Purpose: A service production system has a structure composed of task execution, agents performing tasks and a resulting service output. The purpose of this paper is to understand how such a service production system changes as a consequence of offshoring.Design/methodology/approach: Drawing on practice theory, the paper investigates how offshoring leads to reconfiguration of the service production system. Through a multiple case methodology, the authors demonstrate how agents and structures interact during reconfiguration.Findings: The paper analyses the reconfiguration of components of a service production system in response to change ignited by offshoring. The authors find recurring effects between structures that enable and constrain agents and agents who shape the structure of the production system. Research limitations/implications: The paper offers a novel contribution to the service operations management literature by applying practice theory. Moreover, the authors propose a detailed, activity-driven view of service production systems and service offshoring. The authors contribute to practice theory by extending its domain to operations management.Practical implicationsService production systems have the ability to self-correct any changes inflicted through offshoring of the systems, which helps firms that offshore.Originality/valueThe paper is aimed at service professionals and offshoring managers and proposes a novel presentation of the service production system with a description of how it responds to offshoring. The authors contribute to theory by applying practice theory to the fields of service operations management and offshoring.
The multi-commodity network flow problem is an important sub-problem in several heuristics and exact methods for designing route networks for container ships. The sub-problem decides how cargoes should be transported through the network provided by shipping routes. This paper studies the multi-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time is essential to offer customers a competitive product.
To evaluate the transportation time reliability of the maritime transportation network for China’s crude oil imports under node capacity variations resulting from extreme events, a framework incorporating bi-level programming and a Monte Carlo simulation is proposed in this paper. Under this framework, the imported crude oil volume from each source country is considered to be a decision variable, and may change in correspondence to node capacity variations. The evaluation results illustrate that when strait or canal nodes were subject to capacity variations, the network transportation time reliability was relatively low. Conversely, the transportation time reliability was relatively high when port nodes were under capacity variations. In addition, the Taiwan Strait, the Strait of Hormuz, and the Strait of Malacca were identified as vulnerable nodes according to the transportation time reliability results. These results can assist government decision-makers and tanker company strategic planners to better plan crude oil import and transportation strategies.
Maersk Line er verdens førende containerrederi og blandt de mest betydningsfulde virksomheder i Danmark. Den globale førerposition blev opnået på relativt kort tid og var et resultat af rederiets beslutning i 1973 om at gå helhjertet ind i containerskibsfarten. Beslutningen blev startskuddet til Maersk Lines dybe internationalisering, hvor rederiet ændrede sig fra at være en overvejende danske virksomhed, der betjente internationale markeder, til at være en genuin transnational virksomhed. Med fokus på opbygningen af Maersk Lines globale organisation og særligt etableringen af egne kontorer i udlandet indkredses rederiets tilpasning og udvikling i perioden fra 1973-1999.
This paper addresses the fleet renewal problem and particularly the treatment of uncertainty in the maritime case. A stochastic programming model for the maritime fleet renewal problem is presented. The main contribution is that of assessing whether or not better decisions can be achieved by using stochastic programming rather than employing a deterministic model and using average data. Elements increasing the relevance of uncertainty are also investigated. Tests performed on the case of Wallenius Wilhelmsen Logistics, a major liner shipping company, show that solutions to the model we present perform noticeably better than solutions obtained using average values.
In this paper, we contribute to the literature on uncertainty and the drivers of social exchange. We explore the 2008 financial crisis and hand-collect unique data on more than 2,700 vessel chartering deals closed in the container shipping industry from 2000 to 2011. Our contribution is twofold. We challenge the literature by finding that low and high status players use different collaborative strategies under uncertainty: the high status players are more prone to coopetition and the low status ones reach out to external buyers. We also extend the literature on social exchange and uncertainty and introduce other constructs: strategic versatility and country-level long-term orientation of the suppliers that we study in our model. Our findings are relevant for policy and managerial decision-makers in the industry.
In the present work, the determinants of port choice regarding container cargoes from specific hinterland regions are analyzed, based on an empirical study of Spain. Previous work has been extended by including novel explanatory variables for the market shares of ports in hinterland locations. Discrete choice theory is the methodological approach used here. More specifically, a nested logit model is proposed. As potential explanatory variables, the model includes maritime connectivity to specific overseas regions and intermodal connectivity of the port to specific hinterland locations. The empirical analysis is based on detailed Spanish customs data. The analysis shows that all variables hypothesized to influence the market share of a port in a specific hinterland region (i.e., road distance to the hinterland region, maritime distance, maritime connectivity of the port, and intermodal connectivity of the port) indeed influence significantly its market share, with the signs as expected. The findings add to the understanding of port competitiveness in specific regions with three conclusions: First, port hinterlands are relational, in the sense that they depend on the overseas origin or destination of the cargo; Second, the analysis suggests that ports that predominantly handle transhipment cargoes may have a “transhipment orientation,” which is an impediment for reaching hinterland markets; Third, intermodal connectivity is a determinant of the market share of a port in a certain hinterland region.
Using evidence from 25,250 records of vessels entering and clearing the rivers of the Chesapeake Bay, this article demonstrates that intercolonial trading captains and crews significantly reduced the number of days their vessels spent in port in Virginia between 1698 and 1766. This contraction reflected a quantifying ethos in shipping that emerged during the early age of sail as the result of mutually reinforcing legal requirements and management practices. Responding to these productivity pressures, captains embraced practices that limited sailors’ freedom and turned to enslaved sailors to guarantee their maritime labor force. Embracing unfreedom aided captains to realize the dispatch goals that helped guarantee their investors’ returns.
In this article, we develop a deep neural network model to estimate the wave added resistance. The required data to train the model is generated using strip theory calculations over a wide range of hull geometries and operational conditions. The model is efficient as it only requires the ship’s main particulars: length, beam, draft, block coefficient, and slenderness ratio. In addition, we present an application of this model in a vessel performance framework. This will be used for predicting propulsion power and analyzing the degree of biofouling on ships from the company Ultrabulk2. The study shows that the developed deep neural network model produces reliable results in predicting the added wave resistance coefficient in comparison to strip theory calculations. Also, the developed ship propulsion and biofouling analysis display satisfactory output for monitoring hull performance under actual ship operational conditions.