Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology

2020 ◽  
Vol 120 (6) ◽  
pp. 1149-1174 ◽  
Author(s):  
K.H. Leung ◽  
Daniel Y. Mo ◽  
G.T.S. Ho ◽  
C.H. Wu ◽  
G.Q. Huang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Service Providers ◽  
Series Data ◽  
Illustrative Case ◽  
Implementation Framework ◽  
Inference System ◽  
Content Type

PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

Designing predictive maintenance systems using decision tree-based machine learning techniques

2020 ◽  
Vol 37 (4) ◽  
pp. 659-686 ◽  
Author(s):  
Shashidhar Kaparthi ◽  
Daniel Bumblauskas
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Tree ◽  
Service Management ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Learning Techniques ◽  
Maintenance Systems

PurposeThe after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.Design/methodology/approachWe propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.FindingsWe found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.Research limitations/implicationsThis approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.Practical implicationsThis approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.Social implicationsSustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.Originality/valueThis is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

Country of origin branding: an integrative perspective

2016 ◽  
Vol 25 (4) ◽  
pp. 322-336 ◽  
Author(s):  
Roderick J. Brodie ◽  
Maureen Benson-Rea
Keyword(s):  
Country Of Origin ◽  
Theoretical Framework ◽  
Illustrative Case ◽  
Business Relationships ◽  
Content Type ◽  
Brand Meaning ◽  
Value Propositions ◽  
Illustrative Case Study ◽  
Strategic Perspective

Purpose A new conceptualization of the process of country of origin (COO) branding based on fresh theoretical foundations is developed. This paper aims to provide a strategic perspective that integrates extant views of COO branding, based on identity and image, with a relational perspective based on a process approach to developing collective brand meaning. Design/methodology/approach A systematic review of the literature on COO branding and geographical indicators is undertaken, together with a review of contemporary research on branding. Our framework conceptualizes COO branding as an integrating process that aligns a network of relationships to co-create collective meaning for the brand’s value propositions. Findings An illustrative case study provides empirical evidence to support the new theoretical framework. Research limitations/implications Issues for further research include exploring and refining the theoretical framework in other research contexts and investigating broader issues about how COO branding influences self and collective interests in business relationships and industry networks. Practical implications Adopting a broadened perspective of COO branding enables managers to understand how identity and image are integrated with their business relationships in the context of developing collective brand meaning. Providing a sustained strategic advantage for all network actors, an integrated COO branding process extends beyond developing a distinctive identity and image. Originality/value Accepted consumer, product, firm and place level perspectives of COO branding are challenged by developing and verifying a new integrated conceptualization of branding.

Beyond social capital: a capability approach to understanding Refugee Community Organisations and other providers for “hard to reach” groups

2014 ◽  
Vol 10 (2) ◽  
pp. 61-72 ◽  
Author(s):  
Jennifer Clarke
Keyword(s):  
Capability Approach ◽  
Service Providers ◽  
Structured Interviews ◽  
Content Type ◽  
Refugee Community ◽  
Community Organisations ◽  
Purposive Sample ◽  
The Capability Approach

Purpose – The purpose of this paper is to explore the value of the “capability approach” as an alternative framework for understanding and conceptualising the role of Refugee Community Organisations (RCOs) and other providers for groups conventionally considered “hard to reach”. Design/methodology/approach – A study of the education services of RCOs, drawing primarily on semi-structured interviews with 71 users, is put forward as a case study for how the capability approach can be operationalised. Findings – The capability approach is found to offer various valuable insights, relating to its appreciation of the multi-dimensional nature of human wellbeing, the significance of individual diversity, and the importance of human agency. Research limitations/implications – The case study is based on a relatively small purposive sample, and may have limited external validity. As the research design proved strong for exploring how RCOs develop their users’ capabilities but weak for exploring if and how they may also constrict them, further research in this area is required. Practical implications – A number of valuable attributes of the capability approach are highlighted for broadening the understanding, the role of RCOs and other service providers. Social implications – The paper outlines the potential of the capability approach to contribute to policymaking related to RCOs and other providers, and to debates relating to social exclusion, cohesion and integration. Originality/value – The paper draws attention to the value of the capability approach within the field of migration research.

