Дисертації з теми "Data Product"

This report is about the product data classification methodology that is useable for the Volvo Cars Engine (VCE) factory's production data, and can be implemented in the Teamcenter software. There are many data generated during the life cycle of each product, and companies try to manage these data with some product data management software. Data classification is a part of data management for most effective and efficient use of data. With surveys that were done in this project, items affecting the data classification have been found. Data, attributes, classification method, Volvo Cars Engine factory and Teamcenter as the product data management software, are items that are affected data classification. In this report, all of these items will be explained separately. With the knowledge obtained about the above items, in the Volvo Cars Engine factory, the suitable hierarchical classification method is described. After defining the classification method, this method has been implemented in the software at the last part of the report to show that this method is executable.

This thesis is focused on Master Data Management of Product Data. At present, most publications on the topic of MDM take into account customer data, and a very limited number of sources focus solely on product data. Some resources actually do attempt to cover MDM in full-depth. Even those publications are typically are very customer oriented. The lack of Product MDM oriented literature became one of the motivations for this thesis. Another motivation was to outline and analyze specifics of Product MDM in context of its implementation and software requirements for a vendor of MDM application software. For this I chose to create and describe a methodology for implementing MDM of product data. The methodology was derived from personal experience on projects focused on MDM of customer data, which was applied on findings from the theoretical part of this thesis. By analyzing product data characteristics and their impacts on MDM implementation as well as their requirements for application software, this thesis helps vendors of Customer MDM to understand the challenges of Product MDM and therefore to embark onto the product data MDM domain. Moreover this thesis can also serve as an information resource for enterprises considering adopting MDM of product data into their infrastructure.

In large organisations today the amount of products is numerous and it is challenging for senior management to have proper control and understanding over all products. As the product is the most important aspect for an organisation to consider, senior management must have ability to manage investments on products and follow development of product related indicators. Managing products as investment on portfolio level, where products are divided into a limited amount of portfolios is a solution for achieving decent control over product investments on senior management level. Product portfolio management is decision making oriented, where the goal is to make the best possible strategic and financial decisions when allocating constraint resources across the entire product portfolio. The product portfolio management aims to increase strategic fit of chosen new product projects, balance in product portfolio and maximizing value of the products. The product portfolio management is constantly ongoing, cross-functional decision making function which is present in all lifecycle states of the portfolios. In this research the product portfolios are seen as investments for mainly internal use of a decision making process. The product portfolios are items that are embodied into the case company’s product data management system and the product portfolios have own lifecycle states. Approach in this research is constructive, where a current state of the case company is analysed and based on the analysis and the literature review a construction is established. The Research questions are: 1) What are the required product structures in product data management systems to support product portfolio management practices? 2) What are the information elements and their lifecycle states and what they should be in product data management systems to support product portfolio decisions? Results of this research are the current state analysis committed in the case company and the construction of product portfolio management structure and lifecycle states. In the construction a portfolio package is defined. The portfolio package is the item used for embodying portfolios into the information systems. An information model for implementing the portfolio packages into the product data management system is introduced. The construction also presents product structure for implementing the portfolio package into the product data management system. Relation of lifecycle states between the portfolio package and other items in a product hierarchy is assessed in a nested lifecycle model. Two models, required and recommended, are suggested for the company to consider for managing the lifecycle of the portfolio package item. All the results are validated from several perspectives.

Product Information Management (PIM) is a field that deals with the product master data management and combines into one base the experience and the principles of data integration and data quality. Product Information Management merges the specific attributes of products across all channels in the supply chain. By unification, centralization and standardization of product information into one platform, quality and timely information with added value can be achieved. The goal of the theoretical part of the thesis is to construct a picture of the PIM, to place the PIM into a broader context, to define and describe various parts of the PIM solution, to describe the main differences in characteristics between the product data and data about clients and to summarize the available information on the administration and management of knowledge bases of the PIM data quality relevant for solving practical problems. The practical part of the thesis focuses on designing the structure, the content and the method of filling the knowledge base of the Product Information Management solution in the environment of the DataFlux software tools from SAS Institute. The practical part of the thesis further incorporates the analysis of the real product data, the design of definitions and objects of the knowledge base, the creation of a reference database and the testing of the knowledge base with the help of specially designed web services.

Abstract Products and services are critical for companies as they create the foundation for companies’ financial success. Twenty per cent of company products typically account for some eighty per cent of sales volume. Nevertheless, the product portfolio decisions — how to strategically renew company product offering — tend to involve emotions, pet products and who-shout-the-loudest mentality while facts, numbers, and quantitative analyses are missing. Profitability is currently measured and reported at a company level, and firms seem unable to measure product-level profitability in a constant way. Consequently, companies are unable to maintain and renew their product portfolio in a strategically or commercially balanced way. The main objective of this study is to provide a data-driven product portfolio management (PPM) concept, which recognises and visualises in real-time and based on facts which company products are concurrently strategic and profitable, and what is the share of them in the product portfolio. This dissertation is a qualitative study to understand the topical area by the means combining literature review, company interviews, observations, and company internal material, to take steps towards data-driven decision-making in PPM. This study indicates that company data assets need to be combined and governed company-widely to realise the full potential of company strategic assets — the DATA. Data must be governed separately from business IT technology and beyond it. Beyond data and technology, the data-driven company culture must be adopted first. The data-driven PPM concept connects key business processes, business IT systems and several concepts, such as productization, product lifecycle management and PPM. The managerial implications include, that the shared understanding of the company products is needed, and the commercial and technical product structures are created accordingly, as they form the backbone of the company business as the skeleton to gather all product-related business-critical information for product-level profitability analysis. Also, product classification for strategic, supportive and non-strategic is needed, since the strategic nature of the product can change during the entire product lifecycle, e.g. due to the technology obsolescence, disruptive innovations by competitors, or for any other reason
Tiivistelmä Tuotteet ja palvelut ovat yrityksille kriittisiä, sillä ne luovat perustan yritysten taloudelliselle menestykselle. Kaksikymmentä prosenttia yrityksen tuotteista edustaa tyypillisesti noin kahdeksaakymmentä prosenttia myyntimääristä. Siitä huolimatta tuoteporfoliopäätöksiin — kuinka strategisesti uudistetaan yrityksen tuotetarjoomaa — liittyy tunteita, lemmikkituotteita ja kuka-huutaa-kovimmin -mentaliteettia faktojen, numeroiden ja kvantitatiivisten analyysien puuttuessa. Kannattavuutta mitataan ja raportoidaan tällä hetkellä yritystasolla, ja yritykset eivät näyttäisi pystyvän mittaamaan tuotetason kannattavuutta johdonmukaisesti. Tämä estää yrityksiä ylläpitämästä ja uudistamasta tuotevalikoimaansa strategisesti tai kaupallisesti tasapainoisella tavalla. Tämän tutkimuksen päätavoite on tarjota dataohjattu (data-driven) tuoteportfoliohallinnan konsepti, joka tunnistaa ja visualisoi reaaliajassa ja faktapohjaisesti, mitkä yrityksen tuotteet ovat samanaikaisesti strategisia ja kannattavia ja mikä on niiden osuus tuoteportfoliossa. Tämä väitöskirja on laadullinen tutkimus, jossa yhdistyy kirjallisuuskatsaus, yrityshaastattelut, havainnot ja yritysten sisäinen dokumentaatio, joiden pohjalta pyritään kohti dataohjautuvaa päätöksentekoa tuoteportfolion hallinnassa. Tämä tutkimus osoittaa, että yrityksen data assettit on yhdistettävä ja hallittava yrityksenlaajuisesti, jotta yrityksen strategisten assettien — DATAN — potentiaali voidaan hyödyntää kokonaisuudessaan. Data on hallittava erillään yrityksen IT-teknologiasta ja sen yläpuolella. Ennen dataa ja teknologiaa on omaksuttava dataohjattu yrityskulttuuri. Dataohjatun tuoteportfolionhallinnan konsepti yhdistää keskeiset liiketoimintaprosessit, liiketoiminnan IT-järjestelmät ja useita konsepteja, kuten tuotteistaminen, tuotteen elinkaaren hallinta ja tuoteportfolion hallinta. Yhteisymmärrys yrityksen tuotteista ja sekä kaupallisen että teknisen tuoterakenteet luominen vastaavasti on ennakkoedellytys dataohjatulle tuoteportfolion hallinnalle, koska ne muodostavat yrityksen liiketoiminnan selkärangan, joka yhdistää kaikki tuotteisiin liittyvät liiketoimintakriittiset tiedot tuotetason kannattavuuden analysoimiseksi. Lisäksi tarvitaan tuotteiden kategorisointi strategisiin, tukeviin ja ei-strategisiin tuotteisiin, koska tuotteen strateginen luonne voi muuttua tuotteen elinkaaren aikana, johtuen esimerkiksi teknologian vanhenemisesta, kilpailijoiden häiritsevistä innovaatioista tai mistä tahansa muusta syystä

Manufacturing companies need to be able to respond to customer demand quickly and accurately. This requires the capability to manage product data effectively. Product Data Management (PDM) systems have been identified as a solution to deliver this capability by providing the right information to the right people at the right time and in the right format. The foundation of this research is that the concept of PDM is relevant and important within the product development process. This research focuses upon how the PDM concept is applied in practice to define and configure products and how it can be integrated with other major information systems to enable an enterprise wide information system. To enable the research aim, an extensive review of literature was undertaken to investigate the effectiveness of PDM in enhancing the product definition process and in creating an interface between different business functional areas. A survey ofPDM system usage was undertaken aimed at identifying the current level of PDM usage within manufacturing enterprises in the UK. This was followed up by three industrial case studies to provide some degrees of validation of the results obtained. A need for effective one time order capture was identified from the three case studies which led to the development of a model specification for a late product configuration tool. A prototype system was produced to validate the design specification and was successfully demonstrated to a collaborating company. During the submission of this thesis, the collaborating company and the university are working on funding a project to pursue with its implementation. The work undertaken has firmly established the relevance ofPDM within the product development process and the importance of effective interfaces between PDM and other manufacturing information systems. The research will be of interest to small and medium sized manufacturing companies searching solutions for improving the management of their product data to enhance product definition and configuration.

Context. For the product manager, the product context analysis, which aims to align their products to the market needs, is very important. By understanding the market needs, the product manager knows the product context information about the environment the products conceived and the business the products take place. The product context analysis using the product context information helps the product manager find the accurate position of his/her products and support the decision-making of the products. The product context information generally could be found in the user feedbacks. And the traditional techniques of acquiring the user feedbacks can be replaced by collecting the existed online user feedbacks with a cheaper cost. Therefore, researchers did studies on the online user feedbacks and the results showed those user feedbacks contain the product context information. Therefore, in this study, I tried to elicit the product context information from the user feedbacks posted on Twitter. Objectives. Objectives of this study are 1. I investigated what kinds of Apps can be used to collect   more related Tweets, and 2. I investigated what kinds of product context information can be elicited from the collected Tweets. Methods. To achieve the first objective, I designed unified criteria for selecting Apps and collecting App-related Tweets, and then conduct the statistical analysis to find out what is/are the factor(s) affect (s) the Tweets collection. To achieve the second objective, I conducted the directed content analysis on the collected Tweets with an indicator for identifying the product context information, and then make a descriptive statistical analysis of the elicited product context information. Results. I found the top-ranked Apps or Apps in few themes like “Health and Fitness” and “Games” have more and fresher App-related Tweets. And from my collected Tweets, I can elicit at least 15 types of product context information, the types include “user experience”, “use case”, “partner”, “competitor”, “platforms” and so on. Conclusions. This is an exploratory study of eliciting product context information from the Tweets. It presented the method of collecting the App-related Tweets and eliciting product context information from the collected Tweets. It showed what kinds of App are suitable to do so and what types of product context information can be elicited from the Tweets. This study let us be aware of that the Tweets can be used for the product context analysis, and let us know the appropriate condition to use the Tweets for the product context analysis.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2005.
Includes bibliographical references (p. 106-109).
Implementing a new IT system often requires the enterprise to transform in order to maximally leverage the capabilities generated by the new system. The challenge in using IT as an enabler to change arises from the need to synergistically redesign processes, develop and implement a solution using internal talent and external suppliers, and establish adoption by users. Product Data Management (PDM) technology represents a substantial portion of large industry IT investment over the last decade. The ability to manage and deliver product data throughout the lifecycle has become increasingly important to the aerospace enterprise as products become more complex, cost and development cycles shorten, and customer, partner, and supplier relationships evolve. Currently, the aerospace community does not have capability to provide traceability from requirements and design through field maintenance. While initially an attempt to understand the application of PDM in product development, what emerged was a study in how PDM affects and enables lean enterprise transformation. The selection, development, and deployment of PDM solutions were studied in the aerospace industry in order to enable better implementation decisions in varying complex environments. Organizational, technical, and cultural factors were considered as they contribute to a PDM's effectiveness. .
(cont.) A current-state observation of nine aerospace company sites highlights the difficulty in reaching the technology's full potential to deliver customer value. Data show that PDMs are being used primarily to manage design engineering data and are not tightly integrated with other business systems. The data also show a distinct difference between prime and supplier companies' spending on and capability of their respective data management systems. While the value of PDM to product development includes better data quality, traceability and transparency, value to the enterprise is also found beyond the traditional role of PDM. Looking horizontally across the lifecycle and vertically through the hierarchical relationships, PDM provides opportunities for organizational and process change and stakeholder involvement, both important tenets for evolving into a lean enterprise. This conclusion is supported by both the site interviews and the two case studies
by Erisa K. Hines.
S.M.

Increasingly competitive market environment today forces companies to continuously create new products to serve the widening range of customer’s needs. To be able to survive and success, companies need to develop new products faster while being cost-effective. Configurable products provide a method of increasing product variety while maintaining the economies of scale. Well executed product data management is a crucial enabler of product development processes with constant pressure to shorten time to market. This is especially true when the product is configurable with modular product structure. The product structure of the product needs to be maintained so that variety possibilities of the product are clear and different stakeholders have relevant visibility of the structure available. The domain of this study is product variety creation using rapid productization. Rapid productization refers to a concept, where company decides to rapidly create a new product in response to customer request which cannot be fulfilled with the current product portfolio. Productization itself is a process, where ambiguous solution to the customer problem or need is encapsulated to a defined, standardized and repeatable product. Typically rapid productization is executed by adding something new to the existing product. Rapidly productized products are not necessarily added to the product portfolio. The purpose of this study is to find out what the product data management needs are in rapid productization cases. The research was conducted as a case study and three different case companies were used as a data source. Two companies were large-scale enterprises and one company was medium size enterprise. All case companies had modular product structure and two of them had a configurable product. Based on existing literature, it can be concluded that product data management needs in the productization process are dependent on product structure and a customer order point of the product. Also, product data needs in rapid productization cases are dependent on how the module added in rapid productization interacts with the existing product. If the added component has no integration with the existing product, the stakeholders concerned about the product data are sourcing and the delivery of the product. If the added module has more interaction with the existing product, research and development is a crucial stakeholder and product data need are more or less similar to the normal productization process
Kiristyvä kilpailu ja jatkuva asiakastarpeiden kasvu aiheuttaa yrityksille painetta laajentaa tuoteportfoliota yhä kiihtyvällä tahdilla. Kyky kehittää ja tuottaa uusia tuotteita nopeasti ja kustannustehokkaasti on elinehto yritysten menestymiselle. Yksi kustannustehokas tapa tuottaa laaja tuoteportfolio on konfiguroitavat tuotteet. Hyvin toteutettu tuotetiedon hallinta on toiminnan perusedellytys, kun tuotekehitykselle asetetaan tehokkuus- ja aikavaatimuksia. Erityisen tärkeää tuotetiedon hallinta on konfiguroitavien tuotteiden kehityksessä. Konfiguroitava tuote vaatii tuoterakenteen kuvauksen, joka määrittää eri moduulien yhdistelymahdollisuudet ja toiminnallisuudet. Lisäksi eri sidosryhmillä tulee olla oma näkymänsä tuoterakenteeseen. Tässä työssä tutkitaan tuotteen variointia nopean tuotteistamisen tapauksissa. Tuotteistaminen tarkoittaa prosessia, jossa epämääräinen idea asiakkaan ongelman tai tarpeen ratkaisemiseksi paketoidaan tarkkaan määritellyksi, standardoiduksi ja toistettavaksi tuotteeksi. Nopealla tuotteistamisella viitataan tilanteeseen, jossa yritys luo nopeasti uuden tuotteen sellaisen asiakasvaatimuksen seurauksena, jota ei nykyisellä tuoteportfoliolla pystytä toteuttamaan. Tyypillisesti nopea tuotteistaminen toteutetaan lisäämällä jotain uutta portfoliossa olevaan tuotteeseen. Nopeasti tuotteistettuja tuotteita ei kuitenkaan välttämättä lisätä yrityksen tuoteportfolioon. Tämän tutkimuksen tarkoituksena on selvittää tuotetiedon hallintaa nopean tuotteistamisen tapauksissa. Tutkimus on toteutettu tapaustutkimuksena, jossa tarkasteltavia yrityksiä oli kolme kappaletta. Tutkimuksen kohteena olevista yrityksistä kaksi oli suuryrityksiä, ja yksi keskisuuri yritys. Kaikkien yritysten tuotteet olivat modulaarisia, ja kahdella yrityksellä tuote oli konfiguroitava. Aiemmin kirjoitetun kirjallisuuden perusteella voidaan sanoa, että tuotetiedon hallinnan vaatimukset tuotteistuksessa riippuvat tuotteen modulaarisuudesta ja asiakastilauspisteestä. Nopean tuotteistamisen tapauksissa on tuotetiedon hallinnan näkökulmasta olennaista se, miten olemassa olevaan tuotteeseen lisätty moduuli vaikuttaa tuotteeseen. Jos lisätyllä moduulilla ei ole minkäänlaista vuorovaikutusta tuotteeseen, on tuotetiedon kannalta olennaista määrittää miten lisättävä moduuli hankitaan ja miten se toimitetaan. Jos lisättävä moduuli on vuorovaikutuksissa tuotteen kanssa, vaatii nopea tuotteistaminen myös tuotekehityksen osallistumista, ja myös tuotetietovaatimukset ovat samantyyppiset kuin normaalissa tuotteistusprosessissa

