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In the summer of 1980, a survey of chief corporate planners was conducted in order to update data collected in 1979 (reported in the Fall 1980 issue of The Journal of Business Strategy) and to provide new data about additional aspects of the planning process.

Corporate managers have an increasing need for data on career paths, compensation, and staffing in corporation planning functions in order to better manage their own operations. At the same time, current and potential members of corporate planning staffs need similar information to better guide their careers and activities. This survey was conducted to meet an important part of the needs of both management and staff.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.

Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.

Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.

Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

Levels of selected essential and non‐essential metals (Cd, Cr, Pb, Cu, Fe and Zn) and those of macro‐nutrients (Ca, K, Mg and Na) are estimated in 15 different seasonal fruits available in local Pakistan markets. The wet digestion oxidation method was used for the analysis of samples by the flame atomic absorption technique. The results indicated almost 100 percent incidence of occurrence of trace metals and macro‐nutrients in all fruits. The highest concentration was observed for zinc, ranging between 0.13 and 79.9mg/kg, wet weight, respectively for banana and mango. The iron levels ranged from 0.55 to 44.8mg/kg, wet weight, for pomegranate and mango. The concentrations of Cd, Cr, Pb and Cu remained at marginal levels, except for certain fruits where the concentrations were very high. The data are compared with allowed safe limits laid down by WHO.

The purpose of this research paper is to study the significant enablers for a competitive supply chain and analyze the relationships among them by using multi-criteria decision-making (MCDM) techniques. The supply chain (SC) managers will get better insights from the models of this study to design their SCs that are more competitive for competitive advantage.

After an extensive review of literature followed by experts' opinions, 21 significant enablers for a competitive SC (CSC) were selected for structural modeling using MCDM techniques of total interpretive structural modeling (TISM), Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC), followed by decision-making trial and evaluation laboratory (DEMATEL) approach.

Top management commitment is the most prominent causing enabler of a CSC; customer satisfaction is the topmost effect enabler; the operational performance of individual firms in the supply chain is a crucial enabler of a CSC.

The results and findings of this study would provide better insights to SC professionals and practitioners to comprehend the significant enablers of a CSC for designing and executing SC operations more competitively to achieve better customer satisfaction and sustainable business performance.

To the best of the authors’ knowledge, this is a foremost study focusing on the significant enablers of a CSC by utilizing the TISM along with MICMAC and DEMATEL methods. It is expected that this research will offer useful guidance for assessing and considering the SC enablers for achieving a CSC and facilitate new research in this area with more thrust.

Process mapping can lead to a more holistic understanding of how an organisation works. This paper seeks to discuss how an engineering design consultancy, which had developed a series of process maps on the design of steel frame buildings, developed a powerful management tool, the Management Briefing Sheet which has yielded numerous additional benefits enabling practice to be improved and quality procedures more easily accessed.

To maximise the knowledge and expertise of its supply chain partners and to better understand how it designed steel‐framed buildings, the engineering design consultancy undertook a process‐mapping exercise. Various techniques for documenting the process were considered, but a modified IDEF notation was chosen for its ability to capture the iterative nature of the design process and its methodical approach for deconstructing complicated activities.

Process‐mapping exercises can change the way organisations work and make them more efficient, but to do this the changes that would lead to improvements need to be implemented successfully. Carrying out a process‐mapping exercise in isolation from the end‐user can lead to complications.

The key obstacle to implementing change identified by the engineering design consultancy, with whom the MBS was developed, was delivering the knowledge acquired from the process analysis in a format that end‐users could understand easily and adopt effectively.

This article will be of significant use to any organisation wishing to maximise the knowledge and expertise of its supply chain partners and identify inefficient working practices.

Demonstrates the application of spreadsheets in simulating queuing systems with arrivals from a finite population. The problem is referred to as the machine repair problem where the members of the queue are machines that are breaking down and the servers are the technicians repairing the broken machines. The total number of machines are finite and pre‐specified. The technique for the development of the simulation is illustrated with six machines. Describes the approach for developing a generalized simulation model with any number of machines.

The purpose of this paper is to develop a data system to assess failure probability in small to medium‐sized enterprise (SME) reorganization.

The data system is based on information from 83 reorganized Finnish SMEs. Information is divided into four types: pre‐filing non‐financial, pre‐filing financial, reorganization submission, and reorganization plan information. Partial least squares (PLS) analysis is used in data mining to factorize information for each type of information. Logistic regression analysis is applied to assess failure probability.

Useful data system can be developed on the basis of pre‐filing non‐financial information to support reorganization decision. Pre‐filing financial information only marginally improves quality of information. Submission and reorganization plan information improve quality in terms of fit but do not significantly improve classification accuracy.

The sample is small and should be expanded in further studies. The system is developed for Finnish reorganizing firms. It can be generalized to any similar reorganization process.

The data system is useful for managers, lending specialists, investors, reorganization lawyers, and judges. It warns a SME about reorganization failure before filing petition (passive use). It is also useful in developing successful reorganization plans (active use).

This paper builds an extensive data system for assessing reorganization failure risk. It makes use of many variables that have not been analyzed in reorganization studies earlier. It deals with SMEs that is rare in reorganization studies. The paper utilizes PLS in assessing failure probability. It includes new analytical results on PLS.

On the example of professional football in Germany, this paper analyses the conflict about the punishment of fan misbehaviour within an agency-theoretical framework to cast light on the reasons for the ineffectiveness of the sentences and to show possible solutions.

In a pre-study, more than 1,300 hand-collected past sentences against clubs by the German and European sports courts were analysed to demonstrate the ineffectiveness of the penalties so far. Additionally, in the main study, 26 expert interviews with German representatives of the football association, courts, clubs, sponsors, police and active fan scenes allow a deep insight into the relationships of the involved parties.

The paper suggests that the sentences do not sufficiently consider several agency problems. Due to moral hazard, they exert hardly any influence on fan behaviour and only a small one on the clubs. While the lighting of pyrotechnics is by far the most punished type of misbehaviour, most of the interviewees cite the impossibility of preventing it. Despite the sentences, some clubs make non-public agreements with their fans about still tolerable misconduct or do not pass the penalties on to the polluters as intended by the association. The findings highlight the importance of communication for less misbehaviour.

For the first time, the agency theory and the economic theory of optimal punishment are brought together with insights from interviews with the involved parties. The authors discover a two-stage principal-agent problem and get new insights into stakeholders' hidden motivations and attitudes. The results should encourage a debate on the current penalties and possible solutions to the recurring problem of pyrotechnics.