Utilizing analytic modelling process in industrial retail context

Nykypäivän monimutkaisessa ja epävakaassa liiketoimintaympäristössä yritykset, jotka kykenevät muuttamaan tuottamansa operatiivisen datan tietovarastoiksi, voivat saavuttaa merkittävää kilpailuetua. Ennustavan analytiikan hyödyntäminen tulevien trendien ennakointiin mahdollistaa yritysten tunnistavan avaintekijöitä, joiden avulla he pystyvät erottumaan kilpailijoistaan. Ennustavan analytiikan hyödyntäminen osana päätöksentekoprosessia mahdollistaa ketterämmän, reaaliaikaisen päätöksenteon. Tämän diplomityön tarkoituksena on koota teoreettinen viitekehys analytiikan mallintamisesta liike-elämän loppukäyttäjän näkökulmasta ja hyödyntää tätä mallinnusprosessia diplomityön tapaustutkimuksen yritykseen. Teoreettista mallia hyödynnettiin asiakkuuksien mallintamisessa sekä tunnistamalla ennakoivia tekijöitä myynnin ennustamiseen. Työ suoritettiin suomalaiseen teollisten suodattimien tukkukauppaan, jolla on liiketoimintaa Suomessa, Venäjällä ja Balteissa. Tämä tutkimus on määrällinen tapaustutkimus, jossa tärkeimpänä tiedonkeruumenetelmänä käytettiin tapausyrityksen transaktiodataa. Data työhön saatiin yrityksen toiminnanohjausjärjestelmästä.

Towards a contextual understanding of B2B salespeople's selling competencies – An exploratory study among purchasing decision-makers of internationally-oriented technology firms

ABSTRACT Towards a contextual understanding of B2B salespeople’s selling competencies − an exploratory study among purchasing decision-makers of internationally-oriented technology firms The characteristics of modern selling can be classified as follows: customer retention and loyalty targets, database and knowledge management, customer relationship management, marketing activities, problem solving and system selling, and satisfying needs and creating value. For salespeople to be successful in this environment, they need a wide range of competencies. Salespeople’s selling skills are well documented in seller side literature through quantitative methods, but the knowledge, skills and competencies from the buyer’s perspective are under-researched. The existing research on selling competencies should be broadened and updated through a qualitative research perspective due to the dynamic nature and the contextual dependence of selling competencies. The purpose of the study is to increase understanding of the professional salesperson’s selling competencies from the industrial purchasing decision- makers’ viewpoint within the relationship selling context. In this study, competencies are defined as sales-related knowledge and skills. The scope of the study includes goods, materials and services managed by a company’s purchasing function and used by an organization on a daily basis. The abductive approach and ‘systematic combining’ have been applied as a research strategy. In this research, data were generated through semi- structured, person-to-person interviews and open-ended questions. The study was conducted among purchasing decision-makers in the technology industry in Finland. The branches consisted of the electronics and electro-technical industries and the mechanical engineering and metals industries. A total of 30 companies and one purchasing decision-maker from each company were purposively chosen for the sampling. The sample covers different company sizes based on their revenues, their differing structures – varying from public to family companies –that represent domestic and international ownerships. Before analyzing the data, they were organized by the purchasing orientations of the buyers: the buying, procurement or supply management orientation. Thematic analysis was chosen as the analysis method. After analyzing the data, the results were contrasted with the theory. There was a continuous interaction between the empirical data and the theory. Based on the findings, a total of 19 major knowledge and skills were identified from the buyers’ perspective. The specific knowledge and skills from the viewpoint of customers’ prevalent purchasing orientations were divided into two categories, generic and contextual. The generic knowledge and skills apply to all purchasing orientations, and the contextual knowledge and skills depend on customers’ prevalent purchasing orientations. Generic knowledge and skills relate to price setting, negotiation, communication and interaction skills, while contextual ones relate to knowledge brokering, ability to present solutions and relationship skills. Buying-oriented buyers value salespeople who are ‘action oriented experts, however at a bit of an arm’s length’, procurement buyers value salespeople who are ‘experts deeply dedicated to the customer and fostering the relationship’ and supply management buyers value salespeople who are ‘corporate-oriented experts’. In addition, the buyer’s perceptions on knowledge and selling skills differ from the seller’s ones. The buyer side emphasizes managing the subject matter, consisting of the expertise, understanding the customers’ business and needs, creating a customized solution and creating value, reliability and an ability to build long-term relationships, while the seller side emphasizes communica- tion, interaction and salesmanship skills. The study integrates the selling skills of the current three-component model− technical knowledge, salesmanship skills, interpersonal skills− and relationship skills and purchasing orientations, into a selling competency model. The findings deepen and update the content of these knowledges and skills in the B2B setting and create new insights into them from the buyer’s perspective, and thus the study increases contextual understanding of selling competencies. It generates new knowledge of the salesperson’s competencies for the relationship selling and personal selling and sales management literature. It also adds knowledge of the buying orientations to the buying behavior literature. The findings challenge sales management to perceive salespeople’s selling skills both from a contingency and competence perspective. The study has several managerial implications: it increases understanding of what the critical selling knowledge and skills from the buyer’s point of view are, understanding of how salespeople effectively implement the relationship marketing concept, sales management’s knowledge of how to manage the sales process more effectively and efficiently, and the knowledge of how sales management should develop a salesperson’s selling competencies when managing and developing the sales force. Keywords: selling competencies, knowledge, selling skills, relationship skills, purchasing orientations, B2B selling, abductive approach, technology firms

