Optimal Integrated inventory model with Lost Sale and Backordering

This paper explores the impact of quality improvement on joint economic lot size (JELS) models. It begins with the individual optimality of a supplier, which makes the deliveries during the production period, as soon as the quantity is equal to the batch size. The individual optimality of the buyer is based on the economics order quantity (EOQ) model, whereas the supplier is based on the economic manufacturing quantity (EMQ). A cooperative collaboration is stated based on a long-term agreement for sharing information. The result of the study shows that the JELS with quality improvement will provide smaller sub-lot sizes and lower joint total cost of 2.25% compares to the JELS without quality improvement. The purpose of quality improvement is to increase the proportion of conforming items. The result shows this proportion can only be improved under 0.879 and this value depends on the parameters in the investment function.

A note on “quality improvement and setup reduction in the joint economic lot size model”

Purpose: Provide a coordination strategy using multiple common replenishment epochs (MCRE) for a single-supplier multi-retailer supply chain. Design/methodology/approach: The demand of a product occurs only with a group of heterogeneous and independent retailers with constant rates, whereas all their order requests are fulfilled by the supplier. The supplier decides a set of MCREs with general price and extra bonus to entice the retailers to join any one of the MCREs, or to let them remain with their original order time epochs. A retailer is willing to participate in a CRE as long as the retailer’s cost increase is within its tolerance. This paper provide a mixed integer programming to determine the MCRE strategies in order to minimize the total costs of the supplier. Findings: The results illustrate that MCRE model provided in the paper can generate a better replenishment coordination scheme than single CRE models. Practical implications: Replenishment coordination is one of the most important mechanisms to improve the efficiency in supply chains, e.g., chain convenience stores in the modern retail industry. Originality/value: This is a follow-up research on Joint Economic Lot Size (JELS) models with a focus on multiple retailers with their replenishment coordination.

Forward and backward stocking policies for a two-level supply chain with consignment stock agreement and stock-dependent demand

This paper derives a mathematical integrated production inventory model of single vendor and buyer to investigate the effects of capital investment in setup cost of the vendor and order processing cost of the buyer on the decision variables of the model. Main purpose of this study is to minimize the integrated total cost of the vendor and buyer by optimizing the delivery lot size, lead-time, setup and order processing cost and number of deliveries per order simultaneously while lead-time demand follows normal distribution and unknown distribution, i.e., distribution free case. This model is analyzed by calculus method and justified with appropriate numerical illustrations. Numerical results show that significant savings can be achieved at optimal investments in reducing lead time, ordering cost and setup cost. Finally, sensitivity analysis of the key parameters is carried out and some managerial implications are also included.

In this paper we consider a problem of joint coordination between a firm's manufacturing and supply departments. The manufacturing department is responsible for meeting random demand of a product with a short life cycle. The supply department's responsibility is to provide the sufficient amount of raw materials so that the required production level can be achieved. When coordination prevails, both departments' decisions are jointly made; otherwise, decisions are made independently without an exchange of information. If the random demand exceeds the amount produced, a second production run can be expedited at a substantially higher cost. Our analysis yields insightful results to such a coordination problem between the two departments. We provide conditions under which it is desirable to coordinate, resulting in a significant increase in expected profit. If coordination is optimal, then the supply department would purchase additional reserved material for the second run. Under this scenario, we analyze the effects of joint coordination on the expected profit. We also find explicit conditions for which joint coordination is not beneficial, i.e., the supply department should only order sufficient material to meet the first production run requirements. We provide a detailed set of numerical examples and generate response surfaces indicating the desirability of coordination for different pairs of parameter combinations.

PurposeThe purpose of this paper is to discuss how the configurations of short life cycle, low quality, design and price, influence customer purchase intention in fast fashion and high technology industries in China.Design/methodology/approachThe traditional thinking is that products with high quality and low price will win more customers. However, the authors can notice that high quality products usually have high cost. Therefore, it is necessary to do more research on how customers can accept low quality products. The authors take fast fashion products and smart phones as empirical studies, collecting data from customer’s online survey. Based on the methodology of fuzzy set qualitative comparative analysis, the authors analyse the relationship between the factors of short life cycle, low quality, design and price and influence customer purchase intention.FindingsThe authors find that price is the most important influencing factor. Low price is a strong competitive factor in the market. As to low quality products, low price can be achieved relatively more easily than with high quality products, resulting from relatively poorer raw material or configurations. Hence the connection between quality and price may give an idea to enterprises that customers will accept low quality products with low price. Moreover, according to the research, different generations are equally affected by the low price condition, regardless of customer gender and household income.Research limitations/implicationsBecause the study only focuses on fast fashion and smart phones industries, future work needs to replicate this study with individual data for different industries and with alternative methods to reinforce the confidence in the research. Meanwhile , this research studied mainly the customer perspective, it would be desirable to extend the study to the enterprise perspective and find out the difficulties that limit them in using low quality products to meet market needs. This may revel some cultural differences in purchase behavior among different countries and the discussed industries can be expanded to a larger area.Practical implicationsThe study offers a number of managerial implications. With the rapid changes in people’s aesthetic sense and developing high-tech, it is more and more necessary for companies to think about how to win more customers and earn more profits. Low quality products have advantages as they will lower companies’ costs in many dimensions, improving the speed of supply. It helps firms to take low quality products into consideration and think whether they will influence different aspects of the company assistance firms to get a deeper understanding of customer psychology and make better decisions on their products.Originality/valueThe paper fills the gap in the research field by exploring how consumer behavior is affected by different conditions.

