From mass customization to mass personalization: a strategic transformation

Business and operations strategists have long sought to formulate strategies that would serve profitably for a market of one. Two decades after its conception, there is growing evidence that mass customization strategy is transforming into a mass personalization strategy, making the market of one a reality, at least in select industries. The degree of transformation of a company depends on the extent to which its product is soft, i.e., can be produced electronically. Thus, at the lower end of the personalization spectrum are manufacturing companies engaged in producing hard, configurable products, while on the high end of the spectrum are service companies whose product can be totally configured and delivered electronically. The underlying factors that are enabling this transformation, in our view, are: (1) development of information technologies such as peer to peer (P2P), business to consumer (B2C), and Web 2.0, (2) near-universal availability of the Internet, (3) customer willingness and preparedness to be integrated into the process of product co-design and co-creation, (4) modern manufacturing systems, such as flexible manufacturing and, of course, (5) mass customization tools such as modularity and delayed differentiation, which help reduce manufacturing cost and cycle times and (6) deployment of customer-satisfaction-specific software called customer relationship management (CRM) to engender customer retention. Due to the importance and strategic success of affordable personalization, this issue is dedicated to that theme. The articles included in this issue would, I believe, serve as significant decision support mechanisms for companies pursuing a mass customization and personalization strategy. In addition to providing a brief perspective on articles included in this issue, we also summarize the state of the art of mass customization research.     

Mass Customization: Metrics and Modularity

Mass customization as a competitive strategy is getting progressively increasing attention in business and academic arenas due to its high potential to provide sustained strategic advantage in a unique fashion. It is well documented that a manufacturing company competes with others in its industry on five dimensions: Price, quality, flexibility, delivery, and service. According to the existing literature, mass customization provides significant strategic advantage in two of these dimensions—price and customization. We, however, argue that when properly implemented, the cellular manufacturing structure associated with the appropriate implementation of mass customization strategy, provides additional competitive value in quality and delivery. Furthermore by virtue of the customer co-design as an integral aspect of mass customization, customer satisfaction also improves under this strategy. Mass customization strategy, therefore, provides competitive advantage in all five competitive dimensions simultaneously—a truly unique strategic accomplishment. We also propose, in this paper, new metrics for mass customization strategy that measure the “mass” as well as the “customization” aspects of this strategy. Finally, we describe in the clearest terms the modus operandi of modularity in product design and the role it plays in bringing about high levels of customization on one hand and economies of scale at component level, on the other. We conclude with remarks that underscore the need for conducting research in the areas at the interface of mass customization and supply chain management.     

Observations on the present and future of mass customization

A demanding task for many companies today is that of learning to regard customers as individuals, of proactively developing products and services according to the individual customer preferences, and of subsequently producing and distributing these offerings. Over the last decade, mass customization has emerged as an effective approach for tackling precisely this task. In this paper, I discuss the background of mass customization and the elements of this strategy. I will then comment on the implementation of mass customization in practice. I will end with a brief discussion of alternative strategies in this domain, namely personalization and matching services.     

Reverse simulation for collaborative commerce: A study of integrating object-oriented database technology with object-oriented simulator

Collaborative commerce has been used for communication, design, planning, information sharing, and information discovery in business-to-business (B2B) applications. The collaboration between buyers and sellers enhances product quality and customer satisfaction. However, most effort currently focuses on information sharing with customers and suppliers instead of joint product development or manufacturing. Moreover, traditional analytical methods have limited capability in solving problems. This study presents a framework for doing reverse simulation, where designers can reuse past experiments and change system parameters in manufacturing system for collaborative commerce. The framework integrates the object-oriented simulator and the object-oriented database. In this framework, the object-oriented database records the whole experiment scenarios and allows multiple planners with different expertise to involve concurrently and collaboratively. Then, simulations of advanced planning and scheduling in a product manufacturing environment that involves several planners working collaboratively are used for demonstration.     

Measuring the effectiveness of a mass customization and personalization strategy: a market- and organizational-capability-based index

The research of Kumar (Int J Flex Manuf Syst 16(4):287–312, 2004) is expanded to develop a methodology that measures the effectiveness of a mass customization and personalization strategy using a mass customization and personalization effectiveness index. This index extends Kumar (2004) in three significant ways: (1) it encompasses both service and manufacturing companies, (2) the assessment of product customization considers that customers assign different weights to different product features, and (3) the index captures the impact of both market perception and system capabilities. Three different measures are proposed. Which is appropriate should be a function of a particular company’s parameters. The circumstances under which each measure is best is a subject for future research. The validation and reliability of this index and these measures are also issues that should be addressed by future research.     

Process Variety Modeling for Process Configuration in Mass Customization: An Approach Based on Object-Oriented Petri Nets with Changeable Structures

Mass customization strategies could be usefully deployed by companies whose products are characterized by a modular design. Typically, each module serves a specific aspect of the overall product function at varying performance levels. Each product variant (constructed through a unique combination of modular performance levels), therefore, serves to customize the overall performance of the product, thus serving the unique needs of each customer. The high demand for each module guarantees economies of scale and, eventually, low cost to customer. The rationale of configuring production processes for producing individual product variants originates from the fact that massive process data is commonly available in a firm and there exists a generic process structure underlying the production of similar products in a family. To design a decision support mechanism that constructs process configuration corresponding to a given product configuration, this paper develops a formal modeling of process variety using Petri nets. Object-oriented Petri nets (PNs) with changeable structures (OPNs-CS) are applied to deal with the issues of generic representation, constraints compliance, and operational sequence requirements. Object-oriented PN (OPN) models facilitate generic representation of product and process variety as well as their instantiations. The OPNs-CS model is tested with simulation. Based on simulation results, the optimal configuration of production processes can be determined for each individual product as well as the cohort of a product family. To illustrate the feasibility and potential of OPNs-CS based process variety modeling, a case study of process configuration for mass customized textile spindles is reported.     

Aligning demand and supply flexibility in custom product co-design

Flexibility of supply and demand is essential for successful implementation of a mass customization strategy that delivers sustained competitive advantage. Supply flexibility, i.e., a choice of alternative products designed to perform the same basic function, is made possible by the range of capabilities available in flexible and agile manufacturing systems and in supply chains. Demand flexibility is derived from the degree to which a customer is willing to compromise on product features or performance levels in order to meet budgetary (reflected in price) or schedule (reflected in delivery) constraints. Flexibility of both supply and demand can have significant strategic and financial value if they are properly aligned. However, customers are mostly unaware of mapping of demand flexibility on to supply flexibility and its impact on production cost and time. Recent advances in information technology have made it possible to co-design a product that involves customer on one end and the manufacturer on the other. This creates an aura and an opportunity where a middle ground between the supply and demand flexibility can be explored and a “deal” can be struck where both parties settle for a product that is beneficial to both through a negotiated settlement. In this paper, we develop a framework for such negotiations. The customer requirements are treated as a range of negotiable options instead of a set of fixed inputs. Demand and supply for customization is then matched by aligning the flexibility of manufacturing systems with customers’ requirement options. Based on this framework, a negotiation scheme is developed to assist customers and manufacturers in exploring and utilizing demand and supply flexibility information in co-design. The negotiation scheme is formulated using goal programming. Finally, an interactive problem-solving procedure is developed and implemented with an illustrative example.     

Johnson Matthey electronics — a total quality management approach to customer support

Johnson Matthey is committed to Total Quality Management (TQM) aimed at setting standards in customer support. TQM achieves improvements in product quality and standards of service by continually improving production processes, analytical techniques, data processing and distribution channels.The Company has so far invested £1M in TQM training, which involves every employee what ever their role. In addition to TQM, the Company continues to invest heavily in a long term research and strategic acquisition policy, which achieves not only improvements in product quality but also provides new products and solutions to meet customers changing needs.     

