Prediction of Bike Share Demand by Machine Learning

In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.

The Impact of COVID-19 in Delivering Customer Social Value through Supply Chain

An agile supply chain is important in a struggling post-COVID-19 economy to manage costs and to respond to customer demand. And, the ability to react rapidly is the key to satisfying customer demand. Supply chains, their re-organisation as platform-mediated ecosystems, are now primed for their biggest change yet. The Platform refers to a technology that allows open interaction between market players, such as producers and consumers. A digitised supply chain also provides opportunities for completely new revenue models beyond the redesign of processes. Specifically, a whole range of modern business models are able to monitor a product past the handover to the consumer, and on to actual use. The aim of this chapter is to explain the complexities of the supply chain during the age of COVID-19, which contributed to an improvement in social value for consumers, especially in digitizing the process. This paper is inspired by several sources, such as Deloitte Insight released at the end of 2020, which raises the future of mobility after the COVID-19 pandemic (Corwin, Zarif, Berdichevskiy and Pankratz, 2020). Corwin et al (2020) mention the term ecosystem when describing the reality of mobility during a pandemic. Viswanadham and Samvedi (2013), on the other hand, define the supply chain ecosystem as the elements of the supply chain as well as the entities that control the movement of goods, knowledge, and money across the supply chain. Today’s supply chains, as the final frontier, are being re-architected as environments coordinated by central networks. In this chapter, there are a numbers of case study about limited mobility during a pandemic that triggers service providers to search for innovative ways to survive. Second, with regard to numerous parties promoting increasingly large online shopping activities and enabling the home business sector to be easier and easier to enter the community, this is what is known as consumer social value. This case study will be taken from a variety of Asian countries, one of which is Indonesia. Third, the next challenge facing businesses in managing supply chains will raise the social value of consumers, especially when the pandemic ends.

Melhoria na flexibilização e aumento de produtividade de uma linha de montagem de uma empresa do setor de autopeças através da alteração do arranjo físico do processo

Due to the constant competitiveness among the multinational companies nowadays, these companies intensely seek agility and flexibility in their production processes, so that they can respond quickly to variations in their demands, eliminating waste and making their processes leaner and more competitive. Following this trend, the present work had as a proposal and purpose to restructure the layout of a conventional assembly line (linear layout) for U-shaped manufacturing cells, and it is intended in this way to obtain flexibility improvement and productivity increase of the process. The applied methodology was developed from bibliographical research and analysis of the current situation, where a mapping of the processes was done in order to identify the wastes that had a negative impact on the production line. As for the factors that had a negative impact to the process, it was found that there was a need to change the layout of the production line. As the main results related to the restructuring, there is an increase of 6% in productivity per person, a reduction of approximately 30% in the movements of operators in the process, and an improvement in the flexibility of the assembly line, adapting it according to the variation in customer demand.

Self­-service for public transport payments: A business case for conversational artificial intelligence

<p>Although self-service (i.e. mobile top-ups) is at the heart of Snapper’s customer service offering, customers have a disjointed experience managing their public transport payment cards across a range of customer service touchpoints, including more traditional support channels such as helpdesks and in-person support centres. Customer feedback indicates that some Snapper users perceive the process of resolving support issues through these traditional support channels to be inconvenient and time-consuming. Activity through these traditional channels still forms a large proportion of Snapper’s customer service, despite Snapper’s ongoing investment in their self-service channels; including mobile applications, the website, the MySnapper desktop application, and kiosks. Just as Snapper innovated to meet customer demand for self-service through a mobile app (Snapper Services Ltd., 2017a), the evolution of conversational artificial intelligence (AI), or chatbot technology, presents an opportunity for Snapper to lead the way in meeting customer demand for a faster, more accessible way to resolve common support issues. The successful development of such a solution will further position Snapper as a market-leader in customer-centric innovation. In order to understand the commercial potential of such an automated customer service offering, the research aims to understand customer use and perceptions of Snapper’s support channels; to identify barriers to the adoption of self-service, and understand how these can be addressed; and to understand customer attitudes towards automated customer service. Using a mixed methods approach, research began with analysis of secondary data accessed from Snapper’s internal customer service reporting. Findings validated customer demand for additional self-service options, as well as the repetitive nature of Snapper’s customer service queries, indicating that these are ripe for automation. In-depth interviews were conducted with Snapper cardholders, giving further insight into how they select and interact with Snapper’s customer service channels. The avoidance of perceived effort was identified as a key theme when explaining how customers navigate service channels, supporting the role of “ease of use” in explaining customer adoption of self-service technologies (Davis, Bagozzi, & Warshaw, 1989). Types of perceived effort were identified as social, cognitive and logistical effort. These categories are proposed as an extension to the Technology Acceptance Model (Davis et al., 1989), giving additional insight into what constitutes “ease of use”. Following the in-depth interviews, market analysis and discussions with AI and chatbot service providers explored best practice in automated customer service, to understand the adoption of conversational AI technology in the New Zealand context, as well as how other companies have successfully implemented a chatbot product. The project report concludes with a stand-alone business case for applying conversational AI technology to Snapper’s customer service offering. The business case summarises the business model and delivery methodologies recommended for the project development (see Section 6.1), including LEAN startup methods. The market validation phase (Section 6.2) then addresses the strategic business case, assessing the case for change and incorporating key findings from the customer and market research conducted earlier in the research. Building on the opportunities identified in the PESTEL analysis, the product validation phase (Section 6.3) utilises a SWOT analysis, before providing clear recommendations around the required feature-set of the proposed solution, and a possible roadmap for implementing these features. Finally, the economic, financial and commercial cases are addressed; including a cost-benefit analysis of the proposed solution, a recommended development methodology, and high-level resources and requirements required for implementation. By validating that delivering such an enhanced self-service offering is commercially viable, the project aims to deliver a more delightful experience to Snapper users, driving better uptake of Snapper’s self-service channels.</p>

