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The service industry is a major component of the economy. Raw material, components, assemblies, and finished products are shipped between suppliers, manufacturers, distributors, and retailers. Accordingly, timely receipt of shipped goods is crucial in maintaining the efficiency and effectiveness of such service processes. A service provider offers an incentive to the customer by specifying a competitive target time for delivery of goods. Further, if the delivery time is deviant from the target value, the provider offers to reimburse the customer for an amount that is proportional to the value of the goods and the degree of deviation from the target value. The service provider may set the price to be charged as a function of product value. This price is in addition to the operational costs of logistics that are not considered in the formulated model. For protection against deviation from target due dates, the service provider agrees to reimburse the customer. The reimbursement could be based on an asymmetric loss function influenced by the degree of deviation from the target due date as well as product value. The penalties could be different for early and late deliveries since the customer may experience different impact and consequences accordingly. The chapter develops a model to determine the amount (price) that the provider should add to the cost estimate of the delivery contract for protection against delivery deviations. Such a cost estimate will include the operational costs (fixed and variable) of the shipment, to which an amount is added to cover the expected payout to customers when the delivery time deviates from the target value. The optimal price should be such that the expected revenue will at least exceed the expected payout.

Information technology-enabled gig platforms connect freelancers with consumers to provide short-term services or asset sharing. The growth of gig economy, however, has been accompanied by controversy, and, recently, food delivery platforms have been criticized for using data-driven techniques to set strict delivery time limits, resulting in negative externality. This study aims to provide managerial implications on the decisions of delivery time and subsidy for food delivery platforms.

The authors develop an analytical framework to investigate the optimal delivery time and subsidy provided to delivery drivers to maximize the gig platform's profit and compare the results with those of a socially optimal outcome.

The study reveals that it is optimal for the platform to shorten the delivery time and raise the subsidy when the food price becomes higher; nevertheless, the platform should shorten the delivery time and lower the subsidy in response to a higher delivery fee. Increases in the food price or delivery fee have non-monotonic effects on the number of fulfilled orders and the platform's profit. In addition, the authors solve the socially optimal outcome and find that a socially optimal delivery time is longer than the platform's preferred length when the delivery fee is high and the negative externality is strong.

The food delivery platform's optimal decision on delivery time is derived after taking negative externality into account, which is rarely considered in the prior literature but is a practically important problem.

This paper investigates the impact of sustainability information disclosure on consumers' choice of order-to-delivery lead-time in relation to consumers' sustainability concern.

Based on two choice experiments with participants from the Netherlands (n = 348) and the United Kingdom (n = 1,387), the impact of sustainability information disclosure was examined in connection with consumers' concerns for environmental and social sustainability. Information on environmental impact (carbon emission) and social impact (warehouse workers and drivers' well-being) was considered and compared.

Disclosing sustainability impact information significantly increased consumers' preference and choice for longer delivery times, with equivalent effects for environmental and social impact information. Consumers' relevant (environmental or social) sustainability concern as personality traits enhanced effects on preferences, as did priming of environmental concern.

Future research may consider differences between product categories or e-commerce companies' reputation in sustainability activities.

The findings provide opportunities for online retailers to influence consumer choice of delivery time, especially through disclosing environmental and/or social sustainability information.

This study fills a gap in the literature on sustainability information disclosure to actively steer consumer choice of delivery time, particularly regarding the effect of social sustainability impact information in comparison to its environmental counterpart.

Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.

In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.

The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.

This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

The present work attempts to determine an appropriate number of different categories of Delivery Persons for a Hyper-local Food Delivery Organization for different intervals within a day and across days within a week which would provide a satisfactory level of service to the target customers and at the same time would become cost-efficient.

Currently the firm estimates the required number of Delivery Persons for “lunch peak” and “dinner peak” of the next week's weekdays and weekend based on the maximum number of orders occurring during the same period of both weekdays and weekend in the current week. The proposed approach involves determining the projected demand in every four-hourly interval of both week-days and weekend in the next week. Subsequently, the study has developed a simple integer programming model for determining the optimum number of Delivery Persons based on the projected demand data.

The existing approach followed by the firm indicates that the Delivery Persons remain unutilized during periods of low demand. The proposed model demonstrated savings to the tune of 21.4% in manpower cost without any erosion in the service level.

The study has made three tangible contributions. First, the development of a simple methodology for estimating the demand of next period allows the Managers to utilize dynamic demand data. Second, the development of a simple integer programming model helps managers determine an appropriate number of Delivery Persons in different intervals in both weekdays and weekend. Third, the development of a framework of hiring strategy aids managers in adopting a particular hiring strategy under a particular context keeping in mind the magnitude of demand for food, demand for delivery service and the cost of providing the service.

