Overlapping and communication policies in product development

Shorter product life cycles in many industries impel firms to accelerate the product development process. Overlapping development stages, combined with frequent information exchange, is commonly regarded as a core technique for faster product development. However, overlapping and communication require additional resources and can be costly. We investigate the time-cost tradeoffs involved in concurrent product development to determine the optimal overlapping and communication strategies. The methodology was applied to a refrigerator development process in order to illustrate its utility.     

Managing the exchange of information in product development

In the present paper, we develop a dynamic programming (DP) model of the product development (PD) process. We conceptualize product development as a sequence of decisions: whether to incorporate a piece of information that just arrived (i.e. became available) or wait longer. We utilize this formulation to analyze different situations that depend on the type, and nature of information that is exchanged: stationary versus dynamic information. We derive optimal decision rules to determine whether (and when) to incorporate for each case. An analysis of the model results in several important findings. First, we must not necessarily incorporate all available information that is related to the design activity. Specifically, once the information collection exceeds certain value, the design team should stop collecting further information. Second, only when past design work accumulates to a certain threshold value should the team include the latest information and perform rework. Large uncertainty of the information and large sensitivity of the design activity makes the incorporation of new information less likely. Finally, managerial implications are discussed with several numerical examples.     

Optimal testing strategies in overlapped design process

Testing is an important activity in product development. Past studies, which are developed to determine the optimal scheduling of tests, often focused on single-stage testing of sequential design process. This paper presents an analytical model for the scheduling of tests in overlapped design process, where a downstream stage starts before the completion of upstream testing. We derive optimal stopping rules for upstream and downstream stages’ testing, together with the optimal time elapsed between beginning the upstream tests and beginning the downstream development. We find that the cost function is first convex then concave increasing with respect to upstream testing duration. A one-dimensional search algorithm is then proposed for finding the unique optimum that minimizes the overall cost. Moreover, the impact of different model parameters, such as the problem-solving capacity and opportunity cost, on the optimal solution is discussed. Finally, we compare the testing strategies in overlapped process with those in sequential process, and get some additional results. The methodology is illustrated with a case study at a handset design company.     

A representation model for the solving-time distribution of a set of design tasks in new product development (NPD)

Given the potential risks of new product development projects (NPD), the characteristics of the design tasks solving-time distributions are critical for their effective management. In OR we need to find what operational characteristics of design tasks may delay projects. Other researchers already identified the technological novelty, the magnitude of the design tasks, the interactions between design tasks in an NPD project, and the balancing between projects among the most important causes of the unpredictability of the design tasks lead times in NPD projects.     

An agent-based investigation into the new product development capability

The capability to bring products to market which comply with quality, cost and development time goals is vital to the survival of firms in a competitve environment. New product development comprises knowledge creation and search and can be organized in different ways. In this paper, we study the performance of several alternative organizational models for new product development using a model of distributed, self-adapting (learning) agents. The agents (a marketing and a production agent) are modelled via neural networks. The artificial new product development process analyzed starts with learning on the basis of an initial set of production and marketing data about possible products and their evaluation. Subsequently, in each step of the process, the agents search for a better product with their current models of the environment and, then, refine their representations based on additional prototypes generated (new learning data). Within this framework, we investigate the influence of different types of new product search methods and generating prototypes/learning according to the performance of individual agents and the organization as a whole. In particular, sequential, team-based Trial & Error and House of Quality guided search are combined with prototype sampling methods of different intensity and breadth; also, the complexity of the agents (number of hidden units) is varied. It turns out that both the knowledge base and the search procedure have a significant impact on the agents' generalization ability and success in new product development. Andreas Mild was born in Vienna, Austria, in 1973. He studied business administration in Vienna, in 2000 he received his Ph.D. from the Vienna University of Economics and Business Administration (WU). Since 2003 he is associated professor at the WU. He has been guest professor in Frankfurt, Germany, Sydney, Australia and Bangkok, Thailand. Previous research appeared in Journals such as MIS Quarterly, Management Science and Marketing Science. His research interests currently include agent-based models, new product development and recommender systems. Alfred Taudes was born in Vienna, Austria, in 1959. He studied business administration and management information systems (MIS) in Vienna (doctorate 1984), in 1991 he received his Ph.D. from the Vienna University of Economics and Business Administration (WU). He was assistant professor at the WU (1986–1991) and professor for MIS at the German Universities of Augsburg (1991), Münster (1991/92) and Essen (1992/93). Since 1993, he has been professor for MIS at the WU and Head of the Department for Production Management. Since 2000, Dr. Taudes has been speaker for the Special Research Area SFB # 010 (Adaptive Information Systems and Modelling in Economics and Management Science). His research interests currently include agent-based models of industry structures, management of innovation, technology management and business strategy.     

