供应商参与新产品开发对企业自主创新能力的影响研究

本文以装备制造企业为实证研究对象,探讨了供应商参与新产品开发对企业自主创新能力的影响关系.同时,构建了包括供应商参与新产品开发、关系互动、知识创造、企业自主创新能力四个变量的概念模型,并提出相应的理论假设.通过结构方程模型的检验,表明供应商参与新产品开发对关系互动和知识创造均有显著的正向影响,进而通过两者对企业自主创新能力有显著正向影响.     

Assessing the lead market potential of countries for innovation projects

This paper presents an approach to assessing the potential of countries to lead the global adoption of an innovation and to set a global product or process standard. It can be observed that the specific design of an innovation diffuses worldwide after it has been adopted in a single country. We suggest that there are nation-specific characteristics that increase the likelihood that a locally preferred innovation design will become successful in other countries, too. Once users in a market that has lead market characteristics have adopted a specific innovation design, the possibility increases that users in other countries subsequently adopt it as well. We present a lead market concept for the development of global innovation designs. By focusing on the design of the innovation that responds to the preferences within the lead market, a company can leverage the success experienced in the lead market for a global market launch. In order to follow a lead market strategy of new product development, it is necessary to assess the lead market potential of countries before an innovation is developed and tested in the market. We use an indicator-based methodology that approximates the lead market attributes of countries. The assessment methodology is applied to two innovation projects at the truck division of DaimlerChrysler.     

基于前景理论的新产品开发方案选择方法

为了解决新产品开发中考虑竞争产品方案评价信息的方案选择问题,提出了一种基于前景理论的决策分析方法。在该方法中,依据新产品开发方案与竞争产品方案相比的绩效确定前景参考点,并给出了前景价值函数和权重函数;通过计算和比较新产品开发方案的前景值和竞争产品方案的前景值,得到新产品开发方案的优选结果;最后给出一个算例,说明该方法的可行性和有效性。     

基于CVaR约束的多产品订货风险决策模型

过去随机环境下多产品订货往往以期望值作为唯一决策准则,没有将风险控制纳入决策范畴,与实际决策过程不相符合.为给具有不同风险偏好的决策者提供合适的决策分析工具,文章在分析投资组合和产品组合存在某种相似性的基础上,借鉴金融工程领域广泛应用的条件风险值方法,建立多产品最优订货决策模型,并对模型进行了检验,发现它完全符合决策者的直觉要求.而且,由于所建的模型最终可以表示为一个线性规划问题,因此即使是大规模的产品组合问题也可以借助工具软件求解.     

Effective strategies in emerging industries

This paper presents the methodology and the results of an extensive field study achieved in 1982–1983, focusing on industrial firms' strategies in two emerging industries. Fiftynine firms participated in this work. Four generic strategies consistent with survival were identified, one of them being the most effective for building market share. Complementary studies are needed to test the external validity of these results.     

Textual data transformations using natural language processing for risk assessment

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause–effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.     

A Risk Analysis Model in Concurrent Engineering Product Development

Concurrent engineering has been widely accepted as a viable strategy for companies to reduce time to market and achieve overall cost savings. This article analyzes various risks and challenges in product development under the concurrent engineering environment. A three‐dimensional early warning approach for product development risk management is proposed by integrating graphical evaluation and review technique (GERT) and failure modes and effects analysis (FMEA). Simulation models are created to solve our proposed concurrent engineering product development risk management model. Solutions lead to identification of key risk controlling points. This article demonstrates the value of our approach to risk analysis as a means to monitor various risks typical in the manufacturing sector. This article has three main contributions. First, we establish a conceptual framework to classify various risks in concurrent engineering (CE) product development (PD). Second, we propose use of existing quantitative approaches for PD risk analysis purposes: GERT, FMEA, and product database management (PDM). Based on quantitative tools, we create our approach for risk management of CE PD and discuss solutions of the models. Third, we demonstrate the value of applying our approach using data from a typical Chinese motor company.     

Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies

Abstract

In this contribution, we aim to prioritise the indicators to enhance the organisational supply chain’s (SC) effectiveness from an industrial perspective. It will help industries to develop strategies for managing the SC effectively and ensuring improvement in performance continuously. To achieve this, this work proposes to use a two-phase research methodology. First, 36 SC performance improvement indicators are recognised from a literature survey and from field and industrial expert’s inputs. Secondly, a structural model is proposed using the fuzzy analytical hierarchy process (AHP) to prioritise the indicators strategically to improve the SC performance. The fuzzy AHP method helps determine the priority of concerns of the identified indicators under fuzzy surroundings. Inputs in this research are taken from four ancillary Indian plastic manufacturing firms. Research findings indicate that collaborations and information exchange dimension indicators obtained the highest priority in improving SC performance. The model proposed is considered very useful for the SC managers/practitioners/decision-makers to understand better and distinguish the essential SC performance improvement indicators and to take systematic decisions specifically to improve the performance of business in a SC context. Sensitivity analysis was conducted to examine the priority ranking of the indicators.     

