Forecasting the efficiency of weft knitting production: a decision tree method

Purpose This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor. Design/methodology/approach The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances. Findings The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%. Originality/Values This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.

A Performance-Based Dispatching Rule for Decentralised Manufacturing Planning and Production Control System

Considering a Flow Shop production line in an Industry 4.0 setting where the Cyber-Physical System (CPS) and Internet of Things (IoTs) can be deployed, a newly Performance-based Decentralised Dispatching Rule (PDDR) is proposed. It combines known dispatching rules with the knowledge of the monitored production system state. The goal is to provide a novel dispatching rule based on production line performance oversight. The governance system considers the machine condition in terms of machine utilisation. Regarding the assessment scenario, the proposed rule has been tested and compared with the well-known Short Processing Time (SPT) and the First-In-First-Out (FIFO) rule in a higher generality way by taking into account unforeseen events that may occur in production (such as breakdowns, potential rework, micro-stops, and unplanned machine setups). The simulation results showed interesting results where the flexibility of this rule, as well as its practical use with real hypotheses are its main advantages.

The Manufacturing Planning of Installation Series-Parallel Combination Centrifugal Pump Testing Equipment

This study discusses the planning in making a centrifugal pump installation with a combination of two series, namely series and parallel. The concept of discussion refers more to the upgrading of the installation system and calculates the estimated processing time needed to test the performance of the results of the installation that has been made. From the results of the research, it was found that for the preparation of series series installation it takes ± 8.5 minutes including the cutting process of materials and component installation, while the time required in making the installation of parallel circuits ± 28 minutes so that the total manufacturing of the two types of series takes 36, 5 minutes. In terms of performance generated from each installation obtained at full aperture where for the series of head series the value obtained is 37.6 m with a flow capacity of 3.25 l / s while for the head value on the parallel pump obtained by 27.7 m with a capacity flow of 3.75 l / s and this pump operates at 2900 rpm. So it can be seen from the results of the head and flow capacity obtained values ​​indicate good pump performance and ready to be used as a practical tool in the laboratory.

Accelerate the optimization of large-scale manufacturing planning using game theory

This paper studies a real-world manufacturing problem, which is modeled as a bi-objective integer programming problem. The variables and constraints involved are usually numerous and dramatically vary according to the manufacturing data. It is very challenging to directly solve such large-scale problems using heuristic algorithms or commercial solvers. Considering that the decision space of such problems is usually sparse and has a block-like structure, we propose to use decomposition methods to accelerate the optimization process. However, the existing decomposition methods require that the problem has strict block structures, which is not suitable for our problem. To deal with problems with such block-like structures, we propose a game theory based decomposition algorithm. This new method can overcome the large-scale issue and guarantee convergence to some extent, as it can narrow down the search space and accelerate the convergence. Extensive experimental results on real-world industrial manufacturing planning problems show that our method is more effective than the world fastest commercial solver Gurobi. The results also indicate that our method is less sensitive to the problem scale comparing with Gurobi.

Transformation of the Innovative and Sustainable Supply Chain with Upcoming Real-Time Fashion Systems

Technologies that are ready-to-use and adaptable in real time to customers’ individual needs are influencing the supply chain of the future. This study proposes a supply chain framework for an innovative and sustainable real-time fashion system (RTFS) between enterprises, designers, and consumers in 3D clothing production systems, using information communication technology, artificial intelligence (AI), and virtual environments. In particular, the RTFS is targeted at customers actively involved in product purchasing, personalising, co-designing, and manufacturing planning. The fashion industry is oriented towards 3D services as a service model, owing to the automation and democratisation of product customisation and personalisation processes. Furthermore, AI offers referral services to prosumers or/and customers and companies, and proposes individual designs with perfect styles and measurements using new 3D computer aided design and AI-based product design technologies for fashion and design companies and customers. Consequently, 3D fashion products in the RTFS supply chain are entirely digital, saving time and money with sampling and tracking capabilities, secured, and trusted with personalised service delivery.

A Method for Assessing Manufacturability of Products Using Semantic Models in Digital Manufacturing

The efficiency of machine-building production is largely determined by the development time of new types of high-tech products and their modifications. In these conditions, the time of evaluating product manufacturability at the manufacturing planning stage is crucial. As this evaluation requires processing a substantial amount of information, the process becomes very time consuming. This problem can be resolved through automation. To increase automation of the manufacturability assessment, a method based on a three-stage algorithm for analyzing the availability of design and technological solutions with the production and technological capabilities of the enterprise is developed. The proposed algorithm allows step-by-step identification of structural and technological problems of product manufacturing at a specific enterprise and creation of possible solutions while simultaneously managing modifications of the product and the production system of the enterprise. The method can also be used for identification and exclusion from further analysis of enterprises that require vast investments to prepare and master manufacturing of a product or its components. This will significantly shorten the development time of new types of high-tech products and their modifications.