Evaluation of forecasting models for air cargo

Purpose – Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes. Design/methodology/approach – A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized. Findings – Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors. Originality/value – The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

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Valuing retail lease options through time

Journal of Property Investment and Finance ◽

10.1108/jpif-05-2016-0036 ◽

2017 ◽

Vol 35 (4) ◽

pp. 369-381 ◽

Cited By ~ 1

Author(s):

Jussi Vimpari ◽

Seppo Junnila

Keyword(s):

Option Pricing ◽

Real Option ◽

Real Life ◽

Option Valuation ◽

Content Type ◽

Life Data ◽

Property Owner ◽

Real Life Data ◽

Different Types ◽

Lease Contracts

Purpose Retail properties are a perfect example of a property class where revenues determine the rent for the property owners. Estimating the value of new retail developments is challenging, as the initial revenues can have a significant variance from the long-term revenue levels. Owners and tenants try to manage this problem by introducing different kind of options, such as overage rent and extension rights, to the lease contracts. The purpose of this paper is to value these options through time for different types of retailers, using real-life data with a method that can be easily applied in practice. Design/methodology/approach This paper builds upon the existing papers on real option studies but has a strong practical focus, which has been identified as a challenge in the field. The paper presents simple mathematical equations for valuing overage rent and extension options. The equations capture the value related to uncertainty (volatility) that is missed by standard valuation practices. Findings The results indicate that overage and extension options can represent a significant proportion of retail lease contract’s value and their value is heavily time-dependent. The option values differ greatly between tenants, as the volatilities can have a large spread across tenants. The paper suggests that the applicability of option pricing theory and calculus should not be considered as an insurmountable barrier any more, rather a greater challenge for the practical adaptability of the method can be the availability of real-life data that is a common problem in real option analysis. Practical implications The value of extension and overage options varies greatly between tenants. In general, the property owner can try balance the positive effects from the overage rents to the negative effects of tenant extensions. However, this study tries to highlight that, as usual, using the “law of averages” can result into poor valuation in this context as well. Even the data used in this study provide valuable findings for the property owner as an analytical deduction can be made that certain types of tenants have higher volatilities and this should be acknowledged when valuing options within lease contracts. Originality/value Previous literature in this topic often takes the input data for the option valuation as granted rather than trying to identify the real-life data available for the calculation. This is a common problem in real options valuation and it seems to be one of the reasons why option valuation has not been used widely in practice. This study has used real-life data to assess the problem and more importantly assessed the data across different types of tenants. The volatility spread between different types of tenants has not been discussed previously, even though it has a significant importance when using option pricing in practice.

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Psychometric sifting to efficiently select the right service employees

Managing Service Quality ◽

10.1108/msq-04-2014-0091 ◽

2014 ◽

Vol 24 (5) ◽

pp. 418-433 ◽

Cited By ~ 11

Author(s):

John E.G. Bateson ◽

Jochen Wirtz ◽

Eugene Burke ◽

Carly Vaughan

Keyword(s):

Customer Orientation ◽

Selection Process ◽

Traditional Approach ◽

Real Life ◽

Operational Efficiency ◽

Service Firms ◽

Recruitment Process ◽

Content Type ◽

Service Employees ◽

The Right

Purpose – Service employees in subordinate service roles are crucial for operational efficiency and service quality. However, the stressful nature of these roles, inappropriate hire selection, and the proliferation of job boards have created massive recruitment problems for HR departments. The purpose of this paper is to highlights the growing costs of recruiting the right candidates for service roles while offering an alternative approach to recruitment that is more efficient and effective than the traditional approach. Design/methodology/approach – The study offers empirical evidence of five instances in which the use of psychometric sifting procedures reduced recruitment costs, while improving the quality of the resultant hires. Findings – By standing the traditional recruitment process “on its head” and using psychometric tests at the start of the selection process, the recruitment process can be significantly improved. Such tests efficiently weed out unsuitable candidates before they even enter the recruitment process, leaving a smaller, better-qualified pool for possible recruitment. Practical implications – Firms can safely use the psychometric sifts to select applicants according to their operational efficiency, customer orientation, and overall performance. This paper illustrates the use of both traditional questionnaire measures and situational judgment tests to remove unsuitable applicants at the start of the selection process. A real-life case study suggests that such an approach increases the hiring success rate from 6:1 to 2:1. In the opening of a new supermarket by a UK group, this process saved 73,000 hours of managers’ time, representing $1.8 million savings in opening costs. Originality/value – The paper offers a viable cost-saving alternative to a growing problem for HR departments in service firms and provides directions for further research.

