Two-stage replenishment policies for deteriorating items at Taiwanese convenience stores

In the past decade, convenience stores have generally experienced low profit margins due to the intensive competition that exists in the industry. To reduce operating costs, these stores must be able to efficiently control their stock replenishment, especially for deteriorating items such as meal-boxes. To solve this problem, we employed a two-step model to determine the optimal amount of replenishment. In the first step, we obtained the basic reorder quantity by considering three inventory management methods involving the consideration of the probability forecast of demand, hypothesis testing and the newsboy method. In the second step of our model, a novel warning system is established by employing the support vector machine to modify the basic order quantity, which may be varied due to the effect of uncertain factors such as the weather, climate and economic prospects. Using actual data from a convenience store which was a part of the President Chain Store Corporation in Taiwan, the prediction accuracy of the two-step replenishment policies was evaluated. We also apply two methods to enhance accuracy and provide further insights into the model. The results show that the model is workable, and the results can be used as a valuable reference for future practical applications.     

Restock and straightening system for retail automation using compliant and mobile manipulation

As the retail industry keeps expanding and shortage of workers increasing, there is a need for autonomous manipulation of products to support retail operations. The increasing amount of products and customers in establishments such as convenience stores requires the automation of restocking, disposing and straightening of products. The manipulation of products needs to be time-efficient, avoid damaging products and beautify the display of products. In this paper, we propose a robotic system to restock shelves, dispose expired products, and straighten products in retail environments. The proposed mobile manipulator features a custom-made end effector with compact and compliant design to safely and effectively manipulate products in retail stores. Through experiments in a convenience store scenario, we verify the effectiveness of our system to restock, dispose and rearrange items.     

Mining fuzzy association rules in a bank-account database

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.     

A cloud‐based responsive replenishment system in a franchise business model using a fuzzy logic approach

In today's volatile markets, increasingly unpredictable customer demand is exerting great challenges to responsive replenishment. The complexity of responsive replenishment is higher when the business is global in which demand in both domestic and overseas markets has to be catered for. The emergence of cloud computing has eased the difficulties as it allows nearly real‐time access to a universal platform for information sharing between franchisors and franchisees, creating huge opportunities for understanding global market needs for responsive replenishment. Considering the existence of uncertainties due to the fluctuating demands, fuzzy logic is useful in providing decision support for replenishment in uncertain environments. This paper presents a cloud‐based responsive replenishment system to manage operation data of a franchise business using cloud computing, and for analysis using fuzzy logic in order to provide franchisors with the required inventory levels. To the best of our knowledge, this is the first study that applies cloud computing and artificial intelligence techniques in franchising. A pilot run of the system is conducted in an education company, which is considered to be a good representation of an industry operating with a franchise model. The results show that the system allows franchisors to formulate effective responsive replenishment strategies.     

A study on the promotion of the business service for regional retail store using smart technology

Recently, large supermarkets causing new changes in the domestic distribution structure with price break and large store operation are expanding their advancement of super supermarket (SSM) into small-amount purchasing customers and causing frictions with regional retail stores. So the government passed “the Large, Small and Medium-sized Enterprises Co-prosperity Cooperation Promotion Law (Win-Win Law)” at the National Assembly in November 2010 to protect traditional markets and has been making a lot of effort to find solutions to the problem of coexistence of the SSM with regional retail stores. So this paper attempts to explore the ways for regional retail stores to secure competitiveness against SSM amid the changes in the domestic distribution environment caused by the advancement of SSM.     

Applying virtual reality for trust-building e-commerce environments

The application of virtual reality in e-commerce has enormous potential for transforming online shopping into a real-world equivalent. However, the growing research interest focuses on virtual reality technology adoption for the development of e-commerce environments without addressing social and behavioral facets of online shopping such as trust. At the same time, trust is a critical success factor for e-commerce and remains an open issue as to how it can be accomplished within an online store. This paper shows that the use of virtual reality for online shopping environments offers an advanced customer experience compared to conventional web stores and enables the formation of customer trust. The paper presents a prototype virtual shopping mall environment, designed on principles derived by an empirically tested model for building trust in e-commerce. The environment is evaluated with an empirical study providing evidence and explaining that a virtual reality shopping environment would be preferred by customers over a conventional web store and would facilitate the assessment of the e-vendor’s trustworthiness.     

System support for web hosting services on server clusters

The Web hosting service is becoming increasingly important in which the service providers offer system resources to store and provide Web access to contents from individuals, institutions, and companies who lack resources or expertise to maintain a Web site. Web server cluster is a popular architecture used by the hosting service providers as a way to create scalable and highly available solutions. However, hosting a variety of contents from different customers on such a distributed server system faces new design and management problems and requires new solutions. This paper describes the research work we are pursuing for constructing a system to address the challenges faced by hosting Web content on a server farm environment. We also report performance data measured from a real hosting service.     