Satisfaction of tuberculosis patients with health services in Ghana

2017 ◽  
Vol 30 (6) ◽  
pp. 545-553 ◽  
Author(s):  
Frank Bonsu ◽  
Felix Afutu ◽  
Nii Nortey Hanson-Nortey ◽  
Mary-Anne Ahiabu ◽  
Joshua Amo-Adjei
Keyword(s):  
Client Satisfaction ◽  
Service Providers ◽  
Healthcare Providers ◽  
Large Body ◽  
Service Users ◽  
Content Type ◽  
Sputum Production ◽  
Interview Guide

Purpose Within human services, client satisfaction is highly prioritised and considered a mark of responsiveness in service delivery. A large body of research has examined the concept of satisfaction from the perspective of service users. However, not much is known about how service providers construct client satisfaction. The purpose of this paper is to throw light on healthcare professionals’ perspectives on patient satisfaction, using tuberculosis (TB) clinics as a case study. Design/methodology/approach In-depth interviews were conducted with 35 TB clinic supervisors purposively sampled from six out of the ten regions of Ghana. An unstructured interview guide was employed. The recorded IDIs were transcribed, edited and entered into QSR NVivo 10.0 and analysed inductively. Findings Respondents defined service satisfaction as involving education/counselling (on drugs, nature of condition, sputum production, caregivers and contacts of patients), patient follow-up, assignment of reliable treatment supporters as well as being attentive and receptive to patients, service availability (e.g. punctuality at work, availability of commodities), positive assurances about disease prognosis and respect for patients. Practical implications Complementing opinions of health service users with those of providers can offer key performance improvement areas for health managers. Originality/value To the best of the authors’ knowledge, this is a first study that has examined healthcare providers’ views on what makes their clients satisfied with the services they provide.

Case study of sustainable service design in the hospitality industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-An Chen ◽  
Chun Liang Chen
Keyword(s):  
Sustainable Development ◽  
Customer Value ◽  
Hospitality Industry ◽  
Service Providers ◽  
Corporate Sustainability ◽  
Service Design ◽  
Cultural Resources ◽  
Content Type ◽  
Holistic Understanding

Purpose The purpose of this study is to explore how creative-cultural hotels can achieve sustainable service design through the development of a holistic conceptual framework. Design/methodology/approach The authors created this framework using a qualitative exploratory multi-case study of four creative-cultural hotels in Taiwan. The framework comprises strategic, organizational and interface levels to describe the design process and implementation of service offerings that co-create value within a multifaceted network of actors. Findings The findings of this study show that incorporating local arts and culture into sustainable service design can generate unique value and experiences for customers. From the perspective of sustainable development, these hotels seek to add value by using local creative and cultural resources to ensure that they have a sound commercial base from which to showcase their cultural features. As such, this study recommends that the hotel industry shift its focus to a paradigm that provides a strategic and sustainability-framed vision to create value for society while protecting local natural and cultural resources. Originality/value This multilevel model reframes the development of customer value constellations through a holistic understanding of user experience, eco-design practice, service encounters aligned with user touchpoints and front-line employee capabilities. To integrate the perspectives of both service providers and their customers, the proposed model embeds these stakeholders within a single model through the vehicle of local value co-creation. This holistic framework can assist in designing sustainable service within the hospitality industry to deliver better services and customer experiences. The findings provide an illustration of how the proposed multilevel sustainable-development-oriented service design framework can serve as a useful tool in guiding hotels toward corporate sustainability.

Assessing Industry 4.0 readiness in manufacturing: a self-diagnostic framework and an illustrative case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krishnamurthy Ramanathan ◽  
Premaratne Samaranayake
Keyword(s):  
Diagnostic Tool ◽  
Industry 4.0 ◽  
Emerging Economy ◽  
Illustrative Case ◽  
Assessment Framework ◽  
Structured Interviews ◽  
Readiness Assessment ◽  
Manufacturing Firm ◽  
Content Type

PurposeThe purpose of this paper is to present an Industry 4.0 Readiness Assessment Framework (I4.0RAF) and demonstrate its applicability and practical relevance through a case study of a large manufacturing firm in an emerging economy.Design/methodology/approachThe research firstly involved a synthesis of recent literature for the identification of important determinants, and their constituent criteria, for assessing the readiness of a manufacturing firm to transition to an Industry 4.0 setting and structuring them into a readiness assessment framework that can be used as a self-diagnostic tool. The framework was illustrated through a case study. The empirical findings of readiness assessment are validated using semi-structured interviews of senior management of the organization.FindingsThe proposed I4.0RAF was found to be a practically applicable self-diagnostic tool that can be used to assess a firm's readiness to transition to an Industry 4.0 setting with respect to eight important determinants. Cross-functional participation in the assessment helped the organization to determine priorities and interdependencies among the determinants.Research limitations/implicationsThe determinants and their constituent criteria can be further streamlined using inputs from practitioners, consultants and academics.Practical implicationsThe findings demonstrate the interdependencies between the determinants, help to delineate interventions that can lead to synergistic outcomes and enabls planning to achieve higher levels of Industry 4.0 maturity.Originality/valueA self-diagnostic tool as a basis for an informed discussion on transitioning to an Industry 4.0 setting is presented and illustrated through a case study in an emerging economy.