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Travel time prediction in transport and logistics

VINE Journal of Information and Knowledge Management Systems ◽

10.1108/vjikms-11-2018-0102 ◽

2019 ◽

Vol 49 (3) ◽

pp. 277-306 ◽

Cited By ~ 2

Author(s):

Xia Li ◽

Ruibin Bai ◽

Peer-Olaf Siebers ◽

Christian Wagner

Keyword(s):

Travel Time ◽

Model Fitting ◽

Real Life ◽

Gps Data ◽

Travel Time Prediction ◽

Time Data ◽

Content Type ◽

Time Prediction ◽

Travel Time Data ◽

Real Life Data

Purpose Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions. Design/methodology/approach The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case. Findings The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper. Research limitations/implications The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances. Practical implications The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions. Originality/value This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

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Project management issues: vulnerability management assessment

Kybernetes ◽

10.1108/k-08-2016-0218 ◽

2017 ◽

Vol 46 (7) ◽

pp. 1171-1188

Author(s):

Aleksandar Aleksic ◽

Hrvoje Puskaric ◽

Danijela Tadic ◽

Miladin Stefanovic

Keyword(s):

Project Management ◽

Vulnerability Assessment ◽

Real Life ◽

Future Research ◽

Content Type ◽

Business Operations ◽

Real Life Data ◽

Vulnerability Management ◽

Risk Sources

Purpose The purpose of this paper is to investigate the vulnerability of projects implemented in enterprises. The paper focuses on the issue of vulnerability assessment in the planning stages of a project, before its realization. Design/methodology/approach In this paper, the realization of the project has been analyzed through the phases of delivery, and the fuzzy approach has been deployed for mathematical modeling of uncertainties. An appropriate expert and management team has assessed the variables of the project’s vulnerability by using linguistic expressions, as this way of assessment is close to the human way of thinking. The model of project’s vulnerability assessment has been verified on real life data by means of an illustrative example. Findings A very significant part of business operations in enterprises all over the world is realized through the practice of project management. In daily business practice, project activities may be exposed to different risk sources. These risks may be studied from different perspectives, but without reevaluation, risk sources increase the vulnerability of projects as well as of the whole enterprise. Originality/value The results of the analysis of the obtained data gives good direction to future research in the scope of vulnerability management in the enterprises oriented to long-term sustainability.

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A new algorithm for detecting communities in social networks based on content and structure information

International Journal of Web Information Systems ◽

10.1108/ijwis-06-2019-0030 ◽

2019 ◽

Vol 16 (1) ◽

pp. 79-93

Author(s):

ELyazid Akachar ◽

Brahim Ouhbi ◽

Bouchra Frikh

Keyword(s):

Social Networks ◽

Community Detection ◽

Structural Information ◽

Real Life ◽

Data Sets ◽

Content Type ◽

Structure Information ◽

Novel Approach ◽

Real Life Data ◽

Content Information

Purpose The purpose of this paper is to present an algorithm for detecting communities in social networks. Design/methodology/approach The majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities). Findings To validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information. Originality/value The experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.

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Strategic vehicle fleet management–a joint solution of make-or-buy, composition and replacement problems

Journal of Quality in Maintenance Engineering ◽

10.1108/jqme-04-2020-0026 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Adam Redmer

Keyword(s):

Management Strategy ◽

Large Scale ◽

Real Life ◽

Optimization Methods ◽

Solution Procedure ◽

Fleet Management ◽

Content Type ◽

Fleet Size ◽

Real Life Data ◽

Management Problems

PurposeThe purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.Design/methodology/approachThe three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.FindingsThere are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.Originality/valueThe methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.

