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PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

PurposeThe purpose of this paper is to examine the potential impacts of various variables on product return activities after online shopping. Previous studies on customer behaviour have been predominantly concerned with return on used products and other product-quality-related constructs in the model. This study aims to specially examine the logistics service-related and customer intention–related variables for general products under the e-commerce circumstance.Design/methodology/approachStructured questionnaire data for this study were collected in the two southeast cities of China (162 useable responses). Structural equation modelling was used to examine the latent variables.FindingsThe results confirmed that product return intention has the greatest impact on online shopping returns with a direct effect of 0.63, followed by the flexibility in return (logistics service) with a direct effect of 0.49.Originality/valueSuch a model not only enriches the theoretical understanding of customer behaviour studies but also offers online shopping stores and platforms a quantitative benchmark and new perspective on the design of online shopping supply chains by considering product returns so as to improve the customer satisfaction.

The complexity of today's e-commerce logistics environment compels practitioners to achieve a higher level of operating efficiency. As it is infeasible for operators to process a large number of discrete e-orders individually, warehouse postponement, that is, delaying the execution of a logistics process until the last possible moment, is essential. Yet the question remains as to how one can accurately identify the timing for consolidating e-orders, and subsequently releasing the grouped e-orders for batch order picking. This is a subject, lacking previous research, but is fundamentally crucial in today's e-commerce logistics environment. This paper introduces an integrated autoregressive-adaptive neuro-fuzzy inference system (AR-ANFIS) approach for forecasting e-commerce order arrivals. Two AR-ANFIS models are built for evaluating their prediction ability against ARIMA models. The experimental results confirm the suitability of the hybrid model for forecasting e-order arrivals. To make use of the model output, an algorithm is formulated to convert e-order arrival figures into cut-off time of order grouping. In this sense, this total solution, packaged as a decision support system, namely the E-order arrival prediction system, assists logistics practitioners in judging when to release the grouped e-orders for batch processing, and essentially improves their order handling capability.

Though American sign language (ASL) has gained recognition from the American society, few ASL applications have been developed with educational purposes. Those designed with real-time sign recognition systems are also lacking. Leap motion controller facilitates the real-time and accurate recognition of ASL signs. It allows an opportunity for designing a learning application with a real-time sign recognition system that seeks to improve the effectiveness of ASL learning. The project proposes an ASL learning application prototype. The application would be a whack-a-mole game with a real-time sign recognition system embedded. Since both static and dynamic signs (J, Z) exist in ASL alphabets, Long-Short Term Memory Recurrent Neural Network with k-Nearest-Neighbour method is adopted as the classification method is based on handling of sequences of input. Characteristics such as sphere radius, angles between fingers and distance between finger positions are extracted as input for the classification model. The model is trained with 2600 samples, 100 samples taken for each alphabet. The experimental results revealed that the recognition rate for 26 ASL alphabets yields an average of 99.44% accuracy rate and 91.82% in 5-fold cross-validation with the use of leap motion controller.

PurposeThe purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.Design/methodology/approachPapers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.FindingsFive main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.Research limitations/implicationsProcurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.Originality/valueThe contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

Purpose The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment. Design/methodology/approach This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes. Findings The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome. Research limitations/implications The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results. Originality/value Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

Purpose The purpose of this paper is to examine the strategic response to Industry 4.0 for Chinese automotive industry and to identify the critical factors for its successful implementation. Design/methodology/approach A technological, organizational, and environmental framework is used to build the structural models, and statistical tools are used to validate the model. The data analysis helps to determine which factors have impact on the strategic response and whether their relationships are positive or negative. Interpretive structural modeling method is applied to further analyze these derived factors for depicting the relationship. Findings The result shows that company size and nature do not increase the use of advanced production technologies, while other factors have positive impacts on improving the technology adoption among the companies surveyed. Practical implications A strategic response to Industry 4.0 not only helps in improving organizational competitiveness, but it also has social and economic implications. For this purpose, empirical data are collected to measure the understanding of Industry 4.0 in the Chinese automotive industry. Originality/value Despite the fact that the Chinese Government has proposed the “Made in China 2025” approach as a way to promote smart manufacturing, little empirical evidence exists in the literature validating company’s perspective toward Industry 4.0. This paper is to fill the research gap.