Item-level RFID for enhancement of customer shopping experience in apparel retail

In the customer-oriented apparel retail industry, providing satisfactory shopping experience for customers is a vital differentiator. However, traditional stores generally cannot fully satisfy customer needs because of difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. Therefore, this paper proposes an item-level RFID-enabled retail store management system for relatively high-end apparel products to provide customers with more leisure, interaction for product information, and automatic apparel collocation to promote sales during shopping. In this system, RFID hardware devices are installed to capture customer shopping behaviour and preferences, which would be especially useful for business decision-making and proactive individual marketing to enhance retail business. Intelligent fuzzy screening algorithms are then developed to promote apparel collocation based on the customer preferences, the design features of products, and the sales history accumulated in the database. It is expected that the proposed system, when fully implemented, can help promote retail business by enriching customers with intelligent and personalized services, and thus enhance the overall shopping experience.     

Optimal replenishment decision in an EPQ model with defective items under supply chain trade credit policy

The traditional inventory of the economic order quantity model assumes perfect items in an ordered lot and an infinite replenishment rate. However, such conditions are rare in actual production environments. Additionally, most studies of this problem have only considered suppliers offering the wholesaler a grace period. In practice, wholesalers often extend a fixed credit period to downstream customers as well. This study therefore proposes a production model for a lot-size inventory system with finite production rate and defective quality under the condition of two-level trade credit policy and the condition that defective items involve both imperfect quality and scrap items. Thus, optimal wholesaler replenishment decisions can be determined for defective items under two-level trade credit policy in the EPQ framework. Four theorems for determining the optimal cycle time and the results in this study can be deduced as a special case of earlier models. Finally, illustrative examples are provided to verify the theoretical results.     

Fuzzy Web ad selector based on Web usage mining

Internet and Web technologies are widely available, making it easier for companies to conduct business and transfer information to customers. Moreover, they speed up financial transactions efficiently, reducing the transaction costs of commercial activities that businesses would normally incur. So, Internet business has created a competitive environment, a successful company wanting to survive and gain a competitive advantage must provide an acceptable bundle of customized services that satisfy customers' needs. Despite the Internet's obvious benefits as a new communication medium its advertising gives the same advertising messages to all customers and so has suffered from poor responses. To raise a Web ad's effectiveness, we propose a Web ad selector that personalizes advertising messages for customers based on their preferences and interests. The Web ad selection system divides Web site customers with similar preferences into several segments through Web usage mining. It uses fuzzy rules that express customer segments' surfing patterns on the basis of expert advice, and recommends appropriate ads by fuzzy inference.     

Consideration of purchase dependence in inventory management

This paper emphasizes the importance of identifying and considering ‘purchase dependence,’ which is when the purchase of an item is dependent on the availability of the other items demanded in the same order. Even though purchase dependence is an important factor in designing an inventory replenishment policy, it has remained mostly unaddressed. Purchase dependence is different to demand dependence. Purchase dependence deals with the purchase behavior of customers, whereas demand dependence deals with the correlation among demands. This paper is the first to develop inventory models for purchase-dependent inventory systems. Through simulation experiments, this paper demonstrates that the developed inventory models, which include purchase dependence, incur less inventory operations costs (and satisfy more service level constraints) than other inventory models.     

Discovering patterns of online purchasing behaviour and a new‐product‐launch strategy

Spurred by rapid development of computers and Internet technology, online shopping is gradually overtaking in‐store shopping, because of advantages such as convenience, more choice of products or services etc. Online stores must devote a great deal of time and resources to locating and attracting new customers. Growing a customer base requires first understanding customers and then providing the products or services they need, thus encouraging customers to purchase more. This paper develops a system to analyse customers’ purchasing behaviour and track shifts in their interests. Customers’ purchasing behaviour is measured using proposed standard product loyalty status and standard brand loyalty status. Using these metrics, together with the preference map established for each customer, a marketing specialist can easily locate potential customers to target when a company launches a new product. The new‐product‐launch strategy proposed in this paper can be used to create a list of potential customers for a product being launched under a variety of conditions. A prototype system has been built to test the feasibility of the proposed new‐product‐launch strategy. The result shows almost 40% of potential customers respond to the recommendation positively.     