This research seeks to identify and apply techniques that can be used in a supply chain context to diagnose the causes of variability in delivery lead time.

A literature review was conducted and a number of quality management (QM), techniques were selected as candidates for diagnosing delivery time variability. A case study of the application of these techniques is provided on the UK‐based defence supply chain that supported UK operations in the Iraq war of 2003.

Candidate QM techniques for diagnosing delivery time variability were identified, namely: Process Chart; Histogram; Failure Mode and Effect Analysis; and Cause and Effect Analysis. These techniques were successful in enabling the diagnosis of the causes of delivery time variability in the context of the case study investigated.

The work illustrates how QM techniques can be employed to address issues with supply chains, not least with regard to the important problem of variability in delivery leadtime. In practice, this highlights benefits that result to practitioners in order to improve the performance of operations in a dynamic setting, such as the defence supply chain studied here.

This work has value in presenting the findings of an in‐depth case study on the application of QM techniques in a multi‐echelon supply chain setting. It is also original in employing the FMEA technique together with an end‐customer perspective to assess the effect of failure modes in operations across a supply chain. FMEA also provided the means to examine supply chain risk, thus providing a research instrument for deploying risk as a lens. The application of QM techniques in this novel setting provides support for their application beyond the conventional setting of internal operations.

Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.

This paper presents a case study examining delivery service preference of electronic commerce products. With the increasing of electronic commerce and home shopping market, parcel delivery service market has been growing annually by more than 10% in Korea. However, the quality of delivery service and profitability are gradually decreased due to the price competition. A survey conducted by Korea Chamber of Commerce and Industry revealed that nearly half of the respondents selected delivery accuracy and time as major factors in delivery service, implying that online consumers consider service factors such as delivery time more important rather than delivery cost. In this research, a stated preference survey with 16 scenarios was conducted for online consumers to examine delivery preference depending on the type and value of products that they purchased online. The analysis results show that online consumers have a willingness to pay extra cost for rapid delivery or want lower price although it is much slow. It implies that current uniformed delivery cost of online products can be differentiated with different prices for different consumers and products. In addition, the study shows that different online products have different value of time, 27 won per hour on average.

The purpose of this paper is to investigate the effect of a dimension of logistics service quality (delivery time) interacting with shipping charges and purchase importance on customer satisfaction and purchase intentions in an e-commerce context. Uncertainty in terms of perceived ambiguity and perceived riskiness is shown to be the theoretical mechanism that plays a mediating role in the relationships between delivery time and customer satisfaction, as well as between delivery time and purchase intentions.

This study used a scenario-based role playing experiment. Three variables are manipulated in the design of the study – delivery time, shipping charges, and purchase importance. Participant responses (n=360) were collected through Amazon Mechanical Turk with perceptual measures.

Results indicated that increased delivery time significantly increased customers’ perceived ambiguity and perceived riskiness which reduced satisfaction as well as negatively impacted purchase intentions. Further, free shipping reduced customers’ perceived ambiguity when delivery time was lengthy, but strengthened the perception of ambiguity when the delivery time was short.

This paper sheds light on how a dimension of logistics service quality (delivery time) interacts with shipping charges and purchase importance to impact customer satisfaction and purchase intentions. It introduces uncertainty in the form of perceived ambiguity and perceived riskiness, to the logistics service literature as the mechanism that can explain how delivery time interacting with shipping charges and purchase importance impact customer satisfaction and purchase intentions. The implications for online retailers are that they should display separate shipping charges for shorter delivery times but for longer delivery times they should display a total price for the product which includes the shipping cost. Also when the purchase is important to the customer, they should offer shorter shipping time choices if they want to increase customer satisfaction.

This study aims to examine the effect of situational factors, related to transactions and waiting, on customers' cognitive and affective responses to service delivery time, including their service evaluations.

A web‐based survey included customers of an online credit company.

The authors' results demonstrate that disconfirmation influences cognitive but not affective responses. Both transaction importance and opportunity waiting cost influence wait acceptability, and transaction importance moderates the relationship between disconfirmation and wait acceptability. Delivery time worry and transaction worry increase negative affective responses; affective and cognitive responses then determine service evaluation. Opportunity waiting costs increase the positive effect of wait acceptability on service evaluation.

The main limitations pertain to the small sample size and the restriction of the survey to customers of only one financial company.

The derived guidelines suggest how banks can improve service evaluations by modifying consumer time expectations, as well as reducing opportunity waiting cost, delivery time worry, and transaction worry.

The conceptualization of service delivery time refers to waiting outside the company's premises, as opposed to in‐line waiting on company premises. This study considers the influence of situational factors, such as transaction importance, transaction worry, waiting cost, and delivery time worry, on customers' reactions to service delivery time in the context of online banking services.