In order to achieve the sustainable separation design of azeotropic mixtures, this study proposed a systematic framework for distillation process optimization and intensification based on a composite sustainability index. Binary minimum azeotropic mixture ethanol-ethyl propionate was studied as an illustrative example. Specifically, three extractive distillation configurations were firstly applied on the separation and the sustainability of distillation processes were evaluated comprehensively. The composite sustainability index was constructed with the indicators of the economic, environmental, technical and social dimensions, and fuzzy weighting method was used to determine the weight of each indicator. Then, the process can be optimized with the objective of the composite sustainability index by the mesh adaptive direct search algorithm. After that, the heat-pump is further implemented to make full use of the latent heat of vapor stream in the distillation system . The T-H diagram of two upgraded processes clearly demonstrates the heat recovery while considering the specified minimum heat-transfer temperature difference.

The e-commerce internal order processing flow is streamlined and re-designed.A GA-rule-based system for efficient e-commerce order fulfilment is proposed.An optimal order processing plan is generated by genetic algorithm technique.A system implementation shows a significant order processing time reduction. In today's world of digitization, the rise of the e-commerce business around the globe has brought a tremendous change not only in our purchasing habits, but also to the entire retail and logistics industry. Given the irregular e-commerce order arrival patterns, limited time for order processing in e-fulfilment centres, and the guaranteed delivery schedules offered by e-retailers, such as same-day or next-day delivery upon placing an order, logistics service providers (LSPs) must be extremely efficient in handling outsourced e-commerce logistics orders. Without re-engineering the order fulfilment processes, the LSPs are found to have difficulties in executing the order fulfilment process due to the tight handling requirements. This, in turn, delays the subsequent processes in the supply chain, such as last-mile delivery operations, consequently affecting customer satisfaction towards both the retailer and the LSP. In view of the need to improve the efficiency in handling e-commerce orders, this study aims at re-engineering the fulfilment process of e-commerce orders in distribution centres. The concept of warehouse postponement is embedded into a new cloud-based e-order fulfilment pre-processing system (CEPS), by incorporating the genetic algorithm (GA) approach for e-commerce order grouping decision support and a rule-based inference engine for generating operating guidelines and suggesting the use of appropriate handling equipment. Through a case study conducted in a logistics company, the CEPS provides order handling solutions for processing e-commerce logistics orders very efficiently, with a significant reduction in order processing time and traveling distance. In turn, improved operating efficiency in e-commerce order handling allows LSPs to better align strategically with online retailers, who provide customers with aggressive, guaranteed delivery dates.

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.

It presents an integrated model for supply chain design.It considers three stages and their interactions in the model.It develops an ABC-based solution approach.It demonstrates the efficiency of the ABC-based approach. This study develops a mixed integer nonlinear programming (MINLP) model to design supply chains. In view of the limitations of many available strategic supply chain design models, this model involves three major supply chain stages, including procurement, production, and distribution, and their interactions; it takes into account bill of materials constraints for modeling complex supply chain inter-relationships. In addition, in accordance with the fact that companies nowadays develop product families, our model addresses multi-product supply chain design to respond to diverse customer requirements. Recognizing their importance, this study identifies and formulates constraints related to facility pairwise relationships and supplier priority along with the classical constraints from the available literature. To efficiently solve such a highly constrained, large scale MINLP model, we develop an approach based on an artificial bee colony (ABC) algorithm. Bicycle design and production is used to demonstrate the potential of the MINLP model for designing supply chains and the performance of the ABC-based solution approach in solving the model. The proposed model and solution approach can be considered as two fundamental components of an expert system in the broad sense. Thus, this study is expected to stimulate more future research on the development of practical expert systems for designing supply chains.

Purpose – The purpose of this paper is to determine the optimal sale effort deployment under dual-channel distribution which combines a traditional brick and mortar retail channel from the partner retailer and an online direct channel from the manufacturer. Design/methodology/approach – A sales effort competition game is set up in the dual-channel distribution between the manufacturer and the retailer. Demand under sales efforts is determined based on the consumer valuation, consumer’s channel preference and sales efforts. Then, the optimal sales effort deployment is studied with a game theory approach which allow the retailer and the manufacturer to maximize their own profit. Findings – Consumer’s channel preference is a key parameter of the demand assignment in the dual-channel distribution. Interestingly, the optimal sales effort and the profit of the manufacturer and the retailer can be limited by the other’s efficiency of sales effort. The finding suggests that the manufacturer and the retailer should collaborate to enhance the efficiency of the sales effort. It also shows that the manufacturer can utilize the direct channel as an important marketing channel even though no profit is obtained through the direct channel. Research limitations/implications – This research provides a new method to model the sales effort in the dual-channel distribution. The optimal sales efforts based on the consumer behavior are determined. However, since this study assumes a consistent product price across channels, the results is not applicable for a retailer who can set their own price. Practical implications – It is a win-win situation for adoption of the dual-channel distribution although the manufacturer can benefit more. Additionally, direct channel can be used as an effective marketing channel. Originality/value – This research contributes to a better understanding of demands in dual-channel distribution under sales efforts. Additionally, the research results provide a useful framework of sales effort deployment under different consumers’ channel preferences in the dual-channel distribution.

