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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

Dynamic lot sizing is the problem of determining the quantity and timing of ordering items while satisfying the demand over a finite planning horizon. This paper considers the problem with two practical considerations: minimum order size and lost sales. The minimum order size is the minimum amount of items that should be purchased and lost sales involve situations in which sales are lost because items are not on hand or when it becomes more economical to lose the sale rather than making the sale. The objective is to minimize the costs of ordering, item , inventory holding and lost sale over the planning horizon. To solve the problem, we suggest a heuristic algorithm by considering trade-offs between cost factors. Computational experiments on randomly generated test instances show that the algorithm quickly obtains near-optimal solutions.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

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.

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

The purpose of this paper is to propose a model for planning and controlling the design process in companies that design, manufacture and assemble prefabricated engineer-to-order (ETO) building systems. This model was devised as an adaptation of the Last Planner® System for ETO multiple-project environments.

Design science research, also known as prescriptive research, was the methodological approach adopted in this research. An empirical study was carried out at the design department of a leading steel fabricator from Brazil, in which the proposed model was implemented in six different design teams.

The main benefits of the proposed model were shielding design work from variability, encouraging collaborative planning, creating opportunities for learning, increasing process transparency, and flexibility according to project status. Two main factors affected the effectiveness of the implementation process commitment and leadership of design managers, and training on design management and project planning and control core concepts and practices.

Some limitations were identified in the implementation process: similarly to some previous studies (Ballard, 2002; Codinhoto and Formoso, 2005), the success of constraint analysis was still limited; some of the metrics produced (e.g. ABI, causes of planning failures) have not been fully used for process improvement; and systematic feedback about project status was not properly implemented and tested.

The main contributions of this study in relation to traditional design planning and control practices are related to the use of two levels of look-ahead planning, the introduction of a decoupling point between conceptual and detail design, the proposition of new metrics for the Last Planner® System, and understanding the potential role of visual management to support planning and control.

This paper addresses the trade-off between asset investment and food safety in the design of a food catering production plant. It analyses the relationship between the quality decay of cook-warm products, the logistics of the processes and the economic investment in production machines.

A weekly cook-warm production plan has been monitored on-field using temperature sensors to estimate the quality decay profile of each product. A multi-objective optimisation model is proposed to (1) minimise the number of resources necessary to perform cooking and packing operations or (2) to maximise the food quality of the products. A metaheuristic simulated annealing algorithm is introduced to solve the model and to identify the Pareto frontier of the problem.

The packaging buffers are identified as the bottleneck of the processes. The outcome of the algorithms highlights that a small investment to design bigger buffers results in a significant increase in the quality with a smaller food loss.

This study models the production tasks of a food catering facility to evaluate their criticality from a food safety perspective. It investigates the tradeoff between the investment cost of resources processing critical tasks and food safety of finished products.

The methodology applies to the design of cook-warm production. Catering companies use cook-warm production to serve school, hospitals and companies. For this reason, the application of this methodology leads to the improvement of the quality of daily meals for a large number of people.

The paper introduces a new multi-objective function (asset investment vs food quality) proposing an original metaheuristic to address this tradeoff in the food catering industry. Also, the methodology is applied and validated in the design of a new food production facility.

Recent studies have highlighted the importance of adopting a contingency approach to configuring omnichannel warehouses. Nonetheless, research on how various contextual factors influence the selection of warehouse configuration is scarce. This study fills this knowledge gap by exploring how and why certain configurations fit in different omnichannel contexts.

A case study is conducted with six leading Swedish omnichannel retailers. Focusing on outbound warehouse configurations, data are collected through interviews, on-site observations, and secondary sources. A multistep analysis is made, including both pattern matching and explanation building.

The qualitative analysis reveals 16 contextual factors, of which assortment range, requested online order fulfillment times, goods size and total transactions are the most influential. The study shows how contextual factors create different challenges, thereby influencing the choice of the configurations. In addition to market dynamics and task complexity, the study describes four categories of the factors and related challenges that are particularly important in omnichannels: speed, space, economies of scale and tied-up capital.

The findings highlight the importance of understanding context and imply that multiple challenges may require trade-offs when selecting configurations, for example, regarding what storage, processes and resources to integrate or separate. To confirm, extend, challenge and further operationalize the ideas and observations put forward in this paper, an agenda with future research issues is given for this accelerating, contemporary phenomenon.

Managers could leverage the frameworks proposed for the contextual profiling of their current and future positions. The frameworks provide support for understanding the important challenges and potential trade-offs and developing aligned configurations.

This study is original in the way it provides in-depth, case study findings about contextual factors and their influence on omnichannel warehouse configuration.

This study aims to examine factors that determine the adoption of additive manufacturing by small- and medium-sized industries. It provides insights with regard to benefits, challenges and business factors that influence small- and medium-sized industries when adopting this technology. The study also aims to expand the domain of additive manufacturing by including a broader range of challenges and benefits of additive manufacturing in literature.

Using data collected from 175 small- and medium-sized industries, the study has examined through Mann–Whitney test to understand the difference between owners and design engineers on additive manufacturing technology adoption in small- and medium-sized companies.

This study suggests contribution to academic discussion by providing associated factors that have significant impact on the adoption of additive manufacturing technology. Related advantages of additive manufacturing are reduction in inventory cost, lowering the wastage in production and customization of products. The study also indicates that factors such as cost of machinery, higher level of cost in integrating metal components have a negative impact on the adoption of this technology in small- and medium-sized industries.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further in the field of challenges and growth in other areas of application of additive manufacturing, for instance, medical sciences, fabric and aerospace.

The study provides important implications that are of interest for both research and practitioners, related to technology management in small- and medium-sized industries, e.g. foundry and machining industries.

This work/study fulfills an identified need of the small- and medium-sized companies in adopting new technologies and contribute to their growth by understanding the need to accept and implement technology.

This paper fulfills an identified need to study how small- and medium-scale companies accept new technologies and factors associated with implementation in the manufacturing process of the organization.