Kristina Kjersem
email: kristina.kjersem@himolde.no

Gabriele H Jünge
email: gabriele.junge@ntnu.no

Categorizing Engineer-To-Order Companies through their Project Execution Strategy

Keywords: ETO categorization, Project execution strategy, Manufacturing companies

. The implication of this characteristic reflects in that most of the ETO companies apply own execution strategies to their projects. However, these strategies do not always fit the complexity of ETO projects that usually are phase-based managed and involve several participants in each of the phases. This research paper proposes a new categorization of ETO companies based on the project execution strategy applied by our case companies. The scope of such categorization is to support practitioners in defining their strategies for managing ETO projects.

Introduction and Background

Engineer-To-Order (ETO) is a manufacturing approach where each artifact is customized (from the early design phase throughout the production and delivery phases) according to buyer's preferences. Main characteristics of ETO manufacturing approach are: low-volumes, high degree of customization and project-based environment [START_REF] Haartveit | Integration Alternatives for Ship Designers and Shipyards[END_REF]. In a comprehensive literature review presented by [START_REF] Gosling | Engineer-to-order Supply Chain Management: A Literature Review and Research Agenda[END_REF], the authors outline the commonalities and differences between different types of ETO supply chain. The first commonality is that ETO operates in a project-based environment while the second one is that it results in unique artifacts. Among the differences between ETO approaches can be the reusage of the same design versus creating a new one for each product. Another difference is sector specific like for example in construction, where the team complete the project on a new site each time, whereas in shipbuilding the product is manufactured at the same site [START_REF] Gosling | Engineer-to-order Supply Chain Management: A Literature Review and Research Agenda[END_REF]. One implication of the first commonality is that each ETO company must apply an own project execution strategy to manufacturing their products. However, in [START_REF] Kjersem | Literature Review on Planning Design and Engineering Activities in Shipbuilding[END_REF][START_REF] Koskela | Reforming Project Management: The Role of Planning, Execution and Controlling[END_REF] the authors state that traditional project management approaches do not take into consideration the challenges of ETO environment. Among these challenges, [START_REF] Kjersem | Literature Review on Planning Design and Engineering Activities in Shipbuilding[END_REF] name the network organized projects, iteration of design/engineering activities and concurrent engineering. A network-organized project involves a large number of project participants who do not necessary belong to the company responsible for managing the whole project. Most of the ETO projects involve a large number of participants since more than seventy-five percent of the product's value is built with help from suppliers and subcontractors. Consequently, the company that manages the entire project executes only a small part of the project with own employees and at own facilities [START_REF] Dubois | Supply Strategy and Network Effects-purchasing Behaviour in the Construction Industry[END_REF], which makes difficult to control all activities during the project. However, according to our empirical data, several case companies seem interested in developing project execution strategies that can give them better control over most of the project phases. We define project execution strategy (PES) as the management approaches applied by ETO organizations in order to plan, control and complete a project according to customer's requests. PES is influenced by the company's decision about who performs each activity and where each phase of the project is scheduled for completion.

Based on earlier research projects conducted by Møreforsking Molde AS (a Norwegian research institute), as well as on its long-term analysis of the Norwegian maritime cluster, this paper brings to discussion a new categorization of the ETO type of manufacturing. The background for this study comes from projects where our customers were interested in improving their PES for managing ETO projects. These companies were using traditional project management approaches that needed to be adapted to the challenges of the ETO environment and we learned that any PES must contain both standardized elements as well as customized features. Thus, we attempted to find a categorization of ETO companies by the strategies used during their project execution. The closest categorization we found is the one presented by Hicks et al., [START_REF] Hicks | A Typology of UK Engineer-To-Order Companies[END_REF] where the authors use a similar line of thought. Nevertheless, their categorization is from a high-level management perspective and do not address the project management approached by their case companies. To our knowledge, there is no other studies categorizing ETO companies by their project execution strategies.

