Linking quality management to manufacturing strategy: an empirical investigation of customer focus practices

Quality management (QM) has often been advocated as being universally applicable to organizations. This is in contrast with the manufacturing strategy contingency approach of operations management (OM) which advocates internal and external consistency between manufacturing strategy choices. This article investigates, using the case-study method, whether customer focus practices—a distinctive subset of the whole set of QM practices—are contingent on a plant’s manufacturing strategy context. The study strongly suggests that customer focus practices are contingent on a plant’s manufacturing strategy and identifies mechanisms by which this takes place. The findings inform the implementation of QM programs

A Case for a Programmable Edge Storage Middleware

Edge computing is a fast-growing computing paradigm where data is processed at the local site where it is generated, close to the end-devices. This can benefit a set of disruptive applications like autonomous driving, augmented reality, and collaborative machine learning, which produce incredible amounts of data that need to be shared, processed and stored at the edge to meet low latency requirements. However, edge storage poses new challenges due to the scarcity and heterogeneity of edge infrastructures and the diversity of edge applications. In particular, edge applications may impose conflicting constraints and optimizations that are hard to be reconciled on the limited, hard-to-scale edge resources. In this vision paper we argue that a new middleware for constrained edge resources is needed, providing a unified storage service for diverse edge applications. We identify programmability as a critical feature that should be leveraged to optimize the resource sharing while delivering the specialization needed for edge applications. Following this line, we make a case for eBPF and present the design for Griffin - a flexible, lightweight programmable edge storage middleware powered by eBPF

Toward Customizable Multi-tenant SaaS Applications

abstract: Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.Dissertation/ThesisDoctoral Dissertation Computer Science 201

From Bits to Atoms: 3D Printing in the Context of Supply Chain Strategies

A lot of attention in supply chain management has been devoted to understanding customer requirements. What are customer priorities in terms of price and service level, and how can companies go about fulfilling these requirements in an optimal way? New manufacturing technology in the form of 3D printing is about to change some of the underlying assumptions for different supply chain set-ups. This paper explores opportunities and barriers of 3D printing technology, specifically in a supply chain context. We are proposing a set of principles that can act to bridge existing research on different supply chain strategies and 3D printing. With these principles, researchers and practitioners alike can better understand the opportunities and limitations of 3D printing in a supply chain management context.Comment: Submitted to HICSS-4

On the Notion of Abstract Platform in MDA Development

Although platform-independence is a central property in MDA models, the study of platform-independence has been largely overlooked in MDA. As a consequence, there is a lack of guidelines to select abstraction criteria and modelling concepts for platform-independent design. In addition, there is little methodological support to distinguish between platform-independent and platform-specific concerns, which could be detrimental to the beneficial exploitation of the PIM-PSM separation-of-concerns adopted by MDA. This work is an attempt towards clarifying the notion of platform-independent modelling in MDA development. We argue that each level of platform-independence must be accompanied by the identification of an abstract platform. An abstract platform is determined by the platform characteristics that are relevant for applications at a certain level of platform-independence, and must be established by balancing various design goals. We present some methodological principles for abstract platform design, which forms a basis for defining requirements for design languages intended to support platform-independent design. Since our methodological framework is based on the notion of abstract platform, we pay particular attention to the definition of abstract platforms and the language requirements to specify abstract platforms. We discuss how the concept of abstract platform relates to UML

An exploration of the factors affecting the diffusion of advanced costing techniques: a comparative analysis of two surveys (1996-2005)

