Mathematical and Statistical Analysis of Farm Level Agricultural Sector in Bangladesh under Uncertainty (Published)
This study presents three different mathematical models for profit optimization of agricultural products in Bangladesh. To develop a Mixed Integer Linear Programming (MILP) model and analyze this model for two situation of demand uncertainty. Considering demand will be known before production and demand will be known after production. For the mentions of two cases, we investigate the change of solution applying least demand, maximum perhaps demand and extreme demand scenarios. I think this is real life problem and this analysis will be helpful for all types of agricultural producers. The proposed MILP model is to maximize the total profit and also to estimate the profitable production locations. The formulated MILP model were solved by A Mathematical Programming Language (AMPL) and results obtained by appropriate solver MINOS. Numerical example with the sensitivity of several parameters has been deployed to validate the models. Results show that maximum perhaps demand scenario gets better solution according to our expected value compare of other two scenarios.
The use of CO2 for EOR project can either be (1) capturing flue gas, separate the CO2, store and transport it to injection point or (2) purchase a volume of the CO2 needed for the project. This paper examined the former, for a reservoir that requires CO2 injection for EOR. Economics and stochastic analysis were carried out to ascertain the viability of the CO2-EOR project. The forecast variables (NPV, IRR and PI) from the economic (deterministic) model shows that the project is viable. However, the risk (stochastic) model shows that there is less than 50.725%, 52.274% and 52.274% certainty that the project will yield $631.5MM NPV, 58% IRR and 3.51 PI, with an average of 51,76% uncertainty impacted by the input parameters (CAPEX, OPEX, oil price and discount rate). The CAPEX and discount rate are the major parameters that impact high uncertainty on the project. Therefore, mitigating them will increase the chances of obtaining the values forecast variables.
Sense-Making, Entrepreneurial Orientation and Their Influence on Firm Performance in Kenya (Published)
Manufacturing firms constitute an integral part of the economic rubric of developing countries. In Kenya, they contribute 14% of gross domestic product, and train and employ 30% of the workforce. However, they exhibit low organization capacity, and struggle to survive as competitive enterprises. The purpose of this study was to establish how entrepreneurial orientation (EO) influences the relationship between sense-making and firm performance in Kenya. Anchored on the resource-based view and strategic entrepreneurship concept, the study used a self-administered questionnaire to survey owners/managers of 83 small and medium enterprise (SME) food-manufacturing firms registered by the Kenya Association of Manufacturers. Data were analyzed using structural equation modeling, employing Statistical Software for Social Sciences (SPSS) Version 20 and SmartPLS 3. The study found that EO fully mediates the relationship between sense-making and firm performance. This study concludes that EO is a critical strategy that firms should exploit to maximize their performance. The study recommends that, manufacturing SMEs should encourage employee entrepreneurial behaviours, and the government should support policies that promote entrepreneurial business management capabilities in manufacturing firms.
The “mode” has been proposed as an appropriate statistic to improve estimate especially in situations when data distributions are skewed or contain outliers such as activity duration in project scheduling. Since the underlying distribution of activity duration may be unknown and different modes can be obtained using different bin sizes of the histogram method, this paper,investigates the effect of varying histogram bin width and data distribution on the behaviour of the mode. Random numbers were generated from five distributions commonly used to model project activity duration at five different levels and varying sample sizes. Each set of sample is then binned using varying histogram bin width, Sturges’rule and Scott’s rule. The grand mode for all levels per classification is recorded and analyzed. It was found that bin width does not significantly affect the behaviour of the mode but the value of the mode is significantly dependent on the data distribution and sample size.