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Purpose – The purpose of this study is to explore the concept of the economic lot sizing and the time cycle period of reordering. The stochastic demand is quite common in the real environment of a cement retailer. The study compares three methods to obtain the optimal solution of a lot-sizing ordering from the real case of the previous study where the dataset is collected from the area of some retailers at Banda Aceh Province of Indonesia.

Design/Methodology/Approach – The problem model appears when the retailer with shortage has to fulfill the lot size in the optimal condition to the stochastic demand while at the same time has the backlog condition. Moreover, when the backorder needs the time horizon for replenishment where this condition influences the holding cost at the store, many retailers try to solve this problem to minimize the holding cost, but on the other side, it should fulfill the customer demand. Three methods are explored to identify that condition: a Wagner–Whitin algorithm, the Silver–Meal heuristic, and the holding and ordering costs. The three methods are applied to the lot sizing when there is a backlog.

Findings – The results of this study show that the Wagner–Whitin algorithm outperforms the other two methods. It shows that the performance increases around 27% when compared to the two other methods in this study.

Research Limitations/Implications – All models are almost approximate and useful to determine the cycle period on stochastic demand.

Practical Implications – The calculation of the dataset with the three methods would give the simple example to the retailer when he faces the uncertainty demand models. The prediction of the calculation is done accurately than the constant calculation, which is more economic.

Social Implications – The calculation will contribute to much better predictions in many cases of uncertainty.

Originality/Value – This is a initial comparative model among other methods to achieve the optimal stock and order for a retailer

Purpose – This chapter presents a close examination of how manufacturing managers respond to environmental pressures by formulating and implementing operational strategy.

Methodology – The analysis is based on interviews and observations in 31 manufacturing firms in the US Midwest.

Findings – The study reveals that competitive market pressure is only so effective at penetrating the institutional layers of inter- and intra-firm relations. Even in the highly competitive manufacturing sector, operational strategy is consistently implemented in suboptimal ways. Relatively inefficient routines are commonly institutionalized and inefficient arrangements appear to be able to persist for an indefinite period of time. To the extent that firms with variable capabilities and internal socio-technical systems must process, interpret, and react to complex external pressures and often-ambiguous signals, the sociology of work provides essential insights for the sociology of markets.

Originality – While the findings are subject to the standard caveats regarding nonrandom qualitative samples, the rich data produced and the in-depth analysis of real-world organizational pressures and managerial decision-making provide distinctive insights into how managers must balance external market pressures with internal labor process problems. Individual motivation appears to be at least as important in true organizational innovation as market discipline. While adaptation and learning certainly occur in organizations (and selection also operates through the death of extreme laggards) there exists sufficient institutional space within markets for a range of variation in organizational performance. The findings suggest that the analysis of internal organizational dynamics provides an essential part of a realistic theory of markets.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Theeuwes and Adriaansen (1994), among others, have asserted that activity-based costing (ABC) is inappropriate for operational decision-making. In this article, ABC is modified to reflect separate flexible and committed cost driver rates for an activity. This enables the model to reflect the difference in the behavior of an activity’s flexible and committed costs needed for operational planning decisions. The modified ABC facilitates determining the resources required to produce the product mix developed from the firm’s strategic plan and the excess capacity that will result. The modifications made to ABC aid in determining an optimal product mix when the firm has excess capacity, while the traditional ABC may not. Equally important, it facilitates measuring the financial implications of the resource allocation decisions that comprise the firm’s operational plan. As the operational plan is implemented, operational control is used to ensure that it is performed in an efficient and effective manner. The modified ABC enables the firm’s managers to compute the different types of deviations that arise from using flexible and committed resources at the unit, batch, and product levels of the firm’s operations. This aids in understanding problematic aspects of the firm’s operations and identifying where management resources are needed to improve operational efficiency.

Most setup management techniques associated with electronic assembly operations focus on component similarity in grouping boards for batch processing. These process planning techniques often minimize setup times. On the contrary, grouping with respect to component geometry and frequency has been proved to further minimize assembly time. Thus, we propose the Placement Location Metric (PLM) algorithm to recognize and measure the similarity between printed circuit board (PCB) patterns. Grouping PCBs based on the geometric and frequency patterns of components in boards will form clusters of locations and, if these clusters are common between boards, similarity among layouts can be recognized. Hence, placement time will decrease if boards are grouped together with respect to the geometric similarity because the machine head will travel less. Given these notions, this study develops a new technique to group PCBs based on the essences of both component commonality and the PLM. The proposed pattern recognition method in conjunction with the Improved Group Setup (IGS) technique can be viewed as an extended enhancement to the existing Group Setup (GS) technique, which groups PCBs solely according to component similarity. Our analysis indicates that the IGS performs relatively well with respect to an array of existing setup management strategies. Experimental results also show that the IGS produces a better makespan than its counterparts over a low range of machine changeover times. These results are especially important to operations that need to manufacture quickly batches of relatively standardized products in moderate to larger volumes or in flexible cell environments. Moreover, the study provides justification to adopt different group management paradigms by electronic suppliers under a variety of processing conditions.

This research was prompted by work undertaken by the author on the efficiency of shipping operations in the Suez Canal. The physical limitations of the Canal allow only one-way movement of ships for the greater part of its length, and thus ships are organised in convoys. These convoys have fixed starting times, with normally just one convoy per day operating in each direction. When traffic is heavy in the southbound direction, a second (smaller) relief convoy is organised to reduce waiting times which can otherwise exceed 24 hours. The process can be analysed by means of a bulk-service queueing model, where convoys of ships correspond to service batches of customers.

The model has application in the many other fields of transport where relief services are supplied. For example, a coach or train operator will often provide a relief service when customer demand is high. The process may be extended to cover cases where relief is provided for the relief service, resulting in a “cascade” of relief service queues.