To rapidly respond to uncertainties in the business environment whilst remaining competitive, every organization needs to be able to successfully introduce and manage organizational change. Cognizant of the role of information systems (IS) as an enabler of organizational change, many organizations have paid attention to Enterprise Resource Planning (ERP) systems for successful organizational change primarily because of their change-driving forces across organizations. This study focuses attention on the role of readiness for change in the ERP systems adoption. Readiness for change described as views about the need for organizational change is posited to be an antecedent of two expectancies about the system, performance expectancy and effort expectancy, which lead to actual system use. Based on data gathered from the users of the ERP systems, structural equation analysis using LISREL provides significant support for the proposed relationships.

In reality, we can observe anomalous diffusion patterns of cycle-recycle or rejuvenation. Abrupt changes in the market environment such as sudden currency devaluation or change in government policy or those in marketing strategies such as drastic repositioning can lead to such atypical diffusion patterns. The authors present extended Bass models that incorporate effects of such abrupt changes of external factors into the hazard rate and the market potential. Using a set of compact-car data affected by a drastic change in the government policy, they illustrate the strengths of the proposed models.

The Collaborative Filtering (CF) is one of the popular techniques for personalization in e-commerce storefronts. For CF-based recommendation, every customer needs to provide subjective evaluation ratings for some products based on his/her preference. Also, if an e-commerce site recommends a new product, some customers should rate it. However, there is no in-depth investigation on the impacts on recommendation performance of two number of ratings, i.e. the number of ratings of an individual customer and the number of ratings of an item, even though these are important factors to determine performance of CF methods. In this study, using publicly available EachMovie data set, we empirically investigate the relationships between the two number of ratings and the performance of CF. For the purpose, three analyses were executed. The first and second analyses were performed to investigate the relationship between the number of ratings of a particular customer and the recommendation performance of CF. In the third analysis, we investigate the relationship between the number of ratings on a particular item and the recommendation performance of CF. From these experiments, we can find that there are thresholds in terms of the number of ratings below which the recommendation performances increase monotonically. That is, the number of ratings of a customer and the number of ratings on an item are critical to the recommendation performance of CF when the number of ratings is less than the thresholds, but the value of the ratings decreases after the numbers of ratings pass the thresholds. The results of the experiments provide insight to making operational decisions concerning collaborative filtering in practice.

RFID (Radio Frequency IDentification) is a wireless frequency recognition technology that can be used to recognize, trace, and Identify people, things and animals using radio frequency. Although current RFID technology can provide wide advantages in many fields, most companies do not consider widely adopting this technology yet. This paper presents RFID implementation as a technology innovation. This research selects three affecting factors of IT infrastructure, fitness of Tag, fitness of Frequency, and one moderator of the power of business partners, on RFID adoption based on innovation model, and then empirical survey is conducted among the companies using RFID technology or planing for RFID adoption to investigate the relationship of three affacting factors and one moderator factor. Reserachers can find the direction to activate RFID technology as a competitive advantage.

One of challenging issues of market oriented manufacturing organization is product proliferation. However, increasing product variety not only incurs additional direct and indirect production cost, but also aggravates production system performance such as inventory level, customer lead time and fill rate. Recently, as one of the most beneficial strategic choices to menage the risks associateed with product variety and uncertain sales, special attention is focused on the product/process redesign to delayed product differentiation. In this paper, we develop an analytical model to demonstrate the existence of an optimal level of product differentiation, which can be used to provide theoretical basis in establishing market oriented SCM strategy.

Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples. The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.

In many cases of cluster analysis we are forced to perform clustering without any prior knowledge on the number of clusters. But in some clustering methods such as k-means algorithm it is required to provide the number of clusters beforehand. In this study, we focus on the problem to determine the number of clusters in the given data. We follow the 2 stage approach of ASA clustering algorithm and mainly try to improve the performance of the first stage of the algorithm. We verify the usefulness of the method by applying it for various kinds of simulated data. Also, we apply the method for clustering two kinds of real life qualitative data.

In our model, we keep inventory to satisfy uncertain demands which arrives irregularly. In this situation, we have additional two constraints. First, we need to have certain amount of order consolidation (consolidation constraint) for the orders to replenish the inventory because of production or purchase amount constraint. And also, if we order at a certain date which was set by administrative convenience, we have capacity constraint to order the consolidated order demands (capacity constraint). We show this variant inventory policy is needed in steel industry and note that there will be possible similar case in industry. To deal with this case, we invent a variant replenishment policy and show this policy is superior to other possible polices in the consolidation constraint case by extensive simulation. And we derive a combined solution method for dealing with the capacity constraints in addition to the consolidation constraints. For this, we suggest a combined solution method of integer programming and simulation.

We consider an integrated supply chain model in which multiple suppliers replenish items for a single buyer's demand. Also the buyer specifies a basic replenishment cycle and the suppliers replenish the items only at those time intervals. Namely, we propose a model to study and analyze the benefit by coordinating supply chain inventories through the basic replenishment cycle time. The objective of this model is to minimize the total relevant annual cost of the integrated inventory model. After developing proposed coordinated models, we suggest heuristics for searching the solutions of our models. Finally, numerical and computational experiments for each policy are carried out to evaluate the benefits of those models and the compensation policy is addressed to share the benefits.

Quantity Flexibility contract coordinates individually motivated supplier and buyer to the systemwide optimal outcome by effectively allocating the costs of market demand uncertainty. The main feature of the contract is to couple the buyer's commitment to purchase no less than a certain percentage below the forecast with the supplier's guarantee to deliver up to a certain percentage above. In this paper we refine the previous models by adding some realistic features including the upper and lower limits of the purchase. We also incorporate purchase and canceling costs in a cost function to reflect the real world contracting process more accurately. To obtain the solution of the model, we derive a condition for extreme points using the Leibniz's rule and construct an algorithm for finding the optimal solution of the model. Several examples illustrating the algorithm show that the approach is valid and efficient.

In this paper, we address an extended hub-and-spoke transportation network design problem (EHSNP). In the existing hub location problems, the location and number of spokes, and shipments on spokes are given as input data. These may, however, be viewed as the variables according to the areas which they cover. Also, the vehicle routing in each spoke needs to be considered to estimate the network cost more correctly. The EHSNP is a problem of finding the location of hubs and spokes, and pickup/delivery routes from each spoke, while minimizing the total related transportation cost in the network. The EHSNP is an integrated problem that consists of several interrelated sub-problems. To solve EHSNP, we present an approach based on a symbiotic evolutionary algorithm (symbiotic EA), which are known as an efficient tool to solve complex integrated optimization problems. First, we propose a framework of symbiotic EA for EHSNP and its genetic elements suitable for each sub-problem. To analyze the proposed algorithm, the extensive experiments are performed with various test-bed problems. The results show that the proposed algorithm is promising in solving the EHSNP.

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expects it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. These factors make the sales dynamic and unstable. In this paper we develop a time series model to evaluate the sales patterns with stockpiling and short-term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.