Item-level life-cycle model for maintenance network – from cost to additional value

The importance of industrial maintenance has been emphasized during the last decades; it is no longer a mere cost item, but one of the mainstays of business. Market conditions have worsened lately, investments in production assets have decreased, and at the same time competition has changed from taking place between companies to competition between networks. Companies have focused on their core functions and outsourced support services, like maintenance, above all to decrease costs. This new phenomenon has led to increasing formation of business networks. As a result, a growing need for new kinds of tools for managing these networks effectively has arisen. Maintenance costs are usually a notable part of the life-cycle costs of an item, and it is important to be able to plan the future maintenance operations for the strategic period of the company or for the whole life-cycle period of the item. This thesis introduces an itemlevel life-cycle model (LCM) for industrial maintenance networks. The term item is used as a common definition for a part, a component, a piece of equipment etc. The constructed LCM is a working tool for a maintenance network (consisting of customer companies that buy maintenance services and various supplier companies). Each network member is able to input their own cost and profit data related to the maintenance services of one item. As a result, the model calculates the net present values of maintenance costs and profits and presents them from the points of view of all the network members. The thesis indicates that previous LCMs for calculating maintenance costs have often been very case-specific, suitable only for the item in question, and they have also been constructed for the needs of a single company, without the network perspective. The developed LCM is a proper tool for the decision making of maintenance services in the network environment; it enables analysing the past and making scenarios for the future, and offers choices between alternative maintenance operations. The LCM is also suitable for small companies in building active networks to offer outsourcing services for large companies. The research introduces also a five-step constructing process for designing a life-cycle costing model in the network environment. This five-step designing process defines model components and structure throughout the iteration and exploitation of user feedback. The same method can be followed to develop other models. The thesis contributes to the literature of value and value elements of maintenance services. It examines the value of maintenance services from the perspective of different maintenance network members and presents established value element lists for the customer and the service provider. These value element lists enable making value visible in the maintenance operations of a networked business. The LCM added with value thinking promotes the notion of maintenance from a “cost maker” towards a “value creator”.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Interactions of decision mechanisms in an entrepreneurial firm’s journey to become a major player in its industry

This study discusses the interactions of different decision-making mechanisms in the process of change of a successful entrepreneurial dairy firm in Vietnam. The purpose of the study is to construct a theoretical framework, which explains the interactions between effectual and causal decision-making processes in different phases of business, and to provide a real life example with practical recommendations for entrepreneurs and managers. In order to achieve this purpose, a preliminary theoretical framework was built, using process theories applied to different decision making modes, referred to as causation and effectuation. The case was studied through ethnographic research method, with three semi-structured interviews, one unstructured interview, secondary data and observations within four months in 2013-2014. After the data was analyzed, a modified framework was drawn from the result. The finding of this study shows that there was an interaction between effectual and causal decision-making processes in different stages of the company’s development. The entrepreneur applied effectual decision-making process to develop a unique business model and a new dairy market segment. However, when a new market demand arose, the company’s resources became insufficient, they thus had to shift to causation process to adapt to market change. Simultaneously, with better-accumulated resources, the entrepreneur continued the effectuation process to create another brand new dairy market segment. This study, thus, contributes to effectuation theory, emphasizing the necessity of combining effectual and causal decision-making processes in different phases of business. It is suggested that business would develop with an effectual process until a business model is viable for growth. It continues to use this process up to a certain degree. When the market changes, the company needs to collect more means to adapt to the changes. They need to set new goals and this is a shift to the use of causal process, which builds on prediction. It uses goals and teleology as driving mechanisms and tries to exploit and fill potential resource gaps to achieve these goals. At the same time, there are new iterations that look to establish new lines or types of business with the given means, which are now well established. This again employs effectual mechanisms, which are based on evolutionary process, until they reach the stage of viable tested business model. Moreover, this study hopes to provide know-how to entrepreneurs and managers of small companies in similar situations, suggesting how to combine effectual and causal decision-making processes to deal with various circumstances in different times.

Healthcare Information System Selection Model for Medical Clinics

The issue of selecting an appropriate healthcare information system is a very essential one. If implemented healthcare information system doesn’t fit particular healthcare institution, for example there are unnecessary functions; healthcare institution wastes its resources and its efficiency decreases. The purpose of this research is to develop a healthcare information system selection model to assist the decision-making process of choosing healthcare information system. Appropriate healthcare information system helps healthcare institutions to become more effective and efficient and keep up with the times. The research is based on comparison analysis of 50 healthcare information systems and 6 interviews with experts from St-Petersburg healthcare institutions that already have experience in healthcare information system utilization. 13 characteristics of healthcare information systems: 5 key and 7 additional features are identified and considered in the selection model development. Variables are used in the selection model in order to narrow the decision algorithm and to avoid duplication of brunches. The questions in the healthcare information systems selection model are designed to be easy-to-understand for common a decision-maker in healthcare institution without permanent establishment.

