Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector

This study proposes and follows a specific and systematic framework for implementing Lean Six Sigma (LSS) methodology in a telecom company in order to improve customer satisfaction by minimizing the company’s response time to customer requirements. The goal of this study was achieved by utilizing several LSS tools under five phases of the DMAIC methodology. Unlike previous studies in the telecom sector that used only qualitative method, in this study, both qualitative and quantitative methods were utilized to draw meaningful conclusions. As a result of the implementation of the LSS methodology, the average order fulfilment lead time for sales orders (SO) and value-added service (VAS) orders was reduced from 10.3 to 5.9 days and from 1.5 to 0.5 days, respectively. The reduction in lead time resulted in an increase in the sigma level for SO and VAS orders from 0.44 to 1.26 and from 0.73 to 2.66, respectively. These improvements were expected to lead to a financial benefit in savings of over $600,000 per year in operational costs, enhancements to customer experience and an increase in revenue generating opportunities. Moreover, this article enriches the existing literature on the application of LSS concept in the service industry, and helps the company to speed up the response to customer requirements.     

数据驱动下收货方质量偏好与电商配送服务质量优化

针对"互联网+大数据"优化电商配送服务质量问题，明确不同收货方的质量需求稳定性，引导电商根据收货方不同质量敏感性提供相对精准服务，提升配送服务质量。模拟投票结果的形成过程聚类得到收货方对服务质量偏好的记忆性特征：（1）"无记忆"型收货方；（2）"记忆"型收货方；（3）"不确定"型收货方；（4）收货方总体。进一步推导不同规划类型求解空间，设计得到"无记忆"型收货方动态规划精确求解方法，及其他三种类型近似求解粒子群算法。研究过程中，配送资源质量感知度被嵌入到模型约束；"无记忆"型收货方的质量需求规划问题转化为零库存策略最优解问题，进而证明存在精确解；"记忆"型收货方呈现出对质量感知的累积；"不确定"型收货方模型通过赋值即可得总体收货方表达式。结果表明：数据驱动研究框架借助大数据资源，使得电商更易通过收货方的质量偏好设计更加匹配的配送方案；不确定服务需求得到有效满足，投入成本的利用率更高；通过特征分类的方式，尽可能地抽取能够精确最优化的部分，缩小NP范围，提高整体求解的精确度。     

Employers' perspectives on benefit conditionality in the UK and Denmark

This article examines the under-explored demand-side of active labour market policies (ALMPs). Based on interview data from a comparative study of the UK and Denmark, the paper analyses employers' perspectives and experiences of ALMPs. In both countries, employers were favourably disposed towards employing unemployed jobseekers but held negative views on conditionality. First, benefit conditionality led to employers receiving large numbers of unsuitable and unfiltered job applications, with associated negative resource impacts. Second, employers perceived this as a product of ‘box ticking' and compliance targets. Finally, employers criticised policy and media rhetoric for focusing solely on the supply-side and for problematizing unemployed candidates. The paper argues that these crucial, but neglected, employer perspectives demonstrate that the current benefit conditionality regime in the UK risks irrevocably ‘tarnishing' candidates, which undermines, rather than enhances, their chances of securing employment through ALMPs. This unique dataset provides further evidence that the current direction of policy requires urgent and radical re-thinking.     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

For better,for worse? Public support for the capitalist model of the economy

A capitalist market economy is based on several institutional elements, such as private ownership and competition. Does public support for this economic model rise if the economy prospers, and fall during a downturn? Or is public support largely independent of the ups and downs of economic cycles? We hypothesize that positive economic performance increases support and that persons profiting personally are more supportive of the economic system's constitutive institutional elements. Using multilevel regression we study the determinants of individual-level support for the economic system. We also test for differences in the perception of economic performance due to political attitudes and personal properties. The findings partly support the hypotheses, indicating that macro-economic factors matter for individual-level attitudes towards the economy. Attitudes towards different institutional elements of the economic system also differ in the degree to which they are political or economic, and influenced by economic performance. Individual features – education and personal economic stakes – affect attitudes towards the economy, but a substantial share of the individual-level variation in economic attitudes remains unexplained.     

