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Original equipment manufacturers and other manufacturing companies rely on the delivery performance of their upstream suppliers to maintain a steady production process. However, supplier capacity uncertainty and delayed delivery often poses a major concern to manufacturers to carry out their production plan as per the desired schedules. The purpose of this paper is to develop a decision model that can improve the delivery performance of suppliers to minimise fluctuations in the supply quantity and the delivery time and thus maximising the performance of the supply chain.

The authors studied a single manufacturer – single supplier supply chain considering supplier uncertain capacity allocation and uncertain time of delivery. Mathematical models are developed to capture expected profit of manufacturer and supplier under this uncertain allocation and delivery behaviour of supplier. A reward–penalty mechanism is proposed to minimise delivery quantity and time of delivery fluctuations from the supplier. Further, an order-fulfilment heuristic based on delivery probability is developed to modify the order quantity which can maximise the probability of a successful deliveries from the supplier.

Analytical results reveal that the proposed reward–penalty mechanism improves the supplier delivery consistency. This consistent delivery performance helps the manufacturer to maintain a steady production schedule and high market share. Modified ordering schedule developed using proposed probability-based heuristic improves the success probability of delivery from the supplier.

Practitioners can benefit from the findings of this study to comprehend how contracts and ordering policy can improve the supplier delivery performance in a manufacturing supply chain.

This paper improves the supplier delivery performance considering both the uncertain capacity allocation and uncertain time of delivery.

To develop a systematic procedure and a computerized tool for optimizing the delivery and inventory of materials, as part of a comprehensive material management system in construction projects.

A newly devised approach that employs genetic algorithms (GAs) for the optimization of material delivery schedules and their associated inventory control is presented. The approach is based on the project material requirement plans, and employs an objective function that minimizes the total costs associated with material deliveries. Furthermore, the computer system developed is used to examine and validate the adopted approach.

GA proved to be a satisfactory approach for optimizing material delivery schedules and its associated inventory levels. The selected case study particularly showed the system to produce material delivery plans that have reduced costs compared with their actual counterparts. Also, the computer processing time for developing the optimized plans was rather minimal, which promote its practical use.

The paper addresses one part of the comprehensive material management system; that is the optimization of the material delivery schedules and inventory control. Other future publications by the same authors will address the issues of probabilistic lead time calculations and development of material ordering schedules.

The paper partially fulfills a long‐sought research need for developing comprehensive material management systems specifically tailored to construction projects. The system takes into account several parameters that are not typically incorporated in the economic order quantity models for material management. Furthermore, practicality of the introduced system is augmented by the fact that it is interlinked with one of the most commonly used scheduling software.

In this paper, an integrated model for order release and due‐date management is developed to enhance manufacturers’ confidence in the due‐dates offered to customers. The proposed model utilised the backward finite scheduling order release control method and five different due‐date assignment rules. The proposed model is implemented using C++ computer programming language. Due‐date of each order is calculated by a due‐date assignment rule selected. Then the program tries to schedule the whole order with as lowest value as possible of tightness parameter (k). The output of the C++ program is production schedule of the whole order using backward finite scheduling order release method. Experiment results indicate that the modified number of operations due‐date rule_MNOP is superior with respect to the other due‐dates rules of the whole order.

The purpose of this paper is to logically establish a Non – IT ERP solution for small and medium scale industry which is based on improvement of operational efficiencies and not on ERP implementation. Today's buzzword ERP is not the right fit everywhere. The medium and small segment has many failure stories and causes of dissatisfaction. There are two schools of thought prevailing in this segment of industry. One school of thought is for big investment for ERP. The other school of thought is not to invest in ERP. This paper further strengthens the second school of thought with valid logic and real life case examples.

The design and approach is to logically establish the unacceptability of ERP solution in small segment through industry analysis and ROI analysis and to develop a model for improvement of supply chain efficiency using linear programming and Wagner Whitin algorithm.

A Non IT ERP solution for a segment of industry whose turnover is less than 2.5 M USD (in Indian perspective) which is completely based on standard operating procedure. The benefit of this model has been evaluated logically.

A real substitute of ERP system has been conceptualized in this research paper through improvement in standard operating procedure.

This study aims to propose a new genetic algorithm for solving supply chain scheduling and routing problem in a multi-site manufacturing system. The main research question is: How is the production and transportation scheduled in a multi-site manufacturer? Also the sub-questions are: How is the order assigned to the suppliers? What is the production sequence of the assigned orders to a supplier? How is the order assignment to the vehicles? What are the vehicles routes to convey the orders from the suppliers to the manufacturing centers? The authors’ contributions in this paper are: integration of production scheduling and vehicle routing in multi-site manufacturing supply chain and proposing a new genetic algorithm inspired from the role model concept in sociology.

