Forecast Based on an Empirical Probability Function with an Adjusted Error Using Propagation of Error

This paper addresses a cutting edge method of business demand forecasting, based on an empirical probability function when the historical behavior of the data is random. Additionally, it presents error determination based on the numerical method technique ‘propagation of errors.’ The methodology was conducted characterization and process diagnostics demand planning as part of the production management, then new ways to predict its value through techniques of probability and to calculate their mistake investigated, it was tools used numerical methods. All this based on the behavior of the data. This analysis was determined considering the specific business circumstances of a company in the sector of communications, located in the city of Bogota, Colombia. In conclusion, using this application it was possible to obtain the adequate stock of the products required by the company to provide its services, helping the company reduce its service time, increase the client satisfaction rate, reduce stock which has not been in rotation for a long time, code its inventory, and plan reorder points for the replenishment of stock.

Capacity Flexibility within Production

Due to high dynamics in current markets the expectations regarding logistics increase steadily. However, the complexity and variety of products and production make it difficult to understand the interdependencies between logistical objectives and their determining factors. Therefore specific models are needed to meet this challenge. The Logistic Operating Curves Theory is such a model. With its aid the basic correlations between the logistic objectives can be described. Within this model the capacity flexibility represents an important parameter. However, a proper mathematical description for this parameter is still missing. Within this paper such a description will be developed in order to make the Logistic Operating Curves Theory more accurate.

Use of RFID Technology for Identification, Traceability Monitoring and the Checking of Product Authenticity

This paper is an overview of the structure of Radio Frequency Identification (RFID) systems and radio frequency bands used by RFID technology. It also presents a solution based on the application of RFID for brand authentication, traceability and tracking, by implementing a production management system and extending its use to traders.

Production Structure Monitoring - A Neurologic Based Approach

Manufacturing companies are facing a broad variety of challenges caused by a dynamic production environment. To succeed in such an environment, it is crucial to minimize the loss of time required to trigger the adaptation process of a company-s production structures. This paper presents an approach for the continuous monitoring of production structures by neurologic principles. It enhances classical monitoring concepts, which are principally focused on reactive strategies, and enables companies to act proactively. Thereby, strategic aspects regarding the harmonization of certain life cycles are integrated into the decision making process for triggering the reconfiguration process of the production structure.

The Data Mining usage in Production System Management

The paper gives the pilot results of the project that is oriented on the use of data mining techniques and knowledge discoveries from production systems through them. They have been used in the management of these systems. The simulation models of manufacturing systems have been developed to obtain the necessary data about production. The authors have developed the way of storing data obtained from the simulation models in the data warehouse. Data mining model has been created by using specific methods and selected techniques for defined problems of production system management. The new knowledge has been applied to production management system. Gained knowledge has been tested on simulation models of the production system. An important benefit of the project has been proposal of the new methodology. This methodology is focused on data mining from the databases that store operational data about the production process.

A Hybrid Multi Objective Algorithm for Flexible Job Shop Scheduling

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, it quit difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. In this paper, a Pareto approach to solve the multi objective flexible job shop scheduling problems is proposed. The objectives considered are to minimize the overall completion time (makespan) and total weighted tardiness (TWT). An effective simulated annealing algorithm based on the proposed approach is presented to solve multi objective flexible job shop scheduling problem. An external memory of non-dominated solutions is considered to save and update the non-dominated solutions during the solution process. Numerical examples are used to evaluate and study the performance of the proposed algorithm. The proposed algorithm can be applied easily in real factory conditions and for large size problems. It should thus be useful to both practitioners and researchers.

Evaluating the Innovation Ability of Manufacturing Resources

Due to today-s turbulent environment, manufacturing resources, particularly in assembly, must be reconfigured frequently. These reconfigurations are caused by various, partly cyclic, influencing factors. Hence, it is important to evaluate the innovation ability - the capability of resources to implement innovations quickly and efficiently without large expense - of manufacturing resources. For this purpose, a new methodology is presented in this article. Within the methodology, design structure matrices and graph theory are used. The results of the methodology include different indices to evaluate the innovation ability of the manufacturing resources. Due to the cyclicity of the influencing factors, the methodology can be used to synchronize the realization of adaptations.

Using Radio Frequency Identification Technology in Supply Chain Management

The radio frequency identification (RFID) is a technology for automatic identification of items, particularly in supply chain, but it is becoming increasingly important for industrial applications. Unlike barcode technology that detects the optical signals reflected from barcode labels, RFID uses radio waves to transmit the information from an RFID tag affixed to the physical object. In contrast to today most often use of this technology in warehouse inventory and supply chain, the focus of this paper is an overview of the structure of RFID systems used by RFID technology and it also presents a solution based on the application of RFID for brand authentication, traceability and tracking, by implementing a production management system and extending its use to traders.