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Recently, the popularity of software has grown significantly in the market. Enhancement of software is needed to decrease the burden of getting high-quality and reliable software. To achieve this, the software is upgraded by adding new features to the previous version. Therefore, adding new features in the last version to be consistent with the earlier version is challenging. This paper aims to discuss the optimal software enhancement and customer growth model.

This paper discusses a model when new features are added to the software, and the customers' adoption of the software is presented as a customer growth model. An optimal control problem is introduced to maximize the profit obtained from the software system over the system's lifetime period. Total gain is calculated by the value generated from selling the software over the total expenditure during the software development process. The closed-form solution and some theoretical results are presented using the optimal control-theoretic approach. The theoretical results are supported by a numerical example.

This paper gives several substantive insights during sensitivity analysis and provides essential results. The results discussed here are compatible with the actual scenario and useful in software enhancement.

The authors have proposed a new feature growth and customer growth model to maximize the total profit using optimal control theory.

Many project failures can be attributed to changes in requirements as the project progresses. The purpose of this paper is to review several system dynamics models of the requirements process from the literature, especially the model of Andersson and Karlsson. This model addresses the deficiencies in the system dynamics model devised hitherto to create a model than can be used to predict requirements acquisition and staff resources for different levels of project complexity and size.

A new control‐theoretic model of requirements gathering is devised using the Software Test Model of Cangussu as a framework. Conventional control system techniques are then used to compute typical responses.

The results agree with the limited published data. This model can be used to predict an estimate of the possible number of requirements that could be devised for the project. It allows the incorporation of workforce, quality and complexity information to predict the time to complete the requirements process. This model is then used to predict how the requirements process responds to requirements creep and to changes in workforce used.

This model is theoretical and is proposed so that it can be simple tested. It makes a number of assumptions not yet recorded by industry that would have to be obtained.

If it is supported by experimental data then a better project development model can be created with the prospect of greater control of project development.

This work, although based on the work of Cangussu et al., is devised by the author.

Argues for a reorientation of economic theory around the concept of co‐operative production. Suggests that by definition, co‐operative production exists when different people take different, complementary roles in production. Argues that, while the proposed reorientation has clear roots in the ideas of Adam Smith, it also synthesizes several other key insights of the classical economists, and at the same time leads, by implication, both to the major topics of neoclassical economics and to some important topics of modern applied economics that are often treated in an ad hoc fashion, such as economies of agglomeration, dualism in development and X‐inefficiency.

The cost to supermarkets from shoplifting is a growing problem. Despite huge investments in formal security measures, supermarkets experience shrinkage and face heavy financial losses. Hence, this paper explores an alternative approach to shoplifting prevention. The purpose of this paper is to propose that quality relationships between a supermarket and its customers could be a viable strategy for shoplifting prevention. A conceptual model is presented at the end of this paper for encouraging shoplifting prevention using a theory of planned behaviour perspective.

A systematic search of literature on relationship quality was conducted. Research papers were shortlisted from peer-reviewed journals published between 2007 and 2016. For the convenience of readers, studies on relationship quality are presented in six cross-checked information categories in a table.

This study produced two results. First, it finds three antecedents of supermarket-customer relationship quality. These are as follows: a supermarket’s participation in corporate social responsibility and cause-related marketing initiatives, a supermarket’s service quality, and a customer’s attachment to a supermarket’s place/location. Second, there are three major effective dimensions of relationship quality. These are as follows: satisfaction, trust, and commitment. The proposed model incorporates factors from both findings, to measure a supermarket-customer relationship quality that may influence intention of customers to engage in shoplifting preventive behaviour.

This research paper has reviewed the existing literature to utilise it in the context of shoplifting prevention, and developed a novel model/framework for effective shoplifting prevention with a theory of planned behaviour perspective.

The purpose of this paper is to understand the effect of batching on bullwhip effect in a model of multi‐echelon supply chain with information sharing.

The model uses the system dynamics and control theoretic concepts of variables, flows and feedback processes and is implemented using iThink^® software.

It has been seen that the relationship between batch size and demand amplification is non‐monotonic. Large batch sizes, that when combined in integer multiples can produce order rates that are close to the actual demand, produce little demand amplification, i.e. it is the size of the remainder of the quotient that is the determinant. It is further noted that the value of information sharing is greatest for smaller batch sizes, for which there is a much greater improvement in the amplification ratio.

Batching is associated with the inventory holding and backlog cost. Therefore, future work should investigate the cost implications of order batching in multi‐echelon supply chains.