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Optimising end-to-end maritime supply chains: a carbon footprint perspective

The International Journal of Logistics Management ◽

10.1108/ijlm-01-2013-0002 ◽

2013 ◽

Vol 24 (3) ◽

pp. 407-425 ◽

Cited By ~ 11

Author(s):

Patrick Rigot-Muller ◽

Chandra Lalwani ◽

John Mangan ◽

Orla Gregory ◽

David Gibbs

Keyword(s):

Carbon Emissions ◽

Real Life ◽

Southern Europe ◽

Optimal Route ◽

Content Type ◽

Life Data ◽

First Case ◽

Real Life Data ◽

End To End ◽

The Uk

Purpose – The purpose of this paper is to illustrate an optimisation method, and resulting insights, for minimising total logistics-related carbon emissions for end-to-end supply chains. Design/methodology/approach – The research is based on two real-life UK industrial cases. For the first case, several alternative realistic routes towards the UK are analysed and the optimal route minimising total carbon emissions is identified and tested in real conditions. For the second case, emissions towards several destinations are calculated and two alternative routes to southern Europe are compared, using several transport modes (road, Ro-Ro, rail and maritime). An adapted Value Stream Mapping (VSM) approach is used to map carbon footprint and calculate emissions; in addition Automatic Identification Systems (AIS) data provided information for vessel specification allowing the use of more accurate emission factors for each shipping leg. Findings – The analysis of the first case demonstrates that end-to-end logistics-related carbon emissions can be reduced by 16-21 per cent through direct delivery to the UK as opposed to transhipment via a Continental European port. The analysis of the second case shows that deliveries to southern Europe have the highest potential for reduction through deliveries by sea. Both cases show that for distant overseas destinations, the maritime leg represents the major contributor to CO2 emissions in the end-to-end supply chain. It is notable that one of the main apportionment approaches (that of Defra in the UK) generate higher carbon footprints for routes using Ro-Pax vessels, making those not optimal. The feasibility of the optimal route was demonstrated with real-life data. Originality/value – This research used real-life data from two UK companies and highlighted where carbon emissions are generated in the inbound and outbound transport chain, and how these can be reduced.

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New Solutions for Digital Advertising: Gen Y Playing Roles of Personas

Mednarodno inovativno poslovanje = Journal of Innovative Business and Management ◽

10.32015/jibm.2020.12.2.9.85-95 ◽

2020 ◽

Vol 12 (2) ◽

pp. 85-95

Author(s):

Mateja Mahnič

Keyword(s):

Business Models ◽

Interpersonal Relations ◽

Real Life ◽

Gen Y ◽

Online Study ◽

Real Life Data ◽

Creative Products ◽

The Right ◽

Practical Implications

In this article we continue our interest in generation Y, Millennials, abbreviated Gen Y As we assumed, we confirm with a short survey that they are really innovative and creative. In that article we proved Gen Y’s creativity with using methodology of international survey on DOBA’s online student’s bachelors’ courses of Advertising Campaigns. For these student’s creativity overwhelmingly meant Original/Different/ Unique (64,28 %), therefore we assumed that they were the right personas - first persons to share creative products/services further. This hypothesis was confirmed in a case study in which students play personas. We recommended that our students design persona with characteristics of one of their team-mates, on real-life data. The results of this case study were useful innovations for promoting online study of DOBA Faculty. With this method we could turn business models into reality and make them interesting and worthy of buying from modern consumers. Our practical implications are that a smart company/business/entrepreneur/ creative industry should involve Millennials as their key persona when preparing their advertising campaigns, especially on social networks with its interpersonal relations. This solution could strengthen and empower their link with consumers and increase their profits.

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Selection and industrial applications of panel data based demand forecasting models

Industrial Management & Data Systems ◽

10.1108/imds-08-2015-0345 ◽

2016 ◽

Vol 116 (6) ◽

pp. 1131-1159

Author(s):

Shuyun Ren ◽

Tsan-Ming Choi

Keyword(s):

Panel Data ◽

Model Selection ◽

Demand Forecasting ◽

Industrial Applications ◽

Data Models ◽

Future Research ◽

Panel Data Models ◽

Content Type ◽

Forecasting Models ◽

The Right

Purpose – Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed. Design/methodology/approach – It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed. Findings – This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed. Research limitations/implications – This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered. Practical implications – The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications. Originality/value – This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

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