Guanxi influence and talent management in Chinese organisations; evidence from the real estate sector

The Chinese context has been characterised as one where guanxi influence is significant, shaping the choices, considerations, challenges, and consequences of talent management (TM). There is though little empirical research which shows that this is what is experienced, and that guanxi influence does feature prominently. The purpose of our research is to explore experiences of TM in China, and the extent to which the guanxi influence affects TM.

Case study research to identify and explore guanxi influence on TM was undertaken in three real estate companies in China. Interviews with managers and employees on how talent is defined, attracted, developed, and retained were analysed for guanxi influence. These show that guanxi influence is clearly present in the experience of TM, though this is not the sole or always dominant influence on choices, considerations, challenges, and consequences. This paper contributes to the TM literature by showing that guanxi influences play important roles in defining, attracting, developing, and retaining talent in the Chinese context and also highlighting both advantages and potential disadvantages of guanxi influence in TM activities.

Evidence suggests that guanxi influence is a relevant focus for understanding TM and for framing further research on TM in China. Guanxi influence in this TM context will continue, and by extension in contexts where Chinese organisations develop their presence globally, and adapt to the wider international context of organisation development and change in which best practice TM is a stronger influence.     

The Effect of Distribution Processes on Replenishment Lead Time and Inventory

We investigate the interrelationship of distribution center picking policies and supply chain inventory performance. In particular, we show how the picking sequence in the upstream supply chain link affects the inventory levels of items being replenished to a downstream link for a common “ship‐when‐full” trailer dispatching policy. Perturbing the picking sequence affects items’ inventory levels asymmetrically which causes the aggregate system inventory to vary. We separate the items in replenishment into those units in transit and those awaiting shipment from the upstream distribution step: we call the latter the residual replenishment. We show that the process governing aggregate residual replenishment is Markov and has a stationary distribution that is discrete uniform. Computing the item‐level residual distribution is intractable and so we develop analytical models from which we derive hypotheses for the effectiveness of stable vs. random picking sequences, how item residual replenishment varies with stable picking sequences, and how the aggregate inventory level changes with picking sequence. These suggest a heuristic sequencing algorithm for minimizing inventory, which performs well in simulation tests over a large testbed of parameter sets.     

A classification approach based on the outranking model for multiple criteria ABC analysis

The multiple criteria ABC analysis is widely used in inventory management, and it can help organizations to assign inventory items into different classes with respect to several evaluation criteria. Many approaches have been proposed in the literature for addressing such a problem. However, most of these approaches are fully compensatory in multiple criteria aggregation. This means that an item scoring badly on one or more key criteria could be placed in good classes because these bad performances could be compensated by other criteria. Thus, it is necessary to consider the non-compensation in the multiple criteria ABC analysis. To the best of our knowledge, the ABC classification problem with non-compensation among criteria has not been studied sufficiently. We thus propose a new classification approach based on the outranking model to cope with such a problem in this paper. However, the relational nature of the outranking model makes the search for the optimal classification solution a complex combinatorial optimization problem. It is very time-consuming to solve such a problem using mathematical programming techniques when the inventory size is large. Therefore, we combine the clustering analysis and the simulated annealing algorithm to search for the optimal classification. The clustering analysis groups similar inventory items together and builds up the hierarchy of clusters of items. The simulated annealing algorithm searches for the optimal classification on different levels of the hierarchy. The proposed approach is illustrated by a practical example from a Chinese manufacturer. Furthermore, we validate the performance of the approach through experimental investigation on a large set of artificially generated data at the end of the paper.