考虑多种诱导信息下公众满意度的舆情危机响应决策方法

针对当前研究较少考虑到多种诱导信息情境下舆情危机响应决策问题的现状,首先在介绍研究理论和方法的基础上,对该决策问题进行了描述;然后,通过分析诱导信息的属性结构及其量化方式,运用前景理论从属性层面,提出了舆情危机特定情景和响应方案下公众对多种诱导信息发展态势满意度的计算方法;进一步地,考虑到方案对危机情景演变的影响,运用累积前景理论从公众满意度干预视角,提出了方案筛选的风险决策方法。最后,以一类重大传染病疫情场景中舆情危机响应决策问题为例,介绍该方法的潜在应用和说明其有效性。     

Interpretive structural modeling-based framework for mass customisation enablers: an Indian footwear case

Abstract

In the epoch of open economy and with the emergence of availability of individualised products over the Internet, Indian manufacturing industries are facing an enormous pressure to become more flexible and responsive, to accomplish customer’s varied and increasing demands. Mass customisation (MC) is about developing a customised product on demand for a particular customer after reception of a real order and producing it with the similar operational efficiency as one would anticipate from a mass-produced product. MC takes into account the merits of both the earlier systems of production, i.e. mass production and craft production. The craft production satisfies the personalised demands of customers and the mass production produces a limited variety of products at lower cost. Industries in developing countries such as India confront pressure from several perspectives to adopt MC. This study has been presented in the context of Indian manufacturing industries, and particularly for footwear industries to examine the enablers of MC. Achieving MC, however, require certain enabling technologies and processes in place. Several such enablers have been identified from the research literature. The objective of this study it to key out significantly important enablers for MC using interpretive structural modelling (ISM), and develop a hierarchy of these enablers for the Indian footwear units. ISM results show that modularity-based practices, digital manufacturing practices and supply chain integration are the most important MC enablers. Enhanced flexibility and responsiveness in the footwear production system can be achieved through modular and reconfigurable production system.     

Multiobjective Decision-Tree Analysis1

Single-objective-based decision-tree analysis has been extensively and successfully used in numerous decision-making problems since its formal introduction by Howard Raiffa more than two decades ago. This paper extends the traditional methodology to incorporate multiple noncommensurate objective functions and use of the conditional expected value of the risk of extreme and catastrophic events. The proposed methodology considers the cases where (a) a finite number of actions are available at each decision node and (b) discrete or continuous states of nature can be presented at each chance node. The proposed extension of decision-tree analysis is introduced through an example problem that leads the reader step-by-step into the methodological procedure. The example problem builds on flood warning systems. Two noncommensurate objectives—the loss of lives and the loss of property (including monetary costs of the flood warning system)–are incorporated into the decision tree. In addition, two risk measures—the common expected value and the conditional expected value of extreme and catastrophic events—are quantified and are also incorporated into the decision-making process. Theoretical difficulties associated with the stage-wise calculation of conditional expected values are identified and certain simplifying assumptions are made for computational tractibility. In particular, it is revealed that decisions concerning experimentation have a very interesting impact on the noninferior solution set of options—a phenomenon that has no equivalence in the single-objective case.     

An Exploration of New Product Development's Front‐end Knowledge Conceptualization Process in Discontinuous Innovations

Organizational knowledge creation, as reflected in new product/service development, is a vital process for firms to master. Critical to the development of new products/services is the management of the front‐end phase, which crucially determines the eventual fate of a new product/service idea. However, studies on how knowledge is created at the front‐end of the new product development process are rare. This empirical paper addresses this gap by focusing on knowledge conceptualization – where new knowledge from its generation is crystallized into a concrete and explicit form. Employing a qualitative methodology, the paper examines ten discontinuous projects and develops a front‐end knowledge conceptualization framework. The framework consists of the identified knowledge conceptualization stages and illuminates the pattern of overlaps, outcomes, contributors, knowledge, interactions and volatility across each stage. Our main contribution is a knowledge‐based appreciation of the dynamic and interactive nature of the new product/service development's front‐end phase in the context of discontinuous innovations, and thence the paper provides clear managerial learning points for the effective management of this phase.     

DEA-based production planning

Production in large organizations with a centralized decision-making environment like supermarket chains or factories with many workshops, usually involves the participation of more than one individual unit, each contributing a part of the total production. This study is motivated by a production-planning problem regularly faced by the central decision-making unit to arrange new input and output plans for all individual units in the next production season when demand changes can be forecasted. Two planning ideas have been proposed in this paper. One is optimizing the average or overall production performance of the entire organization, measured by the CCR efficiency of the average input and output levels of all units. The other is simultaneously maximizing total outputs produced and minimizing total inputs consumed by all units. According to these two ideas, we develop two DEA-based production planning approaches to find the most preferred production plans. All these individual units, considered as decision-making units (DMUs), are supposed to be able to modify their input usages and output productions. A simple numerical example and a real world data set are used to illustrate these approaches.     