Life Cycle costing as part of design for environment environmental business cases

Background  In developing products various requirements have to be integrated including functionality, quality, affordability as well as environmental aspects. Often conflicting requirements have to be fulfilled. Therefore, multi-dimensional decision support approaches are necessary. Methods  Here, one approach is to relate the conflicting requirements to each other. Life Cycle Costing (LCC) has the potential to support the trade-off between some environmental targets and overall affordability targets by including all monetary flows along the product life cycle (going beyond the well-known costs of ownership by integrating also long-term use and end-of-life costs). Those solutions can be identified that (a) have the highest efficiencies (where do we get most environmental improvements per Ϊ and (b) have the highest affordability for the customer along the life cycle. Furthermore, on-costs in the design phase can be justified in terms of future savings either for the customer or for the recycling of the products. These represent real business cases for environmental actions. Three types of environmental business cases can be differentiated. Results and Discussion  This paper presents various examples where LCC is integrated into product design. However, there are a number of open issues in the implementation of LCC within real product development including data availability and uncertainty (future costs/ savings), level of discounting, accounting and compensation. Various internal case studies done in the last years showed that already few changes in the costs structure can significantly affect the identi-fied future costs. Recommendation and Outlook  Uncertainties in LCC are higher than in LCA and highest when applied in the stage of product develop-ment, i.e. used to support DfE action. As a consequence, the result-ing figures can only be seen as directional. Therefore, the use of LCC in Design for Environment cannot be recommended without major restrictions in terms of guidance, experience/training. The link-age between LCC and DfE can either be established via (1) experts supporting design teams or (2) as part of a DfE tool. The DfE tool has to include detailed guidance for interpretation, and its application should be based on a solid training for DfE engineers.     

Mass customization of travel packages: data mining approach

This article employs a mass customization strategy to design travel packages that minimize the operation and processing costs for the service provider on one hand, while aligning the components of the packages to maximize customer satisfaction on the other. Data mining is used to identify rules of association to develop this model. Hidden relations in the massive travel agencies’ databases are revealed by using the association rules technique to customize travel packages according to customers’ requirements. This approach leads to fewer, but more manageable and popular travel package promotions. The overall package selection problem is modeled as an integer program that minimizes costs of operation and processing. Two different solution approaches were used: a mathematical modeling language approach and a heuristic algorithm approach. An illustrative numerical example based on a synthetic data set is also presented.     

A two-stage stochastic programming approach for new tape-out allocation decisions for demand fulfillment planning in semiconductor manufacturing

Demand fulfillment and capacity utilization directly affects customer satisfaction, market growth, and the profitability of the company in the semiconductor industry. These characteristics boost the significance of allocating various customer demands to a number of wafer fabrication facilities (fabs) with different capacity configurations. Before volume production, the introduction of new semiconductor product, namely new tape-out (NTO), requires extremely sophisticated and lengthy qualification with high-cost masks and pilot runs in the qualified fabs. Thus, the NTO allocation will affect future product mix of the qualified fabs, and the flexibility to fulfill the volume demands of the allocated NTOs in the corresponding fabs. This research aims to construct a two-stage stochastic programming (2-SSP) demand fulfillment model. The first stage considers NTO allocation decisions to a number of qualified fabs before the corresponding demand volume is realized. The second stage allocates the capacity to fulfill demand requirements based on the results of four options of capacity reconfiguration: (1) qualifying a product to more than one fab (share); (2) physically transferring a set of masks for a product from one fab to another, where a requalification is required (transfer); (3) moving tools from under-loaded fabs to over-utilized fabs (backup); and (4) utilizing different technologies to capacity inside a fab to support the technology with insufficient capacities (exchange). Both the share and transfer options require long lead time for qualification, whereas the backup and exchange options can be accomplished within a planned timeframe. A numerical study based on real settings is conducted to estimate the validity of the proposed 2-SSP model via values of stochastic solution (VSS) and expected values of perfect information (EVPI). The results showed the benefits of adopting 2-SSP models, especially in an environment with high-demand fluctuation. Furthermore, the proposed 2-SSP can provide near-optimal solutions similar to those of deterministic models with perfect information.     