Risk Modeling of Interdependent Complex Systems of Systems: Theory and Practice

The emergence of the complexity characterizing our systems of systems (SoS) requires a reevaluation of the way we model, assess, manage, communicate, and analyze the risk thereto. Current models for risk analysis of emergent complex SoS are insufficient because too often they rely on the same risk functions and models used for single systems. These models commonly fail to incorporate the complexity derived from the networks of interdependencies and interconnectedness (I–I) characterizing SoS. There is a need to reevaluate currently practiced risk analysis to respond to this reality by examining, and thus comprehending, what makes emergent SoS complex. The key to evaluating the risk to SoS lies in understanding the genesis of characterizing I–I of systems manifested through shared states and other essential entities within and among the systems that constitute SoS. The term “essential entities” includes shared decisions, resources, functions, policies, decisionmakers, stakeholders, organizational setups, and others. This undertaking can be accomplished by building on state‐space theory, which is fundamental to systems engineering and process control. This article presents a theoretical and analytical framework for modeling the risk to SoS with two case studies performed with the MITRE Corporation and demonstrates the pivotal contributions made by shared states and other essential entities to modeling and analysis of the risk to complex SoS. A third case study highlights the multifarious representations of SoS, which require harmonizing the risk analysis process currently applied to single systems when applied to complex SoS.     

Decision‐making under uncertainty in child protection: Creating a just and learning culture

The argument is made for having a positive error culture in child protection to improve decision‐making and risk management. This requires organizations to accept that mistakes are likely and to treat them as opportunities for learning and improving. In contrast, in many organizations, a punitive reaction to errors leads to workers hiding them and developing a defensive approach to their practice with children and families. The safety management literature has shown how human error is generally not simply due to a “bad apple” but made more or less likely by the work context that helps or hinders good performance. Improving safety requires learning about the weaknesses in the organization that contribute to poor performance. To create a learning culture, people need to feel that when they talk about mistakes or weak practice, there will be a constructive response from their organization. One aspect of reducing the blame culture is to develop a shared understanding of how practice will be judged and how those appraising practice will avoid the hindsight bias. To facilitate a positive error culture, a set of risk principles are presented that offer a set of criteria by which practice should be appraised.     

不同冷链服务模式下生鲜农产品供应链决策

基于单物流服务商、单供应商和单零售商构成的生鲜农产品供应链，构建3种冷链服务模式：承诺模式、提前要求模式与延后要求模式，探讨不同冷链服务模式对定价与冷链服务水平、企业绩效的影响。研究发现：某种冷链服务模式是否有利于消减数量与质量损耗依赖于产品易腐性、冷链服务成本及其分担比例。供应链成员无法在承诺模式与提前要求模式下形成统一的模式偏好。当物流服务商承担的冷链服务成本比例较低时，相比于提前要求模式，供应商偏好承诺模式，否则，偏好提前要求模式。承诺模式比提前要求模式更有利于物流服务商，然而对于零售商，结果正好相反。数值实验表明，虽然供应链成员无法对冷链服务模式形成整体的一致性偏好，但仍可实现局部的一致性偏好。     

互联网医疗服务法律监管研究

随着互联网技术的进步以及医疗改革的深入,互联网医疗服务成为新生业态.然而,我国互联网医疗服务法律监管体系还不够完善,缺乏统一规范的监管体系,多头监管、个人隐私隐患大、纠纷处理解决机制不健全等现实困境尚未解决,不利于互联网医疗服务行业的发展和人民群众身心健康.为解决上述问题,通过梳理发达国家的成功经验,如美国的移动医疗应用程序监管和个人隐私保护、欧盟的互联医疗数据管理、英国的行业协会监管等,结合我国现有国情,建议从完善立法体系、建设隐私保护机制、构建行业监管、明晰医疗服务主体责任等方面健全互联网医疗法律监管制度,以期推动互联网医疗服务行业发展,保障医患双方的合法权益.     

The University: An Agent of Social Change?

Can universities be agents of progressive social change? How would we know if a university was acting as an agent of social change? Drawing on four case studies, I raise a number of questions to problematize our understanding of the university as an agent of social change. I outline a number of contributing factors that appear to explain successful cases. I conclude by arguing the relevancy of these cases for larger, and more traditional, sociological projects.