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The purpose of this paper is to examine extreme value charts and analyse means based on half logistic distribution.

Variable control charts with subgroup observations based on the extreme values at each subgroup are constructed without specially going to any subgroup statistic. The control chart constants depend on the probability model of the extreme order statistic of each subgroup and the size of the subgroup. Accordingly the proposed chart is normal as extreme value chart. As a by‐product the technique of analysis of means for a skewed population is exemplated through half logistic distribution and extreme value control charts. The results are illustrated by examples on live data.

H.L.D is found to be better test for the data of the three examples, ANOM gave a larger (complete) homogeneity of data than those of Ott.

Supposing arithmetic means of k subgroups of size “n” each drawn from a half logistic model. If these subgroup means are used to develop control charts to assess whether the population from which these subgroups are drawn is operating with admissible quality variations. Depending on the basic population model, we may use the control chart constants developed by the authors or the popular Shewart constants given in any SQC text book. Generally the authors say that the process is in control if all the subgroup means fall within the control limits. Otherwise it is said that the process lacks control.

Half logistic distribution is a better model, exhibiting significant linear relation between sample and population quantiles.

This study aims to determine the contribution of capital expenditure from industries on the employment rate in West Java, Indonesia. Capital expenditure from the private sector is always assumed to positively affect the employment rate because the number of investment realization signifies workforce requirement; however, with rapid technological advancement and changes in the social and business environment, does it still reflect the real situation? The main source of data is taken from the Investment Activity Report or Laporan Kegiatan Penanaman Modal (LKPM), which is a report on the growth of a company’s investment realization and the issues encountered by businesses that are submitted regularly to Badan Koordinasi Penanaman Modal/Kementerian Investasi (Indonesia Investment Coordinating Board/Ministry of Investment) or BKPM. LKPM submission is regulated by BKPM Regulation 7/2018, and the purpose of this study is to observe investment realization growth and foster communication between BKPM and businesses. This study is carried out by evaluating LKPM data from companies in the manufacturing industry that conduct their business in West Java Province and comparing it against the employment rate in West Java Province to find out the effects of investment realization on the employment rate. This study finds that there is an effect of all independent variables on the dependent variable. If the conclusion is drawn, there is an influence between the workforce on the investment amount in 2018.

This study aims to enhance storage and distribution operations at a pharmaceutical supply center (PSC) in primary health care (PH) using lean health care (LH) tools. Supply centers for health products, medications and supplies have unique characteristics compared to centers for other goods due to complex processes, specific services, diverse stakeholders and multiple interactions. The authors adapt LH tools to address these complexities and meet industry-specific needs.

The investigation unit is a PSC in a large southern Brazilian city, and the processes analyzed are the storage and distribution of medications. The authors performed action research from June 2019 to February 2020. Data collection and problem diagnosis involved the development of a value stream mapping.

The authors adapted the overall equipment effectiveness calculation, efficiency analysis, and loss classification for PSC operations. Eighteen core issues were found: waiting, movement, transport, stock, inadequate processing, defects and human potential losses. The authors proposed waste reduction tools and practices. Inadequate storage conditions may compromise medicine quality, efficacy and safety. This can result from lacking physical structures or noncompliance with procedures. Next, the authors recommend simulating scenarios for validation before implementation.

The study explored ways to enhance layout and medicine distribution at the PSC, focusing on reducing loss and cost impact.

Originality lies in LH application in a PSC of PH, often applied in secondary or tertiary health levels like hospitals. The novelty necessitated adaptations of tools for future PSC applications.

The impact of transportation on the supplier selection has received very scant attention in the literature. This is a great limitation because splitting orders across multiple suppliers will lead to smaller transportation quantities which will likely imply larger transportation cost. Moreover, transportation and inventory elements are highly interrelated and contribute most to the total logistics costs. This paper seeks to present a nonlinear multiobjective programming approach of selecting suppliers and allocating the order quantity among them, taking into account transportation.

The model considers the total product cost and the lead‐time as the criteria to minimize simultaneously.

The total cost is the sum of transportation, inventory and ordering costs. The constraints related to suppliers and buyer are also considered in the model. The model is solved several times, evaluating various scenarios. Each scenario depends on the shipment type used between the suppliers and the buyer.

This paper fills a gap in the literature by comprehensively examining the role of transportation in determining the optimal number of suppliers and the portion of the order to allocate to each one.

The purpose of this paper is to present the integration of logistic management with information and communication technologies to largely improve the effectiveness of logistic fleet operations. The work presented here shows a real‐world fleet management system that integrates mobile communication and supports real‐time logistic information flow management.

First, the application of information and mobile communication technologies in providing effective logistic distribution service is introduced. Then, the proposed real‐time fleet management system (RTFMS) architecture is depicted, the technology profiles for mobile data terminal (MDT) and logistic information system are described, and the considerations of various wireless mobile communication technologies for logistic distribution process are also addressed. Finally, the implications of this paper are discussed and plans for further work are outlined.

