This study aims to provide a model for designing logistics networks with multiple products. In such a design model, it is important to determine the operation‐related…
This study aims to provide a model for designing logistics networks with multiple products. In such a design model, it is important to determine the operation‐related parameters accurately.
An iterative process is used to design a logistics network. First, a mixed integer‐programming (MIP) model is used to determine the configuration of the network. Then, based on the output of MIP model, an inventory‐planning model for multiple products is developed to decide the lot size and ordering frequency for each product at each node of the logistics network, and a vehicle routing model is used to find the shortest product delivery routes from wholesalers to retailers. After that, the operation‐related parameters are recalculated and updated, and the configuration of the logistics network is re‐optimized. Such process proceeds until it converges. By this iterative process, the operation‐related parameters can be determined more accurately.
The iterative process proposed in the paper possesses following advantages: determining operation‐related parameters accurately, and adapting to changing market quickly.
A typical logistics network used to distribute multiple products is considered as target system to apply this model. This paper does not handle a real case study as an application example.
This paper proposes a modular design approach: different types of mathematics are used for different modules. The model developed in this paper is realizable as software tool in logistics management.
Purpose – The study sets out to explore the use of an iterative approach for designing distribution chain in an agile virtual environment; in an agile virtual environment…
Purpose – The study sets out to explore the use of an iterative approach for designing distribution chain in an agile virtual environment; in an agile virtual environment, quick adaptation to changing market situation and automation of supply chain management processes are essential. Design/methodology/approach – The iterative approach consists of two parts: the strategic model and the tactical model. First, the strategic model (including number of distributors, location of distributors) is determined. Then, based on the output of the strategic model, the tactical model (i.e. the inventory planning at each node, and vehicle routeing between different nodes of the chain) is determined. After determining the tactical model, the operation‐related parameters from the tactical model are input into the strategic model again, and the configuration of distribution chain is re‐optimized. Such iterations proceed until the design result converges. Findings – The proposed iterative design process provides many advantages such as reuse of knowledge, adaptive to changing market conditions, modular design, and optimal results guaranteed by mixed mathematical usage. The proposed approach is also realizable as a supply chain management software tool. Originality/value – An important contribution of this study is the iterative process that uses three different types of mathematics. For determination of the strategic model mixed integer programming is used. Determination of the tactical model is effected using genetic algorithm and probability theory.
When an enterprise wants to design its distribution chain, it needs first to assess all possible distributors, then select the eligible ones to form the design model. This assessing process can be finished by distributor benchmarking. In this paper, a new approach is developed to benchmark distributors. The benchmarking process is done by the following three steps. First, all factors needed for benchmarking a distributor are identified by a systematic analysis. Second, an internet‐based information acquisition module is developed to get all needed information from possible distributors. Third, an inference module based on the combination of fuzzy logic and array‐based logic is developed to benchmark a distributor. As the information acquisition module is implemented via Internet, and the inference process for benchmarking a distributor is executed by computer applications, it is possible to realize online distributor benchmarking by the approach provided in this paper.
The purpose of this paper is to investigate the relationships between business risks and credit choices of 400 farm households surveyed in Shaanxi province in October 2007…
The purpose of this paper is to investigate the relationships between business risks and credit choices of 400 farm households surveyed in Shaanxi province in October 2007 in the Yangling district. More specifically, this paper investigates whether or not rural farm households in China balance business risks from agricultural production with financial risk from the use of debt.
The data were collected through a survey of 400 farm households in Shaanxi province conducted in October 2007. Four separate regressions are run using a credit measure as the dependent variable and measures of profitability, risk, risk aversion, and demography, and debt source (formal versus informal lending) as independent variables.
The model shows evidence of risk balancing. That is, there is strong evidence that Chinese farmers reduce credit use and financial risk, as business risks increase.
The results suggest that Chinese policy makers could encourage the use of finacial leverage and prudent debt use by offering risk reducing programs such as crop insurance, weather insurance, or price insurance.
This paper uses a unique survey form to collect production risk data as well as gather information on credit use and sources. Data were collected so that risk measures could easily be computed using a triangular distribution. Furthermore, this is believed to be the first empirical validation of the risk balancing hypothesis.