Organizing product releases in time-driven development processes: a probabilistic analysis

In this paper, the problem is addressed of how to organizea new product development process that is fast on the one handand that provides good-quality results on the other hand. Severalplanning techniques, like PERT and CPM, are available to analysethe completion time or product release time of such complexprocesses. Although these techniques are all well known andare widely used in project management, they do not address theiterative mode of operation that is characteristic for suchuncertain processes. Neither do they offer a tool or guidelinesto ‘design’ a composition of iterative processesbecause they are analytical. In this paper, a quantitative concept is presented for modellingthe release time of a single uncertain iterative activity, asa random variable to deal with the probabilistic aspect in asimple way. From this simple model, the complexity is extendedsystematically to model fundamentally different configurations,that are on the one hand simple enough to be studied analyticallyand on the other hand exhibit their fundamentally differentrelease characteristics, as experienced in real life. From theanalysis, guidelines are formulated for organizing or (re)configuringa complex process configuration. It is demonstrated that organizing uncertain processes fora fast product release requires a balance between the exploitationof the principles of concurrent engineering and the risk ofoverrunning time targets. An important factor in this balanceis the decision structure for the release of intermediate results.The ‘empowerment’ structure, a structure where decisionsare not clustered as milestones but are made without any delay,offers great opportunity in terms of small mean release timesand small variances thereof.     

Structuring product development processes

This paper proposes operational frameworks for structuring product development processes. The primary objective of this research is to develop procedures to minimize iterations during the development process which adversely affect development time and costs. Several procedures are introduced to restructure the development process. The computation of the corresponding product development times is facilitated by two Markov models addressing different types of learning. The methodologies are employed to identify a set of managerial concerns in restructuring the product development processes.The developed framework has become an integral part of a re-engineering project for the development of rocket engines at Rocketdyne Division of Rockwell International. Throughout the paper, the methodologies are illustrated with the help of this process.     

Supply chain dynamic configuration as a result of new product development

This study considers supply chain network configuration in an innovative environment while the new product development (NPD) will affect the supply chain configuration (SCC). The time of new product introduction has a significant effect on the market performance while it has an effect on the supply chain configuration. Supplier integration into the new product introduction is the key parameter for successfully new product introduction, which may contribute to supply chain reconfiguration. Consequently By considering the new product development concept, we may face with dynamic supply chain configuration during a planning horizontal time. In this study, a new model is presented to consider the dynamic configuration of a supply chain by developing new products. In the proposed model, the dynamic configuration of a supply chain and the new product launching time is optimized simultaneously. The proposed model considers production, sales and transportation planning for the entire supply chain in order to achieve an integrative and efficient supply as well. Then some numerical analyses have been done to show the applicability of the proposed model. The results show that the new product development has a significant effect on the configuration of supply chain.     