An Assessment Framework for Optimal FMS Effectiveness

Equipment failures in an FMS are significant to performance and can lead to costly, incorrect decisions. Fortunately, effectiveness measurement techniques can be mapped to clever modeling frameworks to help predict, track, and then improve upon the FMS performability or mission effectiveness, and improve maintenance. This article provides sources and guidelines for efficient and effective FMS modeling, a framework for applying the modeling to predict the impact on customers from their point of view, and a method for tying it all together for improving the FMS effectiveness. It is not enough to simply examine the working and failed states of an FMS or even to calculate common reliability metrics. It is necessary to consider the FMS as a whole, and that system includes the needs of the customer and the business. It is also necessary to be purposeful about the measures of performance selected and to support the measures of effectiveness. In this article, we present: a framework for considering customer needs in the measures of effectiveness for FMS; modeling approaches for solving for effectiveness measures; and an example to show how to apply it to an FMS, to improve it or plan for meeting specific customer needs.     

A network approach for modeling and design of agile supply chains using a flexibility construct

Agility can be viewed as a need to encourage the enterprise-wide integration of flexible and core competent resources so as to offer value-added product and services in a volatile competitive environment. Since flexibility is considered a property that provides change capabilities of different enterprise-wide resources and processes in time and cost dimensions, supply chain flexibility can be considered a composite state to enterprise-wide resources to meet agility needs. Enterprise modeling frameworks depicting these composite flexibility states are difficult to model because of the complex and tacit interrelationship among system parameters and also because agility thrives on many business objectives. In view of this, the modeling framework presented in this paper is based on analytical network process (ANP) since this methodology can accommodate the complex and tacit interrelationship among factors affecting enterprise agility. The modeling framework forms a three-level network with the goal of attaining agility from the perspective of market, product, and customer as the actors. The goal depends on substrategies that address the characteristics of the three actors. Each of these substrategies further depends on manufacturing, logistic, sourcing, and information technology (IT) flexibility elements of the enterprise supply chain (SC). The research highlights that, under different environmental conditions, enterprises require synergy among appropriate supply chain flexibilities for practising agility. In the present research, the ANP modeling software tool Super Decisions? has been used for relative prioritization of the supply chain flexibilities. We demonstrate through sensitivity analysis that dynamic conditions do require adjustments in the enterprise-wide flexibility spectrum.     

Estimation of the mean waiting time of a customer subject to balking: a simulation study

To a customer, the waiting time for order processing for a product or service is important information for order placement. If the time foreseen for order fulfillment is long, the order might be lost to a competitor. In particular, modern principles of supply chain management highly suggest information sharing between entities in the chain and information technology has enabled customers to conveniently consider the waiting time for a potential balking decision. To help determine the design and operation of a manufacturing or service system in which a customer may balk based on the foreseen waiting time, this paper develops procedures to estimate the average waiting time of an order. Either the procedures allow the maximum waiting time for a balking decision to be random or do not require knowledge of the arrival process of customers before balking if the balking limit is known. For generality of the model, this paper considers general inter-arrival and service time distributions, and uses the simulation and regression approach.     

Mass Customization in Videotape Duplication and Conversion: Challenges of Flexible Duplication Systems,Fast Delivery,and Electronic Service

With the emergence of electronic commerce technology, marketers gained new methods for personalizing electronic advertisements, automating marketing research, and customizing product designs. These developments suggest that production operations also must be redesigned to support contemporary marketing objectives via mass customization. Yet, operations management research historically has focused on mass production systems buffering the customer from production operations. As such, the literature provides relatively few insights about managing mass customization operations. Thus, in this paper, we consider a previously unexamined type of mass customization production operations. The paper presents a case study of a manufacturer's videotape duplication and conversion system, consisting of a flexible network of heterogeneous, parallel machines used to duplicate and convert videotapes and other multimedia. We describe the characteristics of this flexible duplication system, discuss parallels between these systems and mass customization, and identify issues regarding flexibility, speed of delivery, and electronic service.     