Abstract Master data describes business objects that are shared across an entire enterprise. Master data is a single source of information that should be used across the IT systems and business processes without changing. Definitions and understanding of common data and how well it is understood forms the basis for understanding the master data. The main objective of this study is to clarify how one product data should be understood and defined and to identify the main challenges and the best practices for managing the one product data for business processes. This study approaches one product data for integrated business processes from several perspectives by focusing on one product master data, data ownership, and the importance of a governance model for managing the master data. The means also to determine business value of master data and to ensure that a company’s success in reaching this business value is analysed. The findings of this study reveal the need for balance between business processes, data, and IT systems. The study indicates that a governance model is necessary in conjunction with business processes, data, and IT systems to ensure that an adequate foundation is created for one product data. One product data is the sum of product-related business data and one product master data. One product master data is the “DNA” of a product that is created by the product portfolio management process and is stored and controlled by a Product Lifecycle Management IT-system that updates the receiving systems in business processes with the common product data. One product data forms the basis for integrated business processes. In the product life cycle context, this means that data must be in place from the new product development phase to the maintenance phase, as well as across sales processes, supply chains, and care/service processes. Discontinuous data is harmful as it causes extra costs in management and slows down data analysis, as well as affects the reaction speed around changes on the business side. New business opportunities such as digitalisation may become very difficult if centralised one product data is not in place. It is important to keep in mind that if data integrity and quality are not in place in a company, adding new business models might be very challenging
Tiivistelmä Master data on informaatiota, joka on määritelty yksiselitteisesti ja sitä käytetään muuttumattomana ylitse eri IT- järjestelmien ja -prosessien. Datamäärityksillä tuetaan liiketoiminnan prosesseja. Datan määritelmät ja yleinen datan ymmärtämisen taso yrityksessä ovat tärkeitä elementtejä, muodostaen pohjan Master data -käsitteelle. Tämän tutkimuksen päätarkoituksena on selkiyttää kuinka yksiselitteinen tuotetieto tulisi ymmärtää ja määritellä. Samalla identifioidaan suurimmat haasteet ja parhaat käytänteet yhdenmukaisen tuotetiedon hallinnalle. Tutkimuksessa keskitytään yhtenäisen master datan käsitteistön, datan omistajuuden, sekä hallinnointimallin tärkeiden näkökulmien kautta kokonaisuuden ymmärtämiseen useista eri näkökulmista. Tutkimuksessa perehdytään myös datan liiketoiminnallisen arvon tunnistamiseen. Sen kautta voidaan varmistaa yrityksen kyvykkyys saavuttaa asetetut tavoitteet, jotka johto on määritellyt esim. strategian kautta. Tulokset kertovat, että on äärimmäisen tärkeää löytää oikea balanssi liiketoiminnan prosessien, datan ja tietojärjestelmien kesken. Yksikäsitteinen tuotetieto on summa, joka muodostuu tuotteeseen liittyvästä liiketoimintatiedosta sekä yhtenäisestä tuote master datasta. Yhtenäinen tuote master data on ikään kuin tuotteen DNA tietoa. Yhteenvetona voidaan todeta, että parhaimmillaan data määritellään kerran ja sitä käytetään muuttumattomana eri liiketoiminnan prosessissa hyödyksi. Yhtenäinen tuote data muodostaa pohjan liiketoiminnan prosessien integroimiselle. Tuotteen elinkaaren sisällön osalta tämä tarkoittaa sitä, että data luodaan osana uuden tuotteen kehitysprosessia ottaen huomioon muiden liiketoiminta prosessien tarpeet kuten myynti, logistiikka ja valmistus, huolto jne. On äärimmäisen tärkeää, että datalle ei synny epäjatkuvuuskohtia eri prosessien välille. Datan epäjatkuvuuskohdat voivat tuottaa ylimääräisinä kustannuksia ylläpidon, data analytiikan ja raportoinnin kautta. Yleinen reagointinopeus liiketoiminnan muutoksiin on yleensä hitaampaa. Uusien liiketoimintamahdollisuuksien kuten digitalisaation tai esineiden internetin (IoT) toteuttaminen voi olla haastavaa ja kallista mikäli keskitettyä ja yhtenäistettyä tuote data mallia ei ole. Yhtenäisen tuote master datan käsite ja parhaita käytänteistä toteuttava hallintamalli antavat pohjan tietokeskeiselle ajattelulle yrityksessä

To utilize space better, warehouses stack their products on top of each other. This increases the risk of injury for workers when storing and retrieving the products. Some warehouses counteract this by using robots to retrieve products to a picking area where a human worker picks the products needed to fulfill an order. This means that it is important for the robots to be effective when retrieving products to reduce the time the worker spends waiting in the picking area. This thesis focuses on the grouping of products in the containers when they are stored in the warehouse. The robots will then retrieve one container at a time and if the grouping of products is done correctly this should decrease the number of retrievals required to fulfill an order. In order to make the decision on which products to group together, an application was developed that data mined previous orders that the warehouse had received in an attempt to extract information about the products. With the help of this information the application then suggests different product layouts that focus on different goals when they are created. The different layouts are then compared against each other in order to determine which layout technique produces the best results. This algorithm has been named the PLO-algorithm. The results showed that when a product is placed with the PLO-algorithm, the most important aspect to consider is the relations it has with the other products it is grouped with. The results also showed that data mining orders that are too old can have a negative impact on the result if not handled correctly. The results also showed that when constructing the warehouse you should try to avoid restrictions that affect which products can be placed together as much as possible since these restrictions can impact the effectiveness of the warehouse in a negative way. The thesis draws the conclusion that there is a clear gain in effectiveness for warehouses to have a planed layout for their products. It is recommended to data mine previous orders to extract relations between the products if possible since this piece of information showed the best results in this thesis. It is also in the warehouse best interest to avoid as many restrictions as possible that affect which products can be placed together since this can impact the results in a negative way. It is also beneficial to not include data that is too old in the data mining since this can impact the results in a negative way if not handled correctly.
För att utnyttja sitt utrymme bättre staplar lagerhus sina produkter på höjden. Detta medför högre risker för personskada vid hämtning och lämning av produkter, en del lagerhus löser detta genom att använda sig av robotar som hämtar och lämnar produkterna i lagerhuset. Robotarna hämtar och lämnar produkterna i en plock zon där en mänsklig arbetare plockar de produkter som behövs för en order. Detta innebär att det är viktigt att robotarna är effektiva i sin hämtning av produkter för att minska väntetiden för arbetarna i plock zonen. I ett försök att effektivisera robotarna fokuserar denna avhandling på gruperingen av produkterna i behållarna. Detta innebär att beslutet om vilka produkter som ska grupperas tillsammans i samma behållare är viktig eftersom om rätt produkter lagras tillsammans så kommer detta minska antalet hämtningar och lämningar som krävs för att uppfylla en beställning. För att hjälpa till med detta beslut skapades en applikation som analyserade tidigare beställningar som varuhuset erhållit i ett försök att extrahera information om produkterna. Applikationen skapar sedan olika förslag på produkt placeringar där de olika förslagen fokuserar på olika mål för att undersöka vilket mål som är viktigast att fokusera på när en produkt ska placeras. Algoritmen i denna applikation har valts att kallas för PLO-algoritmen. Resultaten visade att när en produkt ska placeras med PLO-algoritmen så är det viktigt att gruppera produkten med produkter den har starka relationer till. Resultatet visade också att när data ska analyseras bör inte för gammal data analyseras då äldre relationer mellan produkter som inte stämmer längre kan påverka resultatet negativt om algoritmen ej hanterar detta på något sätt. Resultaten visade också att vid konstruktionen av lagerhuset bör restriktioner som begränsar hur produkter kan placeras, undvikas om möjligt då dessa kan påverka lagerhusets effektivitet negativt. Slutsatsen som kan dras är att ett lagerhus kan tjäna väldigt mycket på att ha en plan när de bestämmer hur deras produkter ska placeras. Om det finns möjlighet att analysera tidigare beställningar efter relationer mellan produkter så är detta rekommenderat då det visade bäst resultat i denna undersökning. Det är även till lagerhusets fördel att försöka undvika restriktioner på deras lagersystem när det byggs eftersom det möjliggör för fler kombinationer när produkterna ska grupperas. Till sist så visar avhandlingen att med datan som användes att det var fördelaktigt att inte göra analys på för gammal data, då detta ger sämre resultat.

This diploma thesis deals with the Product Information Management (PIM) and compares PIM software tools. Its goal is to introduce the area of the PIM systems in Czech language. Next subgoal is to define system of criteria. It is also necessary to achieve the last goal -- to analyze and compare PIM software. The method I used is the exploration of information sources; obtaining information through email communication and use of empirical knowledge to define system of criteria. The contribution of this work is the same as its goals. The work is divided into two parts. The first theoretical part deals with PIM definitions, context, functionality, architectures and PIM market developing. The second practical part involves selecting of particular PIM software tools, defining system of criteria and comparison of PIM software tools.

Scania is a Swedish automotive manufacturer for heavy vehicles and engines. It also offers transport solutions and long term commitment for customers. In today's Scania, a modular system provides a huge variety of specifications to meet varying dramatic needs for different customers. In order to be able to meet the diverse requirements of customers, modular approach with the support of reusable components is used to increase the efficiency of designing different products. To customize both product development and product design, computer aided design(CAD) is used to support the process of design and design documentation. "CATIA" is a multi-platform CAD software and "ENOVIA" is a product modeling product offers product database management for virtual model design into CATIA, both "CATIA" and "ENOVIA" are developed by the French company Dassault Systemes are chosen by Scania to support its product development. The modular system approach requires the system support for product structure, which is managed by a mainframe called SPECTRA. The thesis project is mainly about system designing a new module which takes the responsibility for exchanging information between SPECTRA and ENOVIA. In more detail, the new component is to perform a mapping of data in SPECTRA format into a format which ENOVIA can import. The mapping module has several interfaces with other applications in the system. JavaMigrator provides the environment to import data from the mainframe and transfer the data into the module and finally output the expected data format into ENOVIA. To achieve this purpose, several possible solutions were proposed and several methods were tried. Since an in-house developed CAA-module is highly preferred by Scania, the new mapping component will finally be designed into two separate modules, the first part converts the XML extracted from SPECTRA into an intermediate format and the second part is designed to convert the intermediate file into the expected target file. The intermediate file is required, since the format is independent of changes in both SPECTRA and ENOVIA. Furthermore, it is flexible and less complex to maintain than direct mapping from exported XML to ENOVIA. The report focuses on five parts, background, project specification, methodology, implementation, result and future work.

