Marco Barajas

The use of fuzzy logic in product family development: literature review and opportunities

Over the past few years, a number of key issues related to the product family design have been addressed, and a great deal of work has been done to improve it. Many different tools have been employed in this effort, such as mass customization, modularity, delayed differentiation, commonality, platforms, product families, and so on. The purpose of this paper is to analyze how fuzzy logic has been applied and how it can help to improve the entire process of product family development. Given its powerful capability to represent aspects that binary variables cannot, we show how fuzzy logic has been used to take advantage by considering the vague parameters related to the human character in different processes. Our aim is to contribute to the understanding and improvement of product family development process by identifying essential applications of fuzzy logic. An extended overview of the product family development process is provided, and also this work highlights the role of fuzzy logic in it. Fourteen fuzzy logic tools and thirteen topics into the product family development process are identified and summarized as a framework to analyze the role of fuzzy logic and at the same time to identify further application opportunities.

Improved fuzzy ranking procedure for decision making in product design

In this paper, we present a method for ranking any number of normal fuzzy numbers using trapezoidal fuzzy numbers as a general form, where rectangular and triangular fuzzy numbers are particular cases of such a form. This general form is supported by 29 cases, which is enough to consider all the possible situations between two normal fuzzy numbers, such as trapezoidal, triangular, or rectangular. The ranking procedure is performed using four ordering criteria into a pseudo-order preference model considering the type of the fuzzy preference relation. Two examples are given to illustrate and validate the applicability and practicality of this fuzzy ranking method. A comparison and an analysis of the proposed method is presented to demonstrate its usefulness and its contribution to the improvement of the decision making processes as a result of its management of vague or imprecise information, and whether or not that information should be allowed to be entered into such processes.

Market segmentation through data mining: A method to extract behaviors from a noisy data set

Abstract Strategic business planning requires forecasted information that contains a sufficient level of detail that reflects trends, seasonality, and changes while also minimizing the level of effort needed to develop and assess the forecasted information. The balance of information is most often achieved by grouping the customer population into segments; planning is then based on segments instead of individuals. Ideally, separating customers into segments uses descriptive variables to identify similar behavior expectations. In some domains, however, descriptive variables are not available or are not adequate for distinguishing differences and similarities between customers. The authors solved this problem by applying data mining methods to identify behavior patterns in historical noisy delivery data. The revealed behavior patterns and subsequent market segmentation are suitable for strategic decision-making. The proposed segmentation method demonstrates improved performance over traditional methods when tested on synthetic and real-world data sets.

Forecast of individual customer’s demand from a large and noisy dataset

Abstract Optimization of the supply chain relates on data that describes actual or future situation. Besides in many situations available data may not correspond directly to what is expected for the different models because of too large quantity and imprecision of the data that may lead to suboptimal or even bad decisions. Actual problem considers the availability of a large and noisy dataset concerning historical information about each customer that will be used to make improved prediction models, that may fit models to optimize the supply chain. When dealing with large datasets, market segmentation is frequently employed in business forecasting; many customers are grouped based on some measure of similarity. Segment-level forecasting is then employed to represent the population within each segment. Challenges with successfully applying market segmentation include how to create segments when descriptive customer information is lacking and how to apply the segment-level demand forecasts to individual customers. This research proposes a method to create customer segments based on noisy historical transaction data, create segment-level forecasts, and then apply the forecasts to individual customers. The proposed method utilizes existing data mining and forecasting tools, but applies them in a unique combination that results in a higher level of customer-level forecast accuracy than other traditional methods. The proposed forecasting method has significant management applications in any domain where forecasts are needed for a large population of customers and the only available data is delivery data.