Quality issues impacting production planning

Among the various problems affecting production processes, the unpredictability of quality factors is one of the main issues which concern manufacturing enterprises. In make-to-order or in perishable good production systems, the gap between expected and real output quality increases product cost mainly in two different ways: through the costs of extra production or reworks due to the presence of non-compliant items and through the costs originating from inefficient planning and the need of unscheduled machine changeovers. While the first are relatively easy to compute, even ex-ante, the latter are much more difficult to estimate because they depend on several planning variables such as lot size, sequencing, deliveries due dates, etc. This paper specifically addresses this problem in a make-to-order multi-product customized production system; here, the enterprise diversifies each production lot due to the fact that each order is based on the customer specific requirements and it is unique (in example, packaging or textiles and apparel industry). In these contexts, using a rule-of-thumb in overestimating the input size may cause high costs because all the excess production will generate little or no revenues on top of contributing to increasing wastes in general. On the other hand, the underestimation of the lots size is associated to the eventual need of launching a new, typically very small production order, thus a single product will bear twice the changeover costs. With little markups, it may happen that these extra costs can reduce profit to zero. Aim of this paper is to provide a critical analysis of the literature state-of-art while introducing some elements that can help the definition of lot-sizing policies considering poor quality costs

Quality issues impacting production planning

Among the various problems affecting production processes, the unpredictability of quality factors is one of the main issues which concern manufacturing enterprises. In make-to-order or in perishable good production systems, the gap between expected and real output quality increases product cost mainly in two different ways: through the costs of extra production or reworks due to the presence of non-compliant items and through the costs originating from inefficient planning and the need of unscheduled machine changeovers. While the first are relatively easy to compute, even ex-ante, the latter are much more difficult to estimate because they depend on several planning variables such as lot size, sequencing, deliveries due dates, etc. This paper specifically addresses this problem in a make-to-order multi-product customized production system; here, the enterprise diversifies each production lot due to the fact that each order is based on the customer specific requirements and it is unique (in example, packaging or textiles and apparel industry). In these contexts, using a rule-of-thumb in overestimating the input size may cause high costs because all the excess production will generate little or no revenues on top of contributing to increasing wastes in general. On the other hand, the underestimation of the lots size is associated to the eventual need of launching a new, typically very small production order, thus a single product will bear twice the changeover costs. With little markups, it may happen that these extra costs can reduce profit to zero. Aim of this paper is to provide a critical analysis of the literature state-of-art while introducing some elements that can help the definition of lot-sizing policies considering poor quality costs

How Yield Process Misspecification Affects the Solution of Disassemble-to-order Problems

Random yields from production are often present in manufacturing systems and there are several ways that this can be modeled. In disassembly planning, the yield uncertainty in harvesting parts from cores can be modeled as either stochastically proportional or binomial, two of these alternatives. A statistical analysis of data from engine remanufacturing of a major car producer fails to provide conclusive evidence on which kind of yield randomness might prevail. In order to gain insight into the importance of this yield assumption, the impact of possible yield misspecification on the solution of the disassemble-to-order problem is investigated. Our results show that the penalty for misspecifying the yield method can be substantial, and provide insight on when the penalty would likely be problematic. The results also indicate that in the absence of conclusive information on which alternative should be chosen, presuming binomial yields generally leads to lower cost penalties and therefore preferable results

DYNAMIC LOT-SIZING PROBLEMS: A Review on Model and Efficient Algorithm

Due to their importance in industry, dynamic demand lot-sizing problems are frequently studied.This study consider dynamic lot-sizing problems with recent advances in problem and modelformulation, and algorithms that enable large-scale problems to be effectively solved.Comprehensive review is given on model formulation of dynamic lot-sizing problems, especiallyon capacitated lot-sizing (CLS) problem and the coordinated lot-sizing problem. Bothapproaches have their intercorrelated, where CLS can be employed for single or multilevel/stage, item, and some restrictions. When a need for joint setup replenishment exists, thenthe coordinated lot-sizing is the choice. Furthermore, both algorithmics and heuristics solutionin the research of dynamic lot sizing are considered, followed by an illustration to provide anefficient algorithm

