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This paper focuses on decision making related to the use of strategic inventory allocation in product platform design. The purpose of the paper is to present a novel approach to managing product variety by considering product and manufacturing strategy, and considering the supply chain benefits.

Discrete event simulation is used for analyzing product platform performance in terms of inventory level and throughput. Simulation parameters such as lead times and safety stock allocation are optimized by using an analytical model of strategic safety stock allocation.

The results show that strategic safety stock allocation supports product platform strategy by increasing production output, and reducing inventory level and customer order queues at a higher level of product variety.

Linking the safety stock allocation and product platform strategy facilitates simultaneous product and process design by providing the most optimum platform strategy at minimum safety stock allocation.

Strategic safety stock allocation facilitates decision making with regard to lean strategy by reducing inventory level, agile strategy by increasing the amount of product variety and outputs, and responsiveness by reducing the number of customer order queues.

The paper presents an innovative customer order decoupling point decision.

The purpose of this paper is to study the effect of technology adoption on operational competitiveness by taking an example from international manufacturing companies.

This paper develops a conceptual model to generate strategic flexibility and benefit for a manufacturing firm by managing manufacturing strategy and technology adoption with different leadership roles.

The results show that the leadership role is a significant factor to motivate strategic flexibility and generate higher benefit. For intellectuals, the mechanism provides a new approach to explicate technology adoption in a manufacturing company. Besides, it provides a deep‐rooted preliminary point for supplementary empirical assessment. The mechanism facilitates managers to expand their understanding of the leadership role for trust building and knowledge sharing in manufacturing firms.

With that enhanced understanding, the managers can spotlight their actions, which help further to perk up their firm's competitiveness provoked by supporting the knowledge sharing activities through appropriate leadership roles.

The mechanism developed in this study is believed to be the first sober effort to provide a deeper theoretical understanding of trust building for improving operational competitiveness in manufacturing firms with a practical approach.

The purpose of this paper is to present the results of a survey of customer satisfaction with regard to the strategy of total quality management (TQM) adopted in a wheat flour milling company.

A survey was conducted to learn more about customer expectations and satisfaction. The respondents were all customers of a flour‐based food processor. Quality function deployment (QFD) was applied to improve customer service performance by focusing on customer satisfaction, value and retention.

The results show that customer satisfaction has increased steadily over a period of three years. The QFD approach helped to design a competitive product by aligning the company resources to customers’ needs.

The findings are limited to a specific food manufacturing company and for this reason they cannot be generalized to the whole in service sector. Further research is needed for other service companies.

The findings may support manufacturing companies hoping to achieve customer satisfaction by focusing on TQM implementation efforts. Furthermore, companies can focus their efforts on improving the way in which they meet certain customers’ needs if these needs are more extensively related to a certain component of quality management.

The paper presents evidence to managers of the value of implementing TQM strategy to effectively achieve customer satisfaction that ultimately leads to greater market share and profit maximization.

The general purpose of the paper is to improve supply chain (SC) responsiveness and agility by developing advanced planning and scheduling (APS) with collaboration process into agile supply and demand networks (ASDN).

Some industrial examples are presented to extract the APS requirements, then business models that are supported by analytical models are developed into APS modules to respond to the requirements. At the end, the modules are attached into an ASDN simulator to measure the benefit of the APS with collaboration process.

The results show that the APS with collaboration process is superior to existing APS software in terms of promising lead times to customers at minimum inventory level.

Since the APS with collaboration process cannot optimize transportation planning, SCs cannot therefore optimize networks by finding the optimum network configuration. Currently, the simulator needs to be tested in several possible network scenarios to find the optimal network configuration.

The APS with collaboration process makes it possible to give guaranteed lead times at minimum inventory level. Furthermore, it is possible to combine the APS with collaboration process with enterprise resources planning or MRP II by considering the criticality of the planning.

The attachment of APS with collaboration process business into ASDN represents the original aspect of this paper.

