The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

Purpose

The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes.

Design/methodology/approach

The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side.

Findings

The results carried out from this study help the managers’ staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production.

Research limitations/implications

The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated.

Originality/value

The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers’ staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.

Nowadays, the real life constraints necessitates
controlling modern machines using human intervention
by means of sensorial organs. The voice is one of the human
senses that can control/monitor modern interfaces.
In this context, Automatic Speech Recognition is principally
used to convert natural voice into computer text as
well as to perform an action based on the instructions
given by the human. In this paper, we propose a general
framework for Arabic speech recognition that uses Long
Short-Term Memory (LSTM) and Neural Network (Multi-
Layer Perceptron: MLP) classifier to cope with the nonuniform
sequence length of the speech utterances issued
fromboth feature extraction techniques, (1)Mel Frequency
Cepstral Coefficients MFCC (static and dynamic features),
(2) the Filter Banks (FB) coefficients. The neural architecture
can recognize the isolated Arabic speech via classification
technique. The proposed system involves, first, extracting
pertinent features from the natural speech signal
using MFCC (static and dynamic features) and FB. Next,
the extracted features are padded in order to deal with the
non-uniformity of the sequences length. Then, a deep architecture
represented by a recurrent LSTM or GRU (Gated
Recurrent Unit) architectures are used to encode the sequences
ofMFCC/FB features as a fixed size vector that will
be introduced to a Multi-Layer Perceptron network (MLP)
to perform the classification (recognition). The proposed
system is assessed using two different databases, the first
one concerns the spoken digit recognition where a comparison
with other related works in the literature is performed,
whereas the second one contains the spoken TV
commands. The obtained results show the superiority of
the proposed approach.

Efficient routing and scheduling of vehicles has significant economic implications for both the public and private sectors. To this purpose, we propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that we call the Multi-Depot Heterogeneous Vehicle Routing Problem with Time Window (MDHVRPTW) by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and we solve this problem by proposing a new scheme based on the application of the bio-inspired genetic algorithm heuristics and by embedding a clustering algorithm within a VRPTW optimization frame work, that we will specify later. Computational experiments with the benchmark test instances confirm that our approach produces acceptable quality solutions compared with the best previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that our proposed genetic algorithm is effective in solving the MDHVRPTW problem and hence has a great potential.

In this paper, we present the application of knowledge engineering and externalisation of tacit knowledge in manufacturing industry, in order to improve the performance of a production system and save the knowledge capital of the company. The main aim of this study is to propose a knowledge model for manufacturing task combining common knowledge acquisition and design support (CommonKADS) and methodology for acquisition of tacit knowledge (MACTAK) methodologies, using two different knowledge base modelling based on two categories: (i) ontology and (ii) expert knowledge base. In that purpose, we suggest a process dedicated to industrial manufacturing, allowing to capitalise knowledge by: (1) Externalisation of tacit knowledge by MACTAK-methodology in industrial processes, (2) using knowledge engineering method; CommonKADS methodology, (3) Formalizing and modelling the domain knowledge using ontology and inference model, (4) presenting the implementation tool to support the knowledge model and (5) reusing the manufacturing knowledge model in decision support systems. The three pillars of methodology are: the externalisation process, Knowledge representation technique and quality tools. The proposed model is applied in manufacturing monitoring systems.

This study aims are data acquisition, control and online modeling of an oil collection pipeline network using a SCADA «Supervisory Control and Data Acquisition» system, allowing the optimization of this network in real time by creating more exact models of onsite facilities. Indeed, fast development of computing systems makes obsolete usage of old systems for which maintenance became more and more expensive and their performances don't comply any more with modern company operations. SCADA system is a telemetry and control system adapted for particular requirements of an oilfield management. Thanks to its different functions, we take advantage of this system to solve production problems especially those related to oil collecting pipeline network. In fact this network is confronted to some problems, in particular pressure losses which has significant effect on the production. This problem can be taken under control by the awareness of pipeline network operation and all its process data (especially junctions) in real time. This will allow online creation of representative and accurate computerized models for the oil collecting pipeline network including producing wells, collecting pipelines, manifolds and others facilities.

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors.

The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give ASKANDIA, by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26% and could be generalised to other manufacturing or service systems.

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurrent

In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences.