A methodology for controlling the consequences of demand variability in the design of manufacturing systems

Today's unprecedented demand changes flood the global market. Staying competitive is now a matter of responding quickly and cost-effectively to variability. To address this paradigm, flexibility is a key aspect to tackle. Studies show that integrating flexibility in design of systems increases their performance by 25%, yet application procedures are still not very well established. This dissertation proposes a solution methodology for this problem. Aiming control of demand variability consequences, an integrated approach of optimization, screening, and simulation modelling has been developed. Applied to a case study in the furniture manufacturing industry, the methodology highlighted numerous opportunities of improvement in the manufacturing site. Indeed, by applying a flexible design, the overall performance goals were reached and a plan of action was initiated.The results support the proposed methodology as a viable solution for the problem addressed, nevertheless future success involves more than the pure application of this procedure, as flexibility is also a way of thinking

Configuration of robust manufacturing systems

Considering the increasing turbulence in the markets, many companies are faced with the task of responding to changes in customer demand in a flexible and timely manner. A variety of current research projects in terms of configuration of production systems deals with the increasing flexibility of several elements of a production system or the entire system, to meet the need for flexible responses. Furthermore, there is the avoidance or reduction of any kind of waste, including the creation of standards for the information and material flow processes at the heart of the company's efforts. Against this background, also organisationally robust processes are increasingly becoming the focus of operational actors. This paper points out the possibilities of influencing production systems and what characteristics exist regarding the requirement of structural changes. In this context, production control by defined loops and checking structural performance are indicators relevant to the focus of following considerations

Development and validation of resource flexibility measures for manufacturing industry

Purpose: Global competition and ever changing customers demand have made manufacturing organizations to rapidly adjust to complexities, uncertainties, and changes. Therefore, flexibility in manufacturing resources is necessary to respond cost effectively and rapidly to changing production needs and requirements. Ability of manufacturing resources to dynamically reallocate from one stage of a production process to another in response to shifting bottlenecks is recognized as resource flexibility. This paper aims to develop and validate resource flexibility measures for manufacturing industry that could be used by managers/ practitioners in assessing and improving the status of resource flexibility for the optimum utilization of resources. Design/methodology/approach: The study involves survey carried out in Indian manufacturing industry using a questionnaire to assess the status of various aspects of resource flexibility and their relationships. A questionnaire was specially designed covering various parameters of resource flexibility. Its reliability was checked by finding the value of Cronback alpha (0.8417). Relative weightage of various measures was found out by using Analytical Hierarchy Process (AHP). Pearson’s coefficient of correlation analysis was carried out to find out relationships between various parameters. Findings: From detailed review of literature on resource flexibility, 17 measures of resource flexibility and 47 variables were identified. The questionnaire included questions on all these measures and parameters. ‘Ability of machines to perform diverse set of operations’ and ability of workers to work on different machines’ emerged to be important measures with contributing weightage of 20.19% and 17.58% respectively. All the measures were found to be significantly correlated with overall resource flexibility except ‘training of workers’, as shown by Pearson’s coefficient of correlation. This indicates that companies do not want to spend on worker training. Practical implications: The study provides guidelines to managers/ practitioners in assessing and managing resource flexibility for optimum utilization of resources. This study can also help the firm’s management to identify the measures and variables to manage resource flexibility and the order in which stress should be given to various measures and actions. The developed and validated measures can be used globally for managing the resource flexibility in manufacturing sector. Originality/value: In this work, the theoretical perspective has been used to prepare the instrument from a detailed review of literature and then the study carried out using the questionnaire in an area where such studies were not carried out earlier.Peer Reviewe

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Koncepcijsko projektiranje inteligentnog unutarnjeg transporta materijala korištenjem umjetne inteligencije

