Databased Decision Support for the Design of Business Processes in Manufacturing Companies

2021 ◽  
pp. 565-573
Author(s):  
M. Schopen ◽  
L. Geesmann ◽  
S. Schmitz ◽  
A. Gützlaff ◽  
G. Schuh
Keyword(s):  
Decision Support ◽  
Business Processes ◽  
Manufacturing Companies

DECISION SUPPORT FOR A SUSTAINABLE PRODUCTION IN JOB SHOP MANUFACTURING SYSTEMS

10.6036/9917 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 455-459
Author(s):  
MAHDI NADERI ◽  
ANTONIO FERNÁNDEZ ULLOA ◽  
JOSÉ ENRIQUE ARES GÓMEZ ◽  
GUSTAVO PELÁEZ LOURIDO
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Job Shop ◽  
Sustainable Production ◽  
Ease Of Use ◽  
Manufacturing Companies ◽  
Strategic Decisions ◽  
Production Environments

Despite the growing importance that is being given to the concepts of sustainability in many areas, not only in industry but also in the economy and public opinion in general, until now, most research has focused, practically, on the analysis of the concepts, but has not addressed, in a comprehensive way, its impact in decision making probably due to the complex relations of interdependence between its different aspects. In this context, MAPSAM (Methodology for the Assessment of Sustainability in Manufacturing Processes and Systems) was created to help the decision-making process, allowing a conscious and transparent assessment by administrators and managers at the different levels of the structure of companies and organisations. This article explains its development and application in a "job shop" type manufacturing system with an approach that allows the integration of economic, environmental and social criteria. MAPSAM is based on the use of various techniques and tools to quantify the importance of each aspect of sustainability and it has been applied in other production environments, being implemented in different systems, analysing their ease of use and evaluating their behaviour. The objective is to show how it helps to make operational, tactical and strategic decisions in the management on these type of manufacturing companies and, specifically, in this contribution we want to highlight its versatility and applicability, by validating it in a certain type of layout. With this new application, MAPSAM increases its possibilities as an innovative instrument that allows companies to make conscious and sustainable decisions in order to be more efficient, fair, supportive and respectful of the environment. Keywords: Manufacturing System, Simulation, Decision Support, Sustainable Production, Decision-Making

NBIC-TECHNOLOGIES FOR A DIGITAL ECONOMY

2021 ◽  
Author(s):  
О.Н. МАСЛОВ
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Simulation Modeling ◽  
Information Technologies ◽  
Business Processes ◽  
Digital Economy ◽  
Biological Information ◽  
Role Of Information ◽  
New Formation

Дается обоснование необходимости ускоренного внедрения NBIC-технологий (нанотехнологии, информацион -ные, биологические и когнитивные технологии) в отечественное производство на стадии его перехода к цифровой экономике. Рассматривается проблема формирования системы генерации и реализации инновационных знаний; показана ключевая роль информационных технологий (реинжиниринг бизнес-процессов, имитационное моделирование, системы поддержки реше -ний и др.). Отмечена важность подготовки кадров новой формации, способных использовать достижения NBIC-технологий в интересах современного производства. The paper discusses the need for accelerated implementation of NBIC-technologies (according to the first letters of their names: nanotechnology, biological, information, and cognitive technologies) in domestic production at the stage of its transition to the digital economy. The problem of forming a system for generating and implementing innovative knowledge is considered. The key role of information technologies (business processes reengineering, simulation modeling, decision support systems, etc.) in its solution is shown. The importance of training personnel of a new formation, capable of using the achievements of NBIC-technologies in the interests of modern production, is noted.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals Business Process Improvement of Production Systems Using Coloured Petri Nets

2018 ◽  
Vol 7 (1) ◽  
pp. 102-112 ◽  
Author(s):  
Imam Mukhlash ◽  
Widya Nilam Rumana ◽  
Dieky Adzkiya ◽  
Riyanarto Sarno
Keyword(s):  
Petri Nets ◽  
Business Process ◽  
Business Performance ◽  
Business Processes ◽  
Production Systems ◽  
Coloured Petri Nets ◽  
Manufacturing Companies ◽  
Fundamental Factor ◽  
Bottleneck Analysis

The quality of information systems affects the company's business performance. Therefore, it is necessary to analyze business processes to determine any discrepancies between the planned business processes and the actual ones. Based on the results of this analysis, the business process can be improved. The fundamental factor of manufacturing companies is production process. In reality, there are many discrepancies between the actual business processes with the pre-planned, so that there should be analyzed. The analysis can be performed by modeling the business process using Coloured Petri Nets (CPN). In this study, the objectives are to determine the level of conformance checking of business processes, reachability graph and the bottleneck analysis. The results of the analysis are used to construct a recommended model. Based on the analysis of the case study, e.g. a steel industry in Indonesia, the recommended model has a better value than initial model.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Design of a Decision Support System for a Home Telehealth Application