SCANIA does the automobile production based on Modular Approach. A ModularSystem provides a huge variety of specifications to meet varying dramatic needs fordifferent customers. Modular approach with the support of reusable components increasesthe efficiency of designing different products.The main stream product design is performed on CATIA V5 platform with ENOVIAas its PDM vault. But SCANIA uses its own legacy PDM SPECTRA for maintainingthe product structure for modular based product specification.So to design the 3D parts in CATIA, one should import the product structure fromSPECTRA to ENOVIA. But this process includes a conversion from SPECTRA formatto ENOVIA format and currently it is performed using a third party componentnamed ECCO. Now, SCANIA wants to replace ECCO for various reasons with inhousebuild module.This report discusses the background knowledge of the problem, methodology usedin the solution approach, different implementations along with their results and finallyconcluding with future work of solutions. Keywords: CATIA,ENOVIA,CAA,JSDAI,ECCO,EXPRESS,EXPRESS-X

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references.
The online market has expanded tremendously over the past two decades across all industries ranging from retail to travel. This trend has resulted in the growing availability of information regarding consumer preferences and purchase behavior, sparking the development of increasingly more sophisticated product recommendation systems. Thus, a competitive edge in this rapidly growing sector could be worth up to millions of dollars in revenue for an online seller. Motivated by this increasingly prevalent problem, we propose an innovative model that selects, prices and recommends a personalized bundle of products to an online consumer. This model captures the trade-off between myopic profit maximization and inventory management, while selecting relevant products from consumer preferences. We develop two classes of approximation algorithms that run efficiently in real-time and provide analytical guarantees on their performance. We present practical applications through two case studies using: (i) point-of-sale transaction data from a large U.S. e-tailer, and, (ii) ticket transaction data from a premier global airline. The results demonstrate that our approaches result in significant improvements on the order of 3-7% lifts in expected revenue over current industry practices. We then extend this model to the setting in which consumer demand is subject to uncertainty. We address this challenge using dynamic learning and then improve upon it with robust optimization. We first frame our learning model as a contextual nonlinear multi-armed bandit problem and develop an approximation algorithm to solve it in real-time. We provide analytical guarantees on the asymptotic behavior of this algorithm's regret, showing that with high probability it is on the order of O([square root of] T). Our computational studies demonstrate this algorithm's tractability across various numbers of products, consumer features, and demand functions, and illustrate how it significantly out performs benchmark strategies. Given that demand estimates inherently contain error, we next consider a robust optimization approach under row-wise demand uncertainty. We define the robust counterparts under both polynomial and ellipsoidal uncertainty sets. Computational analysis shows that robust optimization is critical in highly constrained inventory settings, however the price of robustness drastically grows as a result of pricing strategies if the level of conservatism is too high.
by Anna Papush.
Ph. D.

Improving product quality is always of industrial interest. Product availability, a function of product maintainability and reliability, is an example of a measurement that can be used to evaluate product quality. Product availability and cost are two units which are especially important to manage in the context of the manufacturing industry, especially where industry is interested in selling or buying offers with increased service content. Industry in general uses different strategies for increasing equipment availability; these include: corrective (immediate or delayed) and preventive strategies. Preventive strategies may be further subdivided into scheduled and predictive (condition-based) maintenance strategies. In turn, predictive maintenance may also be subdivided into scheduled inspection and continuously monitored. The predictive approach can be achieved by early fault detection. Fault detection and diagnosis methods can be classified into three categories: data-driven, analytically based, and knowledge-based methods. In this thesis, the focus is mainly on fault detection and on data-driven models.Furthermore, industry is generating an ever-increasing amount of data, which may eventually become impractical to store and search, and when the data rate is increasing, eventually impossible to store. The ever-increasing amount of data has prompted both industry and researchers to find systems and tools which can control the data on the fly, as close to real-time as possible, without the need to store the data itself. Approaches and tools such as Data Stream Mining (DSM) and Data Stream Management Systems (DSMS) become important. For the work reported in this thesis, DSMS and DSM have been used to control, manage and search data streams, with the purpose of supporting increased availability of industrial products.Bosch Rexroth Mellansel AB (formerly Hägglunds Drives AB) has been the industrial partner company during the course of the work reported in this thesis. Related data collection concerning the functionality of the BRMAB hydraulic system has been performed in collaboration with other researchers in Computer Aided Design at Luleå University of Technology.The research reported in this thesis started with a review of data stream mining algorithms and their applications in monitoring. Based on the review, a data stream classification method, i.e. Grid-based classifier, was proposed, tested and validated (Paper A). Also, a fault detection system based on DSM and DSMS was proposed and tested, as reported in Paper A. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus demonstrating how data stream prediction can be used to gain more time for proactive response actions by industry (Paper B). Further development included an automatic update method which allows the proposed fault detection system to be able to overcome the problem of concept drift (Paper E). The proposed and modified fault detection systems were tested and verified using data collected in collaboration with Bosch Rexroth Mellansel AB (BRMAB). The requirements for the proposed fault detection system and how it can be used in product development and design of the support system were also discussed (Paper C). In addition, the performance of a knowledge-based method and a data- driven method for detecting failures in high-volume data streams from industrial equipment have been compared (Paper D). It was found that both methods were able to detect all faults without any false alert. Finally, the possible implications of using cloud services for supporting industrial availability are discussed in Paper F. Further discussions regarding the research process and the relations between the appended papers can be found in Chapter 2, Figure 4 and in Chapter 5, Figure 21.The results showed that the proposed and modified fault detection systems achieved good performance in detecting and predicting failures on time (see Paper A and Paper B). In Paper C, it is shown how data stream management systems may be used to increase product availability awareness. Also, both the data-driven method and the knowledgebased method were suitable for searching data streams (see Paper D). Paper E shows how the challenge of concept drift, i.e. the situation in which the statistical properties of a data stream change over time, was turned to an advantage, since the authors were able to develop a method to automatically update the safe operation limits of the one-class data-driven models.In general, detecting faults and failures on time prevents unplanned stops and may improve both maintainability and reliability of industrial systems and, thus, their availability (since availability is a function of maintainability and reliability). By the results, this thesis demonstrates how DSM and DSMS technologies can be used to increase product availability and thereby increase product quality in terms of availability.

Godkänd; 2013; 20130423 (ahmalz); Tillkännagivande disputation 2013-05-24 Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Ahmad Alzghoul Ämne: Datorstödd maskinkonstruktion/Computer Aided Design Avhandling: Mining Data Streams to Increase Industrial Product Availability Opponent: Professor Patrik Eklund, Institutionen för datavetenskap, Umeå universitet Ordförande: Professor Lennart Karlsson, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Tid: Måndag den 17 juni 2013, kl 09.00 Plats: E231, Luleå tekniska universitet

The current literature suggests that to enhance the possibility of new products making into the market and being profitable, an organization needs to capture customer requirements and convert them into technical features, plan the roadmap and evaluate the investment endeavour. The requirements for a product originate from various sources. Additional challenge is that the customers do not always know what they want from the product. The study introduces installed base data as a supplementary source of product requirements. Installed base data has not been vastly examined from the product management point of view and during the study, the case company examines the affordances of installed base for product management for the first time. The goal of the research is to determine value adding features and waste contributors, among a release, by utilizing customer installed base information. Additionally, the study aims at finding applicable information from the installed base data, which could be used by product management and marketing. The research uses a single case study approach with constructive and inductive methods. For the data collection, the research utilizes product data, installed base data and interviews. The results demonstrate that the case company do not have a complete process to investigate the investment endeavour of their products. Despite the defective nature of evaluation of the value contributors, the research highlight that highly activated application software and vital base software features are the most valuable for the customers of the case company. Installed base data can be utilized in the case company to determine the wasted commercial potential, designate the sales potential, steer the sales push towards the most potential target, track down and develop the high valued features, emerge new KPIs and expand the risk assessment for the new features. Because of the nature of the research, the affordances are generalizable for other researches as well
Jotta uusi tuote saadaan markkinoille onnistuneesti ja ylläpidettyä tuotteen kannattavuutta, nykyinen kirjallisuus suosittelee seuraavien vaiheiden huolellista läpikäymistä. Ensimmäiseksi asiakkaan vaatimukset tulisi analysoida tarkoin ja muuttaa ne teknisiksi ominaisuuksiksi. Toiseksi ominaisuuksien julkaisu tulisi suunnitella ja ajoittaa siten, että se tuo suurimman hyödyn sekä asiakkaalle että yritykselle. Viimeiseksi investoinnin kannattavuudesta täytyy tehdä laskelmat. Prosessin alussa haasteena on asiakasvaatimusten määrittäminen, sillä vaatimuksia voi tulla useista eri läheteistä, eikä asiakas aina tiedä mitä he oikeasti haluavat. Tämä tutkimus esittelee asennetun laitekantatiedon käyttömahdollisuuksia vaatimusten hallintaa tukevana työkaluna. Asennetun laitekantatiedon hyödyntämistä tuotehallinnassa ei ole tutkittu laajasti kirjallisuudessa. Tutkimuksen yhteydessä laitekantatietoa käytettiin ensimmäistä kertaa hyödyksi kohdeyrityksessä tuotehallinnan osalta. Tämän tutkimuksen tavoitteena on laitekantatietoa hyödyntäen löytää arvoa tuottavia ja arvoa tuottamattomia ominaisuuksia tuotejulkaisusta. Tämän lisäksi tutkimus pyrkii löytämään muita käyttömahdollisuuksia asennetulle laitekantatiedolle. Tämä diplomityö käyttää konstruktiivista ja induktiivista yksittäistapaustutkimusmenetelmää päästäkseen tutkimuksen tavoitteisiin. Empiirisen tiedon lähteenä tässä tutkimuksessa toimii kohdeyrityksen tuotetieto ja heidän asiakkaittensa asennettu laitekantatieto. Lisäksi tutkimuksessa haastatellaan kohdeyrityksen tuotehallinnan henkilöstöä. Tutkimuksen tulokset osoittavat, ettei kohdeyrityksessä ole prosessia, jolla mitattaisiin investoinnin kannattavuutta tuotteen ominaisuustasolla. Vaikka rahallista arvoa ja hukkaa tutkimus ei pysty selvittämään, se kohdentaa, että kohdeyrityksen asiakkaat arvostavat välttämättömiä kantaominaisuuksia sekä eniten aktivoituja soveltavia ominaisuuksia. Tutkimuksen tulokset viittaavat siihen, että laitekantatietoa voidaan käyttää hyväksi osana tuotehallinnan prosessia, määrittämään hukattua kaupallista potentiaalia, ohjaamaan myyntiä kohti potentiaalisia kohteita, määrittämään arvostettuja ominaisuuksia, kehittämään uusia suorituskykymittareita sekä laajentamaan uusien ominaisuuksien riskinarviointia. Asiakkaan ja yrityksen välinen tieto on saatavilla sopimuksen mukaan ja siten sitä voidaan myös käyttää hyväksi samantapaisissa tapauksissa eri tuotannon alueilla