Purpose– The purpose of this paper is to explore the feasibility of adopting crowdsourcing for enhancing innovative problem solving through task design on task attributes. Task attributes have been proven to be an important factor influencing participation and engagement of crowdworkers.Design/methodology/approach– A survey questionnaire was developed and data from potential and experienced crowdworkers was collected and analyzed to identify the influence of task attributes on the quantity and quality of innovative solutions from crowds. This study finds that extrinsic and intrinsic motivation of task attributes are linked to psychological participation attention and the contribution of innovative solutions.Findings– It is found that crowdsourcing projects with higher awards and recognition as extrinsic motivation is positively associated with the quantity of solution. Competitive selection with performance feedback, problems with diversity of knowledge and job autonomy with more information from sponsors are positive associate with the quality of innovative solutions. These results can complement existing research studies on how task attributes influence the performance of existing crowdworkers in crowdsourcing and can also be a starting point for analyzing potential crowdworkers’ psychological perspectives.Originality/value– The novelty of this paper is that it illustrates the influential effects of five selected task attributes – monetary rewards and recognition, competitive selection, knowledge diversity, task complexity and autonomy – to enhance extrinsic and intrinsic motivation toward crowdsourcing feasibility in terms of quantity and quality of innovative solutions.

Purpose – The purpose of this paper is to formulate procurement strategies and determine the optimal procurement quantity in order to maximize profit through forward contracting and the spot market. Design/methodology/approach – The procurement process is modeled at various stages along a time horizon from the perspective of the buyer, with consideration of uncertain yields, stochastic demand and dynamic spot market prices. Monte Carlo simulation based experiments were conducted to figure out the best procurement quantity for five different scenarios. The framework was developed to understand the impact of different uncertain variables on a firm's profit. A case study was carried out in a steel making company in India, with real data. Findings – The results indicate that the proposed approach enables buyers to achieve higher profits under volatile demand conditions. In the case study, it was found that the profit is higher for the spot market than for contract pricing if there is significant demand and spot price volatility. Originality/value – This research considers not only demand uncertainty but also supply uncertainty in the procurement process, and profit analysis was carried out to enable an enterprise to set up a procurement plan by using forward contracting and the spot market. This study should also increase awareness in both academia and industry on the opportunities of using the spot market to enhance flexibility and to mitigate risk in the procurement process.

Petri net (PN) is a classical tool for modeling, simulation, and analysis. With the emergence of distributed manufacturing system (DMS), PN has been evolved into different forms such as colored PN (CPN) and timed PN (TPN). To fulfill the practical requirements, CPN has been extended to a larger and more complex model. Meanwhile, DMS becomes an important issue for industry, and how to model a complex manufacturing network for better throughput needs further investigation. A simple single type of PN only cannot fulfill all contemporary requirements at the same time. This paper proposes a new concept for a new PN-hierarchical timed CPN (HTCPN), which can be a powerful tool for modeling, simulation, and analysis for current large complex distributed manufacturing system. This paper presents a general structure of DMS and uses HTCPN to model the manufacturing system as discrete event with dynamic behavior. The proposed approach shows superiority on modeling large complex manufacturing system as it represents the material flow and transitions clearly and provides overview and detailed description for DMS.

Third-party logistics service providers (3PLs) play a vital role in contemporary supply chain management. Evaluation and selection of the right 3PLs depends on a wide range of quantitative and qualitative criteria rather than cost-based factors. Although various multi-criteria decision making approaches have been proposed, they have not considered the impact of business objectives and requirements of company stakeholders on the evaluating criteria. To enable the “voice” of company stakeholders is considered, this paper develops an integrated approach for selecting 3PL strategically. In the approach, multiple evaluating criteria are derived from the requirements of company stakeholders using a series of house of quality (HOQ). The importance of evaluating criteria is prioritized with respect to the degree of achieving the stakeholder requirements using analytic hierarchy process (AHP). Based on the ranked criteria, alternative 3PLs are evaluated and compared with each other using AHP again to make an optimal selection.

