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.     

Flexibility configurations: Empirical analysis of volume and product mix flexibility

In this paper we address flexibility and investigate the relationship between volume and product mix flexibility. One view of flexibility is that of being a capability in itself; another view is that of flexibility as an enabler, providing the manufacturing system with properties on which other competitive capabilities are built. In this research, the latter view of flexibility is used, where flexibility acts as a second order competitive criterion. The aim is to differentiate between two dimensions of flexibility important to the manufacturing value chain, i.e., volume and product mix flexibility, and to investigate how different flexibility configurations are related to various manufacturing practices. A clustering research approach is used to identify groups of companies based on flexibility configurations. The groups are then analyzed with respect to characteristics and impact on operational performance. For the empirical investigation, we use empirical data from the high performance manufacturing (HPM) study, including three industries and seven countries—a total of 211 plants. We find that flexibility configurations based on high or low levels of volume and mix flexibility combinations show significant differences both in terms of operational performance, and in terms of emphasis put into different flexibility source factors.     

Fuzzy multi-attribute decision-making for evaluating manufacturing flexibility

Evaluation of manufacturing flexibility in operations management is important to determine the competitiveness of manufacturing system, and is being increasing discussed in the literature on manufacturing system. The objective of this research is to develop a multi-attribute decision-making model based on fuzzy set theory to improving the manufacturing flexibility evaluation process. Since most information available in this stage is not numerical, fuzzy linguistic assessment is used to represent the performance rating of all flexibility metrics under flexibility dimensions and importance grade of all flexibility dimensions. This paper proposes a modified LOWA operator, which uses the maximum entropy weights, and uses it to direct computation on linguistic terms. The developed model evaluates the degree of manufacturing flexibility that can assist decision-makers, when making capital investment decisions and measuring performance, in finding the need for improving manufacturing flexibility, and in determining the dimensions of flexibility as the best directions to improvement. Examples using a case study of leading Taiwan firm in the bicycle industry are used to illustrate the concept developed. In addition, sensitivity analysis can be performed to examine the solution reasonability.     

The magic matrix as a smart machine evaluator: Machine performance measurement,level of competitiveness

Today's competitive environment compels companies to be the best at the work they do. Machining efficiency establishes the plant level of performance and thus the ability to compete in the market. However, as product mix changes, and new machines possessing more and better capabilities are introduced, machining efficiency varies over time. Therefore, continuous evaluation of machine fitness for product mix has to be made. This is a huge task and is seldom done due to a lack of efficient tools to perform such an evaluation. This paper presents a computerized method that may be used to perform the evaluation task and establish the company level of competitiveness in an honest, and just way, free from improper influence. Conclusions drawn, and actions and decisions made as a result of the evaluation, are the prerogative of management. The evaluation method employs a matrix concept, which is fully described in this paper.     

CAPACITATED REMANUFACTURING WITH SERVICE LEVEL CONSTRAINTS*

This research examines production planning and control for a remanufacturer that can sell returned items on a graded as‐is basis or remanufacture the returned items. Using a GI/G/1 queuing network, we model the firms decision to remanufacture an optimal product mix over the long run that maximizes profits while maintaining a desired service level. We further use simulation to explore dispatching heuristics that can be used at the shop‐floor level to achieve the desired optimal product mix, while meeting the service level constraint. Our research is grounded in actual practice and the results provide key insights into the decision‐making process required to maximize profits and minimize average flow times for remanufactured products.     

The marketing strategy worksheet—A practical planning tool

The Marketing Strategy Worksheet is a simple aid to strategic planners. It enables a coherent and integrated plan to be produced and communicated to others. It is a three-level flow chart comprising an analytical stage, formulation of some key primary decisions and subsequent settlement of strategic issues within the marketing mix. The analytical stage incorporates both external and internal analysis. The primary decisions concern company objectives, marketing objectives, demand management, target market and positioning. Using the conventional marketing mix format, decisions are made about product, promotion, distribution and price.     

