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The national logistics policy report published by Ministry of Land, Infrastructure and Tourism in 2011 proposes to establish international logistics strategy teams in 10 different regions around Japan to satisfy the increasing demand for advance transportation infrastructure and stay competitive in Asia Pacific. The globalization of world economies creates many opportunities as well as challenges for international logistics companies to gain more business chances in this changing environment. The purpose of this paper is to improve service quality of international logistics companies and explores the quality function deployment in terms of quality evaluation method. The logistics service is particularly characterized by offering a series of transport solution and including other logistics activities. The major customers of the logistics services are the industrial clients. The customer satisfaction is key managerial mission since the competitiveness is a growing issue in this industry. The quality function deployment is one of the unique procedures to expose the requirements of customer and transform them into managerial tasks by cross correlation analysis between requirements and technical measures. The empirical study is performed to investigate service quality of the logistics industry by focusing on a group of leading logistics companies.

The purpose of this study is to present and explain a new customer segmentation approach inspired by failure mode and effect analysis (FMEA) which can help classify customers into more accurate segments.

The present study offers a look at the three most commonly used approaches to assessing customer loyalty:net promoter score, loyalty ladder and loyalty matrix. A survey on the quality of restaurant services compares the results of categorizing customers according to these three most frequently used approaches.

A new way of categorizing customers through loyalty priority number (LPN) is proposed. LPN was designed as a major segmentation criterion consisting of customer loyalty rate, frequency of purchase of products or services and value of purchases. Using the proposed approach allows to categorize customers into four more comprehensive groups: random, bronze, silver and gold – according to their loyalty and value to the organization.

Survey will bring a more accurate way of categorizing customers even in those sectors where transaction data are not available. More accurate customer categorization will enable organizations to use targeting tools more effectively and improve product positioning.

The most commonly used categorization approaches such as net promoter score, loyalty ladder or loyalty matrix offer relatively general information about customer groups. The present study combines the benefits of these approaches with the principles of FMEA. The case study not only made it possible to offer a view of the real application of the proposed approach but also made it possible to make a uniform comparison of the accuracy of customer categorization.

This paper aims to raise awareness of a potential planning pitfall and provide recommendations on how to assess and improve upon current practices. In potential conflict areas, such as the Korean Theater of Operations (KTO), military forces are required to store a portion of their ammunition combat load within depots and ammunition supply points under the control of a servicing ammunition company. This necessitates a lengthy retrieval process, as the ammunition company does not have enough resources to serve all customers simultaneously.

The stored combat load (SCL) retrieval process is modeled as a parallel machine scheduling problem and simulated using synthetic requirements. The current system of retrieval is contrasted against a proposed alternate system through a series of simulations scaled across three factors: number of ammunition company Soldiers, number of customer units and number of magazines.

The proposed alternate system demonstrates a significant potential for reducing the makespan of the SCL retrieval process when more than half of the magazines store SCL for multiple customers and there are more than five customers per Soldier.

Transitioning military units from a peacetime standing to full combat readiness as quickly as possible is of immense value within the KTO and other hostile areas with established troops not actively engaged in combat.

This study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.

The authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.

The sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.

Digital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.

Multi-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.

This study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.

Research suggests that centers of calculation, empowered by accounting inscriptions, are similar to maps: they provide a useful, albeit simplified, version of reality. The purposes of this paper are to examine whether and how digital platforms change the nature of centers of calculation, and to improve the understanding of the relationship between digital platforms and accounting.

An in-depth, single case-study design is used to empirically investigate how a Nordic hotel chain competed with global online travel agencies (OTAs) in the quest for the “new oil”—customer data.

The paper demonstrates how the case organization created a local alternative to global digital platforms with the aim of acquiring customer data, thereby moving from a center of calculation (CoC) to what authors label a “center of data appropriation” (CDA). While CoCs are guided by accounting inscriptions that enable “mapping”, CDAs are constructed around accounting inscriptions with other properties that enable digital “mirrors” of the economic domain. The authors find that this has two governing effects. First, multiple centers emerge that compete for access to the periphery. Second, future forms of competition can follow dynamic trajectories, where mutual dependence between CDAs may lead to coopetition.

Scholars have suggested that surveillance capitalism creates market-power imbalances. This study indicates that the transformation of local organizations into CDAs enables them to challenge global digital-platform organizations. Therefore, authors argue that local organizations may retain some market power by establishing local CDAs.