PurposeThe purpose of this paper is to present a generic framework to assess and simulate outsourcing risks in the supply chain.Design/methodology/approachThis combination approach involves a qualitative risk analysis methodology termed as the supply chain risk‐failure mode and effect analysis (SCR‐FMEA) which integrates risk identification, analysis and mitigation actions together to evaluate supply chain outsourcing risk. The qualitative risk assessment will allow risk manager to provide a visual presentation of imminent risks using the risk map. Monte Carlo simulation (MCS) on the imminent risks of delivery outsourcing using the Milk‐Run system is adopted.FindingsWith basic statistical concepts, key performance variables and the risk of delivery outsourcing are analyzed. It is found that a newly implemented delivery outsourcing arrangement on the Milk‐Run system reduces the average customer lead‐time and total cost. However, a certain extent of risk or uncertainty can still be detected due to the presence of variation.Research limitations/implicationsThis paper reveals that company can manage the risk by adopting a systematic method for identifying the potential risks before outsourcing and MCS can be applied for examining the quantifiable risks such as lead time and cost.Practical implicationsThe paper provides a generic guideline for practitioners to assess logistics outsourcing, especially for logistics management consultants and professionals for evaluating the risk and impact of outsourcing. It is believed that the proposed risk assessment framework can help to analyze the operational cost uncertainty and ensure the stability of the supply chain. However, the limitation of this research is that the full spectrum of outsourcing risk, especially the non‐quantifiable risk may not be analyzed by MCS.Originality/valueThis paper proposed an integrated framework which combines qualitative and quantitative method together for managing outsourcing risk. This research provides a standardized metric to quantify risk in the supply chain so as to determine the effectiveness of outsourcing.

Appropriate organizational resources allocation becomes a major challenge for companies to address the rapid demands for resources from different operational aspects while resource utilization is keeping low. Differentiate exiting customers with common features into smaller groups can serve as a piece of useful reference for decision-making. So far, k-means algorithm is the most commonly used clustering technique for conducting customer grouping. However, k-means limits the grouping consideration to a fixed number of dimensions among each group and the grouping results are significantly influenced by the initial clusters means. In this research, a robust genetic algorithm (GA) based k-means clustering algorithm is proposed in attempt to classify existing customers of the enterprise into groups with consideration of relevant attributes for the sake of obtaining desirable grouping results in an efficient manner. Different from k-means, the proposed GA-based k-means algorithm is able to select which and how many dimensions are better to be considered for each customer group when developing approximate optimal solutions. A case study is conducted on a window curtain manufacturer with the application of software Generator associated with MS Excel.

Research highlights? In the responsive logistics information system, radio frequency identification can provide visibility of product flow and capture the real time inventory data. ? The captured data are analysed by online analytical process to identify the market segment. ? With advert of artificial neural network, the demand pattern is recognized and the corresponding replenishment strategy can be formulated. This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today's globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow.

Reactive ion etching (RIE) is a process in the fabrication of semiconductor devices. The ability to predict the influence of the process parameters of RIE is crucial in terms of machine performance as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this correspondence paper presents a novel performance tradeoff function for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts, taking into full consideration their impact on the performance of the machine involved. An experiment using the RIE machine is provided to validate the practicability of the proposed approach.

Reverse logistics, which is the management or return flow due to product recovery, goods return, or overstock, form a closed-loop supply chain. The success of the closed-loop supply chain depends on actions of both manufacturers and customers. Now, manufacturers require producing products which are easy for disassembly, reuse and remanufacturing owing to the law of environmental protection. On the other hand, the number of customers supporting environmental protection by delivering their used products to collection points is increasing. According to the findings, the total cost spent in reverse logistics is huge. In order to minimize the total reverse logistics cost and high utilization rate of collection points, selecting appropriate locations for collection points is critical in reverse logistics. This paper proposes a genetic algorithm to determine such locations in order to maximize the coverage of customers. Also, the use of RFID is suggested to count the quantities of collected items in collection points and send the signal to the central return center. This can facilitate the vehicle scheduling for transferring the items from collection points to the return center. The significance of this research is the proposal of RFID-based reverse logistics framework and optimization of locations of collection points which allow economically and ecologically reasonable recycling. Simulation results indicated that the genetic algorithm is able to produce good-quality solutions in terms of coverage of collection points by choosing suitable locations for collection points and RFID can help detect the quantity of returned products so as to increase efficiency of logistics operations.