BAYESIAN MODELS FOR NEW PRODUCT PRICING

This paper develops an information decision system for new product pricing based on Bayesian updating of prior estimates of demand distribution parameter values and on optimization by dynamic programming. The model considers the interaction of production and pricing decisions and emphasizes the simultaneous making of both decisions. After presentation of the basic model, approximate techniques are introduced which obtain most of the benefit of the approach while requiring only a fraction of the computer cost and input data. Numerical examples using growing demand and price sensitivity are given to demonstrate the high computer cost of the first model and the relative performance (on a profit basis) of the approximate techniques.     

An investigation of company product decision making

This article is concerned with an investigation of the approaches taken by companies in making product decisions. The first part is concerned with a review of the range of product decisions as presented within the literature. This range is established within the context of corporate planning, with a major split between long term strategic planning product decisions, and short term operational planning product decisions. The second part of the article is concerned with the results of a survey that was designed to investigate the criteria that companies use within their product decision making. These criteria included those applicable to strategic planning, but also incorporated a range of criteria applicable to short term operational planning, as proposed in a recent article by Greenley [8]. The overall conclusion to the survey results was that a common and universal approach to product decision making cannot be identified within this sample of companies. A low level of agreement as to the relative degree of importance of the criteria was evident, and, little attention to differentiating product decisions with time was also evident. The results also challenge the importance given by the literature to the concepts of product life cycle, portfolio analysis and synergy. Finally, the author suggests two implications as a consequence of these results.     

Dynamic Assignment of Flexible Service Resources

Resource flexibility is an important tool for firms to better match capacity with demand so as to increase revenues and improve service levels. However, in service contexts that require dynamically deciding whether to accept incoming jobs and what resource to assign to each accepted job, harnessing the benefits of flexibility requires using effective methods for making these operational decisions. Motivated by the resource deployment decisions facing a professional service firm in the workplace training industry, we address the dynamic job acceptance and resource assignment problem for systems with general resource flexibility structure, i.e., with multiple resource types that can each perform different overlapping subsets of job types. We first show that, for systems containing specialized resources for individual job types and a versatile resource type that can perform all job types, the exact policy uses a threshold rule. With more general flexibility structures, since the associated stochastic dynamic program is intractable, we develop and test three optimization‐based approximate policies. Our extensive computational tests show that one of the methods, which we call the Bottleneck Capacity Reservation policy, is remarkably effective in generating near‐optimal solutions over a wide range of problem scenarios. We also consider a model variant that requires dynamic job acceptance decisions but permits deferring resource assignment decisions until the end of the horizon. For this model, we discuss an adaptation of our approximate policy, establish the effectiveness of this policy, and assess the value of postponing assignment decisions.     

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.     

SIMPLE MODELS FOR MONITORING NEW PRODUCT PERFORMANCE

Several studies have shown that consumers trying a new brand of frequently purchased consumer goods behave differently from repeat buyers. The “customer mix” for a new brand changes over time. Early in a new brand's life, most of the buyers are triers, while later in its life, a significant portion of the buyers are repeaters. The study presented here tests the hypothesis that an aggregate sales forecast, “averaging” the behaviors of triers and repeaters, would be outperformed by a model that treats them separately. A new product model, giving separate consideration to triers and repeaters, is developed. This model is tested on predictive ability against a widely used single-equation aggregate model. Consumer panel purchase diaries and data on advertising in measured media, covering a period of 42 months following introduction of a major brand of cold tablets, serve as the proving grounds. Data for the early part of this period serve as the data base for both models, while data for the rest of the period serve to test the predictive ability of both models. The disaggregate model is shown to perform significantly better than the aggregate model in terms of predictive accuracy. It also offers several other advantages in use as a decision aid, both for GO-NO decisions and for marketing mix decisions. Finally, several problems in the implementation of the proposed model and implications for research strategy are discussed.     

