REENGINEERING CHANNEL REORDERING PROCESSES TO IMPROVE TOTAL SUPPLY-CHAIN PERFORMANCE

This paper discusses the relationship between business process reengineering and channel performance for firms implementing electronic data interchange (EDI) linkages within the U.S. grocery industry. Both quantitative and qualitative data sources are combined to test the hypothesis that channel transformation involving adoption of EDI and redesign of replenishment processes enables performance improvements more than an order of magnitude greater than implementation of EDI alone. This new process, enabled by EDI, provides retailers with 50- 100% higher inventory turns for products on continuous replenishment processes (CRP) relative to retailer performance using traditional ordering processes. Firms adopting EDI without using CRP to reengineer the ordering processes have failed to realize any statistically significant improvements in either inventory levels or warehouse stockouts. This research provides evidence to support the claims of process reengineering that are common in the popular business press, but for which statistically significant empirical evidence is minimal. The findings of the research also demonstrate the potential improvements that firms can realize through extending the business process reengineering concept to include the firms' entire supply chain.     

PROCESS IMPROVEMENT: AN EXPLORATORY DATA ANALYSIS APPROACH WITHIN AN INTERVAL-BASED OPTIMIZATION FRAMEWORK

This article revisits an old problem; “systematically explore the information contained in a set of operating data records and find from it how to improve operational performance by taking the appropriate decisions in the space of operating conditions,” thus leading to continuous process improvement. A series of industrial case studies within the framework of the internships in the Leaders for Manufacturing (LFM) program at Massachusetts Institute of Technology led us to a reexamination of the traditional formulations for the above problem. The resulting methodology is characterized by the following features: (1) problem statement and solutions are expressed in terms of hyperrectangles in the decision space, replacing conventional pointwise results; (2) data-driven, nonparametric learning methodologies were advanced to produce the requisite mapping between performance and decisions; (3) operating performance is in essence multifaceted, leading to a multiobjective problem, which is treated as such. The proposed methodology has been applied to a number of industrial examples and in this paper we provide a brief overview only of those that can be discussed in the open literature.     

MEASURING AND COMPARING VOLUME FLEXIBILITY IN THE CAPITAL GOODS INDUSTRY

This paper presents a theoretical framework for measuring volume flexibility and relating these measures to firm performance. We develop four metrics using the principle that a volume flexible firm can handle similar levels of uncertainty (as measured by sales variability) with smaller fluctuations in inputs (as measured by variability in cost of goods sold and variability in inventory levels). Then, using 20 years of Compustat data on 550 firms in the capital goods industry, we find that on three of four process‐based measures, small firms are more volume flexible. However, when we incorporate financial performance into our fourth metric, we find that large firms are more volume flexible. We conclude that, to be volume flexible is one thing, but to benefit from this flexibility, firms need to focus on the cost of being flexible.     

A COMPARISON OF PRODUCTION SCHEDULING POLICIES ON COSTS,SERVICE LEVEL,AND SCHEDULE CHANGES

We consider a single product, single level, stochastic master production scheduling (Mps ) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the Mps control system: the method used in determining the amount of stock planned for production in each time period. Typically, Mps control systems utilize a single buffer stock. Here, two Mps dual-buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (dea ) methodology to aid in the evaluation of the simulation results, where Dea serves to increase the scope of the experimental design. Results indicate that the dual-buffer control systems outperform existing policies.     

Notes towards a politics of Bartleby The ignorance of chicken

Abstract

Niels Bohr provided the perfect example of how a fetishist disavowal of belief works in ideology: seeing a horse-shoe on his door, the surprised visitor said that he didn't believe in the superstition that it brings luck, to which Bohr snapped back: 'I also do not believe in it; I have it there because I was told that it works also if one does not believe in it!' This is how ideology works today in our epoch, which presents itself as 'post-ideological': we practice our beliefs, even if, consciously, we do not take them seriously. The disavowed ideological dimension is inscribed in everyday material practices and rituals, especially in the apparently innocent reference to pure utility (a man who lives in a large city and owns a Land Rover doesn't simply lead a no-nonsense, 'down to earth' life; rather, he owns such a car in order to signal that he leads his life under the sign of a no-nonsense, 'down to earth' attitude).     

基于综列线性支出系统模型的城镇居民家庭消费分析

本文在分析线性支出系统和扩展线性支出系统模型特点的基础上,结合PANEL DATA设计了综列线性支出系统模型,并结合不同收入等级的城镇居民家庭消费问题,进行了实证分析。     

大数据时代背景下高校学报发展探析

随着社会的发展,科技逐步渗透到生活的每一个角落,人类已经进入了一个技术高速发展的信息化时代。旧的媒介数据不断被数字化,同时新的资讯信息也在不断地被加工创造着,"大数据"(big data)的概念也开始被不断地提及。面对大数据时代下传媒领域产生的一系列新变化,高校学报也开始向数字化、数据化以及网络共享的多元化发展,为了应对这些新变革,高校学报的发展更应该有自己的定位。     

The promise and perils of using big data in the study of corporate networks: problems,diagnostics and fixes

Network data on connections between corporate actors and entities – for instance through co‐ownership ties or elite social networks – are increasingly available to researchers interested in probing the many important questions related to the study of modern capitalism. Given the analytical challenges associated with the nature of the subject matter, variable data quality and other problems associated with currently available data on this scale, we discuss the promise and perils of using big corporate network data (BCND). We propose a standard procedure for helping researchers deal with BCND problems. While acknowledging that different research questions require different approaches to data quality, we offer a schematic platform that researchers can follow to make informed and intelligent decisions about BCND issues and address these through a specific work‐flow procedure. For each step in this procedure, we provide a set of best practices for how to identify, resolve and minimize the BCND problems that arise.     

MEASUREMENT OF MULTIPLE SITES IN SERVICE FIRMS WITH DATA ENVELOPMENT ANALYSIS

Data envelopment analysis (dea) has become an increasingly popular method to measure performance for service firms with multiple sites. DEA is superior to many traditional methods for firms that have multiple goals. The promise of DEA is that the complex, multi-objective problem of performance measurement can be reduced to a single number. Unfortunately, the practice of DEA often belies the promise. Misconceptions concerning the purpose and implementation of DEA can cause DEA applications to be less than successful. Here, the technique is explained, and a guide to the implementation of DEA is proposed, utilizing DEA studies of retail bank branches.