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Present study deals with the most discussed rather than addressed yet still an unsolved problem of supply chain known as the bullwhip effect. Operational variables affecting the bullwhip effect are identified and their role in causing the bullwhip effect has been explored using artificial neural networks. The purpose of this study is to analyze the impact of identified operational reasons that affect the bullwhip effect and to analyze the bunch of variables that are more prominent in explaining the phenomenon of the bullwhip effect.

Ten major sectors of the Indian economy are analyzed for the bullwhip effect in the present study, and the operational variables affecting the bullwhip effect in these sectors are identified. The bullwhip metric is developed as the ratio of variance in production to the variance in the demand. The impact of identified operation variables on the bullwhip effect has been discussed using the artificial neural network technique known as multilayer perceptron. The classification is also performed using neural network, logistic regression and discriminant analysis.

The operation variables are found to be varying with respect to sectors. The study emphasizes that analyzing the right set of operation variables with respect to the sector is required to deal with the complex problem, the bullwhip effect. The operational variables affecting the bullwhip effect are identified. The classification result of the neural network is compared with those of the logistic regression and discriminant analysis, and it is found that the dynamism present in the bullwhip effect is better classified by neural network.

The study used 11 years of observations to analyze the bullwhip effect on the basis of operational variables. The bullwhip effect is a complex phenomenon, and it is explained on the basis of an extensive set of operational variables which is not exhaustive. Further, the behavioral aspect (bullwhip because of decision-making) is not explored in the present study.

The operational aspect plays a gigantic role to explain and deal with the bullwhip effect. Strategies to mitigate the bullwhip effect must be in accordance with the operational variables impacting the sector.

The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of neural networks in which operational variables are taken as predictor variables.

This paper aims to examine how long-lived firms can further develop through digitalisation in terms of actions, conditions and effects from a competitiveness perspective.

This exploratory study follows an inductive approach based on a survey conducted via interviews undertaken with nine long-lived Italian firms. The dimensions of the model (command, continuity, community, connection), elaborated by Miller and Le Breton-Miller (2005) in relation to longevity factors, were chosen to analyse digitalisation’s contribution to these long-lived firms’ development.

The digitalisation implemented by the analysed firms contributed in a variety of ways: (1) improved the efficiency and effectiveness of their business processes, (2) enhanced the understanding of customer experience, (3) supported their craftsmanship and the transmission of the knowledge included in the entrepreneurial path, (4) increased the awareness of the cultural value of the firms’ heritage and (5) allowed for the development of cutting-edge design skills by experimenting with content on different digital platforms and devices.

This study suggests managers of long-lived firms develop digital skills that allow them to interact with the rapid evolution of this context and understand how to effectively implement digitalisation in their specific firm. From this perspective, it is strategic to establish or strengthen collaborative network relationships to acquire such necessary skills.

This study provides novel empirical evidence on how long-lived firms are facing the challenge of digitalisation in terms of actions, conditions and effects to improve their competitiveness and ensure their survival.

The paper offers a comprehensive understanding of how digital transformation affects business models and how firms operate and compete effectively and successfully in a digital economy.

The research adopted an abductive approach (Dubois and Gadde, 2002) through constant movement between theory and empirical evidence. A systematic literature review led the first conceptual development and examples of practices from cultural heritage sectors were used in the theorizing process.

This paper depicts a digital model framework through a set of assumptions about how an organization creates and delivers value in an interconnected way by orchestrating new interactive processes, and providing experience propositions to customers, and about how value is framed in terms of economic, social and cultural outcomes.

The study contributes to the scientific debate by discussing the role of digital business models as enhancements more rather than replacements of traditional business models; it frames a digital business model as consisting of three main pillars: value orchestration, experience propositions and value sharing.