Choi, Tsan-Ming
Yeung, Wing-Kwan
Cheng, T. C. Edwin
Yue, Xiaohang
Motivated by industrial practices, we explore in this paper the optimal supply chain scheduling problem in garment manufacturing with the consideration of coordination and radio frequency identification (RFID) technology. We consider the case in which a garment manufacturer receives orders from multiple retailers, and needs to determine the optimal order set to take and the corresponding optimal production schedule. We model the problem as a flowshop scheduling problem, uncover its structural properties, and prove that the problem is NP-hard in the ordinary sense only. We contribute by first developing a practical and effective pseudopolynomial dynamic programming algorithm to find the globally optimal solution in reasonable time; second, proposing an implementable method to achieve win-win supply chain coordination; and third, showing the good performance of RFID technology deployment. We further determine the critical threshold value of the order number with which the total manufacturing capacity must be increased if companies in the supply chain wish to improve their profits.
We study a coordination contract for a supplier-retailer channel producing and selling a fashionable product exhibiting a stochastic price-dependent demand. The product's selling season is short, and the supply chain faces great demand uncertainty. We consider a scenario where the supplier reserves production capacity for the retailer in advance, and permits the retailer to place an order not exceeding the reserved capacity after a demand information update during a leadtime. We formulate a two-stage optimization problem in which the supplier decides the amount of capacity reservation in the first stage, and the retailer determines the order quantity and the retail price after observing the demand information in the second stage. We propose a three-parameter risk and profit sharing contract that coordinates the supply chain. The proposed contract permits any agreed-upon division of the supply-chain profit between the channel members. (C) 2009 Elsevier B.V. All rights reserved.
The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit. Note to Practitioners-Forecasting is a crucial part for many control and loading problems. Despite the fact that there is no "perfect" forecast, forecasting for highly structured data (e.g., the time series with high seasonality or trend) is known to be "easy" because there are many well-established models which provide the needed analytical formulations. However, for many real-life control applications, the data patterns are notorious for being highly volatile and it is very difficult to analytically learn about the underlining pattern and hence the well-established statistical methods will fail to make a sound prediction for them. As a result, recent advances of artificial intelligence (AI) technologies have offered an alternative way of providing precise and more accurate forecasting result. Although AI methods can produce highly accurate forecasting results, they suffer a major drawback in which they are slow. This shortcoming becomes a major barricade which hinders the application of AI methods for conducting forecasting for control problems in realworld. In this paper, an Intelligent Quick Prediction Algorithm (IQPA) is developed. With publicly available real datasets, we conducted computational experiments to show that the IQPA is versatile and it can finish the prediction task with accurate results within a prespecified time limit.
Supply chain contracts, such as the markdown money policy (MMP), are commonly adopted in the fashion industry. In this paper, we explore how fashion companies can use MMP to enhance economic sustainability from the cross-cultural perspective. We conduct case studies on two fashion firms (suppliers), one from China and one from U.S.A., that are adopting MMP in their respective supply chains. Via semi-structured interviews with staff members and some public data searching of the target companies, we find that the cultural factors, such as power distance and collectivism/individualism, affect contract selection, contract management, supplier-retailer leadership, and supplier-retailer relationship. We use the Hofstede's national cultural dimensions theory to explain our insights. Specifically, in China, a country with a relatively high degree of power distance and collectivism, the companies tend to care more about the group interest and loyalty. The Chinese fashion companies are more willing to play the leading role in managing the relationships with their retailers, and offer MMP to them. In the U.S.A., a country with a relatively low degree of power distance and individualism, the companies are more likely to emphasize their own interest in trading. In fact, we find that American fashion suppliers tend to bargain with their retailers, and they are less willing to proactively provide the markdown money as a sponsor. Finally, managerial implications are provided, and several future challenges on MMP are examined.
Liu, Shuk-Ching
Choi, Tsan-Ming
Au, Raymond
Hui, Chi-Leung
Since the implementation of the Individual Visit Scheme (IVS), the number of tourists from the Chinese Mainland (CM) to Hong Kong (HK) has increased dramatically. These IVS tourists have huge consumption power and account for 50-70% of the total sales revenue of many HK fashion retailers. In this context, the authors explore the consumer attitudes and preferences of the IVS tourists. Based on a random sampling method involving over 2,000 CM tourists, a questionnaire survey was conducted. The findings show that IVS tourists have complex attitudes towards higher-end brands and their extended products. Moreover, statistically significant results are found in relation to: the comparison of HK consumers with IVS tourists; the analysis of gender attitudes; and the regional analysis. Company interviews have been conducted and specific managerial insights are presented.
Chen, Gang
Govindan, Kannan
Yang, Zhong-Zhen
Choi, Tsan-Ming
Jiang, Liping
Long truck queue is a common problem at big marine container terminals, where the resources and equipment are usually scheduled to serve ships prior to trucks. To reduce truck queues, some container terminals adopt terminal appointment system (TAS) to manage truck arrivals. This paper addresses two implementation scenarios of TAS: static TAS (STAS) and dynamic TAS (DTAS). First, a non-stationary M(t)/E-k/c(t) queueing model is used to analyse a terminal gate system, and solved with a new approximation approach. Then, genetic algorithm is applied to optimise the hourly quota of entry appointments in STAS for the derived queueing model. Lastly to relax the assumption of knowing the truckers' preferred arrival pattern in STAS, we propose the concept of DTAS, which is much easier to apply and can assist individual trucker in making appointment by providing real-time estimation of waiting time based on existing appointments. Our analysis reveals DTAS can significantly increase the system flexibility. (C) 2013 Elsevier B.V. All rights reserved.
This paper investigates the issues of channel coordination in a supply chain when the individual supply chain decision makers take mean-variance (MV) objectives. We propose an MV formulation to capture the risk preference of each individual supply chain agent. Through the studies of a wholesale pricing policy, we find that the incorporation of risk concerns into the setting of supply chain coordinating policy is very important because it can substantially affect the achievability of channel coordination. It is also interesting to find that channel coordination depends on how big the net difference between the risk preferences of the supply chain coordinator and the retailer is. Thus, a slightly risk averse supply chain coordinator can successfully coordinate with a slightly risk prone retailer but not a very risk averse retailer. Numerical analyses are included and managerial insights are developed. (c) 2007 Elsevier Ltd. All rights reserved.