Why do quality and reliability feedback loops not always work in practice: a case study

The increasing competition in the market of consumer electronics forces industry to simultaneously improve the functionability, reliability and costs of their products. Due to the strong dynamics of this field an important measure in improving product quality is the feedback of information on actual field behaviour of a product which is extremely relevant for product development. This information, usually gathered by the service department, can help development to learn from the failures of earlier products and to improve the next product generations. This feedback process, however, does not always bring the expected benefits. This paper presents a model, based on an actual case study, of the structure of the feedback process in terms of information flows. The information flow model describes the chain or network of activities in the feedback process as a function of (different) drivers such as cost, time or quality. Using this information flow model it is possible to explain how quality and reliability related information propagates through a business process and where and why communication problems lead to disruptions in the feedback of this information. With this model, it is easier to understand the function of different parties in the service or after sales process, so that management of the information in this process becomes more transparent and can be improved.     

The use of reliability‐oriented field feedback information for product design improvement: a case study

In the innovative industry, four major trends are found to influence product quality and reliability: the increase in product complexity, the strong pressure on time to market, the increasing global economy, and the decreasing tolerance for quality problems. Thus, it becomes more difficult to anticipate all potential failures during the product development process. In this context, an efficient field feedback process should be in place to react to the unanticipated deviations in product performance. Based on a case study made in an innovative company, this paper shows that the problem is not so much in the information collection as in the inherent quality of the information and in the manner the information is processed. Therefore, a new method, presented in this paper, was developed to classify and prioritize field data and to upgrade it into information that can be used for design improvement according to the dominant classes of failures using the four‐phase roller coaster model. Although this newly generated information is richer than raw field data it is not yet detailed enough to allow direct design optimization. Therefore, a second upgrading stage, based on design of experiments, was developed. It uses a method that combines physics‐of‐failure (bottom‐up) and field information (top‐down). As traditional DoE mainly deals with largely time‐independent quality data obtained during the manufacturing process the approach had to be modified to deal with time‐dependent reliability data. Case study results show that it is a promising approach for characterizing and resolving failure mechanisms also in innovative companies. Copyright © 2008 John Wiley & Sons, Ltd.     

Field Reliability Prediction in Consumer Electronics Using Warranty Data

In innovative fast product development processes, such as consumer electronics, it is necessary to check as quickly as possible, using field data, whether the product reliability is at the right level. In consumer electronics, some major companies use the Warranty Call Rate (WCR) for this purpose. This paper discusses extensively the theoretical and practical drawbacks of the WCR. Subsequently, it is demonstrated, using a Weibull failure distribution, that only a few months after product launch, say three months, the warranty data offer the opportunity to estimate the parameters of the failure distribution. Of course, this requires that the warranty data are available in the quality department. Unfortunately, for some companies the field feedback information process from the repair centres to the quality department causes a delay of several months. These companies have to speed up their field feedback information process before they can fully take advantage of the proposed estimation procedure. Copyright © 2006 John Wiley & Sons, Ltd.     

Integrating carbon market uncertainties into a sustainable manufacturing investment decision: a Bayesian NPV approach

Net present value (NPV) is a widely used technique in capital budgeting. In this paper, we develop a Bayesian NPV framework using a Gibbs sampler. This approach allows decision-makers to integrate their knowledge, past experience, and uncertain and volatile cash flows from carbon emissions credits into decisions dealing with energy efficient, sustainable manufacturing equipment. The results indicate NPV is highly dependent on the nature of volatility and uncertainty of the cash flows. Without inclusion of this information through the Bayesian framework results, NPV becomes overstated, and thus it may provide biased guidance for the investment. The results developed in this paper further show that the frequency of very high and low cash flows and to a lesser degree their variability adversely impacts NPV. The results may also explain reasons for the economic phenomenon known as the energy efficiency gap.     

Improving the Quality of Information Flows in the Backend of a Product Development Process: a Case Study

Considerable research has gone into designing effective product development processes. This, coupled with the increasing need for products that are able to deliver reliable, complex functionality with a high degree of innovation, presents a major challenge to modern day industries in the business of developing products. In order to incorporate relevant field experience in the design and manufacturing of new products, increasingly detailed information needs to be retrieved from the market in a very short amount of time. In one particular consumer electronics industry, business process models describing the information flow in the backend of the product development process indicated massive data loss and also serious data quality degradation. This paper attempts to show how such losses can be mitigated and also proposes a business model that can adequately capture information of a higher quality and in a more structured manner. The end result will be a product development process that provides better feedback on current product performance and is more responsive to future market needs. Copyright © 2005 John Wiley & Sons, Ltd.     

