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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Being the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).

Two multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.

Experimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.

The paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.

In this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.

This paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

The software development team reviews the testing phase to assess if the reliability growth of software is as per plan and requirement and gives suggestions for improvement. The objective of this study is to determine the optimal review time such that there is enough time to make judgments about changes required before the scheduled release.

Testing utilizes majority of time and resources, assures reliability and plays a critical role in release and warranty decision-making reviews necessary. A very early review during testing may not give useful information for analyzing or improving project performance, and a very late review may delay product delivery and lead to opportunity loss for developers. Therefore, it is assumed that the optimal time for review is in the later stage of testing when the fault removal rate starts to decline. The expression for this time point is determined using the S-curve 2-D software reliability growth model (SRGM).

The methodology has been illustrated using the real-life fault datasets of Tandem computers and radar systems resulting in optimal review time of 14 weeks and 26 months, respectively, which is neither very early in testing nor very near to the scheduled release. The developer can make changes (more resources or postpone release) to expedite the process.

Most of the literature studies focus on determination of optimal testing or release time to achieve considerable reliability within the budget, but in this study, the authors determine the optimal review time during testing using SRGM to ensure the considerable reliability at release.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

Innovation during project delivery is contested space. The aim in this research is to empirically explore the theory of this contested space and how project implementation can be optimized by the contractor to deliver better outcomes. It is hypothesized that project innovation has a proportional and measurable relationship to contractor success.

Based on a novel conceptual framework, this research applies a case study methodology to analyse 31 construction projects undertaken by a single Australian middle-tier contractor. Benefits from innovation are not often equitably shared. There are risks and rewards. The project innovation zone is defined as a combination of three key performance indicators – efficacy, efficiency and margin – merged into a single index that most likely shows evidence of “working smarter”.

Client–contractor project innovation (c2pi) is demonstrated to be strongly correlated with head contractor success (HCS), yielding an r² value of 71%. Innovative projects mostly show positive change in efficacy, efficiency and margin when comparing “planned” and “actual” outcomes. Across the cases studied, 35% demonstrated likely evidence of innovative delivery and 52% demonstrated evidence of success from the construction contractor's perspective.

These findings verify that, within the studied sample, the pursuit of innovation leads to projects that are likely to also have greater success for the head contractor, evidenced by the mix of five critical success factors: finishing on schedule, making profit, and having less defects, less accidents and higher quality workmanship. These outcomes arguably also apply to sub-contractors, where the head contractor assumes the role of “client”.

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

The study aims to explore the role of media in enhancing financial knowledge, financial self-efficacy, and financial planning propensity among working adults in India.

Primary survey-based data (n = 542) were analyzed using covariance based-structural equation modeling.

Media has a positive impact on financial knowledge. Financial knowledge positively mediates the relationship between media usage and financial self-efficacy and financial planning propensity. Also, financial knowledge and financial self-efficacy positively mediate the relationship between media usage and financial planning propensity.

The role of media as a significant agent of consumer socialization is an under-researched area. The authors contribute to the existing literature by demonstrating the role of media in improving financial knowledge and financial self-efficacy to promote financial planning propensity among working adults.

The aim of this study is to develop a framework for the implementation of a Supplier Kanban System.

The research was conducted within a factory focusing on the warehousing and supplier-related operations using an action research methodology.

The study period spanning over six years was aimed to reduce the inventory and the manpower required to manage this inventory. This initiative led to a substantial average savings of more than 500,000 Malaysian ringgit annually.

The focus of the study is on a framework for implementing a Supplier Kanban System in a small or medium-sized company within the automotive industry.

This framework is designed to be self-sufficient such that the members of the organisation can use it without the help of external experts or consultants.

Reduction in inventory stored in the warehouse, the amount of redundant work carried out by the employees and other non-monetary improvements which are difficult to monetise resulted in a much better work environment and happier employees.

The change in the paradigm of the manufacturing value chain affects the Tier 1 and 2 suppliers in Malaysia. The lines and value chains posing challenges to the suppliers have brought the idea of the Supplier Kanban System. The Supplier Kanban System adopted the action research cycles and continuous learning cycles, and this process was documented and developed as the Framework for the Implementation of a Supplier Kanban System.

This study aims to provide information concerning practices for creating strategic plans in municipal policing organizations and their use in practice.

This study surveyed a sample of Canadian police services, including four of the ten largest services in Canada, to investigate their planning practices and the study’s findings to the results of a content analysis of strategic plans reported by Rogers et al. (2020). This study did not conduct content analysis and therefore relied on the findings of Rogers et al. (2020).

Some respondents indicated they do not apply some practices generally considered advisable. Other respondents indicated they undertake a practice even though doing so is not evident from a review of the associated strategic plans.

This study is based entirely on self-reported survey data. The study did not interview respondents to find out why they responded as they did.

This study points to specific improvements municipal policing organizations could adopt which offer the prospect of creating better strategic plans and better strategic planning outcomes.

Policing organizations are important institutions in society. As a regular part of their activities, they interact with a broad cross-section of the society within which they operate. This paper presents ideas concerning how policing organizations can improve how they adapt themselves to their social environment to improve those interactions.

No other study collects self-reported data on how police services conduct strategic planning and use strategic plans at this level of detail.