Robust product recommendation system using modified grey wolf optimizer and quantum inspired possibilistic fuzzy C-means

In recent years, several researchers have developed web-based product recommendation systems to assist customers in product search and selection during online shopping. In addition, the product recommendation systems deliver true personalization by recommending the products based on the other customer’s preferences. This study has investigated how the product recommendation system influences the customer’s decision effort and quality. In this study, the proposed system comprises of five major phases: data collection, pre-processing, key word extraction, keyword optimization and similar data clustering. The input data were collected from amazon customer review dataset. After the data collection, pre-processing was carried-out to enhance the quality of collected amazon data. The pre-processing phase comprises of two systems lemmatization and removal of stop-words & uniform resource locators (URLs). Then, a superior topic modelling method Latent Dirichlet allocation (LDA) along with modified grey wolf optimizer (MGWO) was applied in order to identify the optimal keywords. The extracted key-words were clustered into two forms (positive and negative) by applying a clustering algorithm named as quantum inspired possibilistic fuzzy C-means (QIPFCM). Experimental results showed that the proposed system achieved better performance in the product recommendation system compared to the existing systems in terms of accuracy, precision, recall and f-measure.

     

Assessment of structural and functional similarity of biosimilar products: Rituximab as a case study

Biosimilars are products that are similar in terms of quality, safety, and efficacy to an already licensed reference/ innovator product and are expected to offer improved affordability. The most significant source of reduction in the cost of development of a biosimilar is the reduced clinical examination that it is expected to undergo as compared to the innovator product. However, this clinical relief is predicated on the assumption that there is analytical similarity between the biosimilar and the innovator product. As a result, establishing analytical similarity is arguably the most important step towards successful development of a biosimilar. Here, we present results from an analytical similarity exercise that was performed with five biosimilars of rituximab (Ristova®, Roche), a chimeric mouse/ human monoclonal antibody biotherapeutic, that are available on the Indian market. The results show that, while the biosimilars exhibited similarity with respect to protein structure and function, there were significant differences with respect to size heterogeneity, charge heterogeneity and glycosylation pattern.     

Modeling customized product configuration in large assembly manufacturing with supply-chain considerations

Dynamic variability in low-volume and highly customized products of a large assembly manufacturing system with an integrated supply chain has been very challenging to capture. Design and product configurations most likely impact outcomes of such broad variability. This article presents a framework to encompass this completely integrated system for using discrete event simulation as a modeling method. The system modeling framework addresses factors including customized configuration attributes and individual customer-preferred considerations for customized configurations. The framework is intended to aid decision-making concerning cost and schedule impacts associated with customization options chosen throughout the supply chain. A real-world example drawn from aerospace is included to demonstrate and validate the operational capability of the proposed framework.     

Analytical laboratory: world class distinction with world-wide connection; from managing instrumentation to managing knowledge

A recent article [F.R. Antonucci, D.A. Barnett, The changing corporate analytical laboratory, CHEMTECH, October 1998] forecasts a change in the way corporate analytical laboratories will operate. At Champion International's Applied Technologies corporate analytical laboratory, this revolutionary change is a movement away from the traditional culture of complete reliance on in-house expertise and instrumentation. Instead, customer needs will be addressed through a network of resources that leverage the both in-house and noncorporate instruments and expertise. We have coined the term “virtual laboratory” for this new laboratory paradigm. This revolution requires the role of corporate analytical scientists to change from one of data and information generators to one of knowledge workers. Associated with this role change is the potential for a dramatic increase in the value of the chemist's contribution to the company, an opportunity for them to function as important business partners with the manufacturing sites and product development centers. The laboratory knowledge workers, the customers, and the virtual laboratory must all work synchronously around prenegotiated expectations regarding cost, quality, and timeliness. The mechanism allowing this change is a Lotus Notes database entitled the Testing Connection. In fact, this database can be viewed as the cornerstone of the virtual laboratory concept. This database allows for the capture of data, information, and knowledge and for collaborative problem solving. All of these capabilities result in faster and better action on customer issues. Also, the migration from problem solving to problem prediction and prevention is achievable.