Data Mining - Concepts and Techniques.

This paper provides a detailed review of the state of the art in product cost estimation covering various techniques and methodologies developed over the years. The overall work is categorized into qualitative and quantitative techniques. The qualitative techniques are further subdivided into intuitive and analogical techniques, and the quantitative ones into parametric and analytical techniques. Each of the techniques is then described and discussed, in detail, with further subdivisions. The paper also signifies the importance of cost estimation in the early phases of the design cycle and, as such, briefly discusses the current trends and future directions in the area. Research work carried out in the field with reference to specific applications is also reviewed. The paper provides a comprehensive literature review in the field and should be useful to researchers and practitioners interested in this field.

Product Cost Estimation: Technique Classification and Methodology Review

Purpose – Although leanness is certainly not a new concept it is doubtless still relevant. Interestingly, newly developed manufacturing paradigms and systems are always examined in relation to leanness. In other words, leanness serves in most cases as the landmark paradigm with which comparisons are being drawn between the latter and recently pioneered approaches. The purpose of this paper is to highlight the evolutional orbit that leanness has followed over the years and serve as a herald of the current state of this evolution, which will be discussed further, in a separate paper.Design/methodology/approach – A great part of this paper is devoted to highlighting the misconceptions surrounding issues such as what leanness really constitutes and what are the key concepts and practices that leanness encompasses. Two successful lean transition stories are presented showing how these lean tools and techniques were implemented in the cases of two UK‐based manufacturing companies.Findings – Because of its inher...

Leanness: experiences from the journey to date

Survey on the use of simulation for manufacturing system design and operation

Digital Twin Enhanced Dynamic Job-Shop Scheduling

Activity-based cost estimation in a push/pull advanced manufacturing system

The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production control especially in dynamic production environments. This study proposes a decision support system (DSS) designed to increase the performance of dispatching rules in dynamic scheduling using real time data, hence an increase in the overall performance of the job-shop. The DSS can work with all dispatching rules. To analyze its effects, it is run with popular dispatching rules selected from the literature on a simulation model created in Arena®. When the number of jobs waiting in the queue of any workstation in the job-shop falls to a critical value, the DSS can change the order of schedules in its preceding workstations to feed the workstation as soon as possible. For this purpose, it first determines the jobs in the preceding workstations to be sent to the current workstation, then finds the job with the highest priority number according to the active dispatching rule, and lastly puts this job in the first position in its queue. The DSS is tested under low, normal, and high demand rate scenarios with respect to six performance criteria. It is observed that the DSS improves the system performance by increasing workstation utilization and decreasing both the number of tardy jobs and the amount of waiting time regardless of the employed dispatching rule.

https://www.mdpi.com/2227-7390/7/3/278/pdf

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

A multi-objective multi-period mathematical model for an industrial symbiosis network based on the forest products industry

The classification of inventories requires using several criteria to control different functions of inventory management. In this study, a new classification algorithm, called the FNS (functional, normal, and small) algorithm, is developed that combines classical ABC classification with a new grouping strategy. In the algorithm, handling frequency, lead time, contract manufacturing process, and specialty are used as input criteria, and the outputs are new classes for the inventories. The algorithm is applied in a large company operating in the defence industry. The main problem in the company is not being able to manage and track inventories effectively. The company has previously used the Pareto analysis approach, but this no longer met the company’s inventory management needs. In our study, the ABC classification method is enriched and combined with the proposed FNS algorithm to create nine different classes for inventories. To achieve this, the classical ABC classification method is integrated with expert systems, clustering, and fuzzy logic methods. Now, inventories can be classified in more detail, and useful counting strategies can be created. The classification system developed is currently being used by the company, and is integrated into its enterprise resources planning (ERP) system.

/pdf/an-inventory-classification-approach-combining-expert-55r1r809ct.pdf

An inventory classification approach combining expert systems, clustering, and fuzzy logic with the abc method, and an application

Recent advances in world order and intensified competitive environment, force firms immediately to incorporate techniques into the company culture that will radically and drastically improve the main performance criterions of quality, service, speed, and cost. This challenging surrounding in commerce and industry which emerged in the second half of the twentieth century, push the limits of the production management techniques. In recent years, the importance of a supply chain management that has the ability to respond agile and flexible market increased. Certainly, the success of a supply chain management depends on various decisive factors and can be achieved by managing several components efficiently. In this study, one of such significant factors, management and selection of vendors is examined and an expert system is designed to give rational decisions in vendor selection. For this purpose, an expert system model is developed for selection and handling of suppliers in Shoes Industries and the corresponding case study is presented.

/pdf/vendor-selection-in-supply-chain-management-by-expert-1kd7ivly63.pdf

Ahmet Kürşad Türker

Papers

Activity-based cost estimation in a push/pull advanced manufacturing system

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

A multi-objective multi-period mathematical model for an industrial symbiosis network based on the forest products industry

An inventory classification approach combining expert systems, clustering, and fuzzy logic with the abc method, and an application

Vendor Selection in Supply Chain Management by Expert Systems and a Case Study