The issue of cost calculation has been largely debated in the last years under the pressure of the perceived lost of relevance of the so called "traditional cost accounting approaches". The enthusiasm for new management accounting techniques has often driven most of attention towards technical or theoretical aspects of the proposed new cost models. In particular, Activity-Based Costing (ABC) implementation literature pinpoints a large number of studies that have looked at technical and organizational/behavioral factors that influence effective implementation. Recently a great attention has been paid by researchers on the contingent factors affecting the adoption of advanced management accounting techniques and the influence of the variables that drive towards higher levels of cost system sophistication. The need is felt for insightful studies regarding processes and contingent variables working through time in relation with these changes. Improved analysis can be obtained by undertaking replication studies based on larger number of responses and/or across geographic and cultural borders. Whitin the boundaries of a contingent framework analysis, this paper has provided additional insights into areas relating to factors influencing the level of sophistication of product cost systems in Italy. The paper presents the comparison of two survey results carried on in a ten years distance on the same sample of Italian largest companies. These two long-distance surveys provide the opportunity to assess the changes occurred in the companies that in 1996 declared the adoption of (or the interest in adopting) ABC and Target costing (Cinquini et al., 1999).Moreover, the time elapsed could allow the perception about adopters’ behavior, along different stages of the diffusion process of advanced costing techniques. The research findings pinpoint that only “importance of cost information” and “cost structure”, among the contextual variables considered in the more recent survey responses, are positive and significant in relation with increasing in implementation of advanced costing techniques. This outcome could open to further studies to assess whether or not adopters are moving from a “fad and fashion” behavior of the early stages, to a more rational approach in which the matching between management needs and tools potentiality is maximized.Management accounting innovations, Activity-Based Costing, Target costing, Product costing design, Cost system sophistication, Contingent factors

Generic Distribution Support for Programming Systems

This dissertation provides constructive proof, through the implementation of a middleware, that distribution transparency is practical, generic, and extensible. Fault tolerant distributed services can be developed by using the failure detection abilities of the middleware. By generic we mean that the middleware can be used for many different programming languages and paradigms. Distribution for each kind of language entity is done in terms of consistency protocols, which guarantee that the semantics of the entities are preserved in a distributed setting. The middleware allows new consistency protocols to be added easily. The efficiency of the middleware and the ease of integration are shown by coupling the middleware to a programming system, which encompasses the object oriented, the functional, and the concurrent-declarative programming paradigms. Our measurements show that the distribution middleware is competitive with the most popular distributed programming systems (JavaRMI, .NET, IBM CORBA)

An exploration of the factors affecting the diffusion of Advanced Costing techniques: a comparative analysis of two surveys (1996-2005)

The issue of cost calculation has been largely debated in the last years under the pressure of the perceived lost of relevance of the so called "traditional cost accounting approaches". The enthusiasm for new management accounting techniques has often driven most of attention towards technical or theoretical aspects of the proposed new cost models. In particular, Activity-Based Costing (ABC) implementation literature pinpoints a large number of studies that have looked at technical and organizational/behavioral factors that influence effective implementation. Recently a great attention has been paid by researchers on the contingent factors affecting the adoption of advanced management accounting techniques and the influence of the variables that drive towards higher levels of cost system sophistication. The need is felt for insightful studies regarding processes and contingent variables working through time in relation with these changes. Improved analysis can be obtained by undertaking replication studies based on larger number of responses and/or across geographic and cultural borders. Whitin the boundaries of a contingent framework analysis, this paper has provided additional insights into areas relating to factors influencing the level of sophistication of product cost systems in Italy. The paper presents the comparison of two survey results carried on in a ten years distance on the same sample of Italian largest companies. These two long-distance surveys provide the opportunity to assess the changes occurred in the companies that in 1996 declared the adoption of (or the interest in adopting) ABC and Target costing (Cinquini et al., 1999).Moreover, the time elapsed could allow the perception about adopters’ behavior, along different stages of the diffusion process of advanced costing techniques. The research findings pinpoint that only “importance of cost information” and “cost structure”, among the contextual variables considered in the more recent survey responses, are positive and significant in relation with increasing in implementation of advanced costing techniques. This outcome could open to further studies to assess whether or not adopters are moving from a “fad and fashion” behavior of the early stages, to a more rational approach in which the matching between management needs and tools potentiality is maximized.Management accounting innovations, Activity-Based Costing, Target costing, Product costing design, Cost system sophistication, Contingent factors

Towards An Efficient Cloud Computing System: Data Management, Resource Allocation and Job Scheduling

Cloud computing is an emerging technology in distributed computing, and it has proved to be an effective infrastructure to provide services to users. Cloud is developing day by day and faces many challenges. One of challenges is to build cost-effective data management system that can ensure high data availability while maintaining consistency. Another challenge in cloud is efficient resource allocation which ensures high resource utilization and high SLO availability. Scheduling, referring to a set of policies to control the order of the work to be performed by a computer system, for high throughput is another challenge. In this dissertation, we study how to manage data and improve data availability while reducing cost (i.e., consistency maintenance cost and storage cost); how to efficiently manage the resource for processing jobs and increase the resource utilization with high SLO availability; how to design an efficient scheduling algorithm which provides high throughput, low overhead while satisfying the demands on completion time of jobs. Replication is a common approach to enhance data availability in cloud storage systems. Previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures while increasing the data availability with the limited resource. The schemes for correlated machine failures must create a constant number of replicas for each data object, which neglects diverse data popularities and cannot utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability hence minimize SLA (Service Level Agreement) violations while minimize cost caused by replication in order to maximize the revenue. In this dissertation, we build a nonlinear programming model to maximize data availability in both types of failures and minimize the cost caused by replication. Based on the model\u27s solution for the replication degree of each data object, we propose a low-cost multi-failure resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. In current cloud, providers still need to reserve resources to allow users to scale on demand. The capacity offered by cloud offerings is in the form of pre-defined virtual machine (VM) configurations. This incurs resource wastage and results in low resource utilization when the users actually consume much less resource than the VM capacity. Existing works either reallocate the unused resources with no Service Level Objectives (SLOs) for availability\footnote{Availability refers to the probability of an allocated resource being remain operational and accessible during the validity of the contract~\cite{CarvalhoCirne14}.} or consider SLOs to reallocate the unused resources for long-running service jobs. This approach increases the allocated resource whenever it detects that SLO is violated in order to achieve SLO in the long term, neglecting the frequent fluctuations of jobs\u27 resource requirements in real-time application especially for short-term jobs that require fast responses and decision making for resource allocation. Thus, this approach cannot fully utilize the resources to process data because they cannot quickly adjust the resource allocation strategy dealing with the fluctuations of jobs\u27 resource requirements. What\u27s more, the previous opportunistic based resource allocation approach aims at providing long-term availability SLOs with good QoS for long-running jobs, which ensures that the jobs can be finished within weeks or months by providing slighted degraded resources with moderate availability guarantees, but it ignores deadline constraints in defining Quality of Service (QoS) for short-lived jobs requiring online responses in real-time application, thus it cannot truly guarantee the QoS and long-term availability SLOs. To overcome the drawbacks of previous works, we adequately consider the fluctuations of unused resource caused by bursts of jobs\u27 resource demands, and present a cooperative opportunistic resource provisioning (CORP) scheme to dynamically allocate the resource to jobs. CORP leverages complementarity of jobs\u27 requirements on different resource types and utilizes the job packing to reduce the resource wastage and increase the resource utilization. An increasing number of large-scale data analytics frameworks move towards larger degrees of parallelism aiming at high throughput. Scheduling that assigns tasks to workers and preemption that suspends low-priority tasks and runs high-priority tasks are two important functions in such frameworks. There are many existing works on scheduling and preemption in literature to provide high throughput. However, previous works do not substantially consider dependency in increasing throughput in scheduling or preemption. Considering dependency is crucial to increase the overall throughput. Besides, extensive task evictions for preemption increase context switches, which may decrease the throughput. To address the above problems, we propose an efficient scheduling system Dependency-aware Scheduling and Preemption (DSP) to achieve high throughput in scheduling and preemption. First, we build a mathematical model to minimize the makespan with the consideration of task dependency, and derive the target workers for tasks which can minimize the makespan; second, we utilize task dependency information to determine tasks\u27 priorities for preemption; finally, we present a probabilistic based preemption to reduce the numerous preemptions, while satisfying the demands on completion time of jobs. We conduct trace driven simulations on a real-cluster and real-world experiments on Amazon S3/EC2 to demonstrate the efficiency and effectiveness of our proposed system in comparison with other systems. The experimental results show the superior performance of our proposed system. In the future, we will further consider data update frequency to reduce consistency maintenance cost, and we will consider the effects of node joining and node leaving. Also we will consider energy consumption of machines and design an optimal replication scheme to improve data availability while saving power. For resource allocation, we will consider using the greedy approach for deep learning to reduce the computation overhead caused by the deep neural network. Also, we will additionally consider the heterogeneity of jobs (i.e., short jobs and long jobs), and use a hybrid resource allocation strategy to provide SLO availability customization for different job types while increasing the resource utilization. For scheduling, we will aim to handle scheduling tasks with partial dependency, worker failures in scheduling and make our DSP fully distributed to increase its scalability. Finally, we plan to use different workloads and real-world experiment to fully test the performance of our methods and make our preliminary system design more mature