Product mix in the TFT-LCD industry

As operational costs and equipment depreciation in the TFT-LCD (thin film transistor-liquid crystal display) industry are a high percentage of the total cost, most manufacturers usually fully utilise their production capacity to reduce the average unit cost. However, when the market demand is less than the supply the stock of panels increases; this forces manufacturers to instigate a price war to reduce levels of stock and results in a wide fluctuation in panel prices. Inventory stocks of panels could be decreased by optimising the product mix. This will help manufacturers to reduce the risk of holding stocks, increase profit, and improve competitive advantage. This study uses mixed integer linear programming (MILP) to construct a product mix for the TFT-LCD industry given the conditions of profit, productivity, raw materials supply, and market demand. A case study shows that this model is proven to be effective in generating product mix for the TFT-LCD industry while improving profit. The product mix generated by this model can provide a reference for the sales department for orders and shipping, for the production department for the order quantity, and for master production scheduling for each product.     

Hedging strategic flexibility in the distribution optimization problem

Motivated by the increasing prevalence of flexibility hedging in corporate-level risk management programs, this paper focuses on the treatment of hedging operational risks in the coordinated replenishment and shipment for distribution systems. The forward option pricing model with the generalized autoregressive conditional heteroskedasticity (GARCH) model for stochastic demand forecasting is adopted for constructing inventory volume flexibility. We therefore propose a hedge-based coordinated inventory replenishment and shipment (HCIRS) methodology for flexibly making inventory hedging and optimal routing assignment decisions as well as coordinating replenishment and shipment policies. The HCIRS methodology provides insight into strategic flexibility adopted for a real-life inventory–distribution problem faced by one of the major East Asia food supply networks and turns out to be very efficient. The proposed HCIRS methodology has provided evidence of better results than the traditional operational techniques for the presented case in this paper.     

Decision model for product rollover in manufacturing operations

The purpose of this study is to investigate critical decisions when planning for product rollover at a manufacturing company, and how to organise these decisions. A literature review and a case study are used to develop a decision model. The findings indicate that product rollover can be organised in a five-phase decision model. The research quality is strengthened by a structured literature review, but it can be argued that more empirical research is needed for validation. For researchers, this paper contributes with the identification of critical decisions and a model for product rollover. For practitioners, the study highlights the need to acknowledge the importance of product rollover and its role as a competitive weapon. In earlier studies, product rollover has mainly been investigated from a market perspective, so that this study contributes by investigating the issue from a manufacturing perspective.     

Impact of Price Postponement on Capacity and Flexibility Investment Decisions

Investments in dedicated and flexible capacity have traditionally been based on demand forecasts obtained under the assumption of a predetermined product price. However, the impact on revenue of poor capacity and flexibility decisions can be mitigated by appropriately changing prices. While investment decisions need to be made years before demand is realized, pricing decisions can easily be postponed until product launch, when more accurate demand information is available. We study the effect of this price decision delay on the optimal investments on dedicated and flexible capacity. Computational experiments show that considering price postponement at the planning stage leads to a large reduction in capacity investments, especially in the more expensive flexible capacity, and a significant increase in profits. Its impact depends on demand correlation, elasticity and diversion, ratio of fixed to variable capacity costs, and uncertainty remaining at the times the pricing and production decisions are made.     

Flexible‐Duration Extended Warranties with Dynamic Reliability Learning

Frequent technological innovations and price declines adversely affect sales of extended warranties (EWs) as product replacement upon failure becomes an increasingly attractive alternative. To increase sales and profitability, we propose offering flexible‐duration EWs. These warranties can appeal to customers who are uncertain about how long they will keep the product as well as to customers who are uncertain about the product's reliability. Flexibility may be added to existing services in the form of monthly billing with month‐by‐month commitments or by making existing warranties easier to cancel with pro‐rated refunds. This paper studies flexible warranties from perspectives of both customers and the provider under customers' reliability learning. We present a model of customers' optimal coverage decisions and show that customers' optimal coverage policy has a threshold structure under some mild conditions. We further show that flexible warranties can result in higher profits and higher attach rates in a homogeneous market as well as in a heterogeneous market with multiple segments differing in various dimensions.     

