Relationship between accounting benefits and ERP user satisfaction in the context of the fourth industrial revolution

The importance of corporate social responsibility is shaping investment decisions and entrepreneurial actions in diverse perspectives. The rapid growth of SMEs has tremendous impacts on the environment. Nonetheless, the economic emergence plan of Cameroon has prompted government support of SMEs through diverse projects. This saw economic growth increased to 3.8% and unemployment dropped to 4.3% caused by the expansion of private sector investments. The dilemma that necessitated this study is the response strategy of SMEs operators towards environmental sustainability. This study, thus seeks to examine the effects of entrepreneurial intentions and actions on environmental sustainability. The research is a conclusive case study design supported by the philosophical underpins of objectivism ontology and positivism epistemology. Data was sourced from four hundred (400) SMEs operators purposively sampled from the Centre and Littoral regions of Cameroon using structured questionnaire. Data was analysed using the Structural Equation Modelling technique with the aid of statistical packages including: SPSS 24 and AMOS 23. The study revealed that entrepreneurial action has weak positive statistical significant impacts on environmental sustainability; whereas entrepreneurial intention has strong positive statistical significant effects on environmental sustainability. Entrepreneurial intention comprised of self-efficacy and perceived control whereas, entrepreneurial actions involved entrepreneurial alertness and uncertainty. This study concludes that entrepreneurs in Cameroon have sustainable intentions to protect the environment but; the current actions taken are inadequate. This research recommends that entrepreneurs should enhance efforts toward attaining the state of genuine sustainabilit

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Managing the bullwhip effect in multi-echelon supply chains

This editorial article presents the bullwhip effect which is one of the major problems faced by supply chain management. The bullwhip effect represents the demand variability amplification as demand information travels upstream in the supply chain. The bullwhip effect research has been attempting to prove its existence, identify its causes, quantify its magnitude and propose mitigation and avoidance solutions. Previous research has relied on different modeling approaches to quantify the bullwhip effect and to investigate the proposed mitigation/avoidance solutions. Extensive research has shown that smoothing replenishment rules and collaboration in supply chain are the most powerful approaches to counteract the bullwhip effect. The objective of this article is to highlight the bullwhip effect avoidance approaches with providing some interesting directions for future research

A Constraint-directed Local Search Approach to Nurse Rostering Problems

In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach

A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure