Adoption of Artificial Intelligence and Cutting-Edge Technologies for Production System Sustainability: A Moderator-Mediation Analysis

2023 | Information Systems Frontiers | Citations: 0

Authors: Chatterjee, Sheshadri; Chaudhuri, Ranjan; Kamble, Sachin; Gupta, Shivam; Sivarajah, Uthayasankar

Abstract: Cutting-edge technologies like big data analytics (BDA), artificial intelligence ... Expand

Abstract: Cutting-edge technologies like big data analytics (BDA), artificial intelligence (AI), quantum computing, blockchain, and digital twins have a profound impact on the sustainability of the production system. In addition, it is argued that turbulence in technology could negatively impact the adoption of these technologies and adversely impact the sustainability of the production system of the firm. The present study has demonstrated that the role of technological turbulence as a moderator could impact the relationships between the sustainability the of production system with its predictors. The study further analyses the mediating role of operational sustainability which could impact the firm performance. A theoretical model has been developed that is underpinned by dynamic capability view (DCV) theory and firm absorptive capacity theory. This model was verified by PLS-SEM with 412 responses from various manufacturing firms in India. There exists a positive and significant influence of AI and other cutting-edge technologies for keeping the production system sustainable. Collapse

Topics: green IS artificial intelligence organizational productivity blockchain quantum computing

Methods: partial least squares path modeling theoretical contribution conceptual modelling theory development partial least squares regression

Theories: dynamic capabilities theory absorptive capacity theory

Digital workplace transformation triggers a shift in the HR function: From resource manager to growth catalyst

2023 | European Conference On Information Systems | Citations: 0

Authors: Gierlich-Joas, Maren; Zimmer, Markus

Abstract: Digital workplace technologies and changing demands of the workforce drive digi ... Expand

Abstract: Digital workplace technologies and changing demands of the workforce drive digital workplace transformation (DWT). In response, the human resources (HR) function implements people analytics (PA), which can either enable traditional HR reporting or facilitate novel personalized employee experiences. This either/or in leveraging PA induces a shift in the HR function's identity. Building upon paradox theory and using the concept of identity, we investigate the occurring tensions due to the implementation of PA. We use a two-staged qualitative research approach, consisting of an ethnography at a company undergoing DWT, extended to insights from 18 expert interviews from the field of PA for theoretical generalization. We make two contributions. First, our findings suggest two HR identity archetypes: HR as resource managers vs. HR as growth catalyst. Second, we extend the paradoxical tension between these two archetypes to DWT literature questioning the pertinent view of identity metamorphosis. Collapse

Topics: organization structure information technology infrastructure operational management organizational context value creation

Methods: qualitative interview ethnography qualitative coding narrative analysis field study

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

2021 | Decision Support Systems | Citations: 0

Authors: Gonçalves, João N.C.; Cortez, Paulo; Carvalho, M. Sameiro; Frazão, Nuno M.

Abstract: Demand forecasting works as a basis for operating, business and production plann ... Expand

Abstract: Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer’s demand for components in the presence of endcustomer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventory-related costs. This paper introduces a multivariate approach to predict manufacturer’s demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component’s life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component’s life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages. Collapse

Topics: supply chain management logistics management procurement management enterprise resource planning decision support

Methods: longitudinal research time series analysis regression analysis method autoregressive integrated moving average case study

Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port

2021 | International Journal of Information Management | Citations: 0

Authors: Muravev, Dmitri; Hu, Hao; Rakhmangulov, Aleksandr; Mishkurov, Pavel

Abstract: Due to numerous uncertainties such as bad weather conditions, frequent changes i ... Expand

Abstract: Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals. In this research, we investigate a two-stage optimization of intermodal terminals main parameters via using AnyLogic simulation platform. We have developed a set of hybrid simulation models to optimize the main parameters of intermodal terminals which are also called dry ports. To make an express evaluation of the preliminary implementation of dry ports, we have developed an agent-based system dynamics simulation model to achieve the stable state of the main parameters of intermodal terminals. To clarify the obtained averaged benefits of the main dry ports parameters while the port managers make key decisions on the investments into implementation of intermodal terminals, we have developed an agent-based discrete-event simulation model of a seaport – a dry port system. We show that the combination of the agent-based modeling with other simulation approaches simplifies the process of designing simulation models and increases their visibility. The developed set of models allows us to compute the balanced values of the parameters, while an effective operation of a seaport – intermodal terminal system is achieved. On the basis of the provided case study on one of the busiest ports in China, we prove the adequacy and validity of the developed simulation models. Due to the lack of systematic approach to optimization of the main parameters of intermodal terminals in logistic industry, our findings set herein could improve the decision-making process related to the selection of strategic facility planning in the field of intermodal terminals. Collapse

Topics: logistics management systems operations conversational agent operational management online learning system

Methods: simulation system dynamics case study discrete event simulation experiment

The Impact of Digitization on Content Markets: Prices, Profit, and Social Welfare

2020 | Management Information Systems Quarterly | Citations: 2

Authors: Shivendu, Shivendu; Ran (Alan) Zhang

Abstract: The pervasiveness of the Internet and digitization has revolutionized the delive ... Expand

Abstract: The pervasiveness of the Internet and digitization has revolutionized the delivery and consumption of information goods. In this paper, we study the impact of digitization and the shift in consumers’ preferences for the digital medium on outcomes including social welfare in content markets. We consider a publisher who offers information goods in the physical and the digital media and also in a bundle of physical and digital media in a market where consumers are heterogeneous in both their valuations for content and their preferences for media. We find that the publisher’s optimal content-medium strategy is to offer the content only in the digital medium under some market conditions, and to offer the content in a bundle of media and the digital medium under other conditions. Interestingly, while the price of the bundle of media increases with the marginal cost of the physical medium, the price of the digital medium may decrease when the marginal cost of the physical medium is sufficiently large. Surprisingly, we find that consumer surplus can decrease as the proportion of digital-savvy consumers (consumers who prefer the digital medium over the physical medium) increases, and social welfare may have a discrete decrease when the proportion of digital-savvy consumers is sufficiently large. Counter to intuition, when more consumers prefer the digital medium, while the digital price may have a discrete increase, the price of the bundle may have a discrete decrease. Collapse

Topics: price management IS economics consumer surplus economic surplus digital media

Methods: mathematical model operations research

Waste of Time and Money? Constructing an Applicability Framework for Organizational Use of Simulation Studies and Digital Twins

2020 | International Conference on Information Systems | Citations: 0

Authors: Trott, Maik

Abstract: While simulation modelling has become a popular tool both in IS research as well ... Expand

Abstract: While simulation modelling has become a popular tool both in IS research as well as organizational practice, the utilization and potential benefits of simulation studies and Digital Twins are still subject to manifold imponderabilities and variables. Hence, this paper proposes a sophisticated applicability framework to provide structural guidance for practitioners and researchers when it comes to assessing the individual suitability of computer simulation-based analysis approaches as well as the adoption of simulation studies and digital twins in particular. The general applicability of the framework is demonstrated by means of a prototypical case study and expert feedback. Collapse

Topics: decision support system operating system IT productivity digital twin operational management

Methods: simulation case study single case study structured literature research design artifact

Decision Support for Optimal Investments in Building Energy Systems

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Brauner, Tim; Kraschewski, Tobias

Abstract: In 2000, the Renewable Energy Sources Act (EEG) initiated a large dissemination ... Expand

Abstract: In 2000, the Renewable Energy Sources Act (EEG) initiated a large dissemination of photovoltaic systems in Germany. The EEG guaranteed a fixed feed-in remuneration for 20 years, which is highly profitable for many private and commercial prosumers. Feed-in tariffs have decreased substantially, resulting in a different economic situation for prosumers. Current prosumers that are about to exit the remuneration in the near future facing the challenge of readjusting their energy infrastructure. Whereas, potential prosumers aiming to establish an efficient building energy system. Both need an artifact that introduces the idea of efficient investments in renewable energy sources technology. Our decision support system (DSS) seeks to provide guidance for private and commercial prosumers by simulating small energy systems, presenting performance indicators and thereby display investments' impact on the building energy system's efficiency. The Green IS based DSS reduces complexity of components and enables prosumers to follow a sustainable transformation path. Collapse

Topics: decision support system energy information system operational management green IS problem solving