This paper aims to focus on duopolistic competition under dynamic price and production postponement for two differentiable products that share common product platform at a certain degree of product commonality.

Both price and production postponement modelling are benchmarked according to their profit in order to investigate the product substitutability effect to the profit and also their appropriateness to different competition situations. In addition, dynamic property is applied to show the demand changes effect of both strategic decisions (price and production) against demand uncertainty.

The results show that the pure price postponement is appropriate to be applied into highly customized products while pure production postponement to configurable products.

Price and production postponement decisions help managers to optimize product development as well as production strategy by considering demand and supply sides.

Price and production postponements give alternative ways to managers for deciding on how to produce products as fighting product and mass customized product.

Innovative price and production postponement models are the novelty of this paper.

The purpose of this paper is to demonstrate modularity degree in terms of interfaces and innovation.

The research objective is achieved through a modeling approach for deciding modular architecture and its implementation regarding unique components and product innovation. A case example is presented to elaborate on the concept of modularity degree and provide an option for choosing the best module from different alternatives.

The presented approach can be considered a product design strategy, in which loose coupling is achieved through standardized component interfaces. Loosely coupled component interfacing is a prerequisite for developing mass customized products. There needs to be a decision support system to formulate the interfacing in order to achieve maximum benefits. This is illustrated in this paper.

The modeling strategy for measuring the modularity level is formulated theoretically. This approach needs to be validated through an empirical study in order to generalize its findings.

In the industrial arena there is a research gap in identifying and measuring the modularity level, which is formulated in the presented approach. It is hoped that this approach will contribute to filling this research gap in the business environment, which would further benefit managers of firms in their corresponding production processes.

The unique contribution of this modeling approach is articulated through analyzing product architecture, with a view to interpreting the component interfaces in a more productive way. This formulation triggers the decision‐making process in complex product development processes.

This paper aims to focus on production ramp up modeling on built‐to‐order (BTO) manufacturers facing customized demand. The general purpose is to present a novel approach to managing collaboration, by considering information exchange between the manufacturer and the supplier.

The methodology applies feedback control mechanism to analyze supplier responsiveness and customer order decoupling point to represent the need for collaboration. A two‐stage game is applied ahead of control system application to optimize the capacity decision, with the ultimate goal being profit maximization.

The results show that a higher product commonality degree gives more opportunity for quick response BTO supply chains, which are managed by feedback control, and at the same time to possibly mitigate the bullwhip effect caused by demand information noise.

The analytical model here focused on one product family development, so the applicability of the proposed model to the whole product portfolio should be investigated in the future.

This paper helps the manufacturer to act optimally by considering the possibility of information exchange with the supplier and deciding on the product commonality degree, in taking into account the customer's lead time requirement.

A control system model of “BTO Supply Chain” is proposed by including product commonality and response analysis in the simulation model. Furthermore, a contribution to collaborative supply chains is shown by applying a synchronized supply model to represent supplier and manufacturer communication.

The aim of this paper is to propose a simulation study of the “steel supply chain” to demonstrate the effect of inventory management and demand variety on the bullwhip effect mitigation.

The relevant literature is reviewed, and then the simulation model proposed.

This study identifies reasons for sharing information under varying levels of demand and some variants, and demonstrates the benefits of mitigating the bullwhip effect by applying a design of experiment. It is shown that the information sharing is able to mitigate the bullwhip effect in the steel supply chain by extending the order interval and minimising the order batch size.

This study explores the factors associated with the bullwhip effect. This research is focused on built‐to‐order simulation, so the results are only oriented on the basis of orders; hence a simultaneous order‐ and forecast‐based steel supply chain should be carried out in the future.

This framework is expected to provide a convenient way to measure the optimum inventory level against a limited level of demand uncertainty, and thus enterprises can promote the supply chain coordination.

An innovative simulation model of the “steel supply chain” is proposed, which includes information sharing in the simulation model. Furthermore, dynamic scheduling is shown by applying a continuous ordering and order prioritization rule to replace traditional scheduling methods.