Political Influence, Economic Interests and Endogenous Tax Structure in a Computable Equilibrium Framework: with Applicatioon to the United States, 1973 and 1983

A full understanding of public affairs requires the ability to distinguish between the policies that voters would like the government to adopt, and the influence that different voters or group of voters actually exert in the democratic process. We consider the properties of a computable equilibrium model of a competitive political economy in which the economic interests of groups of voters and their effective influence on equilibrium policy outcomes can be explicitly distinguished and computed. The model incorporates an amended version of the GEMTAP tax model, and is calibrated to data for the United States for 1973 and 1983. Emphasis is placed on how the aggregation of GEMTAP households into groups within which economic and political behaviour is assumed homogeneous affects the numerical representation of interests and influence for representative members of each group. Experiments with the model suggest that the changes in both interests and influence are important parts of the story behind the evolution of U.S. tax policy in the decade after 1973.

Suppression of long-branch attraction artefacts in the animal phylogeny using a site-heterogeneous model

Affiliation: Département de Biochimie, Université de Montréal

A theoretical and experimental dissociation of two models of decision‐making

La prise de décision est un processus computationnel fondamental dans de nombreux aspects du comportement animal. Le modèle le plus souvent rencontré dans les études portant sur la prise de décision est appelé modèle de diffusion. Depuis longtemps, il explique une grande variété de données comportementales et neurophysiologiques dans ce domaine. Cependant, un autre modèle, le modèle d’urgence, explique tout aussi bien ces mêmes données et ce de façon parcimonieuse et davantage encrée sur la théorie. Dans ce travail, nous aborderons tout d’abord les origines et le développement du modèle de diffusion et nous verrons comment il a été établi en tant que cadre de travail pour l’interprétation de la plupart des données expérimentales liées à la prise de décision. Ce faisant, nous relèveront ses points forts afin de le comparer ensuite de manière objective et rigoureuse à des modèles alternatifs. Nous réexaminerons un nombre d’assomptions implicites et explicites faites par ce modèle et nous mettrons alors l’accent sur certains de ses défauts. Cette analyse servira de cadre à notre introduction et notre discussion du modèle d’urgence. Enfin, nous présenterons une expérience dont la méthodologie permet de dissocier les deux modèles, et dont les résultats illustrent les limites empiriques et théoriques du modèle de diffusion et démontrent en revanche clairement la validité du modèle d'urgence. Nous terminerons en discutant l'apport potentiel du modèle d'urgence pour l'étude de certaines pathologies cérébrales, en mettant l'accent sur de nouvelles perspectives de recherche.

Searching for consensus in ahp-group decision making. A bayesian perspective

This paper sets out to identify the initial positions of the different decision makers who intervene in a group decision making process with a reduced number of actors, and to establish possible consensus paths between these actors. As a methodological support, it employs one of the most widely-known multicriteria decision techniques, namely, the Analytic Hierarchy Process (AHP). Assuming that the judgements elicited by the decision makers follow the so-called multiplicative model (Crawford and Williams, 1985; Altuzarra et al., 1997; Laininen and Hämäläinen, 2003) with log-normal errors and unknown variance, a Bayesian approach is used in the estimation of the relative priorities of the alternatives being compared. These priorities, estimated by way of the median of the posterior distribution and normalised in a distributive manner (priorities add up to one), are a clear example of compositional data that will be used in the search for consensus between the actors involved in the resolution of the problem through the use of Multidimensional Scaling tools

Group preference structures in AHP–group decision making

This paper presents a procedure that allows us to determine the preference structures (PS) associated to each of the different groups of actors that can be identified in a group decision making problem with a large number of individuals. To that end, it makes use of the Analytic Hierarchy Process (AHP) (Saaty, 1980) as the technique to solve discrete multicriteria decision making problems. This technique permits the resolution of multicriteria, multienvironment and multiactor problems in which subjective aspects and uncertainty have been incorporated into the model, constructing ratio scales corresponding to the priorities relative to the elements being compared, normalised in a distributive manner (wi = 1). On the basis of the individuals’ priorities we identify different clusters for the decision makers and, for each of these, the associated preference structure using, to that end, tools analogous to those of Multidimensional Scaling. The resulting PS will be employed to extract knowledge for the subsequent negotiation processes and, should it be necessary, to determine the relative importance of the alternatives being compared using anyone of the existing procedures

Probabilistic models to assist maintenance of multiple instruments

The paper discusses maintenance challenges of organisations with a huge number of devices and proposes the use of probabilistic models to assist monitoring and maintenance planning. The proposal assumes connectivity of instruments to report relevant features for monitoring. Also, the existence of enough historical registers with diagnosed breakdowns is required to make probabilistic models reliable and useful for predictive maintenance strategies based on them. Regular Markov models based on estimated failure and repair rates are proposed to calculate the availability of the instruments and Dynamic Bayesian Networks are proposed to model cause-effect relationships to trigger predictive maintenance services based on the influence between observed features and previously documented diagnostics