In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization's success. In recent years, attention has been focused on intelligent systems which have shown great promise in supporting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to overcome the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ''distributed process mining'' feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems.

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.

PurposeThis paper aims to develop a genetic algorithm (GA)‐based process knowledge integration system (GA‐PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network.Design/methodology/approachThe proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items.FindingsThe application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality.Research limitations/implicationsIn order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes.Originality/valueBecause of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA‐based approach can help to optimize the process parameters along the supply chain network.

The hard disk drive is a reliable and relatively cheap mass storage device used in every computer nowadays. In this study, one major issue affecting the product quality of the fixture inside a hard disk drive is the surface contamination of the arm finger of actuator (AFA). For economical exploitation, a primary concern is to generate a model for optimizing the process parameter settings necessary to sustain the desired cleanliness level in an ultrasonic cleaning process. Two approaches were employed to identify critical process parameters, followed by the determination of the optimal parameter settings. The former approach was a statistical design of experiments (DOE) for developing regression equations for predicting the cleanliness level and finding out the dependence of each parameter and outcome. The latter approach was in using an artificial neural network (ANN) for building prediction models. A comparative study showed that both approaches have advantages over other methods. The results obtained show a reduction in contamination of the AFA; hence it provides an aid in the improvement of product quality.

This paper proposes an energy consumption change forecasting system using fuzzy logic to reduce the uncertainty, inconvenience and inefficiency resulting from variations in the production factors. The proposed fuzzy logic approach helps the manufacturer forecast the energy consumption change in the plant when certain production input factors are varied. Predictions given by the proposed system adopts the fuzzy rule reasoning mechanism so that any changes in the overall energy consumption will neither violate the stable power supply and production schedules nor result in energy wastage. To demonstrate how the fuzzy logic approach is applied to a manufacturing system, a case study of the energy consumption forecast in a clothing manufacturing plant has been conducted in an emulated environment. The result of the case indicates a percentage change in the plant's energy consumption after analyzing three input parameters. This finding is able to provide a solid foundation on which decision makers and systems analysts can base suitable strategies for ensuring the efficiency and stability of a manufacturing system.

In today's competitive logistics business environment, airfreight forwarders need to optimize every aspect of their logistics operations. However, forwarders still heavily rely on human brain and working experiences for calculating complex cargo packing and scheduling problems. Although recent research studies related to cargo packing and scheduling problems have resulted in the development of a number of advanced techniques of cargo planning, it can be seen that most of the research work is focused on the optimization of space in order to achieve the maximum possible amount of cargo to be packed in the minimum of space. After numerous site evaluation and end-user feedbacks, it is found that space optimization does not necessarily cause profit optimization, which is the ultimate aim of logistics providers. A study of contemporary research publications indicates that there are inadequate research studies related to profit-based optimization in cargo packing areas. This paper presents a profit-based air cargo loading information system (ACLIS) that embeds an innovative technology known as heuristics iterative reasoning technology (HIRT) that supports loading plan generation, focusing on maximization of the profit margin. In general, the proposed system is meant to maximize the profit in the airfreight forwarding business. It adopts an objective function governed by a list of constraints together with rule-based reasoning to provide expert advice to support the generation of appropriate loading plans

The globalization of competition and changing customer requirements are the emerging phenomenon of today's manufacturing environment. Nowadays, manufacturers need useful information prior to making important decisions based on their experience and knowledge. However, the lack of seamless data transformation between database and knowledge based system hinders design practitioners from responding rapidly to the changes in the complex and dynamic product development environment. This paper proposes a dynamic product information schema which is characterized by its ability to provide designers with a product data exchange standard, thus, enhancing the responsiveness of product development. The approach is based on the modification of the XML standard which is assimilated with object technology to form a cross-platform protocol that enables enterprises to update their data constantly. In particular, data from the traditional relational database model can be transformed to a format which is acceptable in a knowledge base system. To validate the feasibility of the proposed schema, a trial run has been conducted in a local company based on the suggested approach.

