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Innovation processes are inherently uncertain. They account for a high proportion of risks taken by companies and cause tensions. The purpose of this paper is to consider whether some management tools facilitate the smooth implementation of an innovation process. Does the relevance of these tools depend on the size of the company, its activity sector, the type of innovation expected or the viewpoint of the manager in charge?

The author answers these questions using a quantitative questionnaire survey of 169 companies. This survey allows the author to describe the tools and practices of management control of innovation processes and to highlight the specific needs of different sets of companies.

The collected data show a convergence of tools and practices used, which the literature on managerial control and creativity helps us to understand.

The paper also contributes to reconciling management control and innovation activities. In particular, it investigates how the use of management tools varies depending on managers’ viewpoints. A distinction is made between formal, information system-based tools and more informal, human relationship-based tools.

A particular difficulty arises when lean manufacturing principles are applied to temperature dependent processes. Specifically, when an interruption occurs, lean manufacturing advocates stopping the production line to find the “root cause” of the problem. However, interruptions in temperature dependent processes will cause work‐in‐progress and process equipment to cool down and quality problems are likely when production restarts. This difficulty could be countered through a detailed process model, but apart from the difficulty of creating perfect process models with accurate inputs, the use of such a system would separate shop floor operators from the problem‐solving process. Instead, a response to interruptions is sought in the form of a “script” of actions which can be used to train operators. In order to develop an appropriate script, a generic model of a set of temperature dependent processes is defined in which the states of processes and products are time dependent. A hypothetical profit function is defined for the processes and it is shown how this could be optimized by dynamic programming. The dynamic programming process is then translated into a script to guide operators in decision‐making during interruptions. The script is demonstrated with a simulation model of an extrusion process, and the results suggest that the new script will result in improved throughput and reduced scrap in the presence of interruptions.

Quality function deployment (QFD) is a suitable tool for understanding the expectations of hotel guests from services provided to them and designing the new one. It is also a well-known technique for improving service and product quality in general. First applied by Yoji Akao in 1960, the idea behind QFD is to understand the customer needs and determine the problem which might be associated with product or service provided by an enterprise. The purpose of this paper is to present how check-in operations in hospitality business can be improved with the help of QFD.

In this study, deep interview was employed as main data gathering instrument. Once customer expectations were assessed, a House of Quality scheme was established. Consequently, the QFD matrix was being analyzed as a whole.

The results of the study demonstrate that customers do not want to wait in queue or for any process on the front desk.

The research contributes to the literature a new technical term on hospitality industry, “Voice of Hotel.”

This paper aims to solve the problem of free-form tubes’ machining errors which are caused by their complex geometries and material properties.

In this paper, the authors propose a multi-view vision-based method for measuring free-form tubes. The authors apply photogrammetry theory to construct the initial model and then optimize the model using an energy function. The energy function is based on the features of the image of the tube. Solving the energy function allows to use the gray features of the images to reconstruct centerline point clouds and thus obtain the pertinent geometric parameters.

According to the experiments, the measurement process takes less than 2 min and the precision of the proposed system is 0.2 mm. The authors used simple operations to carry out the measurements, and the process is fully automatic.

This paper proposes a method for measuring free-form tubes based on multi-view vision, which has not been attempted to the best of authors’ knowledge. This method differs from traditional multi-view vision measurement methods, because it does not rely on the data of the design model of the tube. The application of the energy function also avoids the problem of matching corresponding points and thus simplifying the calculation and improving its stability.

Sharding of blockchains consists of partitioning a blockchain network into several sub-networks called “shards,” each shard processing and storing disjoint sets of transactions in parallel. Sharding has recently been applied to public blockchains to improve scalability through parallelism. The throughput of sharded blockchain is optimized when the workload among the shards is approximately the same. The purpose of this paper is to investigate the problem of balancing workload of account-based blockchains such as Ethereum.

Two known consensus-based distributed load-balancing algorithms have been adapted to sharded blockchains. These algorithms migrate accounts across shards to balance transaction processing times. Two methods to predict transaction processing times are proposed.

The authors identify some challenging aspects for solving the load-balancing problem in sharded blockchains. Experiments conducted with Ethereum transactions show that the two load-balancing algorithms are challenged by accounts often created to process a single transaction to optimize anonymity, while existing accounts sparsely generate transactions.

Tests in this work have been conducted on transactions originating from a blockchain platform rather than using artificially generated data distributions. They show the specificity of the load-balancing problem for sharded blockchains, which were hidden in artificial data sets.

This article reports a preliminary investigation into the man‐computer interaction (m‐c‐i) aspects of online searching. It describes the methodology developed for observing and recording m‐c‐i in the use of online information retrieval systems. A brief analysis of the data obtained is also given, together with an assessment of the extent to which the goal of effective m‐c‐i is being achieved.

The conventional textile dyeing process requires various operational characteristics, and determining the most reliable factor in dyeing performance has always been a challenge for the textile industry. Thus, the present paper aimed to evaluate the process sensitivity of C. I. Reactive Blue 194 dyeing of cotton fabric using a statistical technique.

An L₂₇ orthogonal array-based Taguchi's methodology was used with six parameters and three levels of each parameter. The signal-to-noise (S/N) ratio and analysis of variance were studied using total fixation efficiency (T%) as the response of the process sensitivity.

Results showed that dyebath pH was the most influential factor on the process and total fixation efficiency (p-value = 0.00 and contribution percentage 45.03%), followed by dye-fixing temperature, dye mass, electrolyte concentration, dye-fixing time and material to liquor ratio.

Overall this study provides a foundation for the determination of dyeing process sensitivity that will be useful in textile industries toward further development.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

Textile manufacturers worldwide are reformulating their networks, often outsourcing them to maintain a competitive advantage and increase market share. From this perspective, the purpose of this study is support the partnership selection process to develop a sustainable chain that effectively meets customer needs. Brazil has the largest textile and apparel chain in the West and is distinguished by its completeness, from fiber production, spinning, weaving, knitting, finishing and sewing to fashion shows. However, a firm’s relationship, especially in the production stage, is based on informal contracts, which result in a negative operational impact.

A methodological framework was developed based on a stable matching process to determine the optimal supplier network structure. This study presents a model application for the denim apparel chain in northeast Brazil.

In these environments, providing choices and recommending suppliers can be beneficial for effectively attending to demand requests, reducing production costs and improving quality through collaboration with sense relationships in a network. Thus, this study presents a better match from the negotiators’ perspective.

The findings of this research are of primary interest for guiding collaborative network composition in the textile and apparel chain. In particular, apparel domain companies can improve their effectiveness in decision-making by measuring the characteristics and potential of all companies involved in networks.