Considering shared transportation system in production scheduling of a multi-site manufacturer is investigated in this paper. Initially, a mathematical model for the problem is presented. Afterwards, a new genetic algorithm based on the reference group concept in sociology, named Reference Group Genetic Algorithm (RGGA) is introduced for solving the problem. The comparison between RGGA and a developed algorithm of literature closest problem, demonstrates a better performance of RGGA. This comparison is drawn based on many test problems. Moreover, the superiority of RGGA is certificated by comparing it to the optimum solution in the small size problems. Finally, the authors use real data collected from a drug manufacturer in Iran to test the performance of the algorithm. The results show the better performance of RGGA in comparison with obtained outputs from the real case.

The authors presented the mathematical model of the problem and introduced a new genetic algorithm based on the “reference group” concept in sociology. Robert K. Merton is a sociologist who presented the concept of reference groups in society. He believed that some people in each society such as heroes or entertainment artists affect other people. The proposed algorithm uses the reference group concept to the genetic algorithm, namely, RGGA. The comparison of the proposed algorithm with DGA and the optimum solution shows the superiority of RGGA. Finally, the authors implement the algorithm in a real case of drug manufacturing and the results show that the authors’ algorithm gives better outputs than obtained outputs from the real case.

One of the major objectives of supply chains is to create a competitive advantage for the final product. This intension is only achieved when each and every element of the supply chain considers customers’ needs in every function of theirs. This paper studies scheduling in the supply chain of a multi-site manufacturing system. It is assumed that some suppliers produce raw material or initial parts and convey them by a fleet of vehicles to a multi-site manufacturer.

This paper deals with finding economic order quantity, number of orders to be placed and/or the time to place each order for four different special types of problems that may be encountered in practice. The first problem (Problem 1a) assumes a fixed planning horizon and a perishable product such as Christmas trees or fashion merchandise whose value deteriorates as the item gets aged. Under constant demand assumption, solution for this type of problem is worked out by capturing the deterioration in value by increasing holding cost. The second problem (Problem 2) has the same assumption as the first, except that the demand is assumed to increase as we move forward in time. The third problem (Problem 2a) is a restricted version of the second, which allows a specific number of integer orders during the planning horizon. The fourth problem (Problem 3) allows the ordering cost to increase as time progresses. All formulae derived can be easily applied to find numerical answers. The answers may have to be adjusted to reflect container size, minimum order quantity and any other restriction not modeled, or to take into account any violation of the model assumptions.

The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.

The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.

Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.

The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.

The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.

The aim of this paper is to demonstrate how agility as a central focus of an organization can be achieved through a modified balanced scorecard (BSC) system.

An action research approach in a single organization is used to investigate the practicality and usefulness of an agility-focused BSC.

While the theory of constraints (TOC)-based approach was improving agility, it is too short-term. Using a BSC, based on agility principles, staff in the organization had their direction focused on drivers of important strategic issues and enabled a more agile environment.

Managers using the TOC have a way forward to use the TOC system, but build a performance measurement system that leads to agility and more sustained competitive advantage.

The role of a BSC in enabling agility in an environment using TOC has not been previously investigated.

The purpose of this paper is to explore the quality of interactions among new product development team members, wherein functional diversity, team stability, and transactive memory system are antecedents of teamwork quality. Three measures of outcomes are used (team learning, speed to market, product success), and task complexity is a moderating variable.

A theoretical model was developed and tested on the survey data collected from 93 product managers of Turkish companies. The product managers who participated in this study represented various industries including those of telecommunications, food, material, software, machinery, chemicals, and service technologies.

An inverted‐U impact of both functional diversity and team stability on teamwork quality was found, and a positive impact of transactive memory system on teamwork quality. Teamwork quality was significantly related to improved performance, and task complexity moderates this relationship.

The paper is the first attempt to explore the role antecedents and its outcomes in NPD teams.

The purpose of this paper is to describe a flexible architecture concept to provide a ubiquitous computing framework where condition‐based maintenance (CBM) data and information can be easily accessed, and maintenance decisions may be performed wherever required.

The architecture is based on a platform of intelligent web services, and logically structured decision layers based on the open systems architecture for CBM definition, from condition monitoring to decision support, and provides automated extraction of results. The application of web services is demonstrated, first related with an original vision on the adoption of mobile devices for dynamic maintenance management solutions, and then with a more conventional web‐based predictive maintenance management system.

Scenarios for the upgrade of existing preventive maintenance practices towards condition‐based strategies are sufficiently different to require customised solutions. A web‐based platform can work with such differences in a cost‐effective way.

Further research can expand the actual platform by embedding new complementary web services.

Condition‐based strategies can provide clear savings in many maintenance activities. The platform described will provide the necessary flexibility to industrial users to manage the volume of data and information needed in CBM.

The platform is flexible enough to provide intelligent processing “on‐demand” and ubiquitously, with a three‐level configuration of web services, agents and interfaces that facilitates interoperability with existing legacy systems. Finally, the platform can grow according to the needs of the user (e.g. new information, increased knowledge on the process, new measurements, etc.).