This is a contribution to the continuing research into the bullwhip effect, giving supply chain operations managers and designers a practical way into controlling the bullwhip produced by batching across multi‐echelon supply chains.

Previous similar studies have used control theoretic techniques and it has been pointed out that control theorists are unable to solve the lot sizing problem. Therefore, system dynamic simulation has been applied to investigate the impact of various batch sizes on bullwhip effect.

The purpose of this paper is to analyze the effect of batching on bullwhip effect in a model of multi‐echelon supply chain with information sharing.

The model uses the system dynamics and control theoretic concepts of variables, flows, and feedback processes and is implemented using iThink^® software.

It has been seen that the relationship between batch size and demand amplification is non‐monotonic. Large batch sizes, when combined in integer multiples, can produce order rates that are close to the actual demand and produce little demand amplification, i.e. it is the size of the remainder of the quotient that is the determinant. It is further noted that the value of information sharing is greatest for smaller batch sizes, for which there is a much greater improvement in the amplification ratio.

Batching is associated with the inventory holding and backlog cost. Therefore, future work should investigate the cost implications of order batching in multi‐echelon supply chains.

This is a contribution to the continuing research into the bullwhip effect, giving supply chain operations managers and designers a practical way into controlling the bullwhip produced by batching across multi‐echelon supply chains. Economies of scale processes usually favor large batch sizes. Reducing batch size in order to reduce the demand amplification is not a good solution.

Previous similar studies have used control theoretic techniques and it has been pointed out that control theorists are unable to solve the lot sizing problem. Therefore, system dynamic simulation is then applied to investigate the impact of various batch sizes on bullwhip effect.

This paper sets out to describe transform methods to control vendor‐managed inventory (VMI). It aims to examine the limits of modelling approaches within control‐theoretic models.

Modelling was achieved with the Simulink package using the equations developed by Disney and Towill for a two‐tier VMI system. Discrete and continuous models were considered together with two forms of production delays: a finite delay and the Forrester exponential delay. Standard control engineering analyses of these delay representations were compared to illustrate how the system response and stability depend on their formulation and to determine the permissible gains.

Response by a discrete or continuous model to step inputs in sales rates depends on the type of delay representation but the responses do not differ by more than 5 per cent if the same delay form is used in the models. The prime effect of using a finite delay is to deepen the stock‐out and increase the required order rate compared with the same response observed with the use of exponential forms of delay. Total time for recovery is similar with all models. It is shown that the continuous model with an exponential delay is always stable and when using a fixed delay the continuous model can be made stable.

The models presented here illustrate that the various forms of control‐theoretic models present similar results irrespective of technique used, provided that they have the same delay type. The range of gains for the required order rate can be computed for any VMI system, knowing that they can be designed to guarantee stable operation.

This work extends that of Disney and Towill to include different modelling representations. It allows operational gains to be safely chosen for stable operation.

Mathematical models (often differential equations) in Modern Control Theory assume “deterministic interaction” between elements of the system. Management systems, in general, do not possess this property. In this paper we employ Beer's ideas to define special quantities and corresponding mathematical methods by which we are able to adapt non‐deterministic interaction for feedback control treatment.

The use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.

The model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.

The paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.

We have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

This paper seeks to lay the computational and mathematical foundations for a “postmodern cybernetics” based on ancient Kabbalah. This new Kabbalistic cybernetics is introduced to act as an unifying science addressing simultaneously, in a common language and framework the triple nature of human, psychological, social, economic, financial, political, cultural systems and their present multi‐faceted crises and conflicts: cognitive objective level of attaining knowledge; subjective emotional level and physical action and behavioral level.

Feedback, hierarchical control and other system and control theoretic features in the Tree of Life (TL) of Kabbalah were identified. These were used to develop a general system theoretic framework of a new type to address human and societal system dynamics, evolution, interaction and feedback control by simultaneously taking into account their triple nature.

The postmodern cybernetics of the TL introduced here as a general system framework, exhibits feedback control with internal model principle, hierarchical control, system multi‐valued logic, category theory pullback and pushout mechanisms, advanced knowledge engineering to aggregate, learn, evolve and solve problems and crises in an integrated way.

“Kabbalistic postmodern cybernetics” proposed here for human and societal systems is the unified scientific framework to solve problems that led to or are just related to economic, financial, political, cultural, societal and human crises and conflicts by addressing their triple intertwined human nature.

Computational foundations for a postmodern cybernetics based on Kabbalah are introduced, where the cognitive, emotional and behavioral and physical action facets of human systems are dealt with in a unified integrated framework to address global problems and crises.