In today's competitive environment, rapid advances in globalization and information technology have brought change at an unprecedented rate. Manufacturing, merchandising, and service companies are finding that their current approach for existing logistics operation no longer suffices. It is desirable to have a seamless and bi-directional interchange of information, coordination of decisions, and enhancement of assimilation of subsystems within and outside the relevant organization. In this paper, the proposed logistics workflow optimizer embraces the combination of On-Line Analytical Processing and Genetic Algorithm to provide decision support in different logistics activities within the supply chain. In addition, extensible Markup Language is incorporated to support the overall infrastructure, thereby providing essential support in enterprise integration. To validate the feasibility of the Internet-based logistics workflow optimizer, the implementation of the prototype in electronic industry is described...

In today’s globally competitive industries, high-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way to survive in an enterprise. Studies indicate that automating quality audits and adding decision support in quality improvement is an attractive idea. In this environment, production workflow mining is an approach for extracting knowledge from different manufacturing processes in order to assist real-time quality prediction and improvement. This papers attempts to propose an intelligent production workflow mining system (IPWMS) embracing online analytical processing (OLAP) and data mining technology, together with the use of artificial intelligence combining artificial neural networks (ANNs) and fuzzy rule sets to realize knowledge discovery and decision support in high-quality manufacturing. To validate the feasibility of the proposed system, a prototype is developed and evaluated in a company, and a description of this case example is covered in this paper.

PurposeThis paper proposes an infrastructural framework, involving various emerging technologies, for the development of an agent‐based quality enhancement system with the distinct feature of the ability to cope with the potential quality problems from various customers' requirements.Design/methodology/approachThe proposed system is equipped with the “distributed problem solving” feature which automatically identifies and divides the potential quality problems into sub‐problems and directs straight to the associated agents that are capable of interacting and negotiating with each other to support customer needs. The new feature of this agile quality enhancement system is characterized with the incorporation of the quality mining engine through the capabilities of the online analytical processing, case‐based reasoning, fuzzy logic and artificial neural networks, achieving the optimization of the performance of organizations.FindingsIn this paper, the detailed architecture and the components included in the proposed system are described with a case study to validate its feasibility of application in actual environment.Originality/valueThe proposed system is to enhance customer satisfaction by automating problem solving procedures in all business processes.

PurposeThe purpose of this research is to study how knowledge‐based systems are applied in an industrial environment. This paper attempts to propose a system, object‐based knowledge integration system (OBKIS) which supports the early stages of product development.Design/methodology/approachThis proposed system characterizes the “dynamic” information exchange capability through its distinct features to include executable tasks within product information script that is being utilized in various functional groups, thereby introducing the action items to be carried out in relevant areas. To achieve the “dynamic” information exchange capability, object technology, which is favorable to the creation of inter‐related modularized data objects, is incorporated into the product information script to facilitate the active information interchange process. The universal extensible markup language (XML) is also adopted to facilitate data exchange between the database and the knowledge base in order to make real time data and knowledge available throughout the enterprise.FindingsFurther research on developing the well‐structured XML schema is needed in order to provide a well‐understood syntax and self‐defined mark‐up language to suit particular needs of product data exchange. For verification and measurement, it is suggested that one should evaluate the system in terms of data reliability, transformation accuracy and effectiveness for improving product design process.Practical implicationsThe implications of these for information flows and management of product data during product development are discussed. In order to validate the feasibility of the proposed system, a prototype is developed for a local company so as to provide linking between the system design concept and system implementation in a practical environment.Originality/valueThe significance of this research is that a new product data schema for the initial phase of product development is formulated and the proposed system supports invoking various behaviors for the same message and overriding the pre‐defined inherited operation such that a flexible correlation can be formulated in the iterative product design process.

During the 1990s, Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP) have emerged as effective means of improving productivity and efficiency, resulting in widespread research work related to CAD and CAPP integration in the hope of achieving seamless data flow among the relevant computer systems. Unfortunately, the efficiency of process planning systems still needs significant improvement due to the problems of design data loss or design data mis-interpretation. This paper attempts to introduce an intelligent computer-integrated system for reliable design feature recognition in order to achieve automatic process planning. Moreover, it describes a rule-based computer-integrated system for recognizing features of components stored as STEP format, analyzing the design files and designing manufacturing processes. In this approach, the STEP design file is used as the information source for generating detailed manufacturing process. The significance of this research is that the product designs with dissimilar formats from various CAD systems can now be interconnected and automatically coded for multiple manufacturing purposes. It presents an effective method that rule-based reasoning is used to create optimal process plans for metal cutting.