From our portfolio of companies, we selected ten cases manufacturing different ETO products and studied the similarities and differences between their PESs. Consequently, we identified different levels of integration between project phases as well as different strategies for managing these and that point toward a new categorization of ETO companies from the perspective of PES. In this paper, we first describe the models of PESs identified from our empirical data and based on these we group ETO companies in three categories that are relevant from the proposed perspective. The scope of this categorization is to highlight the need for integrated PES that fit different ETO challenges. A second scope of the categorization is to serve as a decision support for project managers when planning the strategy for delivering new ETO products. This is an important aspect when it comes to optimizing the project execution as a whole and not only some of its phases. In the following, we present a short review of the literature we found on ETO categorization.

Literature Review

Willner et al., [START_REF] Willner | Exploring the Archetypes of Engineer-to-order: An Empirical Analysis[END_REF] present a relevant literature review on categorization of ETO companies and according to their findings each classification contains a different ETO perspective like: type of modularization, point of customer involvement, engineer dimension referring to the number of standardized versus customized parts, etc. The same authors propose a categorization based on the complexity of the ETO product where they considered the following two factors: 1) the number of units sold annually and 2) the number of engineering hours used for producing the artifact. Their categorization resulted in a matrix of four groups: 1) Complex ETO, 2) Basic ETO, 3) Repeatable ETO and 4) Non-Competitive ETO [START_REF] Willner | Exploring the Archetypes of Engineer-to-order: An Empirical Analysis[END_REF]. This categorization is suitable at a high-level management when deciding strategies at the company level. Nevertheless, this matrix contributes to better understanding of the diversity of ETO products and the authors propose appropriate strategies for improving the outcome of such projects.

Cigolini et al., [START_REF] Cigolini | When ETO Companies Design the Supply Chain During New Product Development Process[END_REF], categorizes ETO companies based on how the product design affects the timing for sourcing decisions. They identified three types of innovativeness: 1) pure incremental innovation, 2) only technology innovation, 3) technology and marketing innovation, which affect the timing of decision for sourcing activities. Suppliers in the third category are earlier involved in the project.

Another relevant categorization is presented by Hicks et al., [START_REF] Hicks | A Typology of UK Engineer-To-Order Companies[END_REF], who use physical processes and the depth of the product structure for identifying four types of ETO companies specific to UK business environment. These are: 1) vertically integrated, 2) design and assembly, 3) design and contract, and 4) project management. However, these ideal types are quite general, especially when describing their project management approach. We have not identified these types of company among our case companies or other previous projects. To our knowledge, there is no ETO categorization that takes into consideration both the typical phase-based project environment as well as who performs each of the project phases. The categorization presented here is based on the processes defined by our case companies for managing each phase of their projects.

Methodology

The case companies have all had different research projects together with our institute. Through these projects, we performed both value and supply chain analysis, which means that our database is quite extensive. The selected companies manufacture different ETO products like offshore and fishing vessels, cranes, winches and alike. The criteria for selecting these cases was mainly their interest in research projects aiming to improve their project management strategies. We have sent the figures to several of the companies and receive feedback on the phases used for defining the project execution strategy. Moreover, the authors work closely through their PhD studies with several of the case companies. The scope of this study is to explore the factors that contribute to the complexity of project execution strategy in ETO environment. Exploratory case studies as defined by [START_REF] Voss | Case Research in Operations Management[END_REF][START_REF]Case Study Research: Design and Methods[END_REF] are reckoned as particularly suitable for this type of research. Case studies are suited for capturing knowledge from practitioners and use these to develop new theories [START_REF]Case Study Research: Design and Methods[END_REF][START_REF] Van Aken | Reinventing the Future[END_REF].