In recent years manufacturing enterprises are increasingly automated and collect and store large quantities of data relating to their products and production systems. This electronically stored data can hold both process measures and hidden information, which can be very important when discovered. Knowledge discovery in databases provides the tools to explore historic or current data to reveal many kinds of previously unknown knowledge from these databases. Manufacturing enterprises data is complex and may include information relating to design, process improvement and limitations, manufacturing machines and tools, and product quality. This thesis focuses on issues relating to information extraction from engineering databases in general and from manufacturing processes in particular using their historical databases. It also addresses the important issue of how the process or the design of the product can be improved based on such information.

Product Platform Planning is an emerging philosophy that calls for the planned development of families of related products. It is markedly different from the traditional product development process and relatively new in engineering design. Product families and platforms can offer a multitude of benefits when applied successfully such as economies of scale from producing larger volumes of the same modules, lower design costs from not having to redesign similar subsystems, and many other advantages arising from the sharing of modules. While advances in this are promising, there still remain significant challenges in designing product families and platforms. This is particularly true for defining the platform components, platform architecture, and significantly different platform and product variants in a systematic manner. Lack of precise definition for platform design assets in terms of relevant customer requirements, distinct differentiations, engineering functions, components, component interfaces, and relations among all, causes a major obstacle for companies to take full advantage of the potential benefits of product platform strategy. The main purpose of this research is to address the above mentioned challenges during the design and development of modular platform-based product families. It focuses on providing answers to a fundamental question, namely, how can a decision support approach from product module definition to the determination of platform alternatives and product variants be integrated into product family design? The method presented in this work emphasizes the incorporation of critical design requirements and specifications for the design of distinctive product modules to create platform concepts and product variants using a data clustering approach. A case application developed in collaboration with a tire manufacturer is used to verify that this research approach is suitable for reducing the complexity of design results by determining design commonalities across multiple design characteristics. The method was found helpful for determining and integrating critical design information (i.e., component dimensions, material properties, modularization driving factors, and functional relations) systematically into the design of product families and platforms. It supported decision-makers in defining distinctive product modules within the families and in determining multiple platform concepts and derivative product variants.
Ph. D.

With increasing concern about consumer product-related injuries in Australia, product safety regulators need evidence-based research to understand risks and patterns to inform their decision making. This study analysed paediatric injury data to identify and quantify product-related injuries in children to inform product safety prioritisation. This study provides information on novel techniques for interrogating health data to identify trends and patterns in product-related injuries to inform strategic directions in this growing area of concern.

In high-tech companies, user experience has become an important part of data-driven product development, but unfortunately the artifacts created by user experience work are often stored in disparate documents and databases, and it is difficult to get a big picture of all the artifacts that have been created and how they relate to each other and to other product development data. Data visualization is one way to approach solving these issues, but how can this data be presented to users in a meaningful way such that both the artifacts and the relationships between them are understood? Three hierarchical data visualizations - partition, sunburst, and zoomable treemap - were built around some early product development data. These visualizations were tested in a comparative exploratory usability test with six users to find how well users understood the data and the relationships between the data. The most significant results were that most users did not know how to interact with the partition and sunburst visualizations until prompted to do so, users had a difference in understanding the data between the sunburst and partition visualization, and some techniques worked very well to keep users oriented while others did not. Future work should first focus on adding a creative element to the tool, where data can be added and relationships can be built in the visualization itself, and then focus on testing the tool again with a more specific audience of strategic planners, UX designers, and requirements engineers.

Scania has developed a unique modular product system which is an important successfactor, creating exibility and lies at the heart of their business model. R&Duse product and vehicle product properties to describe the product key factors. Theseproduct properties are both used during the development of new features and products,and also utilized by the project oce to estimate the total contribution of a project.Scania want to develop a new method to understand and be able to track and comparethe projects eect over time and also predict future vehicle improvements. In this thesis, we investigate how to quantify the impact on vehicle product propertiesand predict component improvements, based on data sources that have not beenutilized for these purposes before. The impact objective is ultimately to increase the understandingof the development process of heavy vehicles and the aim for this projectwas to provide statistical methods that can be used for investigative and predictivepurposes. First, with analysis of variance we statistically veried and quantied differencesin a product property between comparable vehicle populations with respectto component generations. Then, Random Forest and Articial Neural Networks wereimplemented to predict future eect on product property with respect to componentimprovements. We could see a dierence of approximately 10 % between the comparablecomponents of interest, which was more than the expected dierence. Theexpectations are based on performance measurements from a test environment. Theimplemented Random Forest model was not able to predict future eect based on theseperformance measures. Articial Neural Networks was able to capture structures fromthe test environment and its predictive performance and reliability was, under the givencircumstances, relatively good.

Dagens företagsklimat skapar ökad press på företag att minska sin tid till marknad för nya produkter, samtidigt som konstnader ska minskas och en hög produktkvalitet skall hållas. Ett resultat av detta är att tillverkningsföretag måste utveckla och producera produkter fortare, till en lägre kostnad, med ökande kvalité för att upprätthålla sin konkurrenskraft. Inom marknaden för informations- och kommunikationsteknik sker det snabba förändringar, detta göra att produktutvecklingen är allt mer viktig. Hanteringen av produktdata är en viktig aspekt av produktutvecklingen, men också en av de mest  utmanande.  Målet  med  denna  forskningsuppsats  är  att  undersöka  vilka  processer  inom industrialisering som används för att samla och hantera produktdata. Produktdata och hanteringen av den är en viktig del av industrialiseringsprocessen samt produktutvecklingsprocessen. PIM   (Product   Introduction   and   Maintenance)   RBS   (Radio   Base   Station)   Kista   är   en industrialiseringssite och har valts för denna fallstudie – då de representerar en ledande del av produktutvecklingen för utsedda produkter inom Ericsson som är ett världsledande företag inom informations-och kommunikationstekniks industrin. Denna forskning har utförst i linje med det valda fokusområdet att undersöka, beskriva och analysera de viktigaste metoderna som används inom PIM RBS Kista för att samla in, lagra och använda produktdata under produktutvecklingen i industrialiseringsprocessen.   Syftet   med   forskningen   är   att   bidra   till   forskningsområdet produktdatahantering. Fokus har legat inom Operations, där nya produkter realiseras under olika aktiviteter och från vilken produktdata är det viktigaste resultatet. De  arbetsmetoder  som  har  identifieras  under  fallstudien  diskuteras  och  skapar  insikt  hur produktdatahantering  används  under  förverkligandet  av  nya  produkter  –  med  koppling  till produktionsverkstadsgolvet. Denna forskingsuppsats diskuterar även de huvudsakliga implikationera relaterat till produktdatahantering inom organisationen som är vald för denna fallstudie. Detta för att bidra med förbättringsförslag gällande nuvarande produktdatahanteringsmetod och system, samt verktyg, som finns implementerade idag.
In today’s market there is an increasing pressure on companies to reduce their time-to-market and lower their cost whilst maintaining a high quality on their products. As a result, manufacturing firms have to develop and produce products faster, at lower costs, and with increased quality in order to maintain their competiveness. The information and communications technology (ICT) market is a fast  changing  market,  which  makes  the  development  process  all  the  more  important.  The management of product data is an important aspect of the product development process, but also one of the most challenging. Product data and product data management (PDM) are important aspects of the new product introduction (NPI) process and in turn the product development process. This research is based on a case study research conducted at PIM (Product Introduction and Maintenance) RBS (Radio Base Station) Kista. PIM RBS Kista is a lead-site responsible for NPI and product development for certain appointed products within Ericsson, a world leading multinational corporation in the ICT industry. In alignment with the research focus the main processes used within PIM RBS Kista to gather, store, and use product data during product development in the NPI process has been described and analysed – in order to contribute to the PDM research field. The focus has been within the Operations department, in which new products are realised during different activities and from which product data is the main output. The processes identified and analysed provides insight how PDM is used during product realisation and its connection to the production shop floor. The thesis also discusses the main complications within the case organisation and suggests improvements regarding the current PDM processes and systems/tools used.