Self­-service for public transport payments: A business case for conversational artificial intelligence

<p>Although self-service (i.e. mobile top-ups) is at the heart of Snapper’s customer service offering, customers have a disjointed experience managing their public transport payment cards across a range of customer service touchpoints, including more traditional support channels such as helpdesks and in-person support centres. Customer feedback indicates that some Snapper users perceive the process of resolving support issues through these traditional support channels to be inconvenient and time-consuming. Activity through these traditional channels still forms a large proportion of Snapper’s customer service, despite Snapper’s ongoing investment in their self-service channels; including mobile applications, the website, the MySnapper desktop application, and kiosks. Just as Snapper innovated to meet customer demand for self-service through a mobile app (Snapper Services Ltd., 2017a), the evolution of conversational artificial intelligence (AI), or chatbot technology, presents an opportunity for Snapper to lead the way in meeting customer demand for a faster, more accessible way to resolve common support issues. The successful development of such a solution will further position Snapper as a market-leader in customer-centric innovation. In order to understand the commercial potential of such an automated customer service offering, the research aims to understand customer use and perceptions of Snapper’s support channels; to identify barriers to the adoption of self-service, and understand how these can be addressed; and to understand customer attitudes towards automated customer service. Using a mixed methods approach, research began with analysis of secondary data accessed from Snapper’s internal customer service reporting. Findings validated customer demand for additional self-service options, as well as the repetitive nature of Snapper’s customer service queries, indicating that these are ripe for automation. In-depth interviews were conducted with Snapper cardholders, giving further insight into how they select and interact with Snapper’s customer service channels. The avoidance of perceived effort was identified as a key theme when explaining how customers navigate service channels, supporting the role of “ease of use” in explaining customer adoption of self-service technologies (Davis, Bagozzi, & Warshaw, 1989). Types of perceived effort were identified as social, cognitive and logistical effort. These categories are proposed as an extension to the Technology Acceptance Model (Davis et al., 1989), giving additional insight into what constitutes “ease of use”. Following the in-depth interviews, market analysis and discussions with AI and chatbot service providers explored best practice in automated customer service, to understand the adoption of conversational AI technology in the New Zealand context, as well as how other companies have successfully implemented a chatbot product. The project report concludes with a stand-alone business case for applying conversational AI technology to Snapper’s customer service offering. The business case summarises the business model and delivery methodologies recommended for the project development (see Section 6.1), including LEAN startup methods. The market validation phase (Section 6.2) then addresses the strategic business case, assessing the case for change and incorporating key findings from the customer and market research conducted earlier in the research. Building on the opportunities identified in the PESTEL analysis, the product validation phase (Section 6.3) utilises a SWOT analysis, before providing clear recommendations around the required feature-set of the proposed solution, and a possible roadmap for implementing these features. Finally, the economic, financial and commercial cases are addressed; including a cost-benefit analysis of the proposed solution, a recommended development methodology, and high-level resources and requirements required for implementation. By validating that delivering such an enhanced self-service offering is commercially viable, the project aims to deliver a more delightful experience to Snapper users, driving better uptake of Snapper’s self-service channels.</p>

Peningkatan Produktivitas Kearifan Lokal Kerajinan Tangan Tas Rajut Sebagai Bentuk Identitas Bangsa

Tujuan kegiatan Pengabdian Masyarakat ini adalah untuk meningkatkan produktivitas kearifan lokal terutama kerajinan tangan tas rajut menjadi komoditas bangsa yang dapat menjadi salah satu potensi identitas kebangsaan. Kreativitas melalui kerajinan tangan dengan mengangkat kearifan lokal dikembangkan agar terbentuk menjadi UMKM yang kuat. Premium Crochet & Leather Bag Tatti Crochet diproduksi di kota Malang sudah memiliki ciri khas, namun tidak dapat berkembang karena produktivitasnya terhambat karena tidak memiliki sarana produksi yang memadai. Pendekatan yang diberikan untuk memberikan bantuan kepada produsen tas rajut ini adalah dengan memberikan bantuan berupa sarana produksi untuk mendukung permintaan pelanggan menjadi lebih cepat dengan harga yang tetap dapat bersaing di pasaran. Hasil yang dicapai dari pemberian bantuan sarana produksi berupa alat jahit kulit ini adalah percepatan proses produksi dan penekanan harga jual produk sehingga dapat memiliki daya saing yang kuat. The purpose of this Community Service activity is to increase the productivity of local wisdom, especially knitting bag handicrafts, to become a national commodity that can become a potential national identity. Creativity through handicrafts by raising local wisdom is developed to form a strong MSME. Premium Crochet & Leather Bag Tatti Crochet produced in the city of Malang already has a characteristic but cannot develop because its productivity is hampered because it does not have adequate production facilities. The approach given to aid this knitting bag manufacturer is to aid in the form of production facilities to support customer demand faster at prices that can still be competitive in the market. The results achieved from the provision of production facilities in the form of leather sewing equipment are the acceleration of the production process and the suppression of the selling price of the product so that it can have a strong competitiveness.

Mining Express Service Innovation Opportunity from Online Reviews

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

Mining Express Service Innovation Opportunity From Online Reviews

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.