Optimal allocation of reliability tasks to mitigate faults during system development

^** Email: walter.johnston{at}baesystems.com^*** Email: j.quigley{at}strath.ac.uk^**** Email: lesley.walls{at}strath.ac.uk This paper considers reliability planning for a concept designfor a new system where a portfolio of possible reliability developmenttasks exists; the goal is to find a selection and sequence oftasks to achieve reliability targets subject to time constraintsat minimal cost. This is non-trivial given that each task potentiallycan expose several different weaknesses and each weakness potentiallycan be exposed by several different tasks. We use a Bayesianpoint process model to estimate the system reliability. Theprior distribution maps to a fault register and relates directlyto a set of potential engineering modifications.The likely impactof each task can be assessed using the point process model.An integer programming approach is used to sequence and scheduletasks under the constraint that contractual reliability requirementsmust be met. An illustrative example is provided and an extensionto system availability is proposed.     

Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions

New product development involves several critical decisions. A key decision making area in new product development is the evaluation of the viability and the market potentials of a new product. In the absence of any relevant historical data, companies ask the potential buyers of their products about their intentions to buy those products when assessing their viability. Despite the popularity of the use of behavioral intentions in predicting the market acceptance of new product ideas, both survey and empirical studies suggest that the accuracy of such predictions is usually very low. Although earlier case-based studies suggest that a number of factors can affect the quality of new product decisions, it is still empirically unclear how product knowledge and the type of new products might impact the predictive accuracy of intentions-based new product forecasting. This study utilized a longitudinal research design and empirically tested the hypotheses across two new products. The study first collected purchase intentions data about the new products. Second, it collected subsequent actual purchase data about the new products. The results of series of hierarchical regression analyses comparing the initial purchase intentions and subsequent actual behaviors showed that while product knowledge is positively related to the predictive accuracy and consistency of intentions-based new product forecasting, product type is negatively related to them.     

Comparison of two models for managing reliability growth during product design

Relying on reliability growth testing to improve system designis neither usually effective nor efficient. Instead it is importantto design in reliability. This requires models to estimate reliabilitygrowth in the design that can be used to assess whether goalreliability will be achieved within the target timescale forthe design process. Many models have been developed for analysisof reliability growth on test, but there has been much lessattention given to reliability growth in design. This paperdescribes and compares two models: one motivated by the practicalengineering process; the other by extending the reasoning ofstatistical reliability growth modelling. Both models are referencedin the recently revised edition of international standard IEC61164. However, there has been no reported evaluation of theirproperties. Therefore, this paper explores the commonalitiesand differences between these models through an assessment oftheir logic and their application to an industrial example.Recommendations are given for the use of reliability growthmodels to aid management of the design process and to informproduct development.     

On using clique overlapping for detecting knapsack constraint redundancy and infeasibility in 0–1 mixed integer programs

Summary In this note we present fast procedures for detecting knapsack constraint redundancy and infeasibility in 0–1 mixed integer programs by using information from probing analysis and overlapping clique identification. The new procedures improve current preprocessing techniques for size reduction of integer programs.     

A case study of an integrated fuzzy methodology for green product development

Green product development has become a key strategic consideration for many companies due to regulatory requirements and the public awareness of environmental protection. Life cycle assessment (LCA) is a popular tool to measure the environmental impact of new product development. Nevertheless, it is often difficult to conduct a traditional LCA at the design phase due to uncertain and/or unknown data. This research adopts the concept of LCA and introduces a comprehensive method that integrates Fuzzy Extent Analysis and Fuzzy TOPSIS for the assessment of environmental performance with respect to different product designs. Methodologically, it exhibits the superiority of the hierarchical structure and the easiness of TOPSIS implementation whilst capturing the vagueness of uncertainty. A case study concerning a consumer electronic product was presented, and data collected through a questionnaire survey were used for the design evaluation. The approach presented in this research is expected to help companies decrease development lead time by screening out poor design options.     

A possibilistic decision model for new product supply chain design

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.     