Concurrent design for optimal quality and cycle time

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.Includes bibliographical references (p. 113-116).Product and manufacturing system design are the core issues in product development and dominate the profitability of a company. In order to assess and optimize the product and manufacturing system design, an objective evaluation framework is needed. Despite the many existing tools for product and manufacturing system design, there is a missing link between the product design and the production performances under system variability. The goal of the thesis is to explore and understand the interactions among part design and tolerancing, processes and system variability, and system control decision, then provide an integrated model to assess the total cost in a system. This model will be used to aid part design, tolerancing, batching, as well as strategy analysis in process improvement. A two-stage modeling approach is used to tackle the problem: quality prediction and production prediction. The quality prediction model projects the process variations into the output quality variations at each manufacturing stage, then predict the yield rate from the stochastic behavior of the variations and the tolerance. The production prediction model projects the demand rate and variability, processing times and variability, yield rates and batch-sizes into the manufacturing cycle time and inventories. After the performances are predicted through the previous two models, concurrent optimization of part design, tolerance, and batch-sizes are achieved by varying them to find the minimum cost. A case study at Boeing Tube shop is used to illustrate this approach. The result shows that the costless decisions in part design, tolerancing, and batch- sizes can significantly improve the system performance. In addition, conducting them separately or without using the system performance as the evaluation criteria may only lead to the local optima.by Yu-Feng Wei.Ph.D

The value of flexibility

"Revised: March 1986."--3rd prelim. page.Includes bibliographical references.Research support from the School of Management, Boston University.by Nalin Kulatilaka

Anticipatory Batch Insertion To Mitigate Perceived Processing Risk

The literature reviewed on lot-sizing models with random yields is limited to certain random occurrences such as day to day administrative errors, minor machine repairs and random supply due to faulty delivery of parts. In reality however, the manufacturing industry faces other risks that are non random in nature. One example would be yield discrepancies caused by non random triggers such as a change in the production process, product or material. Yield uncertainties of these types are temporary in nature and usually pertain until the system stabilizes. One way of reducing the implications of such events is to have additional batches processed earlier in the production that can absorb the risk associated with the event. In this thesis, this particular approach is referred to as the anticipatory batch insertion to mitigate perceived risk. This thesis presents an exploratory study to analyze the performance of batch insertion under various scenarios. The scenarios are determined by sensitivity of products, schedule characteristics and magnitude of risks associated with causal triggers such as a process change. The results indicate that the highest return from batch insertion can be expected when there are slightly loose production schedules, high volumes of sensitive products are produced, there are high costs associated with the risks, and the risks can be predicted with some degree of certainty

The value of flexibility

This paper develops a framework to evaluate the economic value derived from a firm's ability to switch between different modes of production in the face of uncertain prices. The model, cast as a set of simulataneous stochastic dynamic programs, is solved for the ex-ante value of flexibility, the optimal technology choice, and critical prices at which switching is optimal. This general model of flexibility is used to synthesize several recent studies of real options encountered in capital budgeting. For example, the model yields as special cases (a) the value of waiting to invest, (b) the option to abandon, (c) the value of having an option to shut down, (d) the replacement timing and technology choice, and (e) the "time to build" option for irreversible projects that require sequential outlays. We use an illustrative example with two modes to show that the value of flexibility is monotonically increasing with price variability and switching frequency. The value of flexibility can contribute about a 15 percent improvement over the better fixed technology. Early in the life of the project it is optimal to switch modes when the difference between values under one mode (for the current period and optimal switching thereafter) and the other mode exceeds the switching cost. Towards the end of the economic life, the above difference must be significantly larger for swithcing to occur.School of Management, Boston Universit

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform