Hector Guerrero

Demand management strategies for assemble-to-order production environments

This research focuses on the relatively new topic of demand management (DM) in an assemble-to-order production environment. Its objective is to present a framework for analysing and understanding the complex issues of DM. Special attention is given to situations where production is sustained at a relatively constant rate and demand management is needed to consume production capacity in an efficient manner. The research presents: (1) an examination of the assemble-to-order environment and several naive but logical strategies for demand management; (2) definitions of order types experienced by demand management systems; (3) an explanation of a simulation model and factors; and (4) an assessment of the simulated performance of the strategies.

An Alternative FMEA Method for Simple and Accurate Ranking of Failure Modes

Failure modes and effects analysis (FMEA) is a methodology for prioritizing actions to mitigate the effects of failures in products and processes. Although originally used by product designers, FMEA is currently more widely used in industry in Six Sigma quality improvement efforts. Two prominent criticisms of the traditional application of FMEA are that the risk priority number (RPN) used to rank failure modes is an invalid measure according to measurement theory, and that the RPN does not weight the three decision criteria used in FMEA. Various methods have been proposed to mitigate these concerns, including many using fuzzy logic. We develop a new ranking method in this article using a data-elicitation technique. Furthermore, we develop an efficient means of eliciting data to reduce the effort associated with the new method. Subsequently, we conduct an experimental study to evaluate that proposed method against the traditional method using RPN and against an approach using fuzzy logic.

A general search algorithm for cell formation in group technology

Abstract Group Technology (GT) is a manufacturing approach, which organizes and uses the information about an items similarity (parts and/or machines) to enhance efficiency and effectiveness of batch manufacturing systems. The application of group technology to manufacturing requires the identification of part families and formation of associated machine-cells. One approach is the Similarity Coefficient Method (SCM), an effective clustering technique for forming machine cells. SCM involves a hierarchical machine grouping process in accordance with computed ‘similarity coefficients’. While SCM is capable of incorporating manufacturing data into the machine-part grouping process, it is very sensitive to the data to be clustered (Chan and Milner 1982). It has been argued that for SCM to be meaningful, all machines must process approximately the same numbers of parts (Chan and Milner 1982).We present a new approach, based on artificial intelligence principles, to overcome some of these problems by incorporat...

Analysis of Quantitative Data

In this chapter we continue our study of data analysis, particularly the analysis of quantitative data. In Chap. 2 we explored types and uses of data, and we performed data analysis on quantitative data with graphical techniques. This chapter will delve more deeply into the topic of quantitative data analysis, providing us with a strong foundation and a preliminary understanding of the results of a data collection effort. Some statistical tools will be introduced, but more powerful tools will follow in later chapters.

A Framework for RFID Deployment in Supply Chains

The proposed framework shows how the granularity of tagging and deployment scope affect RFID implementation, helping managers identify RFID benefits and barriers and align deployment with their companys business strategy and return-on-investment goals.

A rule-based robot scheduling system for flexible manufacturing cells

In this research we examine a class of flexible manufacturing cells (FMC) containing a robot. The role of the robot is to load parts onto machines, to unload parts from machines, and to transport parts between machines. Since the productivity of an FMC is directly proportional to the level of productive work performed by the robot, the manner in which robots move between machines affects productivity. The problem of finding efficient robot schedules/tours is therefore one of substantial economic significance in the operation of a FMC. Unfortunately, in many practical situations it is difficult to develop efficient robot schedules given the dynamic environments in which they exist. We devise a rule-based system to assist the cell supervisor in making good decisions by dynamically coordinating the available information during the production process. The rule-based system combines an algorithmic procedure to deal with a well-structured environment and a flexible heuristic approach employed to deal with less well-structured environments. Both the algorithmic and heuristic procedures are applied separately, then together, to control the robots movement in a simulation experiment. We show that there is a predictable tradeoff between the quality of the resulting schedule and the information contents of heuristic used.

Analyzing an inventory system with multiple reorder points and periodic replenishment

Abstract This paper investigates multiple reorder point, periodic replenishment systems similar to those utilized on board some U.S. naval vessels for Selected Item Management (SIM). This inventory system is of interest since it involves both regular and priority replenishment, three reorder points, and cyclical restocking of regular orders. A simulation model is developed to study the impact of demand distribution, cycle time, priority order leadtime, and the quantity of units ordered on the performance of the system. It is found that the frequency of service and days without shortages performance measures can be maximized by setting the reorder points at equal intervals between the zero level and the high level. Also, a power approximation model is presented that estimates the high level that will lead to a desired percent days without shortage percentage.

Presentation of Quantitative Data

We often think of data as being strictly numerical values, and in business, those values are often stated in terms of dollars. Although data in the form of dollars are ubiquitous, it is quite easy to imagine other numerical units: percentages, counts in categories, units of sales, etc. This chapter, and Chap. 3, discusses how we can best use Excel’s graphics capabilities to effectively present quantitative data (ratio and interval), whether it is in dollars or some other quantitative measure, to inform and influence an audience. In Chaps. 4 and 5 we will acknowledge that not all data are numerical by focusing on qualitative (categorical/nominal or ordinal) data. The process of data gathering often produces a combination of data types, and throughout our discussions it will be impossible to ignore this fact: quantitative and qualitative data often occur together.

Analysis of Qualitative Data

There are many business questions that require the collection and analysis of qualitative data. For example, how does a visitor’s opinion of a commercial website relate to their purchases at the website? Does a positive opinion of the website, relative to a bad or mediocre opinion, lead to higher sales? This type of information is often gathered in the form of an opinion and measured as a categorical response. Also accompanying these opinions are some quantitative characteristics of the respondent; for example, their age or income. Thus, a data collection effort will include various forms of qualitative and quantitative data elements. Should we be concerned with the type of data we collect? In the prior chapters we have answered this question with a resounding yes. It is the type of the data—categorical, interval, ratio, etc.—that dictates the form of analysis that we can perform.

Modeling and Simulation: Part 1

The previous six chapters have provided us with a general idea of what a model is and how it can be used. Yet, if we are to develop good model building and analysis skills, we still need a more formal description, one that permits us a precise method of discussing models. Particularly, we need to understand a model’s structure, its capabilities, and its underlying assumptions. So let us carefully re-consider the question—what is a model? This might appear to be a simple question, but as is often the case, simple questions can often lead to complex answers. Additionally, we need to walk a fine line between an answer that is simple, and one that does not over-simplify our understanding. Albert Einstein was known to say—“Things should be made as simple as possible, but not any simpler.” We will heed his advice.