A tabu search procedure for coordinating production,inventory and distribution routing problems

This paper addresses the problem of optimally coordinating a production‐distribution system over a multi‐period finite horizon, where a facility production produces several items that are distributed to a set of customers by a fleet of homogeneous vehicles. The demand for each item at each customer is known over the horizon. The production planning determines how much to produce of each item in every period, while the distribution planning defines when customers should be visited, the amount of each item that should be delivered to customers and the vehicle routes. The objective is to minimize the sum of production and inventory costs at the facility, inventory costs at the customers and distribution costs. We also consider a related problem of inventory routing, where a supplier receives or produces known quantities of items in each period and has to solve the distribution problem. We propose a tabu search procedure for solving such problems, and this approach is compared with vendor managed policies proposed in the literature, in which the facility knows the inventory levels of the customers and determines the replenishment policies.     

传统企业成功转型——“杭州祜康”O2O模式

O2O是指线上线下电子商务,把线上的消费者带到现实商店中:在线支付线下商品、服务,再到线下享受服务。同时,把线下商店的消息推送给互联网用户,从而将他们转换为自己的线下客户。杭州祐康采用的就是通过线上下单,由公司旗下的社区便利店等方式实现配送等业务的O2O模式。     

Optimal dynamic pricing and replenishment policy for perishable items with inventory-level-dependent demand

An inventory system for perishable items with limited replenishment capacity is introduced in this paper. The demand rate depends on the stock quantity displayed in the store as well as the sales price. With the goal to realise profit maximisation, an optimisation problem is addressed to seek for the optimal joint dynamic pricing and replenishment policy which is obtained by solving the optimisation problem with Pontryagin’s maximum principle. A joint mixed policy, in which the sales price is a static decision variable and the replenishment rate remains to be a dynamic decision variable, is presented to compare with the joint dynamic policy. Numerical results demonstrate the advantages of the joint dynamic one, and further show the effects of different system parameters on the optimal joint dynamic policy and the maximal total profit.     

Optimal joint replenishment,delivery and inventory management policies for perishable products

In this paper we analyze the optimal joint decisions of when, how and how much to replenish customers with products of varying ages. We discuss the main features of the problem arising in the joint replenishment and delivery of perishable products, and we model them under general assumptions. We then solve the problem by means of an exact branch-and-cut algorithm, and we test its performance on a set of randomly generated instances. Our algorithm is capable of computing optimal solutions for instances with up to 30 customers, three periods, and a maximum age of two periods for the perishable product. For the unsolved instances the optimality gap is always small, less than 1.5% on average for instances with up to 50 customers. We also implement and compare two suboptimal selling priority policies with an optimized policy: always sell the oldest available items first to avoid spoilage, and always sell the fresher items first to increase revenue.     

Price versus privacy: an experiment into the competitive advantage of collecting less personal information

In previous privacy studies, consumers have reported their unease with online retailers that collect a lot of personal data. Consumers claim they will switch to alternative providers or cancel transactions if data collection is deemed excessive. Therefore, privacy appears to be a competitive factor in electronic commerce. This paper describes a study which quantifies the degree to which privacy is a competitive advantage for online retailers. In an experiment, we offered 225 participants the option to purchase one DVD from one of two online stores. Throughout the study, one online shop asked for more invasive personal data—as confirmed by an exit-questionnaire. In the test treatment, the privacy-invasive store sold DVDs for one Euro less than the other, and in the control treatment, both stores sold DVDs for the same price. Across both treatments, 74 participants made a purchase and had the DVD they bought delivered. In our study we found that, when the price of DVDs was the same between both stores, the shop asking for less personal data did not amass the entire market. When consumers were offered a trade-off between price and privacy, the vast majority of customers chose to buy from the cheaper, more privacy-invasive, firm; this firm got both a larger market share and higher revenue. The cheaper shop generated strong dissatisfaction with their privacy practises; in contrast, consumers of the more expensive store displayed only weak dissatisfaction with price. We established the validity of our analysis by checking users made informed choices, and did not select one firm over the other due to hasty decision-making or ordering effects. We found no support for either a materialistic lifestyle nor the quest for immediate gratification as to why customers chose the cheaper but privacy-unfriendly store.     

Personalization of Supermarket Product Recommendations

We describe a personalized recommender system designed to suggest new products to supermarket shoppers. The recommender functions in a pervasive computing environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers who now visit the store less frequently. Recommendations are generated by matching products to customers based on the expected appeal of the product and the previous spending of the customer. Associations mining in the product domain is used to determine relationships among product classes for use in characterizing the appeal of individual products. Clustering in the customer domain is used to identify groups of shoppers with similar spending histories. Cluster-specific lists of popular products are then used as input to the matching process. The recommender is currently being used in a pilot program with several hundred customers. Analysis of results to date have shown a 1.8% boost in program revenue as a result of purchases made directly from the list of recommended products. A substantial fraction of the accepted recommendations are from product classes new to the customer, indicating a degree of willingness to expand beyond present purchase patterns in response to reasonable suggestions.