Potential benefits of building information modeling for building material supplier

This thesis investigated building information modeling (BIM) from a material supplier’s point of view. The objective was to gain understanding about how a building material supplier could benefit from the growing use of BIM in the AEC (architectural, engineering and construction) industry. Increasing amount of inquiries related to BIM from customers and other interest groups had awoken target company’s interest towards BIM. This thesis acts as a pre-study for the target company related to potential of BIM. First of all BIM and its meaning from a material supplier’s point of view was defined based on a literature review. To reveal the potential benefits of BIM for a material supplier a questionnaire survey and in total of 11 interviews were conducted. Based on the literature review and analyzed results it came clear that BIM offers benefits also for material suppliers. Product libraries and material databases for BIM tools can act as an important marketing channel for material suppliers. Material suppliers could also utilize the information from the BIM models to schedule their deliveries more precisely and potentially even to schedule their own production. All this needs deeper cooperation between material suppliers, contractors and other stakeholders in the AEC industry. Based on the results also first steps for the target company to utilize the growing use of BIM were defined.

Risk Assessment of a Supplier in the Pharmaceutical Industry

The topic of this Master’s Thesis is risk assessment in the supply chain, and the work was done for a company operating in the pharmaceutical industry. The unique features of the industry bring additional challenges to risk management, due to high regulatory, docu-mentation and traceability requirements. The objective of the thesis was to generate a template for assessing the risks in the supply chain of current and potential suppliers of the case company. Risks pertaining to the case setting were sought mainly from in-house expertise of this specific product and supply chain as well as academic research papers and theory on risk management. A questionnaire was set up to assess the found risks on impact, occurrence and possibility of detection. Through this classification of the severity of the risks, the supplier assessment template was formed. A questionnaire template, comprised of the top 10 risks affecting the flow of information and materials in this setting, was formulated to serve as a generic tool for assessing risks in the supply chain of a pharmaceutical company. The template was tested on another supplier for usability and accuracy of found risks, and it demonstrated functioning in a differing supply chain and product setting.

Supplier Delivery Quality Evaluation in Maintenance Supporting Spare Part Business

The focus of this thesis is the issues related to the operational purchasing level supply chain process. Due to this limitation, the physical product quality issues do not belong to the primary concerns of this thesis, whereas the bilateral processes between the supplier and the purchasing organizations are of interest. Also, the issues related to the delivery timing are excluded from the study. Because the perspective is on the issues involved in the supply chain operations, the bilateral communication issues between supplier representative and purchaser are also considered.

Supplier codes of conduct: Acting responsibly in global supply chains

Nowadays the Western companies are considered responsible for the social and environmental issues in their whole supply chains. To influence the practices of their suppliers the Western companies have created suppliers codes of conduct (SCCs) which express their requirements. Suppliers’ compliance with the SCCs is checked through audits. The purpose of this thesis is to analyze SCCs as a means for Western companies to ensure socially and environmentally responsible actions in their global supply chains, and the sub-objectives are to find out 1) how well do the SCCs and their auditing work at suppliers’ production sites and 2) how can possible problems related to SCCs and their auditing be solved. This is a qualitative research carried out in the form of a case study with two case companies. In this study both primary and secondary data is used. The primary data is collected in the form of interviews of the case company representatives and three external experts. Based on a theoretical framework of previous research in the fields of corporate social responsibility and supply chain management, a model with eleven factors, which influence the success of SCC implementation and the auditing of SCC –implementation, is drafted. Also several different best-practices to help to solve and avoid possible problems related to SCC -implementation and auditing have been identified from previous research. Based on the findings of this study the theoretical model has been updated adding two new influential factors. It seems that how well the SCC and its auditing work at suppliers’ production sites depends on the joint effect of thirteen influential factors: buyer’s purchasing policy, supplier’s motivation, buyer’s commitment, the solving of agency problems, the contents of the SCC, supplier’s role and the buyer-supplier –relationship, complexity of supply chain, the limitations of the smaller buyers, cooperation through a business association or multi-stakeholder system, the role of supplier’s employees, SCC –related communication and supplier’s understanding, cheating in audits and the auditors. The possible problems related to SCCs and their auditing can be solved by adopting best-practices. Nine of the theoretical best-practices stand out from the findings of this study: 1) two-way communication and collecting feedback from suppliers, 2) the philosophy of continuous improvement, 3) long-term business relationships with the supplier, 4) informing the supplier about the advantages of SCC –compliance, 5) rewarding code-compliant suppliers, 6) building collaborative, good buyer-supplier relationships, 7) supporting and advising the supplier, 8) joining a business association or multi-stakeholder system and 9) interviewing supplier’s employees as a part of the audits.

Segmentation of overlapping convex objects

This thesis presents a framework for segmentation of clustered overlapping convex objects. The proposed approach is based on a three-step framework in which the tasks of seed point extraction, contour evidence extraction, and contour estimation are addressed. The state-of-art techniques for each step were studied and evaluated using synthetic and real microscopic image data. According to obtained evaluation results, a method combining the best performers in each step was presented. In the proposed method, Fast Radial Symmetry transform, edge-to-marker association algorithm and ellipse fitting are employed for seed point extraction, contour evidence extraction and contour estimation respectively. Using synthetic and real image data, the proposed method was evaluated and compared with two competing methods and the results showed a promising improvement over the competing methods, with high segmentation and size distribution estimation accuracy.

Green supplier selection via Multiple Criteria Data Envelopment Analysis

An appropriate supplier selection and its profound effects on increasing the competitive advantage of companies has been widely discussed in supply chain management (SCM) literature. By raising environmental awareness among companies and industries they attach more importance to sustainable and green activities in selection procedures of raw material providers. The current thesis benefits from data envelopment analysis (DEA) technique to evaluate the relative efficiency of suppliers in the presence of carbon dioxide (CO2) emission for green supplier selection. We incorporate the pollution of suppliers as an undesirable output into DEA. However, to do so, two conventional DEA model problems arise: the lack of the discrimination power among decision making units (DMUs) and flexibility of the inputs and outputs weights. To overcome these limitations, we use multiple criteria DEA (MCDEA) as one alternative. By applying MCDEA the number of suppliers which are identified as efficient will be decreased and will lead to a better ranking and selection of the suppliers. Besides, in order to compare the performance of the suppliers with an ideal supplier, a “virtual” best practice supplier is introduced. The presence of the ideal virtual supplier will also increase the discrimination power of the model for a better ranking of the suppliers. Therefore, a new MCDEA model is proposed to simultaneously handle undesirable outputs and virtual DMU. The developed model is applied for green supplier selection problem. A numerical example illustrates the applicability of the proposed model.

Linking supplier delivery risk to outbound delivery service performance improvement

Customer satisfaction should be the main focus for all of the parts of the business. Usually supply chain behind the business is in a key role when this focus is pursued especially in repair service business. When focusing on the materials that are needed to make repairs to equipment under service contracts, the time aspect of quality is critical. Do late deliveries from supplier have an effect on the service performance of repairs when distribution center of a centralized purchasing unit is acting as a buffer between suppliers and repair service business? And if so, how should the improvement efforts be prioritized? These are the two main questions that this thesis focuses on. Correlation and linear regression was tested between service levels of supplier and distribution center. Percentage of on-time deliveries were compared to outbound delivery service level. It was found that there is statistically significant correlation between inbound and outbound operations success. The other main question of the thesis, improvement prioritization, was answered by creating material availability based supplier classification and additional to that, by developing the decision process for the analysis of most critical suppliers. This was built on a basis of previous supplier and material classification methods.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.