We study an extension of the economic production lot size model, where more than one production rate can be used during a cycle. Moreover, the production rates, as well as their corresponding runtimes are decision variables. We decompose the problem into two subproblems. First, we show that all production rates should be chosen in the interval between the demand rate and the production rate which minimizes unit production costs, and should be used in an increasing order. Then, given the production rates, we derive closed‐form expressions for all optimal runtimes as well as the minimum average cost. This analysis reveals that it is the size of the setup cost that determines the need for being able to use several production rates. We also show how to derive a near‐optimal solution of the general problem.

In the first 15 years of the 21st century, the Chinese video industry continued growing and evolving at a rapid pace. With the changes in the video streaming market, producing original content became a hot topic within major video streaming websites. This study provided a competitive analysis of Chinese video streaming websites. The research applied a comparison study to understand the differences of original content on Chinese video streaming websites. A survey about the online audience's using habits, attitudes toward original contents and ads on video streaming websites was conducted. SWOT analyses in this research also provided an overall understanding of the competitiveness of major video streaming websites. Facing diverse internal and external challenges, the original content on major video platforms had to overcome serious weaknesses such as a lack of creativity, an unstable user experience, and a short life cycle. Therefore, increasing audience loyalty is a critical goal. The main recommendations would be to improve the quality of original content and branding which are the most important aspects for the future development of Chinese video streaming websites.

With the development of competition in business and also today’s rapid changes in competitive market, understanding these changes are the key factor of being more efficient in markets. CRM which is known as basic structure for describing customer’s needs for efficiently understand customer’s behavior and finely get the maximum of market share and profit. There are major differences between B2B and B2C businesses such as long term purchase cycle, purchase interests and the amount of the transactions. These differences needs more interactive strategies. The knowledge that gets from CRM is extremely related to market changes. In recent years data mining increasingly help organizations to get and understand customer’s behavior. But with the rapid changes in market these procedures must be change too. Change mining as the higher order of data mining tries to get knowledge by analysis patterns instead of data. In this paper we attempt to calculate customer’s value by using RFM model and K-MEANS clustering method and then analysis changes in deferent time periods. We tries to find out cluster transitions and most frequent customer value changing trend for proactive decision making. For this purpose we use customer purchase transactions in insurance industry which are gathered in 3 years.

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX’s performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods.

Populationen und ihre Dynamik Over the last few decades changes in land use have caused a drastic decline in the area of extensively used calcareous grasslands in central Europe. The persistence of many vulnerable plant species now depends on relict populations. We use phytosociological records from nutrient-poor calcareous grasslands in the Swiss Jura mountains to study local extinctions of plants from 1950-1985. We tested whether the risk of local extinction is higher in small populations, higher for plants with a short life cycle than for those with a longer life cycle, higher for specialist plant species, and higher for species with a high sensitivity to nutrient availability. We considered the possibility that species could have been missing in the new records without actually having gone extinct, if new and old phytosociological records were not taken at the same position (pseudo-turnover). We found pseudo-turnover between records taken at defined misplacement distances at two sites independent of life form and habitat specificity of a species and mainly confined from 1950-1985 and for which new and old records existed from similar positions. In 1950, 1181 local populations of 185 species were recorded in these sites, yet in the new records 462 (39%) populations were no longer present. Whereas the frequency of habitat specialist per site decreased (p > 0.05), the frequency of generalists increased (p > 0.01), leaving the total number of species per site unchanged. Local extinction was more likely for small populations (p > 0.01), for species with a short life cycle (p > 0.05), and for species with high habitat specificity (p > 0.05). Local extinctions did not depend on species sensitivity to nutrient availability. When recordings of species with an abundance of 0.1% were omitted because of potential pseudo-turnover, the results remained unchanged. Fragmentation and isolation of nutrient-poor calcareous grassland in the Swiss Jura mountains has progressed to such a degree that many plants of these habitats are going locally extinct, even in intact remnants.

The lack of available historical data resulting from increasing market turbulence and very short product life cycles means that the demand in an inventory system varies non-stochastically and must therefore be subjectively determined. This paper investigates the economic production lot size inventory problem, in which a positive lead time and shortages are allowed, with fuzzy demands and an unknown explicit membership function. An analysis method based on an $\alpha $ -cut representation and fuzzy ranking is proposed to derive the optimal inventory policy. In addition, the necessary and sufficient conditions for the optimal inventory policy are determined. The merits of the proposed approach include the ability to obtain a numeric and precise optimal policy rather than a fuzzy policy and no requirement for explicit forms of the membership functions of the fuzzy demands, which increases the generality of the proposed model. Additionally, practical insights are provided.

In most economic lot size models, the demand during stockout period is either completely backordered, or lost. Also, the quantity received is assumed to be the same as the quantity ordered. In this paper, a lot size model is described in which the demand during stockout period is either completely or partially backordered, and also the quantity received is uncertain. The standard deviation of the quantity received is taken to be independent of, or proportional to, the quantity ordered. In both cases, the results obtained are shown to be extensions of the well known results of the simple lot size model with backordering.

Economic production lot size model for deteriorating items with partial back-ordering