Cyclic optimisation for localisation in freeform surface inspection

Over the last decade, several papers have been published on the contribution of the Theory of Constraints (TOC) and Throughput Accounting to product mix decisions. In these papers, the quality of the TOC-based approach to generate good or even optimal solutions is assessed with very different results, particularly when compared with other product mix decision tools. It is the objective of this article to expose the often, not explicitly revealed, premises that are mandatory for generating optimal product mix decisions using a TOC-based approach. By analysing several examples, which are modifications of one and the same basic example, premises on the solution space and the objective function are examined and conclusions made, which are shown in a checklist at the end of the paper. If more than one binding multi-product constraint occurs, optimal product mix decisions can only be derived in special cases. Also, the assumption of integer solutions may lead to non-optimal TOC-based solutions. Furthermore, a correct cost allocation is mandatory, even if sometimes not easily conductible. In particular, taking into account only (all) material costs may lead to wrong decisions. Finally, it is shown that discontinuities in the objective functions deriving, for example, from price discounts, may affect the product mix decision and require special modifications of the TOC-based approach.     

Sensitivity analysis for decision-making using the MORE method—A Pareto approach

Integrated Assessment Modelling (IAM) incorporates knowledge from different disciplines to provide an overarching assessment of the impact of different management decisions. The complex nature of these models, which often include non-linearities and feedback loops, requires special attention for sensitivity analysis. This is especially true when the models are used to form the basis of management decisions, where it is important to assess how sensitive the decisions being made are to changes in model parameters. This research proposes an extension to the Management Option Rank Equivalence (MORE) method of sensitivity analysis; a new method of sensitivity analysis developed specifically for use in IAM and decision-making. The extension proposes using a multi-objective Pareto optimal search to locate minimum combined parameter changes that result in a change in the preferred management option. It is demonstrated through a case study of the Namoi River, where results show that the extension to MORE is able to provide sensitivity information for individual parameters that takes into account simultaneous variations in all parameters. Furthermore, the increased sensitivities to individual parameters that are discovered when joint parameter variation is taken into account shows the importance of ensuring that any sensitivity analysis accounts for these changes.     

Combining LCT tools for the optimization of an industrial process: material and energy flow analysis and best available techniques

Life cycle thinking (LCT) is one of the philosophies that has recently appeared in the context of the sustainable development. Some of the already existing tools and methods, as well as some of the recently emerged ones, which seek to understand, interpret and design the life of a product, can be included into the scope of the LCT philosophy. That is the case of the material and energy flow analysis (MEFA), a tool derived from the industrial metabolism definition.This paper proposes a methodology combining MEFA with another technique derived from sustainable development which also fits the LCT philosophy, the BAT (best available techniques) analysis. This methodology, applied to an industrial process, seeks to identify the so-called improvable flows by MEFA, so that the appropriate candidate BAT can be selected by BAT analysis. Material and energy inputs, outputs and internal flows are quantified, and sustainable solutions are provided on the basis of industrial metabolism.The methodology has been applied to an exemplary roof tile manufacture plant for validation. 14 Improvable flows have been identified and 7 candidate BAT have been proposed aiming to reduce these flows.The proposed methodology provides a way to detect improvable material or energy flows in a process and selects the most sustainable options to enhance them. Solutions are proposed for the detected improvable flows, taking into account their effectiveness on improving such flows.     

