Manufacturing lead time estimation using data mining

2012

Lead time estimation (LTE) is difficult to carry out, especially within the RFID-enabled real-time manufacturing shopfloor environment since large number of factors may greatly affect its precision. This paper proposes a data mining approach with four steps each of which is equipped with suitable mathematical models to analysis the LTE from a real-life case and then to quantitatively examine its key impact factors such as processing routine, batching strategy, scheduling rules and critical parameters of specification. Experiments are carried out for this purpose and results imply that batching strategy, scheduling rules and two specification parameters largely influence the LTE, while, processing routine has less impact in this case. Keywords Data mining Á Lead time Á Radio frequency identification (RFID) Á Real-time Á Shopfloor production 33.1 Introduction Lead time estimation (LTE) is significant since it exclusively influences customer relations and shopfloor management practices (Alexander 1980). The direct outcome of LTE is due data quoting which indicates the commitment to meet

Procedia CIRP, 2018

In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

2012

In recent years data mining has become a very popular technique for extracting information from the database in different areas due to its flexibility of working on any kind of databases and also due to the surprising results. This paper is an attempt to introduce application of data mining techniques in the manufacturing industry to which least importance has been given. A taste of implement-able areas in manufacturing enterprises is discussed with a proposed architecture, which can be applied to an individual enterprise as well as to an extended enterprise to get benefit of data mining technique and to share the discovered knowledge among enterprises. The paper proposes conceptual methods for better use of different data mining techniques in product manufacturing life cycle. These techniques include statistical techniques, neural networks, decision trees and genetic algorithms. An integrated and unified data mining platform is anticipated then to improve overall manufacturing process.

Journal of Manufacturing Science and Engineering-transactions of The Asme, 2006

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

IFAC-PapersOnLine, 2018

The paper proposes an approach to the early detection of factors implying the need in production schedule update. Resource state prediction methods are based on the development of a binary model and a machine learning techniques called association rules search.

IEEE Transactions on Semiconductor Manufacturing, 2006

An estimate of cycle time for a product in a factory is critical to semiconductor manufacturers (and in other industries) to assess customer due dates, schedule resources and actions for anticipated job completions, and to monitor the operation. Historical data can be used to learn a predictive model for cycle time based on measured and calculated process metrics (such as work-in-progress at specific operations, lot priority, product type, and so forth). Such a method is relatively easy to develop and maintain. Modern data mining algorithms are used to develop nonlinear predictors applicable to the majority of process lots, and three methods are compared here. They are compared with respect to performance in actual manufacturing data (to predict times for both final and intermediate steps) and for the feasibility to maintain and rebuild the model.

2022

In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of laser manufacturing processes. We introduce probabilistic modelling in the domain and compare the models in terms of different abilities. While providing a comparison between the models in real-life data, our work has many use cases and substantial business value. Our results indicate that all of the models beat the company estimation benchmark that uses domain experience and have good calibration with the empirical frequencies.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

Recently due to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, "association rules" algorithms "Apriori algorithm", and "chi-square automatic interaction detection (CHAID) analysis tree" have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.

47th CIRP Conference on Manufacturing Systems, 2014

Product complexity leads to increased unpredictability of indices related to manufacturing performance estimation. This phenomenon is intensified in companies that produce engineered-to-order products, such as the knowledge and labour intensive mould-making industry. During the initial capturing of product specifications formalisation difficulties arise. Moreover, the estimation of delivery times for new moulding project is solely based on the engineers’ experience. A methodology, which has been developed into a software tool is proposed that exposes graphical interfaces for customers to submit new orders and establish a formalised communication with the engineering team. The collected data are stored in a knowledge repository and are processed by a case-based reasoning mechanism for the lead time estimation. A real-life pilot installation has been initiated to a mould making SME. Preliminary results depict a significant reduction in the number of iterations between customers and engineering department compared to the traditional approach followed by the company, and improved accuracy of lead time estimation.

IFAC-PapersOnLine, 2017

The Industry 4.0 concept assumes that modern manufacturing systems generate huge amounts of data that must be collected, stored, managed and analysed. The case study is focused on predicting the manufacturing process behaviour according to production data. The paper presents the way of gaining knowledge about the future behaviour of manufacturing system by data mining predictive tasks. The proposed simulation model of the real manufacturing process was designed to obtain the data necessary for the control process. The predictions of the manufacturing process behaviour were implemented varying the input parameters using selected methods and techniques of data mining. The predicted process behaviour was verified using the simulation model. The authors analysed different methods. The neural network method was selected for deploying new data by PMML files in the final phases. The objectives of the research are to design and verify the data mining tools in order to support the manufacturing system control by aiming at improving the decisionmaking process. Based on the prediction of the goal production outcomes, the actual control strategies can be precisely modified. Then they can be used in real manufacturing system without risks.