The optimal decision of customer order decoupling point for order insertion scheduling in logistics service supply chain

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Abstract

Customer demand for order insertions impacts the position of the customer order decoupling point (CODP) in a logistics service supply chain (LSSC). This paper focuses on how the parameters of inserted orders affect the position of the CODP when a logistics service integrator (LSI) is operating a mass customization logistics service. Considering customer demand for orders to be inserted, a CODP decision model for LSSCs is developed based on a two-echelon LSSC consisting of an LSI and several customers. The objectives of the model are to maximize the profit and to ensure the comprehensive satisfaction of the LSI. Conducting numerical analyses on a specific dataset, it was discovered that the scale effect coefficient and service volume of the inserted order have a clear influence on the CODP decision, while the order variation coefficient does not. Furthermore, the LSI will not accept an inserted order when the inserted order has a high variation coefficient or a low price.

Introduction

Faced with the growing demand for customized logistics services, many logistics enterprises are expanding their businesses beyond a mass service or change logistics service model to provide customized services. Specifically, these enterprises are starting to provide mass customization logistics services (MCLS, see Appendix: Acronyms List for quick reference to all the acronyms used throughout this paper), instead of mass logistics services (Chandra and Grabis, 2004). In an MCLS environment, many logistics enterprises cooperate and integrate to form a logistics service supply chain (LSSC) to meet the individualized service requirements of customers and to achieve the capabilities for offering mass services (Choy et al., 2007, Liu et al., 2011). As a result, the key factor in determining the competitiveness of the LSSC has become whether it can offer customized services at the cost of mass services through reasonable scheduling (Liu et al., 2012).

Order insertion is a situation that occurs often in supply chain scheduling, both in production supply chains (Hoogeveen et al., 2012) and in service supply chains (Oliveira and Lourenço, 2002), especially in LSSCs. Order insertion in an LSSC implies that the logistics service integrator (LSI) must insert a new order into the scheduling plan that has already been created for the original orders. In this situation, the LSI must accommodate the new order by developing a new optimal scheduling plan (Hoogeveen et al., 2012). In the MCLS environment, the customer order decoupling point (CODP) is the key point that separates the customized service from the mass service. When a new order needs to be inserted into the schedule, the abruptness and urgency of the new order will change the optimal CODP, as well as the total cost and the comprehensive satisfaction of the LSI. Therefore, for the LSI, the impact of order insertion on the CODP should be explored and corresponding management countermeasures developed.

Many studies have emphasized this topic from a theoretical point of view. Recent studies on the CODP have mainly focused on production supply chain scheduling, including the definition and implications of the CODP (Ji et al., 2007, Olhager, 2003, Rafiei and Rabbani, 2011, Wikner and Rudberg, 2005), the changeable position of the CODP (Qin, 2011, Rafiei and Rabbani, 2011, Rudberg and Wikner, 2004, Van Donk, 2001), and the method for determining the position of the CODP (Berry and Hill, 1992, Ji et al., 2007, Olhager, 2003, Olhager et al., 2001). However, the problem of inserting an order is still a relatively new issue in the service supply chain field. Some quantitative studies on the CODP in the operation of mass customization services have enriched the research in this field, although these studies have mainly investigated how a particular service company determines the CODP of its service process (Tang and Chen, 2009, Tang and Chen, 2010). In addition, Liu et al. (2013) found that the scale effect coefficient (SEC) influences the CODP in a particular way, namely, that the LSI can move the CODP earlier and increase the level of customization by increasing the value of the SEC. When the SEC is greater, the LSI prefers to move the CODP later in order to take advantage of the scale effect. From the extant literature, it is clear that there has been no consensus on the impact of order insertion on the CODP. Addressing the complexity and significance of the CODP decision in an order insertion situation, this study offers two contributions. The first contribution is to expand CODP decision-making research into the context of complex scheduling, which will provide a useful reference for further research on order insertion scheduling. The second contribution is a unique modeling approach, which will help in establishing other models for making order insertion decisions.

Based on a review of the literature and specific practical observations related to logistics enterprises, it seems that in the MC service environment, the LSI needs to focus on solving three problems when scheduling an order insertion. These three problems have been overlooked in the research, but provide the focus of the discussion in this paper. First, how can the difference between original orders and inserted orders be expressed and what parameters should be considered? Second, which parameters will affect the insertion decision and which parameters will affect the CODP decision? What are the rules of such effects? Third, how can the LSI make better decisions according to these rules?

The problems mentioned above will be discussed in this paper. Thus, a multi-objective CODP decision model is developed. The model has two goals: to maximize profit and to ensure the comprehensive satisfaction of the LSI. To solve the model, the ideal point method is used to simplify the multi-objective programming model into a single-objective one. Since many papers have already studied the various solution methods, this paper does not emphasize the selection and comparison of different methods. Instead, a suitable method is chosen, and then a numerical study of the model is conducted, exploring the effects of the new order parameters on the CODP decision. In doing so, some important findings are obtained. For example, the SEC and the service volume of the inserted order have an obvious impact on the CODP decision, while the order variation coefficient does not. In cases where the new order has a high order variation coefficient or low price, the LSI will not accept the order.

The rest of the paper is organized as follows: Section 2 reviews previous studies on mass customization services, the CODP, and order insertion. Section 3 gives the problem description and assumptions of this paper. Section 4 builds a multi-objective model of CODP decisions and offers the solution method of the model. In Section 5, an empirical analysis was conducted to demonstrate the model’s application and to determine the effects of new order parameters on CODP decisions. The data were selected from an LSI: the Baoyun logistics company in Tianjin, China. Finally, Section 6 summarizes the main conclusions and managerial implications.

Section snippets

Literature review

This research mainly concerns CODP decisions regarding order insertions in the MCLS environment. Thus, the literature review focuses on studies related to MC, the CODP, and order insertion. The direction of this present study will be proposed after summarizing the status of the current literature and its deficiencies.

Problem description

Suppose an LSSC consists of an LSI and several customers. The LSI acts as a decision maker, integrating the logistics service resource of its functional logistics service providers (FLSPs) and providing the logistics service to its customers. The logistics service demand of these customers is composed of multiple procedures. Some procedures are common and others are customized. As shown in Fig. 1, the LSI integrates the common procedures of the three orders, providing a mass logistics service.

Model building

In this section, the CODP decision model of LSSC considering order insertion is presented. Section 4.1 gives the objective functions. Section 4.2 gives the constraints of the model. Section 4.3 gives the complete description of the CODP decision model. In Section 4.4, the ideal point method is adopted to solve the CODP decision model, and the specific steps are presented.

Numerical analysis

In this section, numerical analysis is used to explore the effects of the new order parameters on the CODP decision. In Section 5.1, the basic data used for this numerical analysis are selected from the Baoyun Logistics Company in Tianjin, China. Section 5.2 analyzes the rules of CODP movement from five perspectives, namely, scale effect coefficient, order variation coefficient, service volume of the new order, price of the new order, and cost coefficient of the new order. The scale effect

Conclusion

In this paper, a CODP decision model considering order insertion was developed. The effects of single order parameters (e.g., order variation coefficient, service volume, price, and cost coefficient) and combined order parameters on the CODP decision and order insertion decision were studied through numerical analyses. The expected conclusions derived from the analyses are as follows:

  • 1.

    For an LSI in the LSSC, the greater the SEC, the more favorable a postponement strategy will be. In other words,

Limitations and future work

This paper uses a multi-objective programming model to address the problem of CODP decisions in LSSCs considering order insertion. However, this study has some limitations. For instance, the numerical analyses could not account for all situations encountered in practice. Further studies should expand the dataset under analysis in order to increase the significance of the results in practice. The study only considered the CODP decision problem of LSIs with inserted orders from customers and

Acknowledgment

This research is supported by the National Natural Science Foundation of China (Grant No. 71372156), supported by Humanity and Social Science Youth foundation of Ministry of Education of China (Grant No. 13YJC630098), sponsored by China State Scholarship Fund (Grant No. 201308120087) and Independent Innovation Foundation of Tianjin University (Grant No. 2014XST-0007). The reviewers’ comments are also highly appreciated.

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