基于SGS模型与AHP的侦测无人机造型设计研究

目的 为了满足无人机用户需求,完善侦测无人机造型设计方法,进一步拓展产品设计路径。方法 将管理学中的门径管理模型(SGS)融入产品系统开发设计流程;调研用户需求并结合层次分析法(AHP)构建判断矩阵,计算各个设计指标权重并排序,以此促进设计决策,完成侦测无人机造型设计方案;采用7点量表法进行初步方案设计评价,验证设计模型和过程的科学性。结论 以侦测无人机造型设计为例,通过构建SGS产品系统开发模型,运用AHP分析方法进行设计方案指标决策,为侦测无人机造型设计拓展设计路径,为相关产品的设计提供新思路。     

Conceptual robustness in simultaneous engineering: An extension of Taguchi's parameter design

Simultaneous engineering processes involve multifunctional teams; team members simultaneously make decisions about many parts of the product-production system and aspects of the product life cycle. This paper argues that such simultaneous distributed decisions should be based on communications about sets of possibilities rather than single solutions. By extending Taguchi's parameter design concepts, we develop a robust and distributed decision-making procedure based on such communications. The procedure shows how a member of a design team can make appropriate decisions based on incomplete information from the other members of the team. More specifically, it (1) treats variations among the designs considered by other members of the design team asconceptual noise; (2) shows how to incorporate such noises into decisions that are robust against these variations; (3) describes a method for using the same data to provide preference information back to the other team members; and (4) provides a procedure for determining whether to release theconceptually robust design or to wait for further decisions by others. The method is demonstrated by part of a distributed design process for a rotary CNC milling machine. While Taguchi's approach is used as a starting point because it is widely known, these results can be generalized to use other robust decision techniques.     

RFID增加回收率的再制造闭环供应链模型

在再制造闭环供应链的实践中,从市场上回收废旧品是一个关键环节,同时也是一个难点。而本文假设RFID可以提高废旧品的回收率,同时也要花费相应的成本。为对比分析RFID使用前后的效果,按照负责回收的供应链成员不同,建立了一体化和非一体化的再制造闭环供应链模型,并讨论了使用RFID额外增加回收率规模、RFID标签成本和RFID固定投入成本对供应链定价决策及各成员利润的影响。     

新产品开发前端信息处理分析

新产品开发可以分为信息加工与物料加工两个大阶段,前一个阶段具有不确定性与模糊性。通过对比分析发现两个阶段的加工过程具有一定的相似性,如果将信息加工对象比拟为“虚拟工件”,信息加工的人脑与电脑可视为物料加工的“虚拟设备”。这样,则可以利用后阶段成熟的排序策略进行虚拟工件在虚拟设备上计划安排及能力平衡,减少信息加工的不确定性与模糊性,在理论上统一了新产品开发的全过程。     

Dynamic disassembly planning for remanufacturing of multiple types of products

With the increased need for remanufacturing of end-of-life products, achieving economic efficiency in remanufacturing is urgently needed. The purpose of this study was to devise a cost-minimisation plan for disassembly and remanufacturing of end-of-life products returned by consumers. A returned end-of-life product is disassembled into remanufacturable parts, which are supposed to be used for new products after being remanufactured. Each end-of-life product is disassembled into parts at variable levels and through variable sequences as needed, taking into account not only disassembly but also manufacturing, remanufacturing, and holding inventory of remanufacturable parts. This study proposes a mixed integer linear programming (MILP) model for derivation of the optimal disassembly plan for each returned product, under deterministically known demand and return flows. For the purposes of an illustrative example, the proposed model was applied to the formulation of an optimal disassembly and remanufacturing plan of ‘fuser assembly’ of laser printers. The solution reveals that variable-level disassembly of products saves a significant remanufacturing cost compared with full disassembly.     

Operational planning models for distribution networks

The design and optimisation of a logistic network deals with a wide set of decisions, e.g. the determination of the best location and capacity of the different logistic facilities (production plants, distribution centres, transit points, wholesalers, etc.), the allocation of the product demand coming from customers in presence (or absence) of fractionable flows of material, the determination of the best transportation mode (truck, rail, etc.) as well as loading and routing of vehicles. These decisions involve multiple stages of a distribution network: customers-regional distribution centres (RDC), RDCs-central distribution centres (CDC) and CDCs-production plants and sources, in presence of multiple products and the variable time (i.e. time-dependent product demand and flows of material). This paper presents a top-down methodology that joins the strategic planning, the tactical planning and the operational planning of distribution networks with a special focus on the development of effective heuristic methods to face the vehicle routing problem. Original models and heuristic algorithms for the operational planning are illustrated. The impact of the strategic and tactical decisions on the performance of the operational planning is evaluated by the application of the proposed hierarchical approach to two realistic case studies. Obtained results are illustrated in a what-if experimental analysis conducted on multiple problem settings and realistic scenarios.     

Some factors to consider in developing a quality-related feedback system

This paper reports on work which has been carried out in an attempt to identify some of the key factors in the development of a quality-related feedback system associated specifically with major installations. The main reasons why the majority of manufacturers have not developed systems to obtain data from users is due to the difficulties encountered in obtaining meaningful information. The current main sources of feedback are from technical sales representatives, customer complaints and internal feedback obtained from monitoring recurrent orders for spares. The information being generated by users of computerized maintenance systems has, to date, been little used by manufacturers to initiate quality-improving changes. However, most manufacturers said they would use such data if it was offered. It is also pointed out that designers can play a key role in improving the quality of plant and equipment by requesting feedback information in those areas where there might be design uncertainty and by giving consideration to the possibility of condition monitoring when a product is being designed.     

LIFE CYCLE COST: A MODEL AND APPLICATIONS

This research is an effort in understanding and modeling benefits of the interaction between the design and engineering stage, and field service support. We first present a model which estimates discounted and undiscounted life cycle cost of a durable product We incorporate the production costs and demand effects, and take into account the interaction between design engineering parameters and field service support functions. The demand side explicitly models the nonlinearities that exist in cost structure experienced by users of durable products. Implications for choosing alternative design configurations and advantages of including field failure data in making decisions at the early stages of product development are discussed.     

A tool for meaning driven materials selection

There are several tools used in materials selection processes by designers. However, they are mostly engineering based tools, which are dominated by numerical (or technical) material data that is mostly of use in embodiment or detailed design phases of new product development. On the other hand, product designers consider certain aspects such as product personality, user-interaction, meanings, emotions in their material decisions. In this regard, existing tools and methods do not fully support designers in their materials selection processes. This paper describes the development of a new materials selection tool holding the idea of [meaning driven materials selection]. In addition, the paper consists of a study conducted to create data for a dummy application.     

Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field. Deep Extreme Learning Machine (DELM) framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments. The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions. It leads to the concept of vehicle controller making self-decisions. The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations. This overcomes inadequacy of the current in-vehicle route-finding technology and its support. All the relevant route-related information for the ride will be provided to the user based on its availability. Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained. During investigation, it has been observed that proposed framework has the highest accuracy rate with 70% of training (1435 samples) and 30% of validation (612 samples). Simulation results validate the intelligent prediction of the proposed method with 98.88%, 98.2% accuracy during training and validation respectively.     

Application of magnetic resonance imaging techniques to particulate systems

Magnetic resonance imaging (MRI) is a well-established technique in the medical field, typically for imaging liquid water in the human body, but it is increasingly being used in the field of engineering and materials science. A particular section of this is in the area of particulate systems and granular material flows. MRI is being used to provide a unique insight into particle distribution and motion with in situ measurements. In this paper we discuss how judicious choice and development of imaging technique applied to various different granular systems can provide us with valuable new data on the processes occurring in granular flows. Experimental results focus on rotating bed segregation, velocity imaging in vertical fluidized beds and phase-resolved velocity distributions within vertical vibro-fluidized beds. A discussion of the various imaging techniques used to acquire these data is also given.     

What does return on investment (ROI) mean to the pharmaceutical/biotechnology industry?

Abstract

Return on investment (ROI) calculations are common tools used by pharmaceutical and biotechnology companies in evaluating product returns, guiding decisions on further product development or terminating programs prior to company losses. ROI calculations compare the estimated profits from a product to the investments necessary to get the product developed, approved and marketed. Risk factors are incorporated into the ROI, which consider multiple factors including competitor products and market demand. ROI calculations enables a company to set and apply metrics for the evaluation of projects that can be applied consistently, across a portfolio, and goes beyond simple profitability assessments. When coupled with additional financial tools and strategic evaluations such as strengths, weaknesses, opportunities and threats (SWOT) analysis, ROI can provide invaluable and comparable insight into the value of potential projects and products. Here, we present a framework for executing ROI calculations to enable investment or termination decisions for research and development projects. This is augmented with insight on how to apply the framework to academic, government and NGO evaluations where ‘profits’ are not always measured in purely economic terms. Implementing ROI in the evaluation process facilitates go/no go decisions and the comparison of projects using a simple, consistent financial approach.