Decision knowledge sharing: flexible supply chains in KM context

Supply chain excellence has a real impact on business strategy. Building supply chains as flexible systems represents one of the most exciting opportunities to create value and one of the most challenging tasks for the policy makers. It requires integrated decision making amongst autonomous chain partners with effective decision knowledge sharing between them. The key to success lies in knowing which decision has more impact on the overall performance and this can be achieved by appropriate knowledge sharing. In this context, knowledge management (KM) can be used as an effective approach to achieve knowledge sharing and decision synchronisation in supply chains. Flexible supply chains (FSC_s) are more complex and involve multiple autonomous players with varying technical cultures (affects knowledge mindsets), managerial background (affects decision knowledge) and supply chain management (SCM) exposures (affects knowledge sharing attitudes). Thus there is a need to develop demo models that can encourage chain managers towards collaborative knowledge sharing in the supply chains. This paper presents the application of one such model based on decision knowledge sharing (DKS) for improved supply chain management. A simulation model of a flexible supply chain based on DKS framework is developed for demo purposes. The key results are highlighted along with industry implications. The cost based performance of DKS at different levels of flexibility is studied. Thus a careful analysis of the chain with a focus on collaborative decisions is useful to ensure success. This paper addresses this interesting and challenging domain.     

多个不确定性因素下跨国公司投资策略

利用实物期权方法,在汇率、国内外生产成本和产品价格均不确定的情况下,把跨国企业迁移生产和销售的空间决策柔性(spatial decision flexibility)模型为交换期权,分别给出了迁移生产的最优下界和迁移销售的最优上界、及其迁移决策规则,并讨论了关键参数对迁移决策规则的影响。最后,给出应用迁移生产和销售决策规则的数值模拟算例,体现了本文给出的决策规则在实际应用中具有可操作性。     

On flexible product-mix decision problems under randomness and fuzziness

This paper considers several models of product-mix decision problems and production planning problems under uncertain conditions, and shows that these are extensional and versatile models for resolving previous product-mix problems. These proposed models include randomness derived from statistical analysis based on historical data, ambiguity of decision maker's intuition and the quality of received information, and flexibility in accomplishing the original plan. Furthermore, given that the upper limit values of some constraints have flexibility, and given a decision maker's level of satisfaction, we propose a flexible product mix of problems using the theory of constraints (TOC), and develop an efficient solution method. We then provide a numerical example that compares our models with some previous basic models. Efficiency of flexibility is obtained when our proposed models are applied to several conditions, such as measurable changes from the expected value of future returns.     

Scenario aggregation in single-resource production planning models with uncertain demand

In this paper we present an application of the scenario aggregation approach proposed by Rockafellar and Wets to a simple standard multi-product multi-period production planning problem with uncertain demand and setup cost modelled by logical zero-one variables. The uncertainty in demand is expressed by a number of demand scenarios. As compared with more traditional approaches that require distributional assumptions and/or estimates of parameters from historical demand data, the scenario approach offers greater flexibility and makes it possible to take subjective information into account. The scenario aggregation principle and the corresponding progressive hedging algorithm offer a theoretically sound basis for generating consistent solutions for production planning models with uncertain demand. Since the production planning problem studied in this paper is of mixed-integer type the original scenario aggregation approach cannot be applied directly. However, since the integer variables in the production planning model are indirectly coupled to the continuous production decisions an alternative method in which only the production quantities are used to couple the different realizations can be used. This paper is a first attempt to perform this form of coupling. We illustrate the ideas on a small example and use this example to demonstrate how the solution can be evaluated in terms of flexibility measures.