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In past literature, employee has been extensively utilized as input in most data envelopment analysis (DEA) studies, but different labor types are identically defined to be the same input factor without a specific assumption for their heterogeneity. The influence of manual and non-manual labor utilization on performance also has not been investigated in hotel efficiency analyses. The purpose of this study is to assess inefficiency indices derived from manual and non-manual labor, and analyze the influence of labor utilization on hotel’s operational efficiency.

Based on the different features of the two labor types, performance indicators are evaluated through the hybrid DEA model.

More than 32 per cent of tourist hotels are evaluated as efficient and more than half the hotels have an efficiency score lower than the average. The author evaluated the inefficiency caused by radial inputs that have a greater influence on efficiency. This finding indicates that most hotels are efficient in their utilization of non-manual labor. The investigation of external factors shows that excessive utilization of non-manual labor results in a slight influence on operational efficiency across many non-chain hotels. The author also found the efficiency of non-manual labor utilization to be lower at hotels located in resort areas.

This study used the hybrid DEA model, in which non-manual and manual labors are assumed as non-radial and radial inputs, respectively, to evaluate efficiency. Finding the significance of heterogeneous assumptions for manual and non-manual labor types is the main contribution to the theory of hotel efficiency measurement.

Using labour efficiently in service organisations is an important management objective since labour frequently accounts for more than 70 per cent of total operating costs. While most service organisations can use flexible scheduling rules to improve the utilisation of labour, selecting a good or “best” set of work schedules from a large set of alternatives has proved to be a formidable task. A new modelling procedure is described which allows optimal decisions to be made when the desired level of scheduling flexibility results in a very large population of possible schedules. Flexibility in shift scheduling is increased primarily through increasing the number of different shift lengths and allowing flexible placement of breaks. The power of the new modelling approach was used in an experiment described here to assess the relative impact of shift‐length and break‐placement flexibility on labour utilisation. The results indicated that, while either type of flexibility can improve labour utilisation, there was a rather strong synergistic impact from using both simultaneously.

The effects of cellular manufacturing (CM) on increased delivery speed and resource utilization along with its interaction with volume, mix, routing, and labor flexibilities are not clear for manufacturers and supply chain managers. Aims to focus on this.

Based on real data from a US screen‐printing company, a simulation model is designed to determine the influence of volume, mix, routing and labor flexibilities in presence of volatile demand. Simulation of one and two cell configuration is compared with job shop to determine the shortest delivery and highest utilization.

As volume flexibility increases, delivery is faster in presence of CM compared to job shop. Furthermore, added routing flexibility results in 70 percent shorter lead time with low volume flexibility, and 85 percent shorter lead time with high volume flexibility. Additionally, in the two‐cell design, assignment of fewer, but more multi‐skilled workers shared between cells results in higher utilization and lower lead time.

This study contributes to the manufacturing research by revealing the benefits of CM, and the importance of volume, routing, and labor flexibilities reacting quickly to volatile demand in today's dispersed manufacturing environment. Also, this study demonstrates that labor allocation is equally important in manufacturing cells as the equipment and part decisions are.

The findings provide manufacturers a guideline on how to best set up CM and operational flexibilities to respond faster to volatile demand. The simulation model is successful in showing that cells and manufacturing flexibilities are strong enablers of faster delivery lead time and higher resource utilization.

The view has been popular since the early or mid sixties that many developing countries are in the midst of “employment crises” resulting from low and stagnant demand for labour; major manifestations of this crisis are, according to this view, the rising open unemployment rates, falling participation rates, and continuing prevalence of the subtler “disguised” unemployment and underemployment. “Fixed proportion” conceptualizations of the economy and prominence of labour‐saving technical innovation contribute to the plausibility of this hypothesis. This essay questions the general validity of the “crisis” interpretation, based on data for Colombia, one of the many countries to which it has been applied. It argues

Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level.

More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time.

This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry.

The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Job‐shop operations, the emerging norm of industry, require reform of the management methods employed to achieve cost control and efficiency. Maximisation of capacity utilisation, the standard of efficiency for more than a century, does not work in the job shop. Queueless work flow and the flexible labour policies that support it offer potential for reform which can substantially enhance customer service and quality. Simulation of work flow and capacity to identify bottlenecks along with scheduling of set‐ups on a leadtime basis are potentially important supporting methodologies.

YET another book relating to industrial engineering arrives from the United States. Why is it that no English author ever writes a book about new developments in work study? All the tomes on industrial engineering come from across the Atlantic Ocean. Is it because no one has the initiative, or is it the lack of “know‐how”? A combination of the two, no doubt. We have complained before of the indolent attitude of work study technicians, and we shall complain again—they are completely pen‐shy!

The purpose of this paper is to focus on investigating labour productivity in the agricultural sector of Sub-Sahara Africa (SSA) countries between the periods of 2010 and 2017.

The study adopted descriptive design. The sample size for this research includes 43 SSA nations. Measuring SSA nation’s agricultural productivity in this study was based on input and output factors relating to the labour resource utilisation between the periods of 2010 and 2017. Data envelopment analysis (DEA) and panel regression analysis were carried out to examine labour productivity within the set periods.

The findings from the study suggest that labour productivity in the agricultural sector of SSA countries can be improved from its presently low state of productivity. The statistical analysis showed that between the periods of 2010 and 2013, only about 34.9 per cent of countries in the region were technically efficient in the utilisation of labour resources for productive use. More disturbing was that, from 2014 to 2017, labour productivity drooped to 11.6 per cent. Meanwhile, employment of labour in the agricultural sector revealed as low as 1.58 percentage to crop production index in the region. Notably, there is the potential of labour employment to derive as high as 80 per cent yield to the gross domestic product of economies in the SSA region.

Considering the strategic role of labour to the agricultural sector of SSA countries, there must be a stakeholders approach to stimulating the interest of the populace of these countries and getting them actively involved in the agricultural sector. This imply that government, investors, support agencies from developed economies and populace of the SSA nations must support the drive towards agricultural productivity of the SSA nations.

This study established a research agenda that involved a paradigm shift from the more rampant literature on foreign investments, agricultural research, rural livelihood and well-being, among others to focusing on issues that pertain to labour productivity for sustainable agricultural yields in SSA countries. Also, the methodology adopted in the study, such as application of DEA and regression analysis to panel data, shows a departure from single units of analysis adopted by existing studies.

In the context of a somewhat turbulent graduate labour market, attention is being focused on the employment of graduates in small‐ and medium‐sized enterprises (SMEs). This paper takes an initial “sounding” of our understanding about the transition of graduates into such organisations. While research data provides some insight into the barriers which work to discourage more SMEs from recruiting graduates, this understanding appears inadequate and insufficiently segmented to provide a detailed knowledge of the problems. A prevailing assumption is that graduates lack skills required by SME employers. Yet the limited research findings reveal ambiguity about the extent to which SMEs effectively deploy graduate labour. The article proposes an agenda that highlights the need for two types of research. First, a clearer picture of current trends in the SME graduate labour market. Second, a richer understanding of the real experience of graduates, and their managers, in relation to employment in an SME and the implications of such for both the supply and demand sides of the graduate labour market.