This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

PurposeThe purpose of this paper is to propose an adjusted approximate regenerative model (ARM) for a constant‐work‐in‐process (CONWIP)‐based production system that solves the problem of deciding the number of intermediate bulk container in a pharmaceutical company to hold the work in process.Design/methodology/approachThe problem was modeled as a CONWIP system and the ARM was adjusted to estimate the throughput.FindingsBy comparing the results from the original ARM and adjusted ARM, a clear superiority of the proposed method is shown for the real case. In addition, the robustness of the adjusted ARM is demonstrated in terms of the violation of rigid assumption and the impacts brought by the limitation of buffer space before the bottleneck station.Originality/valueThe novelty of the proposed model is to take the long transportation time between machines into consideration.

Product quality is difficult to be traced and monitored in the distributed manufacturing network. This paper proposes and develops a new RFID-based CPN modeling method where the colored tokens are evolved to color-tagged tokens carrying the product information of real-time status. With this new real-time modeling method, the performance of manufacturing systems such as yield rate and throughput can be realized. In this paper, a case study has been conducted to examine the feasibility and effectiveness of the proposed method. The simulation results show that the new modeling method is able to complete the preliminary real-time quality status analysis of a manufacturing system so as to handle dynamic and stochastic manufacturing network effectively and enable decision making for process improvement.

A unified approach for constructing strictly passive systems has been proposed by Feng and Fei (1999). However, we have found that contradictory cases exist and additional prerequisites might be required for the proofs of the lemmas. Illustrations and proofs are provided in this paper to act as counterexamples.

The purpose of this article is to propose a Supply Chain Knowledge Discovery System including Radio Frequency Identification (RFID) technology together with temporal rule induction that the retailers and distributors can have a better Customer Relationship Management by realising the change of customer purchasing behaviour with latest information. The aim of this research is to identify how information system manipulates the Point-Of-Sales data and customer historical data so as to formulate the cross-selling strategy. With the advert development and application of RFID, the data collected in the linear time interval reveal the characteristics of temporal features of daily transactional data. The proposed system discovers the knowledge by querying data in temporal database interactively and recursively with Temporal Query and Mining Language. Data cleaning process is carried out prior to applying temporal association rule for basket market analysis. The significance of this research is the demonstration of the synergy of using a combination of latest technologies to form an integrated system to improve direct marketing campaign.

With the globalisation of world business, the border-crossing activities between organisations have substantially increased. Organisations not only need to handle supply functions, but also play a tremendous role in demand simulation through integration both inside the firm and outside with business partners and customers. Logistics has become more and more mature and sophisticated by taking on an external focus, incorporating suppliers and customers in the business processes, with all the supply chain functions integrated into a whole. By minimising the costs in the value chain or providing customised services, logistics acts as a major source of competitive advantages and profitability. To meet this goal, it would require the integration of activities to focus on customer-oriented measures. Customer service and logistics activities are a chain of interdependent activities that supplement each other to facilitate the flow of information, goods and cash within the value chain. The absence of one activity may imply some specific channels need to supplement another unit. Generally, this paper is to study the impact of corporate strategy, technology and customer satisfaction on the firm's performance, filling the gap of good customer service effects on long-term profits. Two international delivery providers, UPS and FedEx, are studied to realise the critical success factors of express logistics.

In a dispersed manufacturing environment where the production value chain is being dissected and rationalised to take advantage of dissimilar core competencies of geographically isolated firms, manufacturing strategy is required to modify from time to time in accordance with the turbulent market. A 'smart' Decision-Support System (DSS) is proposed with the distinct feature of artificial intelligence capabilities to achieve progressive knowledge acquisition and creation to the entity it is associated with. In particular, this proposed system is characterised by the inclusion of a Neural Object Agent (NOA), which is built upon a newly developed technology coined as neural object technology.

