A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants

Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we can not only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models

Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

Discovering Process Reference Models from Process Variants Using Clustering Techniques

In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms

Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria

Optimum currency area (OCA) theory attempts to define the geographical region in which it would maximize economic efficiency to have a single currency. In this paper, the focus is on prospective and current members of the Economic and Monetary Union. For this task, a self-organizing neural network, the Self-organizing map (SOM), is combined with hierarchical clustering for a two-level approach to clustering and visualizing OCA criteria. The output of the SOM is a topologically preserved two-dimensional grid. The final models are evaluated based on both clustering tendencies and accuracy measures. Thereafter, the two-dimensional grid of the chosen model is used for visual assessment of the OCA criteria, while its clustering results are projected onto a geographic map.Self-organizing maps, Optimum Currency Area, projection, clustering, geospatial visualization

What are the Problem Makers: Discovering the Most Frequently Changed Activities in Adaptive Processes

Recently, a new generation of adaptive Process-Aware Information System (PAIS) has emerged, which enables dynamic service changes (i.e., changes of instances derived from a composite service and process respectively). This, in turn, results in a large number of process variants derived from the same process model, but differing in their structure due to the applied changes. Since such process variants are expensive to maintain, the process model should evolve accordingly. It is therefore our goal to discover those activities that have been more often involved in process (instance) adaptations than others, such that we can focus on them when re-designing the process model. This paper provides two approaches to rank activities based on their involvement in process adaptations and process configurations respectively. The first approach allows to precisely rank the activities, but it is very expensive to perform since the algorithm is at $\mathcal{NP}$ level. We therefore provide as alternative approach an approximation ranking algorithm which computes in polynomial time. The performance of the approximation algorithm is evaluated and compared through a comprehensive simulation of 3600 process models. By applying statistical significance tests, we can also identify several factors which influence the performance of the approximation ranking algorithm

Kuluttajatuotteiden toimitusketjun kehittäminen: tapaustutkimus suomalaisessa energiayhtiössä

