Abdullahil Azeem

Employee performance evaluation: a fuzzy approach

Purpose - Managers encounter many decisions that require the simultaneous use of different types of data in their decision-making process. A critical decision area for managers is the performance evaluation of personnel, whether individually or as a member of a team. Performance evaluation is critically essential for the effective management of the human resource of an organization and evaluation of staff that help develop individuals, improve organizational performance, and feed into business planning. Design/methodology/approach - Performance evaluations require and often involve disparate types of information that are vague, incomplete, objective, and subjective. This paper proposes a performance evaluation system of employees considering various performance evaluation criteria using fuzzy logic. The main task in the proposed approach involves determining the performance indices of employees considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best employee who holds highest performance index comparing all the indices. Findings - A model is developed for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff. Fuzzy control is used to determine the overall performance index by combining results of the performance in selected criteria and provided it in numerical values which will undoubtedly ensure convenience of the concerned human resource personnel during performance rating calculation. Originality/value - This is the first time, a performance evaluation model is developed using fuzzy approach for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff.

An integrated approach for multiple criteria supplier selection combining Fuzzy Delphi, Fuzzy AHP & Fuzzy TOPSIS

In todays competitive business, supplier selection problem plays a significant role as a strategic feature in companys success. As variety of uncontrollable and unpredictable factors affect the evaluation and decision-making process at different levels, supplier selection problem is usually very complex and unstructured, Various decision making approaches have been proposed to tackle the problem as part of a general tendering process, particularly those of multi-criteria analysis which use both quantitative and qualitative data. The aim of this paper is to integrate Fuzzy Delphi method with fuzzy analytic hierarchy process (AHP) and Fuzzy TOPSIS based approach to select the best supplier providing the highest satisfaction for the criteria determined. Fuzzy Delphi method is used to identify the most important and significant criteria then Fuzzy AHP is used to obtain the relative importance of the evaluation criteria and finally Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is used to rank the suppliers with a view to select the best one from them. In order to demonstrate the applicability of proposed approach an illustrative example is presented and the result is analyzed at the end of this paper.

Optimisation of a production inventory model with reliability considerations

In this paper, a production inventory model with reliability of production process is developed to minimise total inventory cost. Production, setup, holding, inspection, depreciation, rejection and backorder cost are considered to develop the model. The economic production lot size and the reliability of the production process along with the production period are the decision variables and total cost per cycle is the objective function which is to be minimised. A meta-heuristic particle swarm optimisation (PSO) algorithm is applied to solve the unconstrained non-integer non-linear form of objective function. Some numerical examples have been presented to explain the model. The results obtained from PSO algorithm are compared with results obtained from genetic algorithm (GA) applying on the same inventory model. Comparison clearly shows the superiority of PSO results over GA results thus makes PSO a better choice for this kind of modelling.

A neural approach to product demand forecasting

This paper develops an artificial neural network (ANN) model to forecast the optimum demand as a function of time of the year, festival period, promotional programmes, holidays, number of advertisements, cost of advertisements, number of workers and availability. The model selects a feed-forward back-propagation ANN with 13 hidden neurons in one hidden layer as the optimum network. The model is validated with a furniture product data of a renowned furniture company. The model has also been compared with a statistical linear model named Browns double smoothing model which is normally used by furniture companies. It is observed that ANN model performs much better than the linear model. Overall, the proposed model can be applied for forecasting optimum demand level of furniture products in any furniture company within a competitive business environment.

Insights of apparel supply chain operations: a case study

Considering the important role of apparel manufacturers within the global chain, we conduct a research on the Bangladesh garments industry and focus on several important supply chain operational issues. The objectives are to analyse apparel supply chain matters such as new orders, raw materials supply, production processes and logistics related to finished goods delivery. We collect data for trade statistics, conduct structured interviews and send survey questionnaires to garment manufacturers. Analysis shows apparel manufacturers are striving for sustainable business growth. Manufacturers are working towards minimising longer lead time and focus more on issues such as environmental friendly manufacturing, child labour and health and safety in the workplace. We identify related supply chain practices influencing the industry, set guidelines for improvement and offer recommendations for sustainability.

Multiple criteria supplier selection: a fuzzy approach

A company must purchase a lot of diverse components and raw materials from different upstream suppliers to manufacture or assemble its products. However, it is not only a very complicated and perplexing task to select outstanding suppliers for decision-makers of strategic purchasing, but also it involves uncertainty and produces erroneous results while considering single criteria. For this reason, the decision-makers of strategic purchasing greatly require an efficient, valid and fair tool to assist them in selecting appropriate suppliers forthwith. This paper proposes a supplier selection model for any kind of company by using MATLAB fuzzy logic toolbox to help the purchasing department in selecting the most appropriate supplier. The main task in the proposed model involves determining the numerical score for different suppliers considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best supplier having highest score. Fuzzy control is used to determine the best supplier by calculating the score in selected evaluation criteria which are provided in numerical values for the convenience of calculation.

Ordering policy in a supply chain with adaptive neuro-fuzzy inference system demand forecasting

Determining ordering policy has incisive impacts on the success or letdown of an organization. This research has considered reliability while developing a method for finding ordering policy for multiple supply chain stages through optimal lot sizing. Setup cost, production cost, inspection cost, rejection cost, interest and depreciation cost, holding cost, etc. are considered for each supply chain stage whereas the demand inputs in the costs are taken from an adaptive neuro-fuzzy inference system generated forecasting method. Later, a genetic algorithm has been applied to find the optimum lot size at multiple levels of supply chain network to minimize total cost. Optimal lot size, reliability and total cost are determined and the costs are accumulated to determine total minimum supply chain cost. To validate the model, a comparison with the current situation clearly indicates the superiority of proposed model over the usual company approach to ordering policy.

A comparative analysis of power demand forecasting with artificial intelligence and traditional approach

Power demand forecasting is a significant factor in the planning and economic and secure operation of modern power system. This research work has compared different forecasting techniques and opted to find out better technique in context of power generation, which varies rapidly from time to time. The dataset has been generated from yearly demand of electricity of Bangladesh for last five years. Year, irrigation season, temperature and rainfall amount have been considered as input parameters where as single output is demand of load in adaptive neuro-fuzzy inference system ANFIS. Another artificial intelligence technique, artificial neural network ANN has been used to validate the output results. The best suited traditional technique for forecasting power generation is seasonal forecasting. Seasonal forecasting is also used to compare with ANFIS and ANN to find out better technique. The result of experiment indicates that ANFIS is superior method to tackle forecasting of power generation from different error measures.

Development of a CNC interpolation scheme for CNC controller based on Runge-Kutta method

The parametric interpolators of modern CNC machines use Taylors series approximation to generate successive parameter values for the calculation of x, y, z coordinates of tool positions. In order to achieve greater accuracy, higher order derivatives are required at every sampling period which complicates the calculation for contours represented by NURBS curve. In addition, this method calculates the chordal error in a given segment through estimation of the curvature neglecting a fraction of the error. In order to avoid calculating higher derivatives and make the calculations simpler, this paper proposes the classical fourth-order Runge-Kutta (RK) method for the determination of successive tool positions requiring the calculation of the first derivatives only. Furthermore, a method of estimating the chordal error on the average value of parameters at the end points of a given curve segment is proposed here that does not require the calculation of curvature at every segment. Finally, a variable feedrate interpolation scheme is designed combining the RK method of parameter calculation and the proposed method of chordal error calculation. Results show that reduced chordal error and feedrate fluctuations are achievable with the proposed interpolator compared to the conventional interpolator based on Taylors approximation with higher order terms.

Operational performance analysis of a public hospital laboratory

Efficient utilization of scarce resources is an issue for any healthcare system. In developing countries, proper tools, techniques, and resources must be widely used in healthcare operational planning. Considering the necessity of effective resource planning, this study focuses on the rural healthcare system of Bangladesh and concentrates on the sub-district government hospital laboratory. The authors’ determine possible ways to improve operations of laboratory facilities. To analyze existing system efficiency, sample laboratory data is fed into a simulation model. This paper identifies several possible ways for future expansion and suggests using simulation for better planning and analysis.