The airfreight forwarding business requires the application of stochastic search techniques to support the development of the industry. In the workflow of airfreight forwarding, the cargo loading process is believed to be the most probable step to find room for further improvement. How to carry cargoes efficiently needs to be taken into consideration to maximise the profit without any violation of the volume and weight constraints. Among those search techniques, Genetic Algorithms (GA), Tabu Search (TS) and Simulated Annealing (SA) are prevalently used to deal with the optimisation problems. As an illustration of the application of the three search techniques to the cargo loading problem, it is suggested that GA is the most appropriate method to apply in the optimisation of freight forwarding application. This paper begins with a glance at the cargo loading problem and the airfreight forwarding profit model. Then the working procedures of stochastic search techniques, including GA, TS and SA, are described as they are applied to the cargo loading problem. Subsequently, a qualitative comparison among these three approaches is made to suggest a search technique that is found to be suitable for optimising cargo loading plans in the airfreight forwarding business.

The lack of seamless information interchange hinders the accomplishment of manufacturing activities, which are related to the efficient bi-directional flow of information, coordination of decisions and enhancement of assimilation of practices within and outside the relevant organisation. In particular, a flexible information management system is essential to play an instrumental role of coping with unexpected changes, which normally occur in actual operational situations. To achieve the distinct feature of managing the 'unpredictables', object technology, which is favourable to the creation of inter-related modularised data objects, is incorporated to form a flexible information management system, which is able to support relevant manufacturing activities even under adverse conditions. This paper covers the underlying framework of this information management system, focusing on the role of object technology in enhancing the flexibility of such a system.

Competitive advantage is gained through operational excellence strategy while quality decisions, reviews and plans facilitate the production of quality products through the adoption of best practice as well as reduction of minimal waste and downtime. Efficient data collection with high accuracy is one of the key success factors to generate quality information, which has sufficient ability to transform data into economic value. However, inefficiency and ineffectiveness in the information life-cycle, including activities such as collect, store, use, analyse and share data, are often the common sick symptoms for most organisations. In general, the enterprises spend too much unnecessary resource on data collection and storage, and less on facilitating data analysis and sharing. This paper demonstrates how to analyse, design and develop an object-oriented information management system to re-engineer and improve the existing problems of inefficient data and information flow. In addition, it supports the data and information analysing technique to facilitate the decision-making and manufacturing process improvement. A case study on a manufacturing small and medium-sized enterprise has been conducted with promising results to validate the feasibility of the approach.

In today's competitive environment, efficient data exchange in a heterogeneous environment is an issue that needs to be addressed. In general, there are a great variety of computer systems that make the linking of computers to enable electronic data interchange even more difficult and costly. To make things even more complicated, complex data such as multimedia clips are likely to be included in the data interchange process, causing extra overhead to the data communication networking system. To overcome this problem, a system that enables the sharing of data, even complex data, among various computer systems, whether within or outside the company, is needed. In this paper, a generic framework for achieving information exchange among business objects residing in dissimilar computer systems is presented. This framework is designed with reference to the software bus communication analogy, incorporating the deployment of an object database system, which may handle the storage and retrieval of complex data such as video, animation and audio clips. The significance of this research can be realised when dealing with the problem of complex data interchange among computer systems in an industrial environment. In order to show the usability of Object Communication Bus Network (OCBN), a case study is outlined in the paper with results in favour of the proposed approach.

Inefficient data interchange and analysis obstruct the accomplishment of enterprise integration in terms of bidirectional interchange of information, coordination of decisions and enhancement of assimilation of subsystems within and outside of the relevant organisation. An efficient information exchange mechanism requires appropriate tools for data mining, thereby allowing the transformation of clusters of data into organised information. In particular, a data-mining approach that provides a solution to accomplish enterprise integration is needed. This paper proposes a generic roadmap for the design and implementation of a multiagent model which integrates the data-mining technique called On-Line Analytical Processing (OLAP) and Genetic Algorithm (GA), which is incorporated to achieve optimisation of workflow, thereby providing essential supports in enterprise integration. To validate the feasibility of the generic roadmap for such a model, a system prototype has been developed for managing workflows in a company.

To meet changing needs of customers and to survive in the increasingly globalised and competitive environment, it is necessary for companies to equip themselves with intelligent tools, thereby enabling managerial levels to use the tactical decision in a better way. However, the implementation of an intelligent system is always a challenge in Small- and Medium-sized Enterprises (SMEs). Therefore, a new and simple approach with 'process rethinking' ability is proposed to generate ongoing process improvements over time. In this paper, a roadmap of the development of an agent-based information system is described. A case example has also been provided to show how the system can assist non-specialists, for example, managers and engineers to make right decisions for a continual process improvement.