This paper investigates the performance assessment of liner shipping industry and presents a multi-dimensional evaluation framework to ensure both financial and non-financial monitoring. The traditional performance assessment approach is based on the financial indicators such as ratio analysis, but it is limited to the fiscal perspective. The meaning of performance has dramatically changed and non-financial (and intangible) assets increased their importance in recent years. Under these circumstances, the multi-attribute performance assessment methods play a critical role to combine many aspects of the business. Balanced Scorecard (BSC) is originally developed for the multi-attribute performance assessment and its philosophy on business process evaluation pioneered the importance of key performance indicators and the quality management issues including the internal customer. Service quality and the business performance assessment are some of the hot issues in the liner shipping industry and the long term competitiveness is a critical concern in the recent liner shipping business. The BSC method is utilized to ensure a cumulative analysis of the short/long and tangible/intangible indicators of performance and computes the weight of each criterion by using Fuzzy-AHP method in the liner shipping industry.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.

The purpose of this study is to apply hybrid cluster analysis in classifying PT Pelindo I customers based on the level of customer satisfaction with passenger services of PT Pelindo I.

Hybrid cluster analysis is a combination of hierarchical and non-hierarchical cluster analysis. This hybrid cluster analysis appears to optimize the advantages of hierarchical and non-hierarchical methods simultaneously to obtain optimal grouping. Hybrid cluster analysis itself has high flexibility because it can combine all hierarchical and non-hierarchical methods without any limits in the order of analysis used.

The results showed that 72% of PT Pelindo I customers felt PT Pelindo I service was special, while the remaining 28% felt PT Pelindo I service was good.

In total, 117 customers of PT Pelindo I were involved in a study using the non-probability sampling method.

This paper aims to highlight a model of industry drivers (industries’ environmental reputation and competitive intensity) that affect the sustainability marketing strategy segmentation, targeting and positioning based on customers’ environmental concern and explore the circumstances under which such a strategy affects performance.

The authors examined 64 Iranian export companies, which adopted sustainability marketing strategies across seven different industries. Achieved data are analyzed using a structural equation model methodology.

The results indicate that industries’ environmental reputation is positively related to the sustainability marketing strategies based on customers’ environmental concern and leads to superior financial and market performance. They also posit that competitive intensity has no significant effect on sustainability marketing strategies.

This study specifically examines the impact of industry drivers on sustainability marketing strategy and performance. Logically, there might be other factors affecting the sustainability or other value dimensions that are not addressed in this study.

This paper provides some understanding of how organizations strength their sustainability marketing strategy, and they have to consider what factors to adopt such strategy. This paper also facilitates a better understanding of the customers’ needs and concern as a factor influencing sustainability marketing strategy adoption and implementation. Identifying the customer segmentation and market targeting based on the industry’s environmental can lead to the business will normally tailor the marketing mix (4Ps) with the needs and expectations of the target in mind.

This paper strengthens the effect of environmental concern of customer to understand what influences the success of the sustainability marketing adoption and implementation by investigating the most influential factors such as industries’ environmental reputation and competitive intensity.

Este artículo pretende poner de manifiesto un modelo de impulsores de la industria (reputación ambiental e intensidad competitiva de las industrias) que afecta a la segmentación, orientación y posicionamiento de la estrategia de marketing de sostenibilidad basada en la preocupación ambiental de los clientes y explora las circunstancias en las que dicha estrategia afecta al rendimiento.

Se han examinado 64 empresas exportadoras iraníes que adoptaron estrategias de marketing sostenible en siete industrias diferentes. Los datos obtenidos se analizan utilizando SEM.

Los resultados indican que la reputación ambiental de las industrias se relaciona positivamente con las estrategias de marketing sostenibles basadas en la preocupación ambiental de los clientes y conlleva un rendimiento financiero y de mercado superior. También se afirma que la intensidad competitiva no tiene un efecto significativo en las estrategias de marketing sostenible.

Este estudio examina específicamente el impacto de los impulsores de la industria en la estrategia y el rendimiento de marketing sostenible. Lógicamente, podría haber otros factores que afecten a la sostenibilidad u otras dimensiones de valor que no se abordan en este estudio.

Se analiza cómo las organizaciones fortalecen su estrategia de marketing sostenible y tienen que considerar qué factores adoptar en dicha estrategia. Este artículo facilita también una mejor comprensión de las necesidades y preocupaciones de los clientes como un factor que influye en la adopción e implementación de la estrategia de marketing sostenible. La identificación de la segmentación de clientes y el mercado basado en el entorno ambiental de la industria puede llevar a que el negocio adapte su marketing mix (4Ps) teniendo en cuenta las necesidades y expectativas del público objetivo.

Esta investigación refuerza el efecto de la preocupación ambiental del cliente para comprender qué influye en el éxito de la adopción e implementación del marketing sostenible al investigar los factores más influyentes, como la reputación ambiental y la intensidad competitiva de las industrias.

Sostenibilidad, Estrategia de marketing, Industria, Impacto medioambiental, Clientes, Preocupación ambiental, Intensidad de la competencia, Exportación, Rendimiento financiero, Rendimiento de mercado.

Estudio de caso

This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.

The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.

The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.

Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.

The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.