This master’s thesis studies how a non-manufacturing firm should design and manage a supply chain for consumer products in a global environment where the firm has limited experience and competence on managing material flows to consumer customers. Existing literature on supply chain design and management is highly focused on manufacturing companies and thus, this study tries to fill this knowledge gap by addressing non-manufacturing companies with purchasing-intensive operations. The study follows a problem-solving focused research approach, design science, by dividing the research into two main phases. First, a literature review is conducted focusing on the themes of supply chain design and management in order to highlight the major elements is prior research and to form a theoretical foundation for the practical solution. Second, a case study is conducted in a Finnish utility company to develop, test and evaluate the solution’s applicability in the context of the case company. Research indicates that supply chain design process starts by analyzing product types, customer needs and marketplace requirements, and linking the supply chain strategy and type to the firm’s context. It was found that a supply chain combining lean and agile, leagile, was most suitable for the case company. After determining the supply chain type, the actual supply chain structure is formed. A primary decision factor is the degree of logistics postponement which determines the number and location of warehouses and distribution channel choices in the supply chain. Finally, supply chain management must be ensured by defining the basic processes and the required level supply chain integration with external actors. The proposed solution provides concrete guidelines and decision factors related to supply chain design and management to be utilized in a purchasing-intensive supply chain environment solving the case company’s problem. Additional benefits for the case company include increased supply chain understanding, more efficient and simple material flows, and probable operational performance improvements. Research partly confirms existing theory by showing that certain elements in traditional supply chain design and management theory can also be applied in a purchasing-intensive environment lacking manufacturing operations. In addition, research identifies elements and decisions factors that are relevant especially for non-manufacturing companies such as the high focus on inventory management.Diplomityö tutkii miten hankintaan keskittyvän yrityksen tulisi suunnitella ja hallita toimitusketjuaan ympäristössä, jossa sillä ei ole aikaisempaa kokemusta tuotteiden toimittamisesta kuluttaja-asiakkaille. Aikaisempi tutkimus toimitusketjun suunnittelun ja hallinnan alueilla on painottunut valmistaviin yrityksiin, joten tutkimus pyrkii luomaan uutta tietoa tutkimalla yrityksiä, joilla ei ole omaa valmistusta ja näin keskittyen valmiiden tuotteiden hankintaan toimittajilta. Tutkimus noudattaa suunnittelutieteen (design science) lähestymistapaa pyrkien ratkaisemaan yrityksen konkreettisen ongelman ratkaisuehdotuksella. Tutkimusprosessi aloitettiin kirjallisuuskatsauksella keskittyen pääosin toimitusketjun suunnitteluun ja hallintaan liittyvään kirjallisuuteen tavoitteena tunnistaa tutkimusalueen pääteemat sekä luoda teoreettinen pohja ratkaisuehdotukselle. Tutkimuksen toinen vaihe suoritettiin tapaustutkimuksena suomalaisessa yrityksessä, missä ratkaisuehdotusta kehitettiin edelleen ja lopulta sen toimivuutta arvioitiin tapausyrityksen kontekstissa. Tutkimus osoittaa, että toimitusketjun suunnittelu tulee aloittaa tuotetyyppien, asiakastarpeiden sekä toimintaympäristön analysoinnilla. Lisäksi on tärkeää, että toimitusketjustrategia ja -tyyppi linkitetään aina yrityksen kontekstiin. Tehokasta (lean) ja ketterää (agile) yhdistävän toimitusketjutyypin havaittiin olevan paras ratkaisu tapausyritykselle. Toimitusketjutyypin valitsemisen jälkeen määritetään ketjun varsinainen rakenne, joka hankintaan keskittyvillä yrityksillä liittyy vahvasti siihen kuinka paljon logistiikkaa viivästytetään. Viivästyttämisen aste pääosin määrittää varastojen määrä ja paikan sekä jakelukanavat toimitusketjussa. Viimeisenä on määritettävä prosessit toimitusketjun hallintaan sekä missä määrin on tarpeen tehdä yhteistyötä yrityksen ulkopuolisten toimijoiden kanssa. Esitetty ratkaisu tarjoaa tapausyritykselle konkreettisia suuntaviivoja ja ohjeita toimitusketjun suunnitteluun ja hallintaan hankintaan painottuvassa toimintaympäristössä. Ratkaisu tarjoaa lisäksi yritykselle lukuisia käytännön hyötyjä kuten lisääntyneen toimitusketjutuntemuksen, tehokkaammat ja yksikertaisemmat materiaalivirrat sekä todennäköisesti kasvavan toimitusketjun suorituskyvyn. Tutkimus osaltaan vahvistaa olemassa olevaa teoriaa näyttäen, että valmistaviin yrityksiin keskittyvää teoriaa ja malleja voidaan hyödyntää myös hankintaan keskittyvässä toimitusketjussa. Lisäksi tutkimus tuottaa uutta hyödyllistä tietoa mitkä elementit ja kriteerit ovat erityisen tärkeitä nimenomaan hankintaan keskittyville yrityksille ilman omaa tuotevalmistusta

Inventory drivers in a pharmaceutical supply chain

In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold

What are the Problem Makers: Discovering the Most Frequently Changed Activities in Adaptive Processes

Recently, a new generation of adaptive Process-Aware Information System (PAIS) has emerged, which enables dynamic service changes (i.e., changes of instances derived from a composite service and process respectively). This, in turn, results in a large number of process variants derived from the same process model, but differing in their structure due to the applied changes. Since such process variants are expensive to maintain, the process model should evolve accordingly. It is therefore our goal to discover those activities that have been more often involved in process (instance) adaptations than others, such that we can focus on them when re-designing the process model. This paper provides two approaches to rank activities based on their involvement in process adaptations and process configurations respectively. The first approach allows to precisely rank the activities, but it is very expensive to perform since the algorithm is at $\mathcal{NP}$ level. We therefore provide as alternative approach an approximation ranking algorithm which computes in polynomial time. The performance of the approximation algorithm is evaluated and compared through a comprehensive simulation of 3600 process models. By applying statistical significance tests, we can also identify several factors which influence the performance of the approximation ranking algorithm