This project investigated how NoSQL databases can be used together with a logical layer, instead of a relational database with separated backend logic, to search for products with customer specific constraints in an e-commerce scenario. The motivation behind moving from a relational database was the scalability issues and increased read latencies experienced as the data increased. The work resulted in a framework called Cauldron that uses pipelines a sequence of execution steps to expose its data stored in an in-memory key-value store and a document database. Cauldron uses write replication between distributed instances to increase read throughput at the cost of write latency. A product database with customer specific constraints was implemented using Cauldron to compare it against an existing solution based on a relational database. The new product database can serve search queries 10 times faster in the general case and up to 25 times faster in extreme cases compared to the existing solution.
Projektet undersökte hur NoSQL databaser tillsammans med ett logiskt lager, istället för en relationsdatabas med separat backend logik, kan användas för att söka på produkter med kundunika restriktioner. Motivationen till att byta ut relationsdatabasen berodde på skalbarhetsproblem och långsammare svarstider när datamängden ökade. Arbetet resulterade i ett ramverk vid namn Cauldron som använder pipelines sammankopplade logiska steg för att exponera sin data från en minnesbunden nyckel-värde-databas och en dokumentdatabas. Cauldron använder replikering mellan distribuerade instanser för att öka läsgenomstömmningen på bekostnad av högre skrivlatenser. En produktdatabas med kundunika restriktioner implementerades med hjälp av Cauldron för att jämföra den mot en befintlig lösning baserad på en relationsdatabas. Den nya databasen kan besvara sökförfrågningar 10 gånger snabbare i normalfallen och upp till 25 gånger snabbare i extremfallen jämfört med den befintliga lösningen.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 68-69).
Effective execution and alignment of data management across development and manufacturing teams is essential for Amgen's Drug Product Technology group to realize its main goals of shortening the development timeline and ensuring robust commercial manufacturing. The right data management strategy can help address these goals by accelerating development work and regulatory filing as well as improving commercial manufacturing efficiency. In the face of challenges associated with rapid growth and an expanding product pipeline, Amgen's commitment to standardizing development work and digitizing both clinical and commercial manufacturing has introduced many opportunities for new data management initiatives, improvements, and a revamped overall data management strategy. We identify a framework for the development of a data management strategy for the Drug Product Technology group to enable greater efficiency and alignment across development and manufacturing teams. The primary steps in data management and objectives at each step were determined. While a full data management strategy has been recommended to the Drug Product Technology group as a set of current and future projects, this thesis focuses on three specific case study projects within the overall strategy: (1) data generation and collection in drug product manufacturing, (2) real-time multivariate statistical process monitoring of lyophilization in clinical manufacturing, and (3) integration of development study data through electronic lab notebook software. Based on insights from these case studies, we make specific recommendations for further improvements in data management.
by Spencer C. Anderson.
M.B.A.
S.M.

Background: As the requirements for the modern-day comforts are raising from day to day, the great evolution in the field of lawn-mowers is recorded. This evolution made companies produce a fleet of lawn-mowers(commercial, house-hold) for different kinds of usages. Despite the great evolution and market in this field, to the best of our knowledge, no effort was made to understand customer usage by analysis of real-time usage of lawn-mowers. This research made an attempt to analyse the real-time usage of lawn-mowers using techniques like machine learning. Objectives: The main objective of the thesis work is to understand customer usage of lawn-mowers by analysing the real-time usage data using machine learning algorithms. To achieve this, we first review several studies to identify what are the different ways(scenarios) and how to understand customer usage from those scenarios. After discussing these scenarios with the stakeholders at the company, we evaluated a suitable scenario in the case of lawn-mowers. Finally, we achieved the primary objective by clustering the usage of lawn-mowers by analysing the real-world time-series data from the Controller Area Network(CAN) bus based on the driving patterns. Methods: A Systematic literature review(SLR) is performed to identify the different ways to understand customer usage by analysing the usage data using machine learning algorithms and SLR is also performed to gain detailed knowledge about different machine learning algorithms to apply to the real-world data. Finally, an experiment is performed to apply the machine learning algorithms on the CAN bus time-series data to evaluate the usage of lawn-mowers into various clusters and the experiment also involves the comparison and selection of different machine learning algorithms applied to the data. Results: As a result of SLR, we achieved different scenarios to understand customer behaviours by analysing the usage data. After formulating the best suitable scenario for lawn-mowers, SLR also suggested the best suitable machine learning algorithms to be applied to the data for the scenario. Upon applying the machine learning algorithms after making necessary pre-processing steps, we achieved the clusters of usage of lawn-mowers for every driving pattern selected. We also achieved the clusters for different features of driving patterns that indicate the various characteristics like a change of intensity in the usage, rate of change in the usage, etc. Conclusions: This study identified customer behaviours based on their usage data by clustering the usage data. Moreover, clustering the CAN bus time-series data from lawn-mowers gave fresh insights to study human behaviours and interaction with the lawn-mowers. The formulated clusters have a great scope to classify and develop the individual strategy for each cluster formulated. Further, clusters can also be useful for identifying the outlying behaviour of users and/or individual components.

As E-commerce continues to take market share from traditional brick and mortar businesses, there are few choices left for managers apart from migrating their sales online. While the topic of online adoption has been studied extensively, this thesis attempts to investigate one of the major drivers of complexity within the industry - the role of structured product data. The study was performed on a major Nordic online retailer, and identified a set of six guiding propositions on the topic of structured product data in e-commerce from interviews with industry professionals. Contemporary data science literature contributes to the body of evidence suggesting a strategically prioritized focus on creating and maintaining structured product data is the way of the future for ecommerce, aligning with much of the interview results. Furthermore, the propositions were thoroughly examined through multiple linear regression analysis on data from the same firm. The study gives empirical support for significant positive impact on most studied metrics from having structured product data available on the website as well as within the internal systems, with slight discrepancies across product categories.
I takt med att e-handeln fortsätter att ta marknadsandelar från traditionella fysiska butiker finns det få alternativ för ledningsgrupper förutom att migrera sin försäljning online. Online-migrering som ämne har studerats i stor utsträckning tidigare, men denna uppsats försöker utforska en av huvuddrivarna till branschens komplexitet – rollen av strukturerad produktdata. Studien gjordes på en större nordisk e-handlare, och identifierade sex ledande teman inom ämnet för strukturerade produktdata i e-handel genom intervjuer med experter på bolaget. Kontemporär litteratur inom datavetenskapen bidrar till belägg för att ett strategiskt prioriterat fokus på att skapa och managera strukturerad produktdata är vägen framåt för e-handeln, vilket ligger i linje med resultaten från intervjuerna inom studien. Vidare analyserades de identifierade temana genom multipel linjär regression genom data från bolaget. Studien ger empiriska belägg för att strukturerad produktdata på e-handlarens hemsida samt i de interna systemen ger signifikant och positiv påverkan på de flesta responsvariabler, med vissa diskrepanser mellan produktkategorier.

We are currently living in the Internet of Things (IoT) era, which involves devices that are connected to Internet and are communicating with each other. Each year, the number of devices increases rapidly, which result in rapid growth of data that is generated. This large amount of data is sometimes titled as Big Data, which is generated from different sources, such as log data of user behavior. These log files can be collected and analyzed in different ways, such as creating product recommendations. Product recommendations have been around since the late 90s, when the amount of data collected were not at the same level as it is today. The aim of this thesis has been to investigating methods to process and create product recommendations to see how well they are adapted for Big Data. This has been accomplished by three theory studies on how to process user events, how to make the product recommendation algorithm called collaborative filtering scalable and finally how to convert implicit feedback to explicit feedback (ratings). This resulted in a recommendation engine consisting of Apache Spark as the data processing system, which had three functions: read multiple log files and concatenate log files for each month, parsing the log files of the user events to create explicit ratings from the transactions and create four types of recommendations. The NoSQL database MongoDB was chosen as the database to store the different types of product recommendations that was created. To be able to get the recommendations from the recommendation engine and the database, a REST API was implemented which can be used by any third-party. What can be concluded from the results of this thesis work is that the system that was implemented is partial scalable. This means that Apache Spark was scalable for both concatenating files, parse and create ratings and also create the recommendations using the ALS method. However, MongoDB was shown to be not scalable when managing more than 100 concurrent requests. Future work involves making the recommendation engine distributed in a multi-node cluster to utilize the parallelization of Apache Spark. Other recommendations include considering other NoSQL databases that might be more scalable than MongoDB.

En la indústria AEC (Arquitectura, Enginyeria, Construcció), és cada vegada més necessari automatitzar l’intercanvi d'informació en els processos en els quals intervé la tecnologia BIM (Building Information Modelling). Els experts que participen en aquests processos (arquitectes, enginyers, constructors, etc.) utilitzen diferents tipus d’aplicacions per dur a terme tasques específiques d’acord al seu àmbit de coneixement i la seva responsabilitat. Tot i que cada una d’aquestes aplicacions, separadament, compleix la seva funció, la interoperabilitat entre elles segueix sent un problema a resoldre. En aquests processos es requereix, a més, accedir a dades de fonts diverses i diferents formats, per integrar-los i fer-los accessibles a les aplicacions BIM. En aquesta tesi s’investiguen les dificultats subjacents en aquests dos problemes –la interoperabilitat entre aplicacions i la integració d’informació de múltiple fonts i formats en el context dels processos basats en tecnologies BIM– i es proposen solucions per superar-les. En primer lloc s’han examinat les ineficiències que actualment existeixen en l’intercanvi d’informació entre sistemes i aplicacions utilitzats en projectes AEC que empren la tecnologia BIM. Un cop identificades, es planteja la seva superació a través de l’aplicació de tecnologies de la Web Semàntica. Per a això, s’analitza la capacitat d’aquestes tecnologies per a integrar dades heterogènies de diferents fonts i àmbits mitjançant ontologies. Finalment, es considera la seva aplicació en el desenvolupament de projectes AEC. A partir d’aquest estudi previ, s’ha pogut concloure que les solucions per millorar la interoperabilitat entre BIM i altres aplicacions a partir de les tecnologies semàntiques estan lluny de proporcionar una solució definitiva al problema de la interoperabilitat. Per tal de proposar solucions basades en la Web Semàntica per a la integració de dades en processos en què intervenen les tecnologies BIM, s’ha acotat la investigació a un cas d’estudi: la creació d’un catàleg de components prefabricats de formigó amb tecnologies de la Web Semàntica i compatible amb la tecnologia BIM. En el context d’aquest cas d’estudi s’han desenvolupat mètodes i eines per a: 1) integrar dades de components i productes constructius en un catàleg amb contingut semàntic accessible a aplicacions BIM, i 2) aplicar regles d’inferència semàntica per examinar els components inclosos en un model BIM i proporcionar productes compatibles extrets del catàleg. La viabilitat dels mètodes i eines s’ha demostrat en un cas d’aplicació: pre-dimensionat d’elements constructius que compleixen les normatives de seguretat estructural i recerca automatitzada de components alternatius en el catàleg. Tot i demostrar el benefici potencial de les tecnologies de la Web Semàntica per millorar els processos BIM integrant dades externes, encara hi ha alguns reptes a superar, entre ells, l’escassetat de dades en format RDF i la dificultat en mantenir els enllaços entre dades quan aquests canvien. Els resultats obtinguts en aquesta investigació podrien continuar desenvolupant-se en dues direccions: 1) ampliant el catàleg a nous productes i incorporant noves fonts de dades relacionades amb els mateixos i 2) creant eines que facilitin la creació i el manteniment de regles d’inferència.
En la industria AEC (Arquitectura, Ingeniería, Construcción), es cada vez más necesario automatizar el intercambio de información en los procesos en los que interviene la tecnología BIM (Building Information Modelling). Los expertos que participan en estos procesos (arquitectos, ingenieros, constructores, etc.) utilizan diferentes tipos de aplicaciones para llevar a cabo tareas específicas de acuerdo a su ámbito de conocimiento y su responsabilidad. Aunque cada una de estas aplicaciones, separadamente, cumple su función, la interoperabilidad entre ellas sigue siendo un problema a resolver. En estos procesos se requiere acceder a datos de fuentes diversas y distintos formatos, para integrarlos y hacerlos accesibles a las aplicaciones BIM. En esta tesis se investigan las dificultades que subyacen en estos dos ámbitos –la interoperabilidad entre aplicaciones y la integración de información de múltiple fuentes y formatos en el contexto de los procesos basados en tecnologías BIM– y se proponen soluciones para superarlas. En primer lugar, se han examinado las ineficiencias que actualmente existen en el intercambio de información entre sistemas y aplicaciones utilizados en proyectos AEC que emplean la tecnología BIM. Una vez identificadas, se plantea su superación a través de la aplicación de tecnologías de la Web Semántica. Para ello, se analiza la capacidad de estas tecnologías para integrar datos heterogéneos de diferentes fuentes y ámbitos mediante ontologías. Finalmente, se considera su aplicación en el desarrollo de proyectos AEC. A partir de este estudio previo, se ha podido concluir que las soluciones para mejorar la interoperabilidad entre BIM y otras aplicaciones a partir de las tecnologías semánticas están lejos de proporcionar una solución definitiva al problema de la interoperabilidad. Con el fin de proponer soluciones basadas en la Web Semántica para la integración de datos en procesos en los que intervienen las tecnologías BIM, se ha acotado la investigación a un caso de estudio: la creación de un catálogo de componentes prefabricados de hormigón con tecnologías de la Web Semántica y compatible con la tecnología BIM. En el contexto de este caso de estudio se han desarrollado métodos y herramientas para: 1) integrar datos de componentes y productos constructivos en un catálogo con contenido semántico accesible a aplicaciones BIM, y 2) aplicar reglas de inferencia semántica para examinar los componentes incluidos en un modelo BIM y proporcionar productos compatibles extraídos del catálogo. La viabilidad de los métodos y herramientas se ha demostrado en un caso de aplicación: pre-dimensionado de elementos constructivos que cumplen las normativas de seguridad estructural y búsqueda automatizada de componentes alternativos en el catálogo. A pesar de demostrar el beneficio potencial de las tecnologías de la Web Semántica para mejorar los procesos BIM integrando datos externos, todavía hay algunos retos a superar, entre ellos, la escasez de datos en formato RDF y la dificultad en mantener los enlaces entre datos cuando estos cambian. Los resultados obtenidos en esta investigación podrían continuar desarrollándose en dos direcciones: 1) ampliando el catálogo a nuevos productos e incorporando nuevas fuentes de datos relacionadas con los mismos y 2) creando herramientas que faciliten la creación y el mantenimiento de reglas de inferencia.
In the AEC industry (Architecture, Engineering, Construction), it is increasingly necessary to automate the exchange of information in processes involving BIM (Building Information Modelling) technology. The experts involved in these processes (architects, engineers, builders, etc.) use different types of applications to carry out specific tasks according to their scope of knowledge and their responsibility. Although each of these applications separately fulfils its function, interoperability between them remains a problem to be solved. In these processes it is also necessary to access data from different sources and different formats to integrate them and make them accessible to BIM applications. This research investigates the difficulties that underlie these two problems – interoperability between applications and the integration of information in the context of processes based on BIM technologies – and propose solutions to overcome them. In the first place, the inefficiencies that currently exist in the exchange of information between systems and applications used in AEC projects using BIM technology have been examined. Once identified, our objective has been to overcome them through the application of Semantic Web technologies. To do this, the ability of these technologies to integrate heterogeneous data from different sources and domains using ontologies is analysed. Finally, we considered their application in the development of AEC projects. From this previous study, it has been concluded that developed solutions to improve interoperability between BIM and other applications using semantic technologies are still far from providing a definitive solution to the problem of interoperability. In order to propose solutions based on the Semantic Web for the integration of data in processes involving BIM technologies, the research has been limited to a case study: the creation of a catalogue of precast concrete components with semantic technologies which are compatible with BIM technology. In the context of this case study, we have developed methods and tools to (1) integrate data on components and constructive products in a catalogue with semantic content compatible with BIM technology, and (2) apply the rules of semantic inference to examine the components used on a BIM model and provide compatible products extracted from the catalogue. The feasibility of the methods and tools has been demonstrated in an application case: pre-dimensioned structural elements that comply with structural safety regulations and the automated search of alternative components in the catalogue. Despite demonstrating the potential of Semantic Web technologies to improve BIM processes by integrating external data, there are still some challenges to overcome, including the shortage of data in RDF format and the difficulty in the maintenance of the links between the data when they change. The results obtained in this research could continue to be developed in two directions (1) expanding the catalogue to new products and integrating new data sources related to them and (2) creating tools that facilitate the creation and maintenance of inference rules.

Software product development companies are increasingly striving to become data-driven. The access to customer feedback and product data has been, with products increasingly becoming connected to the Internet, demonetized. Systematically collecting the feedback and efficiently using it in product development, however, are challenges that large-scale software development companies face today when being faced by large amounts of available data. In this thesis, we explore the collection, use and impact of customer feedback on software product development. We base our work on a 2-year longitudinal multiple-case study research with case companies in the software-intensive domain, and complement it with a systematic review of the literature. In our work, we identify and confirm that large-software companies today collect vast amounts of feedback data, however, struggle to effectively use it. And due to this situation, there is a risk of prioritizing the development of features that may not deliver value to customers. Our contribution to this problem is threefold. First, we present a comprehensive and systematic review of activities and techniques used to collect customer feedback and product data in software product development. Next, we show that the impact of customer feedback evolves over time, but due to the lack of sharing of the collected data, companies do not fully benefit from this feedback. Finally, we provide an improvement framework for practitioners and researchers to use the collected feedback data in order to differentiate between different feature types and to model feature value during the lifecycle. With our contributions, we aim to bring software companies one step closer to data-driven decision making in software product development.

