On the Interface Between Operations and Human Resources Management

Operations management (OM) and human resources management (HRM) have historically been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications and staffing. Human responses to operations management systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields

Manufacturing in the 1990s - productivity, flexibility and innovation

The article outlines the evolution of large multinationals as a result of the appearance of new market demands. Companies having to meet specific market demands, are shown to possess certain characteristics, related to the market demands concerned. The analysis shows that innovativeness will, in all probability, be the new market demand in the 1990s, in addition to the already existing ones of efficiency, quality and flexibility. Descriptions of ideal types illustrate the evolution of companies as they move from the Efficient Firm to the Quality Firm on to the Flexible Firm to, finally, the Innovative Firm. The phase model also includes the symptoms of crisis, when moving from one phase to another. Skipping phases appears to be difficult, if not impossible. The same holds true for moving to the next phase, while the organization has not finished with the preceding phase

Automatic Throughput and Critical Path Analysis of x86 and ARM Assembly Kernels

Useful models of loop kernel runtimes on out-of-order architectures require an analysis of the in-core performance behavior of instructions and their dependencies. While an instruction throughput prediction sets a lower bound to the kernel runtime, the critical path defines an upper bound. Such predictions are an essential part of analytic (i.e., white-box) performance models like the Roofline and Execution-Cache-Memory (ECM) models. They enable a better understanding of the performance-relevant interactions between hardware architecture and loop code. The Open Source Architecture Code Analyzer (OSACA) is a static analysis tool for predicting the execution time of sequential loops. It previously supported only x86 (Intel and AMD) architectures and simple, optimistic full-throughput execution. We have heavily extended OSACA to support ARM instructions and critical path prediction including the detection of loop-carried dependencies, which turns it into a versatile cross-architecture modeling tool. We show runtime predictions for code on Intel Cascade Lake, AMD Zen, and Marvell ThunderX2 micro-architectures based on machine models from available documentation and semi-automatic benchmarking. The predictions are compared with actual measurements.Comment: 6 pages, 3 figure

The Effect of Learning on Assembly Line Balancing: A Review

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems

Kerncraft: A Tool for Analytic Performance Modeling of Loop Kernels

Achieving optimal program performance requires deep insight into the interaction between hardware and software. For software developers without an in-depth background in computer architecture, understanding and fully utilizing modern architectures is close to impossible. Analytic loop performance modeling is a useful way to understand the relevant bottlenecks of code execution based on simple machine models. The Roofline Model and the Execution-Cache-Memory (ECM) model are proven approaches to performance modeling of loop nests. In comparison to the Roofline model, the ECM model can also describes the single-core performance and saturation behavior on a multicore chip. We give an introduction to the Roofline and ECM models, and to stencil performance modeling using layer conditions (LC). We then present Kerncraft, a tool that can automatically construct Roofline and ECM models for loop nests by performing the required code, data transfer, and LC analysis. The layer condition analysis allows to predict optimal spatial blocking factors for loop nests. Together with the models it enables an ab-initio estimate of the potential benefits of loop blocking optimizations and of useful block sizes. In cases where LC analysis is not easily possible, Kerncraft supports a cache simulator as a fallback option. Using a 25-point long-range stencil we demonstrate the usefulness and predictive power of the Kerncraft tool.Comment: 22 pages, 5 figure

Extending the production dice game.

The production dice game is a powerful learning exercise focusing on the impact of variability and dependency on throughput and work-in-process inventory of flow lines. In this paper we will extend the basic dice game along the following lines. First, we allow that the operations take place concurrently as opposed to the more traditional way of playing the game sequentially. Second, we allow both starvation and blocking of the line. Third, we consider balanced lines with work stations characterized by different degrees of variability. Fourth, we use different sets of dice in order to represent a wide range of coefficients of variation of the production line. The game can be played manually in a classroom setting, but it is also modelled as an easy-to-use simulation tool.

Application of Learning Curves in Operations Management Decisions

In the time of industry 4.0 and big data, methods which are based on the collection and the processing of a large amount of data in order to support managerial decisions have outstanding significance. The learning curve theory pertains to these methods. The purpose of this paper is to explore some application possibilities of the classical learning curve in manufacturing and service operations. The learning effect assumes that as the quantity of units manufactured increases, the time needed to produce an individual unit decreases. The function describing this phenomenon is the learning curve. Various learning curves have been developed and applied in the area of production economics and much research studies the significance of the learning effect in management decisions. This study summarizes the main learning curve models and demonstrates how learning can be considered in three classical areas of operations management. First, the calculation of economic manufacturing quantity in the presence of learning is studied. Next, the effect of learning in break-even analysis and assembly line balancing is explored. The results show that with the consideration of the learning effect, calculations become more complex and require greater efforts, but the application of the learning curve concept can provide valuable insight both at operational and strategic levels