Assessing ventilation strategies in a school with observed indoor air problems

Facilities ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ulrika Uotila ◽  
Arto Saari ◽  
Juha-Matti Kalevi Junnonen ◽  
Lari Eskola
Keyword(s):  
Decision Making ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Final Decision ◽  
Target Number ◽  
Content Type ◽  
Existing Building ◽  
Ventilation Strategies

Purpose Poor indoor air quality in schools is a worldwide challenge that poses health risks to pupils and teachers. A possible response to this problem is to modify ventilation. Therefore, the purpose of this paper is to pilot a process of generating alternatives for ventilation redesign, in an early project phase, for a school to be refurbished. Here, severe problems in indoor air quality have been found in the school. Design/methodology/approach Ventilation redesign is investigated in a case study of a school, in which four alternative ventilation strategies are generated and evaluated. The analysis is mainly based on the data gathered from project meetings, site visits and the documents provided by ventilation and condition assessment consultants. Findings Four potential strategies to redesign ventilation in the case school are provided for decision-making in refurbishment in the early project phase. Moreover, the research presents several features to be considered when planning the ventilation strategy of an existing school, including the risk of alterations in air pressure through structures; the target number of pupils in classrooms; implementing and operating costs; and the size of the space that ventilation equipment requires. Research limitations/implications As this study focusses on the early project phase, it provides viewpoints to assist decision-making, but the final decision requires still more accurate calculations and simulations. Originality/value This study demonstrates the decision-making process of ventilation redesign of a school with indoor air problems and provides a set of features to be considered. Hence, it may be beneficial for building owners and municipal authorities who are engaged in planning a refurbishment of an existing building.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Integration between knowledge management and business process management and its impact on the decision making process in the construction sector: a case study of Jordan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sami Wasef Abuezhayeh ◽  
Les Ruddock ◽  
Issa Shehabat
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Decision Makers ◽  
Decision Making Process ◽  
Construction Sector ◽  
Content Type

Purpose The purpose of this paper is to investigate and explain how organizations in the construction sector can enhance their decision-making process (DMP) by practising knowledge management (KM) and business process management (BPM) activities. A conceptual framework is developed that recognises the elements that impact DMP in terms of KM and BPM. The development of this framework goes beyond current empirical work on KM in addition to BPM as it investigates a wider variety of variables that impact DMP. Design/methodology/approach A case study is undertaken in the context of the construction industry in Jordan. A theoretical framework is developed and assessment of the proposed framework was undertaken through a questionnaire survey of decision-makers in the construction sector and expert interviews. Findings The outcomes of this research provide several contributions to aid decision-makers in construction organizations. Growth in the usage of KM and BPM, in addition to the integration between them, can provide employees with task-related knowledge in the organization’s operative business processes, improve process performance, promote core competence and maximise and optimise business performance. Originality/value Through the production of a framework, this study provides a tool to enable improved decision-making. The framework generates a strong operational as well as theoretical approach to the organizational utilization of knowledge and business processes.

Understanding tenants’ motivations for co-living arrangements: a case study of Klang Valley, Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vania Christy ◽  
Teck Hong Tan
Keyword(s):  
Living Arrangements ◽  
Service Providers ◽  
Living Arrangement ◽  
Content Type ◽  
Living Space ◽  
Depth Interview ◽  
Klang Valley ◽  
User Ratings ◽  
Method Approach

Purpose The purpose of this study is to fill a knowledge gap by analyzing the motivations of tenants to co-living spaces in Klang Valley, Malaysia as the motives of co-living spaces are related to how well that space supports their needs. Design/methodology/approach Tenants’ behaviors were examined using a convergent parallel mixed-method approach, which included a survey and an in-depth interview. A total of 175 respondents were interviewed using purposive sampling. Findings The results show that the preference for co-living attributes has changed during the pandemic. User ratings of preference for physical and leasing attributes of co-living spaces are significant in terms of co-living motivations. The findings also revealed that tenants prefer twin-sharing and master bedrooms when choosing a co-living space to stay in. Research limitations/implications Identifying the factors that influence such motivations is critical for housing developers and co-living service providers to pay close attention to improving tenants’ living experiences. Originality/value There is interest in the co-living spaces that are available for rent. However, very little research is based on an understanding of how the tenants in Klang Valley, Malaysia perceive this type of living arrangement. A better understanding and prediction of tenants’ needs and preferences may lead to a better understanding of the attributes that influence their motivations for using co-living spaces.

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