A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

A. I. Utilization in the Construction Business: A review on present state and potential for Elenia Oy

The thesis examines the present applications of artificial intelligence in the construction busi-ness domain. Nowadays, businesses are focusing on the safety of an operating environment. In a project-based business, managing projects and portfolios with safety management is significantly important. Lack of knowledge is rarely a root cause of undesired deviations. More often, the deviations in processes are related to an irregularity in compliance with the instructions and rules. With the assistance of AI-based tools, such as machine learning, one can improve efficiency on safety and project management tasks. The thesis provides a gen-eral view of artificial intelligence and a review of present approaches on AI utilization in the construction domain. Also, the thesis suggests the next steps for the utilization of AI in Elenia’s construction business. The first section of the thesis gives an overall view of artificial intelligence. In the second and third sections, a review of the present utilization approaches is examined. In the second section, the utilization is examined in the construction site safety domain. In the third section the examined field is related to the project management do-main. The most common way to utilize AI were to exploit existing data for risk prediction and relationship detection. The risks differ from the examined domain. Thus, building a machine learning model is use-case related. There are various ways to utilize different models to achieve the benefits of machine learning. In Elenia Oy’s activities managing projects have a key role for achieving company’s mission: Electrifying life. The electric grids demand continu-ous maintenance and consistent development. One part of the development is replacement of components that have reached end of the technical lifecycle. For example, replacement can be executed in Elenia’s Säävarma projects. The development of occupational safety Elenia together with its partners has committed for safety manifesto. The key theme of safe-ty manifesto is to render everyone related to Elenia’s work field to return home in good health. The key approach of thesis was to find widely different approaches to utilize an AI for the development of safety and project objectives.Tässä diplomityössä selvitettiin rakentamisliiketoimintoihin liittyviä tekoälyn käyttökohteita. Nykyisin liiketoiminnoissa keskitytään operatiivisten toimintojen turvallisuuteen. Projektiliike-toiminnassa projektien ja portfolioiden johtaminen yhdessä turvallisuusjohtamisen kanssa on huomattavan tärkeää. Tiedon puute on harvoin juurisyy ei-toivotuille poikkeamille. Useammin poikkeamat prosesseissa johtuvat epäsäännöllisyydestä ohjeistuksien ja sääntöjen noudatta-misen suhteen. Tekoälyyn pohjautuvien työkalujen, kuten koneoppiminen, avulla on mahdol-lista kehittää turvallisuuteen ja projektijohtamiseen liittyvien tehtävien tehokkuutta. Tutkielma sisältää yleisen katsauksen tekoälyyn ja tarkastelun nykyisistä lähestymistavoista tekoälyn hyödyntämiseen rakentamisliiketoimintoihin liittyen. Lisäksi työssä muodostetaan ehdotukset tuleville vaiheille tekoälyn hyödyntämiseen Elenian rakentamisliiketoiminnassa. Ensimmäisessä osassa käydään läpi yleiskatsaus tekoälyyn liittyen. Toisessa ja kolmannessa osassa työtä tar-kastellaan nykyisiä tekoälyn käyttökohteita. Toisessa osassa tarkastellaan rakentamistöiden turvallisuuteen liittyviä hyödyntämiskohteita. Kolmannessa osassa vastaava tarkastelu keskit-tyy projekti ja portfoliojohtamisen toimintaympäristöön. Yleisin tapa hyödyntää tekoälyä on selvittää ja tunnistaa toimintaympäristön riskeihin liittyvien tekijöiden suhteita toisiinsa. Erilai-sissa toimintaympäristöissä on erilaisia riskejä, joiden esiintymisen todennäköisyyttä on syytä pienentää. Koneoppimismallien rakentamisen toteutus on käyttökohde sidonnainen, joten on monia tapoja hyödyntää koneoppimista. Elenia Oy:n toiminnassa projektit ja niiden hallinta ovat keskeisessä osassa mahdollistamassa yhtiön missiota: Elämää sähköistämässä. Sähköver-kot vaativat jatkuvaa kunnossapitoa ja johdonmukaista kehittämistä. Osa tätä kehittämistä on teknisen käyttöiän saavuttaneiden komponenttien uusinta, esimerkiksi Elenian Säävarma-hankkeissa. Työturvallisuuden edistämiseksi Elenia on yhdessä kumppaniensa kanssa allekir-joittanut Turvallisuusmanifestin, jonka keskeinen teema on mahdollistaa kaikkien Elenian töissä olevien henkilöiden turvallisen palaamisen terveenä kotiin. Tutkielman keskeisenä lähestymis-tapana oli etsiä laajasti erilaisia tapoja hyödyntää tekoälyä liittyen turvallisuus- ja projektita-voitteiden kehittämiseen

