Prasun Das

Six Sigma project selection methodology

Purpose – The selection of right projects in a Six Sigma program is a major concern for early success and long‐term acceptance within any organization. One of the ever‐increasing challenges is to define and select right measure for improvement and appropriate problem definition. Many projects encounter the problem of no linkage with business objectives or customer needs, too large or high‐level project scope along with unclear problem and goal statement. Improperly, chosen metrics lead to sub‐optimal behavior and can lead people away from the organizations goal instead of joining them. This paper aims to propose a project selection methodology for different situations.Design/methodology/approach – This research develops a model for project identification; ensuring well‐defined projects are selected having large impact on customer satisfaction or bottom line. The model is described for the situations: availability of performance data, balanced business score card implemented and no data is available.Findi...

Prediction of retail sales of footwear using feedforward and recurrent neural networks

Fluctuation of sales over time is one of the major problems faced by most of the industries. To alleviate this problem management tries to base their plans on forecast of sales pattern, which are mostly adhoc and rarely provides solid foundation for the plans. This study makes an attempt to solve this problem by taking a neural network approach, at the process of sales of footwear, and arriving at an optimum neural network model. The algorithms used for developing such model through neural network are both feedforward and recurrent Elman network. The data used in this work are the weekly sales of footwear and the information about the seasonality of sales process. While solving the problem, the focus is on forecasting of weekly retail sales as per the requirement of management. This work would reduce the uncertainty existing in the short-term/middle term planning of sales and distribution logistics of footwear over different time horizons across the entire supply chain of footwear business.

An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.

A Comparative Study for Modeling of Hot-Rolled Steel Plate Classification Using a Statistical Approach and Neural-Net Systems

Classification of flat steel products from the point of view of reaching the target property is a common practice in industries. In most classification problems, standard statistical methods generally place constraints such as continuous, differentiable, otherwise well behaved, etc. However, Artificial Neural Networks (ANN) has an ability to learn and generalize any complex system without making any model assumptions. This work emphasizes on making performance evaluation of usual statistical techniques such as general clustering like K-means, partition around medoid (PAM), classification and regression tree (CART), linear discriminant analysis (LDA) vis-a-vis usage of multilayer perceptron (MLP) learning algorithm, radial basis function (RBF) family of methods and Kohonen networks. To recommend the utility of modeling, some real-life industrial databases are used. It can be observed from the results that learning of networks through back-propagation yielded minimum misclassification of two groups of heats including minimization of train-test error. The statistical techniques such as LDA and CART provide the same results of misclassification along with the results obtained from perceptron learning, RBF network algorithm and Kohonen learning with learning-vector quantization (LVQ) algorithm.

An application of six sigma methodology to reduce lot-to-lot shade variation of linen fabrics

There was a problem of shade variation of dyed fabrics in a reputed textile company leading to the increase in process cycle time due to the extra amount of color addition or stripping. The extent of reprocessing fabrics was estimated to be around 75%. The objective of this work was to reduce the shade matching time in the fabric dyeing process by optimizing the effect of the controllable parameters. The problem was tackled using the DMAIC cycle of disciplined Six Sigma methodology. Initially, the process baseline sigma level was found as 0.81 and a target sigma level was set at 1.76. The outcome of the analyses observed at every phase was explained based on textile technology. Actions taken on the critical activities led to the reduction in average excess time as 0.0125 h/m. The yield of the overall process has improved to 82% with an improved sigma level of 2.34. The estimated annual saving is to the tune of Rupees eighteen lakhs (over

Six Sigma principles in marketing: an application

40,000). The proposed control schemes from this study are already in place.

A meta-heuristic approach to car allocation problem to reduce transportation cost over a fixed number of routes

The marketing process in any business plays a major role in attaining growth for a company. Adopting Six Sigma principles to business marketing can strengthen factual decision-making activities leading to customer satisfaction. This paper addresses the important issue of ensuring continuous availability of fresh consumer products at retail levels through a warehouse-retail distribution network operating under the marketing discipline of an Indian company. Defects, according to Six Sigma parlance, are defined here based on the declared norms of the company. These defects are measured, Sigma rating is evaluated and process performance target for improvement is set. Working through the DMAIC cycle, substantial improvement after implementation of the identified action plans, has been observed in terms of higher Sigma level and the market share of the company. An extension of this work can be considered in improving several process measures of marketing and related processes with the aim of cost reduction, productivity improvement and better customer retention.

Measuring Six Sigma Project Effectiveness using Fuzzy Approach

This study addresses the problem of car allocation to different routes under certain restrictions with the objective of reducing the excess cost of transportation. A meta-heuristic approach based on Ant Colony Optimisation (ACO) algorithm is proposed and implemented to schedule the cars efficiently along the routes under the existing logistics. A mathematical model with two objectives is formulated for this purpose and solved in two phases. In the first phase, sequences of allocated cars are determined while in the second, car allocation scheme for each trip is determined using ACO algorithm. The simulation study is carried out from the empirical distributions of distance and time, followed by the sensitivity analysis on the basis of their stochastic behaviour. The cost benefit analysis shows a projected savings in terms of reduction of cost of travel, both with respect to distance and time, through the solutions obtained.

Exploring the non-linearity in empirical modelling of a steel system using statistical and neural network models

Six Sigma methodology for process improvement is being used by industries to improve customer satisfaction, business results or both. The success of Six Sigma implementation can be measured by evaluating the effectiveness of the completed projects. The other objective of project effectiveness measurement scheme is to keep the team focused and motivated. A good measurement system should be able to measure and compare projects of various types and need, including benefits from the projects. The project effectiveness measurement scheme should include success factors like project selection, involvement of management, results achieved, conduct of the project and monitoring and review of the project. A Six Sigma project effectiveness evaluation system is generally based on the perception of people that can result in unreliable measurement. To overcome this deficiency, we used a fuzzy approach based on linguistic variables and fuzzy numbers for measuring the project effectiveness in this study by using the perception of management. Two methods for measuring effectiveness of selected sample projects are suggested. The outcome from this research would be helpful for the practicing industries to use this methodology for an unbiased evaluation of completed projects. Copyright

In search of best alternatives: a TOPSIS driven MCDM procedure for neural network modeling

The relationship between the physical properties of metal is often very complex in nature with its chemistry and several other rolling parameters in operation. Non-linear regression models play a very important role in modelling the underlying mechanism, provided it is known. Artificial neural networks provide a wide class of general-purpose and flexible non-linear regression models. The most commonly used neural networks, called multi-layered perceptrons, can vary the complexity of the model from a simple parametric model to a highly flexible nonparametric model. In this particular work, an industry-based data set is used for learning and optimizing the neural network architecture using some well-known algorithms for prediction under neural-net systems. The outcome of the analysis is compared with the results achieved through empirical statistical modelling from its prediction error level and the knowledge of materials science.