Descriptive Results of Design in Industry Enhanced by BDA with Industrial Cases
4.2.3. Discovering Research Opportunities in PSS Conceptual Design Enhanced by BDA
5. Discussion
5.1. Scientific Contributions
5.2. Environmental Implications
5.3. Implications of BDA on Customization
5.4. Practical Contributions
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
A List of Acronyms
BDA | big data analytics |
ISI | Institute for Scientific Information |
MTBF | mean time between failures |
MTTF | mean time to failure |
NPD | new product development |
PBSA | Pahl’s and Beitz’ systematic approach |
PSS | product/service system |
R&D | research and development |
RQ | research question |
SCI | Science Citation Index |
SCM | supply chain management |
SSCI | Social Sciences Citation Index |
Appendix A
Appendix B
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Facet of PSS design | [1] | [2] | [26] | [34] | [35] | [63] | [64] | [65] | [66] | [67] |
---|---|---|---|---|---|---|---|---|---|---|
1. Functionality-oriented designing | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
2. Identification of relevant actors along PSS lifecycle | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
3. Value propositions | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
4. Development and integration of system elements | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
5. Examination of the balance of the integration | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
PBSA Framework for Conceptual Design | Distilled Essence of PSS Design | Consolidated Schema for Conceptual PSS Design |
---|---|---|
1. Abstract to identify the essential problems | 1. Functionality-oriented designing 2. Identification of relevant actors along the PSS lifecycle | 1. Functional unit definition 2. Stakeholder identification 3. Requirement consolidation |
2. Establish function structures: overall function—subfunctions | ||
3. Search for working principles that fulfil the sub-functions | 3. Value proposition 4. Development and integration of system elements | 4. Value proposition 5. Criterion identification 6. Element integration |
4. Combine working principles into working structures | ||
5. Select suitable combinations | 5. Examination of the balance of the integration | 7. Balance examination 8. Selecting combinations |
6. Firm up into principle solution variants | ||
7. Evaluate variants against technical and economic criteria | No essence applicable | 9. Evaluating combinations 10. Solution selection |
Type | Data Source | Design Object |
---|---|---|
PP | Use of products | Product |
PS | Use of products | Service |
SP | Production and use of services | Product |
SS | Production and use of services | Service |
PP (Product to Product) and PS (Product to Service) | SP (Service to Product) and SS (Service to Service) | |
---|---|---|
Step 1: Functional unit definition | T | T |
Step 2: Stakeholder identification | T | |
Step 3: Requirement consolidation | T | T |
Step 4: Value proposition | T | T |
Step 5: Criterion identification | ||
Step 6: Element integration | T | T |
Step 7: Balance examination | T | T |
Step 8: Selecting combinations | ||
Step 9: Evaluating combinations | T | T |
Step 10: Solution selection |
Ref. | Type | Data used | Aim | Method Used | Result | Aspects 1 | |||
---|---|---|---|---|---|---|---|---|---|
[94] | PP | Operating conditions and the responses (e.g., a steady-state condition for 120 minutes with 5-minute interval) | Optimize operations in oil re-refining processes | Principal component analysis | Fewer experiment sets for design of experiment: eventual improvements were quantified in terms of product yield (e.g., 84% increased), process quality (e.g., 47% increased), environmental impacts (e.g., 91% improved in acidification potential), etc. | Q | C | T | E |
[95] | PP | 26,000 customers involved: Social media data (130,000 comments) and machine-generated/sensor data | Better understand customers in NPD at a wearable medical equipment manufacturer | Data (including text) mining and clustering analysis | More precise insights of customer perceptions (trends, expectations, preferences, etc.), leading to NPD with <5 months, that is, less than half in time and 2 million USD (a fraction of economic cost) compared to a traditional NPD project | Q | C | T | |
[96] | PP | Seven peer brands’ sugar levels and prices, and consumer feedbacks on lemonade quality | Support NPD for a lemonade with a reduced sugar level | Text mining | Reducing the NPD costs by 33% and time by 10% without compromising the quality | Q | C | T | e |
[97] | PP | Customers’ preferences registered via the official company website, product information (including videos), social media (10 million threads), and customer locations. | Better manage competences for NPD in a Chinese manufacturer of athletics goods owning three main manufacturing facilities. | Data mining | Strategies such as an optimal expansion on the company’s existing competences by considering internal and external competences: seven golf clubs (e.g., 3-iron and 460cc 10.5° driver) were analyzed with 14 competences (e.g., wood manufacturing and stamping technologies). | Q | C | T | |
[98] | PP | 100,000 user evaluations of fitness mobile apps on social media | Analyze competing products | Natural language processing and machine learning | Four clusters of ca 500 apps and an app’s position in relation to peers, including the product functionality similarity, contributing to making strategies on pricing models, product differentiation, and faster adaptation | Q | C | T | |
[99] | PP PS | Data of one million bridges in the USA; e.g., structural types, condition ratings, geographical zones, and traffic volumes | Analyze and evaluate conditions (incl. degradation) of constructed bridges | ANOVA (analysis of variance) | Insights such as the adequate selection of structural types (e.g., concrete cast-in-place) dependent on the use environments, enhancing the performance and longevity of the bridges, and planning better inspection/maintenance based on deterioration | Q | e | ||
[100] | SS | Purchase transactions by 110,000 customers at this agency and its competitors’ pricing data | Analyze customer behaviors and predict their next purchases of flight tickets at an online travel agency | Data mining and customer segmentation | Patterns and correlations in customer purchasing behaviors, contributing to better customer relationship management, such as targeted promotion | Q | c | t | |
[101] | SS | Data about the software, such as purchase, 330,000 renewals, 120,000 non-renewals, download, problems, and evolution | Predict the risks of software license cancellation in combination with domain knowledge | Machine learning | The framework for prediction tested at IBM, attesting the usefulness of the framework in industry | q | c | t | |
[102] | SS | Three million tweets on Twitter by 1200 companies and their financial data | Examine the relationships of product-related communication and the financial performance of manufacturing firms | Text mining | Positive association between divulging product-related information and the firm value on the market | c | |||
[103] | SS | 4900 online reviews on TripAdvisor for 200 Spa hotels | Segment hotel customers and the prediction of their choices | Machine learning and clustering | Accurate prediction of user choices per segment, which is expected to contribute to optimal marketing expenditures | Q | c | T | |
[104] | SS SP | 2600 online reviews on TripAdvisor for 20 hotels in Taipei | Understand hotel guests’ perceptions | Text mining | Through extracting Kansei words (e.g., excellent and friendly) and hotel service characteristics (e.g., facilities and service delivery) as well as their relationships, a guideline for hotel service development was proposed | Q | C | T |
PP (Product to Product) and PS (Product to Service) | SP (Service to Product) and SS (Service to Service) | |
---|---|---|
Step 1: Functional unit definition | O | O |
Step 2: Stakeholder identification | [103] | |
Step 3: Requirement consolidation | [95,96] | [100,104] |
Step 4: Value proposition | [96,98] | [100] |
Step 5: Criterion identification | ||
Step 6: Element integration | [99] 1 | O |
Step 7: Balance examination | O | O |
Step 8: Selecting combinations | ||
Step 9: Evaluating combinations | O | O |
Step 10: Solution selection |
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Sakao, T.; Neramballi, A. A Product/Service System Design Schema: Application to Big Data Analytics. Sustainability 2020, 12, 3484. https://doi.org/10.3390/su12083484
Sakao T, Neramballi A. A Product/Service System Design Schema: Application to Big Data Analytics. Sustainability. 2020; 12(8):3484. https://doi.org/10.3390/su12083484
Chicago/Turabian StyleSakao, Tomohiko, and Abhijna Neramballi. 2020. "A Product/Service System Design Schema: Application to Big Data Analytics" Sustainability 12, no. 8: 3484. https://doi.org/10.3390/su12083484
APA StyleSakao, T., & Neramballi, A. (2020). A Product/Service System Design Schema: Application to Big Data Analytics. Sustainability, 12(8), 3484. https://doi.org/10.3390/su12083484