Warranty and optimal reliability improvement through product development

For products sold with warranty, the warranty servicing cost can be reduced by improving product reliability through a development process. However, this increases the unit manufacturing cost. Optimal development must achieve a trade-off between these two costs. The outcome of the development process is uncertain and needs to be taken into account in the determination of the optimal development effort. The paper develops a model where this uncertainty is taken into account.     

The impact of environmental regulations on forest product trade in China

Forest product trade plays an important role in the development of the Chinese forest industry. The trading value of forest product has shown a yearly growth rate of 12% during the last five recent years. Stringent environmental regulations in China have a profound impact on raw material supplies and industrial production in the forest sector; however, their impact on the forest product trade is still unclear. This study applies fixed and random effects models as well as a seemingly unrelated regression model to investigate the impact of environmental regulations on the trade of forest product from 2002 to 2015. The results indicate that the stringent environmental regulations promoted the import but restricted the export of forest product in general. Specifically, the stringent environmental regulations stimulated the import but had an ambiguous impact on the export of the paper product. The stringent environmental regulations had also stimulated the import of wood product but inhibited the export. In contrast, wooden furniture had been affected minimally; only export got slightly negatively affected by environmental regulations. Recommendations for resource managers:

• Trade‐offs between economic growth and environmental regulations are needed to smoothly promote the forest product trade in China.
• Paper and wooden furniture product sectors are less likely to be affected by stringent environmental regulations, because high value‐added products could compensate for environmental costs.
• The wood product sector is more likely to be negatively affected by stringent environmental regulations because environmental costs could severely impact the competitiveness of low value‐added products.

     

Alternative supply chain production-sales policies for new product diffusion: An agent-based modeling and simulation approach

Applying agent-based modeling and simulation (ABMS) methodology, this paper analyzes the impact of alternative production-sales policies on the diffusion of a new generic product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers’ new product adoption decisions are influenced by marketing activities as well as positive and negative word-of-mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the “best” production-sales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth in choosing production-sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods.     

A model of the executive's decision processes in new product development

This paper introduces the concept of fuzziness to deal quantitatively with the imprecision of the meaning of the executive's judgment stated in a natural language and presents a model of the executive's decision processes for the new product introduction which contain fuzzy-2 states, fuzzy-2 information systems, fuzzy-2 information signals, fuzzy-2 strategy are presented. The committee decision problem under fuzzy-2 constraints is dealt with.     

Optimal procedures for the discrete time/cost trade-off problem in project networks

We describe two algorithms, based on dynamic programming logic, for optimally solving the discrete time/cost trade-off problem (DTCTP) in deterministic activity-on-arc networks of the CPM type, where the duration of each activity is a discrete, nonincreasing function of the amount of a single nonrenewable resource committed to it. The first algorithm is based on a procedure proposed by Bein, Kamburowski and Stallmann for finding the minimal number of reductions necessary to transform a general network to a series-parallel network. The second algorithm minimizes the estimated number of possibilities that need to be considered during the solution procedure. Both procedures have been programmed in C and tested on a large set of representative networks to give a good indication of their performance, and indicate the circumstances in which either algorithm performs best.     

Optimal mix between pay-as-you-go and funding for DC pension schemes in an overlapping generations model

Public pension systems are usually pay-as-you-go financed, that is, current contributions cover the pension expenditures. However, some countries combine funding and pay-as-you-go within the first pillar. This article studies a mixed system where a part of the individual’s contribution accrues funded rights whereas the other part accrues pay-as-you-go rights. Diversification conditions between these two financing techniques are derived in a mean–variance framework for two distinct contexts: for a cohort entering the system (named ex-ante case) and for multiple cohorts coexisting at the same period of time (named ex-post case). The diversification benefits in presence of a liquidity constraint which ensures that the income from contributions is sufficient to cover the pension expenditures are also studied. We show that, on the one hand, diversification benefits individuals when the economy is dynamically efficient for the ex-ante case. On the other hand, diversification is unattractive when pay-as-ou-go and funding are positively correlated for the ex-post case.