Managing Technology Selection and Development Risk in Competitive Environments

Managing development decisions for new products based on dynamically evolving technologies is a complex task, especially in highly competitive industries. Product managers often have to choose between introducing an incrementally better, safe new product early and a superior, yet highly risky, product later. Recommendations for managing such performance vs. time‐to‐market trade‐offs often ignore competitive reactions to development decisions. In this paper, we study how a firm could incorporate the presence of a strategic competitor in making technology selection and investment decisions regarding new products. We consider a model in which an innovating firm and its rival can introduce a new product immediately or pursue a more advanced product for later launch. Further, the firm can reduce the uncertainty surrounding product development by dedicating more resources; the effectiveness of this investment depends on the firm's innovative capacity. Our model generates two sets of insights. First, in highly competitive industries, firms can adopt different technologies and effectively use introduction timing to mitigate the effects of price competition. More importantly, the firm could strategically invest in the advanced product to influence its rival's technology choice. We characterize equilibrium development and investment decisions of the firms, and derive innovative capacity hurdles that govern a firm's choice between the risky and safe alternatives. The effects of development flexibility—where firms might have the option to revert to the safe product if the advanced product fails—are also considered.     

Industry Clockspeed and the Pace of New Product Development

Recent empirical literature describes an industry's clockspeed as a measure of the evolutionary life cycle, which captures the dynamic nature of the industry. Among other factors, the rate of new product development is found to be associated with an industry's clockspeed. Yet the notion of an industry clockspeed and the essential factors driving suitable decision making in this area have remained relatively unexplored. We develop a simple definition and a corresponding analytic model which explains the interdependent relationship between a firm's own new product development activities and an industry clockspeed. Results from the single firm model show the conditions under which particular firms have an incentive to accelerate their new product development activities. Moreover, we link the single firm's NPD clockspeed decisions to the industry level by creating appropriate metrics which characterize different types of industries. Examples from high‐tech industries such as the personal computer and aerospace industries are included to illustrate our findings. Our intention is not only to offer analytical insights into factors driving the clockspeed for these industries, but also to establish a fundamental structured decision making approach, thereby stimulating future research on this important topic.     

Stock Evaluation Using a Preference Disaggregation Methodology

This paper presents a real application of a multicriteria decision aid (MCDA) approach to portfolio selection based on preference disaggregation, using ordinal regression and linear programming (UTADIS method; UTilités Additives DIScriminantes). The additive utility functions that are derived through this approach have the extrapolation ability that any new alternative (share) can be easily evaluated and classified into one of several user-predefined groups. The procedure is illustrated with a case study of 98 stocks from the Athens stock exchange, using 15 criteria. The results are encouraging, indicating that the proposed methodology could be used as a tool for the analysis of the portfolio managers' preferences and choices. Furthermore, the comparison with multiple discriminant analysis (either using a stepwise procedure or not) illustrates the superiority of the proposed methodology over a well-known multivariate statistical technique that has been extensively used to study financial decision-making problems.     

Best-worst multi-criteria decision-making method

In this paper, a new method, called best-worst method (BWM) is proposed to solve multi-criteria decision-making (MCDM) problems. In an MCDM problem, a number of alternatives are evaluated with respect to a number of criteria in order to select the best alternative(s). According to BWM, the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria are identified first by the decision-maker. Pairwise comparisons are then conducted between each of these two criteria (best and worst) and the other criteria. A maximin problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives with respect to different criteria are obtained using the same process. The final scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on which the best alternative is selected. A consistency ratio is proposed for the BWM to check the reliability of the comparisons. To illustrate the proposed method and evaluate its performance, we used some numerical examples and a real-word decision-making problem (mobile phone selection). For the purpose of comparison, we chose AHP (analytic hierarchy process), which is also a pairwise comparison-based method. Statistical results show that BWM performs significantly better than AHP with respect to the consistency ratio, and the other evaluation criteria: minimum violation, total deviation, and conformity. The salient features of the proposed method, compared to the existing MCDM methods, are: (1) it requires less comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results.     

Modelling to Learn — A Case Study in International Minerals Investment

This paper presents the system dynamics modelling methodology as a means of capturing the nature of complex systems. A case study is used to expose the fine points of the methodology. The theoretical underpinning for this case study is based on a highly detailed simulation model designed to examine the factors impacting the flow of international minerals investment funds. Emphasis here is on the usefulness of the modelling methodology as an aid to decision-making, the reader is referred elsewhere for a discussion of model development.     

The implementation of digital technologies for operations management: a case study for manufacturing apps

Abstract

Manufacturing applications address business to business (B2B) with highly customised applications developed for specific requirements, offering highly specialised solution-oriented and service-based software components, systems, and digital tools that aim at a fast and accurate decision-making support system. The purpose of this paper is to describe the implementation of digital technologies for operations management using manufacturing or engineering apps (eApps), for product design and manufacturing processes. In particular, starting from the specific needs of two companies from mature European industries as automotive and food, this work depicts how this kind of solutions can support companies and improve their operations. In particular, related benefits and challenges faced for the full implementation of the developed tools are highlighted. Moreover a business model to exploit the manufacturing apps is also proposed. The business model proposed for the exploitation of the eApps supports the commercialisation of all the revenue streams offered by this rapidly growing sector taking into account the specific needs of the concerned stakeholders through a diversified value proposition.