Currently, computer-based support tools are widely used to facilitate the design process and have the potential to reduce design time, decrease product cost and enhance product quality. PDM (Product Data Management) and PLM (Product Lifecycle Management) are two types of computer-based information systems that have been developed to manage product lifecycle and product related data. While promising, significant limitations still exist, where information required to make decisions may not be available, may be lacking consistency, and may not be expressed in a general way for sharing among systems. Moreover, it is difficult for designers to consider multiple complex technical and economical criteria, relations, and objectives in product design simultaneously. In recent years, ontology-based method is a new and promising approach to manage knowledge in engineering, integrate multiple data resources, and facilitate the consideration of the complex relations among concepts and slots in decision making. The purpose of this research is to explore an ontology-based method to solve the limitations in present computer-based information systems for product design. The field of Design for Manufacturing (DFM) is selected for this study, and three primary aspects are investigated. First, a generalized DFM ontology is proposed and developed. The ontology fulfills the mathematical and logical constraints needed in DFM, as well as ontology editor capabilities to support the continuous improvement of the ontology. Second, the means to guide users to the proper information and integrate heterogeneous data resources is investigated. Third, based on the ontology and information integration, a decision support tool is developed to help designers consider the design problem in a systematic way and make design decisions efficiently based on accurate and comprehensive data. The methods and tools developed in this research are refined using example cases provided by the CFSP (The NSF Center for Friction Stir Processing). This includes cost models and a decision support environment. Errors that may occur in the research are categorized with management methods. An error ontology is built to help root cause analysis of errors and further reduce possible errors in the ontology and decision support tool. An evaluation methodology for the research is also investigated.
Ph. D.

Abstract Data is becoming more critical success factor as business processes rely increasingly on information systems. Product data is required to produce, sell, deliver, and invoice a product in information systems. Traditionally, product data and product data management (PDM) studies have focused on product development and related activities, with less attention being paid to PDM in other lifecycle phases. The purpose of this doctoral dissertation is to clarify challenges and prerequisites for company-wide PDM. The study covers the entire product lifecycle and provides potential solutions for developing company-wide PDM and enhancing PDM understanding as a company-wide action. The study was realised by collecting and analysing data from those ICT companies that are seeking for better ways to manage a wide product-range, technologically complex products and comprehensive solutions by enhancing their data management practices. The empirical practitioner’s experiences and perceptions are seen to have increased the knowledge in company-wide PDM. This study adopted a case study approach and utilises interviews as the main data collection method. This study indicates that company managers have already realised that successful business operations require a higher-level understanding of products and related product data. In practice, however, several challenges hinder the ability to achieve the goal of higher-level business-driven PDM. These challenges include product harmonisation, PDM process development requirements and information systems development requirements. The results of this research indicate that product harmonisation is required to better support efficient product data management. Understanding the true nature of product data, that is combination of product master data and other general product data, and the content of product data from different stakeholder perspectives are prerequisites for functional company-wide PDM. Higher-level product decisions have a significant impact on product data management. Extensive product ranges require general guidelines in order to be manageable, especially as even single products are complex. The results of this study indicate that companies should follow a top-down approach when developing their PDM practices. The results also indicate that companies require a generic product structure in order to support unified product management. The main implication of this dissertation is the support it provides for managers in terms of developing true company-wide product data management practices
Tiivistelmä Tiedosta on tullut tärkeä liiketoiminnan menestystekijä liiketoimintaprosessien hyödyntäessä yhä vahvemmin tietojärjestelmiä. Tuotteisiin liittyvä tieto on olennaista, jotta tuote voidaan valmistaa, myydä, toimittaa ja laskuttaa. Tuotetietoa ja sen hallintaa on perinteisesti tarkastelu tuotekehityslähtöisesti kun tämä tutkimus pyrkii ymmärtämään tuotetiedon hallintaa kattaen myös edellä mainitut yrityksen toiminnot. Tämän tutkimuksen tavoitteena on tunnistaa haasteita ja perusedellytyksiä yrityksenlaajuisten tuotetiedonhallinnan käytäntöjen kehittämiseksi. Tuotetiedon hallinta yrityksen laajuisena toimintona vaatii ymmärrystä eri toimijoista, jotka käyttävät tuotetietoa; tiedon luonteesta sekä tiedon hyödyntämisestä eri prosesseissa. Tutkimus toteutettiin ICT yrityksissä, joissa tuotetiedon käytäntöjä tehostamalla haetaan keinoja hallita laajaa tuotteistoa, teknologisesti monimutkaisten tuotteita sekä kokonaisratkaisuja. Käytännön toimijoiden kokemukset ja käsitykset ovat ensiarvoisen tärkeitä lisätessä tietoa yrityksen laajuisesta tuotetiedonhallinnasta. Tutkimus toteutettiin tapaustutkimuksen menetelmin, joissa pääasiallisena tiedonkeruu menetelmänä hyödynnettiin haastatteluja. Tämä tutkimus osoittaa, että liiketoimintalähtöisen tuotetiedon hallinan kehittäminen on ajankohtaista yrityksissä. Tutkimuksessa tunnistetaan lukuisia haasteita, jotka ovat estäneet liiketoimintalähtöisen tuotetiedonhallinnan saavuttamisen. Näitä haasteita ovat: tuotteen harmonisointi yrityksen eri toiminnoissa, tuotetiedon hallinnan prosessien kehittämisen vaatimukset sekä tietojärjestelmien kehittämisen vaatimukset. Tutkimustulosten mukaan tuotteiston harmonisointi on yksi perusedellityksistä tehokkaalle tuotetiedon hallinnalle. Yrityksen kattava tuotetiedoen hallinta vaatii myös tuotetiedon todellisen luonteen ymmärtämistä, joka koostuu tuotteen master datasta sekä muusta tuotetiedosta. Lisäksi on olennaista ymmrättää tuotetiedon sisältö sen todellisten käyttäjien näkökulmasta käsin. Tämän tutkimuksen tulokset osoittavat myös, että tuotetiedon hallinnan kehittäminen pitäisi edetä ”top-down” eli ylhäältä-alas periaatteen mukaan. Tulokset myös viittaavat siihen, että geneerinen tuoterakenne tukee yhdenmukaisia tuotehallinta käytäntöjä. Nämä tulokset tarjoavat työssä esitettyjen kuvausten ja mallien ohella tukea tuotetiedon hallinnan käytäntöjen kehittämiseen yrityksen laajuisesti

Natural products have withstood the test ot time as therapeutics but new lead generation strategies have focused away from natural products. This study reports a new approach that uses natural product recognition to drive an understanding of biology space which may provide an impetus for renewed focus on natural product starting points.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Eskitis Institute for Cell and Molecular Therapies
Science, Environment, Engineering and Technology
Full Text

The exceeding supply over demand and very hard competitive conditions are nowadays the main features of the majority of sectors. A successful company is the one that is able to satisfy specific customers' needs, the one that has efficient cooperation with its suppliers throughout the whole supply chain and also the one that is able to speed up the in-house information exchange. Thus the company has to seek constantly new and innovative solutions. This is not possible without standardization and automatization of business processes. This master's thesis is dedicated to one of the possible solutions -- the Product Information Management (PIM). Since it is intended for business managers (without deep IT knowledge), at the beginning it answers the question why it is so important to know master data and to manage it. It specializes in managing product data, brings its comprehensive overview and identifies the advantages and drawbacks of the implementation as well as financial and organizational impacts. The consecutive chapter deals with simplified yet applicable approach to data management analysis (with emphasis on the PIM) and based on research, it mentions main mistakes of the implementation. In addition to the overview of main vendors of the PIM solution, it presents the latest trends in the PIM. Besides internal data synchronization, the thesis analyses several product standards -- the fundamental step towards external data synchronization, the key topic of the practical part. The whole thesis is conceived to provide an organization with a simple yet compact and therefore very effective tool for master product data insight and thus to help it to gain a competitive advantage.

Data from the 2001 and 2003 timber product output (TPO) studies for Georgia were explored to determine new methods for handling missing data and finding suitable sampling estimators. Mean roundwood volume receipts per mill for the year 2003 were calculated using the methods developed by Rubin (1987). Mean receipts per mill ranged from 4.4 to 14.2 million ft3. The mean value of 9.3 million ft3 did not statistically differ from the NONMISS, SINGLE1, and SINGLE2 references means (p=.68, .75, and .76 respectively). Fourteen estimators were investigated to investigate sampling approaches, with estimators being of several means types (simple random sample, ratio, stratified sample, and combined ratio) as well as employing two methods for stratification (Dalenius-Hodges (DH) square root of the Frequency method and a cluster analysis method. Relative efficiency (RE) improved when the number of groups increased and when employing a ratio estimator, particularly a combined ratio. Neither the DH method nor the cluster analysis method performed better than the other. Six bound sizes (1, 5, 10, 15, 20, and 25 percent) were considered for deriving samples sizes for the total volume of roundwood. The minimum achievable bound size was found to be 10 percent of the total receipts volume for the DH-method using a two group stratification. This was true for both the stratified and combined ratio estimators. In addition, for the stratified and combined ratio estimators, only the DH method stratifications were able to reach a 10 percent bound on the total (6 of the 12 stratified estimators). The remaining six stratified estimators were able to achieve a 20 percent bound of the total. Finally, nonlinear repeated measures models were developed to spatially allocate mill receipts to surrounding counties in the event of obtaining only a mill's total receipt volume. A Gompertz model with a power spatial covariance was found to be the best performing when using road distances from the mills to either county center type (geographic or forest mass). These models utilized the cumulative frequency of mill receipts as the response variable, with cumulative frequencies based on distance from the mill to the county.
Ph. D.

In order to ensure effective decisions are made at each stage in the design and manufacture process, it is important that software tools should provide sufficient information to support the decision making of both designers and manufacturing engineers. This requirement can be applied to fixturing where research to date has typically focused on narrow functional support issues in fixture design and planning. The research reported in this thesis has explored how models of fixturing information can be defined, within an integrated information environment, and utilised across product design as well as manufacture. The work has focused on the definition of fixturing information within the context of a wide-ranging model that can capture the full capability of a manufacturing facility.

Thesis (M.Eng.)--University of Louisville, 2007.
Title and description from thesis home page (viewed May 14, 2007). Department of Industrial Engineering. Vita. "May 2007." Includes bibliographical references (p. 36-37).