2013 ◽  
Vol 4 (3) ◽  
pp. 68-79 ◽  
Author(s):  
Mas S. Mohktar ◽  
Kezhang Lin ◽  
Stephen J. Redmond ◽  
Jim Basilakis ◽  
Nigel H. Lovell
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Open Source Software ◽  
Business Processes ◽  
Rule Engine ◽  
Clinical Support ◽  
Tier System ◽  
Home Telehealth ◽  
Data Layer

A decision support system (DSS) that has been designed to manage patients using a home telehealth system is presented. The DSS has been developed to assist home telehealth clinical support staff with their workload, and to provide more effective communication between multiple home telehealth users. The three-tier system architecture that consists of a data layer; a business logic layer; and a front-end layer employs business processes and uses a rule engine for its logic and knowledge base. This paper discusses the design considerations involved in the construction of a DSS for the purpose of home telehealth, and illustrates how it may be developed using entirely open source software.

Is there any Significant Effect of Business Process Re-Engineering Strategy on Service Delivery in the Contextualization of Food Manufacturing Companies in Kenya?

2020 ◽  
Vol 8 (9) ◽  
pp. 189-203
Author(s):  
Richard Nyaanga Ongeri ◽  
Peterson Obara Magutu ◽  
Kate Litondo
Keyword(s):  
Service Delivery ◽  
Business Process ◽  
Significant Relationship ◽  
Business Processes ◽  
Empirical Relationship ◽  
Good Representation ◽  
Manufacturing Companies ◽  
Food Manufacturing ◽  
Engineering Strategy ◽  
Functional Teams

The main objective of the study was to determine if there any significant effect of business process re-engineering strategy on service delivery in the contextualization of food manufacturing companies in Kenya. Accordingly, the study sought to determine the effect of business process re-engineering strategy on service delivery of companies manufacturing food in Kenya. The population of the study comprised of the company’s manufacturing food in Kenya. A descriptive cross-sectional survey design was adopted in data collection and analysis. Primary data was collected from respondents using a structured questionnaire, while secondary data was collected from published firm’s reports. Out of the 75 respondents targeted by the study, 44 respondents forming 56.67% response rate, which was considered adequate for analysis with good representation from all the subsectors. On hypotheses testing, it was established that, 58.1% of variations in the service delivery are explained by variations in the BPR strategy namely resources mobilization for BPR, sponsorship and commitment, BPR cross-functional teams, analytical processes selection, BPR prototypes, management of re-engineered processes, clear BPR definition and vision. Thus, there is a significant relationship between BPR strategy and service delivery of companies manufacturing food in Kenya. HA1 is therefore supported. In conclusion, the study confirmed that there is a positive and statistically significant relationship between BPR strategy and service delivery of companies manufacturing food in Kenya, where 58.1% of variations in the service delivery is explained by variations in the BPR strategy namely resources mobilization for BPR, sponsorship and commitment, BPR cross functional teams, analytical processes selection, BPR prototypes, management of re-engineered processes, clear BPR definition and vision. The results therefore support the anchoring theory of resource advantage theory. This study has contributed in different areas including implications to theory, policy, management practice and methodological contributions as discussed in the subsequent paragraphs. First, this study has advanced frontiers of knowledge from the study findings;  this study confirms that today’s competitive environment compels organizations to re-engineer their business processes to effect perfect service delivering for customer satisfaction which eventually leads to improved overall FP (Hussein, Bazzi, Dayekh & Hassan, 2013; Jurisch, Ikas, Wolfgang, Wolf & Kurcmar, 2012). The research findings have addressed the key gaps in this study. Secondly, this study has contributed to theory: the empirical relationship between BPR strategy and service is significant where BPR strategy constructs independently and positively influences improvements in service delivery with four significant predictors: resources mobilization for BPR; BPR cross-functional teams; sponsorship and commitment of top management; and the management of re-engineered processes. This study confirms and supports the use of resource based view theory. Thirdly on the study’s policy contributions: the study will guide policy makers to develop BPR strategies that will lead to improved service with the understanding that improved business processes facilitates organizations to maximize the value addition which eventually leads to improved service delivery. Lastly on the methodological contributions: key methodological contribution is the use of a quantitative composite index in computing the SD index, the use an integrated empirical model to test the relation between BPR strategies and service delivery; the study used a number of indicators to measure each construct, which improved the construct validity.

Sensor-based ambient intelligence for optimal energy efficiency

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 170-181 ◽  
Author(s):  
David Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Energy Consumption ◽  
Energy Management ◽  
Research Work ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Content Type ◽  
Energy Management Systems

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency. Findings – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems. Research limitations/implications – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). Practical implications – A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. Originality/value – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

Sign in / Sign up

Export Citation Format

Share Document