Methods: simulation case study design theory

A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse

2019 | International Journal of Information Management | Citations: 0

Authors: Mahroof, Kamran

Abstract: The explosive rise in technologies has revolutionised the way in which business ... Expand

Abstract: The explosive rise in technologies has revolutionised the way in which business operate, consumers buy, and the pace at which these activities take place. These advancements continue to have profound impact on business processes across the entire organisation. As such, Logistics and Supply Chain Management (LSCM) are also leveraging benefits from digitisation, allowing organisations to increase efficiency and productivity, whilst also providing greater transparency and accuracy in the movement of goods. While the warehouse is a key component within LSCM, warehousing research remains an understudied area within overall supply chain research, accounting for only a fraction of the overall research within this field. However, of the extant warehouse research, attention has largely been placed on warehouse design, performance and technology use, yet overlooking the determinants of Artificial Intelligence (AI) adoption within warehouses. Accordingly, through proposing an extension of the Technology–Organisation–Environment (TOE) framework, this research explores the barriers and opportunities of AI within the warehouse of a major retailer. The findings for this qualitative study reveal AI challenges resulting from a shortage of both skill and mind-set of operational management, while also uncovering the opportunities presented through existing IT infrastructure and pre-existing AI exposure of management. Collapse

Topics: artificial intelligence logistics management technology adoption usability ergonomics

Methods: qualitative interview personal interview operations research case study

Theories: theory of everything

Advanced turbidity prediction for operational water supply planning

2019 | Decision Support Systems | Citations: 0

Authors: Stevenson, Matthew; Bravo, Cristián

Abstract: Turbidity is an optical quality of water caused by suspended solids that give th ... Expand

Abstract: Turbidity is an optical quality of water caused by suspended solids that give the appearance of ‘cloudiness'. While turbidity itself does not directly present a hazard to human health, it can be an indication of poor water quality and mask the presence of parasites such as Cryptosporidium. It is, therefore, a recommendation of the World Health Organisation (WHO) that turbidity should not exceed a level of 1 Nephelometric Turbidity Unit (NTU) before chlorination. For a drinking water supplier, turbidity peaks can be highly disruptive requiring the temporary shutdown of a water treatment works. Such events must be carefully managed to ensure continued supply; to recover the supply deficit, water stores must be depleted or alternative works utilised. Machine learning techniques have been shown to be effective for the modelling of complex environmental systems, often used to help shape environmental policy. We contribute to the literature by adopting such techniques for operational purposes, developing a decision support tool that predicts >1 NTU turbidity events up to seven days in advance allowing water supply managers to make proactive interventions. We apply a Generalised Linear Model (GLM) and a Random Forest (RF) model for the prediction of >1 NTU events. AUROC scores of over 0.80 at five of six sites suggest that machine learning techniques are suitable for predicting turbidity peaking events. Furthermore, we find that the RF model can provide a modest performance boost due to its stronger capacity to capture nonlinear interactions in the data. Collapse

Topics: decision support system missing data operational information system online learning system individual characteristic

Methods: machine learning correlation analysis longitudinal research random forest classification descriptive statistic

Intelligent Business Processes in CRM: Exemplified by Complaint Management

2018 | Business & Information Systems Engineering | Citations: 0

Authors: Zaby, Christopher; Wilde, Klaus D.

Abstract: Customer relationship management (CRM) is becoming a critical source of competi ... Expand

Abstract: Customer relationship management (CRM) is becoming a critical source of competitive advantage for businesses today. However, many CRM business processes are deficient and inflexible. For example, many customers are dissatisfied with complaint management. Still, companies seldom systematically adapt the complaint management process. In theory, operational and analytical CRM form a closed loop: analytical CRM uses business intelligence (BI) tools to analyze operational data and the knowledge gained is used for continual optimization of operations. One special approach in establishing this loop is to continually support decision points in operational processes with knowledge from BI. In this way, the use of BI becomes an integral part of business processes, which are then referred to as intelligent business processes. However, in CRM not much is known about this approach. Based on an extensive review of the literature, the study explores the state of theory and practice in the field of intelligent business processes in CRM, with special attention to complaint management because of its considerable importance and application potential. In Collapse

Topics: business process management Oracle database industrial analytics production management operational management

Methods: case study literature study design science