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

Curbing cyberloafing: studying general and specific deterrence effects with field evidence

2021 | European Journal of Information Systems | Citations: 6

Authors: Hensel, Przemysław G.; Kacprzak, Agnieszka

Abstract: Although the General Deterrence Theory has frequently been employed to study the ... Expand

Abstract: Although the General Deterrence Theory has frequently been employed to study the prevention of misconduct associated with computer use, the common reliance on survey data makes it difficult to measure the general and specific deterrence effects. We use one-group pre-test-post-test quasi-experiment based on data from a monitoring system covering 230 employees for nine months to study general and specific effects of two interventions: the reminder about possible punishment and the actual punishment. The interrupted time series analysis (ITSA) with ARIMA modelling employed to study the longitudinal effects of the interventions allowed to deliver novel findings. Punishing the violators of organisational policy affected both the punished and unpunished employees (decrease in cyberloafing by 41% and 24%, respectively). The effect was maintained for three months, that is until the end of our dataset. The punishment had a stronger effect on unpunished employees located closer in the organisational structure to the punished ones than on the remaining unpunished employees. Moreover, we show that reminder about the possibility of punishment brought no statistically significant effect, likely due to the earlier experience of punishment avoidance. We discuss implications for theory and designing organisational policies intended to curb cyberloafing. Collapse

Topics: electronic surveillance internet technology human resource management information system use wireless LAN

Methods: longitudinal research qualitative interview time series analysis autoregressive integrated moving average autocorrelation analysis

Theories: general deterrence theory

The Voice of the Customer: Managing Customer Care in Twitter

2020 | Information Systems Research | Citations: 6

Authors: Mousavi, Reza; Johar, Monica; Mookerjee, Vijay S.

Abstract: In recent years, managing customer sentiment—particularly on social media—has be ... Expand

Abstract: In recent years, managing customer sentiment—particularly on social media—has become crucial as more customers use social media to seek help from firms. Therefore, we strive to determine an optimal strategy to manage customer sentiment on social media sites such as Twitter. We also aim to identify factors and external events that can influence the effectiveness of customer care. To understand the antecedents of digital customer care, we model a diffusion process of customer sentiment over time. This diffusion process is influenced (or controlled) by the firm through the strategy employed to respond to customer tweets. We then use real data consisting of sentiments expressed by customers directed at Twitter’s service accounts of four major U.S. telecommunication-service providers (AT&T, Verizon, Sprint, and T-Mobile) to estimate the parameters in our analytical model and shed several insights into digital customer care in this industry. First, we find a clear separation among the firms in terms of digital customer care effectiveness. Second, we find that good customer care is not merely a matter of responding to customer tweets. Third, the quality of digital customer care that customers expect varies across firms: Customers of higher priced firms (e.g., Verizon and AT&T) expect better customer care. Fourth, seemingly unrelated events (such as signing an exclusive contract with a celebrity) can impact digital customer care. Our study has important implications for managers as it can help firms determine the optimal strategy to influence customer sentiment. Collapse

Topics: Twitter customer service application programming interface social media Apple iPhone

Methods: sentiment analysis longitudinal research stochastic model simulation generalized autoregressive conditional heteroskedasticity model