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matlab© software package is used for developing genetic algorithms, manufacturing process simulation, implementing search algorithms and neural network training. The obtained paths are tested by means of the Khepera II mobile robot system within a static laboratory model of manufacturing environment. The experiment results clearly show that an intelligent mobile robot can follow paths generated by using genetic algorithms as well as learn and predict optimal material transport flows thanks to using neural networks. The achieved positioning error of the mobile robot indicates that the conceptual design approach based on the axiomatic design theory can be used for designing the material transport and handling tasks in intelligent manufacturing systems.Pouzdan i efikasan transport materijala je jedan od ključnih zahtjeva koji utječe na povećanje produktivnosti u industriji. Iz tog razloga, u radu su predložena dva pristupa za inteligentan transport materijala korištenjem mobilnog robota. Prvi pristup se zasniva na primjeni genetskih algoritama za optimizaciju tehnoloških procesa. Optimalna putanja se dobiva korištenjem optimalnih tehnoloških procesa i genetskih algoritama za vremensko planiranje, uz minimalno vrijeme kao kriterij. Drugi pristup je temeljen na primjeni teorije grafova za generiranje putanja i neuronskih mreža za učenje generirane putanje. Matlab© softverski paket je korišten za razvoj genetskih algoritama, simulaciju tehnoloških procesa, implementaciju algoritama pretraživanja i obučavanje neuronskih mreža. Dobivene putanje su testirane pomoću Khepera II mobilnog robota u statičkom laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju kako inteligentni mobilni robot prati putanje generirane korištenjem genetskih algoritama, kao i da uči i predviđa optimalne tokove materijala zahvaljujući neuronskim mrežama. Ostvarena pogreška pozicioniranja mobilnog robota ukazuje da se koncepcijski pristup baziran na aksiomatskoj teoriji projektiranja može koristiti u projektiranju transporta i manipulacije u inteligentnom tehnološkom sustavu

Directions of Production Planning & Production Control System: Mathematical Evolution from the Flexibility Point of View

Production Planning and Production Control systems are some of the keys that determine the development of modern production systems. The article presents the importance of a flexibility factor in the process of manufacturing, planning, and control systems development during the 20th century. Finally, actual problems and trends in the production system design and control over the design were perceived in addition to possible feasible directions of the development of manufacturing systems, probably in the 21st century were specified. Production planning and production control are usually painstaking to be one of the most noteworthy issues in the planning and operation of a manufacturing structure. A better planning system has a significant bang on cost reduction, increased productivity, customer satisfaction, and overall spirited improvement for a product. Also, the current customer demand for prominent diversity products has put into an increase in product complications that further lay emphasis on necessitates for superior planning. Proficient planning leads to the amplification in capability exploitation competence and, therefore, thereby reducing the operational time required to intact jobs and accordingly escalating the profitability of an organization in the current spirited environment. There are different systems of manufacturing, planning, and control for a job-shop together with flow-shop in which the jobs are to be a progression through a series of machines for an optimizing number of required performance measure

Analytics for Autonomous C4ISR within e-Government: a Research Agenda

e-Government enables big data analytics to support decision processes in governing. C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance) is essentially e-Government scoped to military decision processes. The value of big data and its challenges are common to both. High variety and demand for veracity compel domain expertise-specific data analysis, and increasing volume and velocity hinder data analytics at scale. These conditions challenge even highly automated methods for comprehensive cross-domain analytics, and motivate cognitive approaches such as underlie Autonomous Systems (AS) aimed at C4ISR. A C4ISR framework is examined by parts, linking each C to ISR capability, and a taxonomy of analytics is extended to include cognitive autonomy enablers. Coupling these frameworks, the authors propose an extension of cognitive approaches for autonomy in C4ISR to e-Government in general and outline a research agenda for attaining it

Job adjustment strategy for predictive maintenance in semi-fully flexible systems based on machine health status

Complex systems consist of multiple machines that are designed with a certain extent of redundancy to control any unanticipated events. The productivity of complex systems is highly affected by unexpected simultaneous machine failures due to overrunning of machines, improper maintenance, and natural characteristics. We proposed realistic configurations with multiple machines having several flexibilities to handle the above issues. The objectives of the proposed model are to reduce simultaneous machine failures by slowing down the pace of degradation of machines, to improve the average occurrence of the first failure time of machines, and to decrease the loss of production. An approach has been developed using each machine’s degradation information to predict the machine’s residual life based on which the job adjustment strategy where machines with a lower health status will be given a high number of jobs to perform is proposed. This approach is validated by applying it in a fabric weaving industry as a real-world case study under different scenarios and the performance is compared with two other key benchmark strategies.This work has been funded by the Department of Science and Technology, Science and Engineering Research Board (DST-SERB), a statutory body established through an Act of Parliament: SERB Act 2008, Government of India with sanction order no. ECR/2016/001808, and also by the FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020