Äriprotsesside ajaliste näitajate selgitatav ennustav jälgimine

Kaasaegsed ettevõtte infosüsteemid võimaldavad ettevõtetel koguda detailset informatsiooni äriprotsesside täitmiste kohta. Eelnev koos masinõppe meetoditega võimaldab kasutada andmejuhitavaid ja ennustatavaid lähenemisi äriprotsesside jõudluse jälgimiseks. Kasutades ennustuslike äriprotsesside jälgimise tehnikaid on võimalik jõudluse probleeme ennustada ning soovimatu tegurite mõju ennetavalt leevendada. Tüüpilised küsimused, millega tegeleb ennustuslik protsesside jälgimine on “millal antud äriprotsess lõppeb?” või “mis on kõige tõenäolisem järgmine sündmus antud äriprotsessi jaoks?”. Suurim osa olemasolevatest lahendustest eelistavad täpsust selgitatavusele. Praktikas, selgitatavus on ennustatavate tehnikate tähtis tunnus. Ennustused, kas protsessi täitmine ebaõnnestub või selle täitmisel võivad tekkida raskused, pole piisavad. On oluline kasutajatele seletada, kuidas on selline ennustuse tulemus saavutatud ning mida saab teha soovimatu tulemuse ennetamiseks. Töö pakub välja kaks meetodit ennustatavate mudelite konstrueerimiseks, mis võimaldavad jälgida äriprotsesse ning keskenduvad selgitatavusel. Seda saavutatakse ennustuse lahtivõtmisega elementaarosadeks. Näiteks, kui ennustatakse, et äriprotsessi lõpuni on jäänud aega 20 tundi, siis saame anda seletust, et see aeg on moodustatud kõikide seni käsitlemata tegevuste lõpetamiseks vajalikust ajast. Töös võrreldakse omavahel eelmainitud meetodeid, käsitledes äriprotsesse erinevatest valdkondadest. Hindamine toob esile erinevusi selgitatava ja täpsusele põhinevale lähenemiste vahel. Töö teaduslik panus on ennustuslikuks protsesside jälgimiseks vabavaralise tööriista arendamine. Süsteemi nimeks on Nirdizati ning see süsteem võimaldab treenida ennustuslike masinõppe mudeleid, kasutades nii töös kirjeldatud meetodeid kui ka kolmanda osapoole meetodeid. Hiljem saab treenitud mudeleid kasutada hetkel käivate äriprotsesside tulemuste ennustamiseks, mis saab aidata kasutajaid reaalajas.Modern enterprise systems collect detailed data about the execution of the business processes they support. The widespread availability of such data in companies, coupled with advances in machine learning, have led to the emergence of data-driven and predictive approaches to monitor the performance of business processes. By using such predictive process monitoring approaches, potential performance issues can be anticipated and proactively mitigated. Various approaches have been proposed to address typical predictive process monitoring questions, such as what is the most likely continuation of an ongoing process instance, or when it will finish. However, most existing approaches prioritize accuracy over explainability. Yet in practice, explainability is a critical property of predictive methods. It is not enough to accurately predict that a running process instance will end up in an undesired outcome. It is also important for users to understand why this prediction is made and what can be done to prevent this undesired outcome. This thesis proposes two methods to build predictive models to monitor business processes in an explainable manner. This is achieved by decomposing a prediction into its elementary components. For example, to explain that the remaining execution time of a process execution is predicted to be 20 hours, we decompose this prediction into the predicted execution time of each activity that has not yet been executed. We evaluate the proposed methods against each other and various state-of-the-art baselines using a range of business processes from multiple domains. The evaluation reaffirms a fundamental trade-off between explainability and accuracy of predictions. The research contributions of the thesis have been consolidated into an open-source tool for predictive business process monitoring, namely Nirdizati. It can be used to train predictive models using the methods described in this thesis, as well as third-party methods. These models are then used to make predictions for ongoing process instances; thus, the tool can also support users at runtime

Collaborative Software Performance Engineering for Enterprise Applications

In the domain of enterprise applications, organizations usually implement third-party standard software components in order to save costs. Hence, application performance monitoring activities constantly produce log entries that are comparable to a certain extent, holding the potential for valuable collaboration across organizational borders. Taking advantage of this fact, we propose a collaborative knowledge base, aimed to support decisions of performance engineering activities, carried out during early design phases of planned enterprise applications. To verify our assumption of cross-organizational comparability, machine learning algorithms were trained on monitoring logs of 18,927 standard application instances productively running at different organizations around the globe. Using random forests, we were able to predict the mean response time for selected standard business transactions with a mean relative error of 23.19 percent. Hence, the approach combines benefits of existing measurement-based and model-based performance prediction techniques, leading to competitive advantages, enabled by inter-organizational collaboration

Predictive analysis of incidents based on software deployments

A high number of IT organizations have problems when deploying their services, this alongside with the high number of services that organizations have daily, makes Incident Management (IM) process quite demanding. An effective IM system need to enable decision makers to detect problems easily otherwise the organizations can face unscheduled system downtime and/or unplanned costs. By predicting these problems, the decision makers can better allocate resources and mitigate costs. Therefore, this research aims to help predicting those problems by looking at the history of past deployments and incident ticket creation and relate them by using machine learning algorithms to predict the number of incidents of a certain deployment. This research aims to analyze the results with the most used algorithms found in the literature.info:eu-repo/semantics/publishedVersio

A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field

Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production