The proposed architecture for a real‐world logistic fleet management system, the RTFMS, can be served as reference architecture for real‐time logistic fleet management design. The major components of the RTFMS have been described in UML use cases to facilitate reuse of this design. This paper presents the RTFMS architecture with associated information flows and timing considerations could be used for the architecture adaptation in similar applications. Wireless technologies provide the logistics feet management with bi‐directional real‐time information flows as shown in this paper, and this would stimulate new ideas in logistics management and services models.

This paper provides a reference model with implementation in adopting wireless technologies in logistics distribution process. However, the services provided by each specific system would depend on all stakeholders in specific chain of logistics service provider and consumer.

The work presented here shows a real‐world fleet management system that integrates mobile communication and supports real‐time logistic information flow management.

This paper aims to explore the interdisciplinary nature of coordination challenges in the logistic response to food safety incidents while distinguishing the food supply chain positions involved.

This adopts an exploratory qualitative research approach over a period of 11 years. Multiple research periods generated 38 semi-structured interviews and 2 focus groups. All data is analysed by a thematic analysis.

The authors identified four key coordination challenges in the logistics response to food safety incidents: first, information quality (sharing information and the applied technology) appears to be seen as the biggest challenge for the response; second, more emphasis on external coordination focus is required; third, more extensive emphasis is needed on the proactive phase in the logistic response; fourth, a distinct difference exists in the position’s views on coordination in the food supply chain. Furthermore, the data supports the interdisciplinary nature as disciplines such as operations management, strategy and organisation but also food safety and risk management, have to work together to align a rapid response, depending on the incident’s specifics.

The paper shows the need for comprehensively reviewing and elaborating on the research gap in coordination decisions for the logistic response to food safety incidents while using the views of the different supply chain positions. The empirical data indicates the interdisciplinary nature of these coordination decisions, supporting the need for more attention to the interdisciplinary food research agenda. The findings also indicate the need for more attention to organisational learning, and an open and active debate on exploratory qualitative research approaches over a long period of time, as this is not widely used in supply chain management studies.

The results of this paper do not present a managerial blueprint but can be helpful for practitioners dealing with aspects of decision-making by the food supply chain positions. The findings help practitioners to systematically go through all phases of the decision-making process for designing an effective logistic response to food safety incidents. Furthermore, the results provide insight into the distinct differences in views of the supply chain positions on the coordination decision-making process, which is helpful for managers to better understand in what phase(s) and why other positions might make different decisions.

The findings add value for the general public, as an effective logistic response contributes to consumer’s trust in food safety by creating more transparency in the decisions made during a food safety incident. As food sources are and will remain essential for human existence, the need to contribute to knowledge related to aspects of food safety is evident because it will be impossible to prevent all food safety incidents.

As the main contribution, this study provides a systematic and interdisciplinary understanding of the coordination decision-making process for the logistic response to food safety incidents while distinguishing the views of the supply chain positions.

This paper aims to focus on the question whether formalisation of the ordering process can be helpful in achieving responsiveness, while remaining efficient.

Three dimensions of the ordering process are discussed, namely logistical control, information processing and the organisational setting of the ordering process. Data were gathered from case studies at five different production companies.

It is suggested that a highly formalised logistical control structure is essential in achieving responsiveness and efficiency. From the formalisation strategies applied by the companies it can also be concluded that a formalised organisational setting of the ordering process is necessary for being responsive in case the logistical control is characterised by a low degree of formalisation.

The paper presents a detailed operationalisation of the formalisation of three dimensions of the ordering process. This is helpful in formulating guidelines for structuring the ordering process to become more responsive.

The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features.

To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation.

The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions.

(1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.

In this study the factors behind the decision‐makers’ erroneous judgements regarding failure prediction (classification of firms as bankrupt and non‐bankrupt) are analysed. The purpose is to find out the factors causing incorrect responses, i.e. the cases in which the decision‐maker is for some reason incapable of using the given information to arrive at the correct classification. The following five possible sources of disturbance in this decision‐making were hypothesized: firm‐specific factors, data, decision‐maker‐specific factors, external factors, and failure process. In further analysis these factors were empirically operationalized and their significance was tested applying logistic (logit) analysis separately for the Type I and Type II classification errors identified in an HIP study. The results indicated that the effect of all of the five hypothesized factors on misclassifications is statistically significant. The inconsistency of the cues (firm‐specific factors) may be the main factor causing errors in evaluation. Moreover, the failure process is another important factor (Type I error). Thus, human bankruptcy prediction can be improved mainly by checking the consistency of financial statements (that they give a true view of the firm’s economic status) and by paying special attention to timely identification of the possible failure process. Future HIP studies on bankruptcy prediction and also other economic events should pay attention to control the kinds of sources of disturbance identified in this study, to maintain validity.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.