ETO Project Phases

In order to grasp the intricacy of the ETO projects, we decided to map the most significant phases that influence the strategy applied to manage these projects. Several internal and external suppliers must deliver information, technical documentation -on specific systems and equipment -during each of the project phases described here. We illustrates each project phase in Figure 1, and describe them next. During the concept phase, a company develop a new product either by own internal projects or by collaborating with customers interested in improving their own business quality. Basic Design is an activity performed by the project responsible company in collaboration with the customer who is interested to decide and approve the main features of the product. This phase contains both 2D drawings and 3D models and some of the case companies chose to outsource partly or entire 3D modelling activities. The company who is responsible for managing the project performs usually the procurement activities. However, in some of the cases, the procurement responsibility is divided between two or three project participants (e.g., one company procures the steel and some of the systems, while the leading company buys all major equipment). The company in charge for the whole project performs and delivers 2D drawings. These types of drawings are considered an important core capability of the company and are very rarely outsourced. Among our cases, there is only one company who does not produce 2D drawings in-house, but collaborate closely with the one producing these drawings.

After completing 2D drawings, these are introduced in a 3D model. The majority of our case-companies perform this phase in-house with own people or people from own subsidiaries. Three of the case companies outsource big parts of 3D modeling activities to other project partners. The reason is that performing such activities necessitates considerable amount of time and resources.

Production drawings represent the drawings used during the production phase. These drawings contain many small details necessary to produce each piece of the product. In some cases, these drawings are produced by the steel frame/hull producing companies, while in other cases, these drawings are produced by the company delivering the basic design and detail engineering. Steel frame/hull production phase is in most of the cases outsourced both internal (the main company owns facilities outside Norway) or external (find new collaborating partners in Norway or other countries).

Outfitting and commissioning phases are most of the time performed by the companies managing the whole project. These phases are considered important core capabilities within the company. However, in one of the cases, the company performing these phases is not the one producing the basic design and detail engineering.

Mapping Project Execution Strategy

The table under is a visual presentation of how our case companies have chosen to execute their ETO projects. The colored boxes represent phases performed by partially owned or completely external companies with the symbol "out" as a shortcut for outsourcing. The "x" symbol confirms in-house execution of the respective phase. The last three columns denote producing equipment (Prod. equip.), procurement (Proc.) and final user. These columns represent an interesting trend among our case companies where at least two of them (A and D) started to produce specialized equipment that was previously supplied by external suppliers (Figure 2). The results show a certain similarity in that most of the companies choose to keep in-house the concept, basic design and some parts of the detail engineering phases. However, company C decided to approach a different strategy and outsourced most of the design, engineering and procurement phases keeping a minimum of engineering hours in-house while producing the steel frame, outfitting and commissioning (the column Com in the table) the final product before delivery. An interesting observation here is that this company seems to achieve quite good results during the present economic crisis. Engineers at company C work together with the company that deliver the design and engineering phases and give them the production perspective for the solutions applied to the product features. Companies C and D seem to complete each other's project phases in a feasible way. Company A is the one with a vertically integrated supply chain executing all the project phases in-house. The term in-house refers here to ownership of the process performed in each phase even when the companies performing these activities are not located in Norway, but are completely owned by a case company.

Company G, decided to outsource a big part of their procurement activities while company F decided to outsource the production of steel frame, keeps the outfitting and the commissioning in-house, but they invested in the final phase of the supply chain, namely using the final product in an own subsidiary. Another observation we made during our mapping process is that two of the companies have started to create departments or new subsidiaries that research and develop complex equipment. That means more vertical integration of their supply chains and more risk to be taken when the market is on a down turn. This evolution seem to contrast the trend towards vertical disintegration identified by Hicks et al. [START_REF] Hicks | Supply Chain Management: A Strategic Issue in Engineer to Order Manufacturing[END_REF] several years ago. Analyzing the similarities and differences between our case companies, we identified a common need for dynamic and more integrative PES that would fit the challenges of ETO complex projects.

Categorization

From the mapping process presented above, we can identify three relevant categories of ETO companies: 1) a vertically integrated type 2) design and engineering type 3) production and testing type.

The vertically integrated companies own most of the processes in each project phase. They acquired smaller local or global companies that deliver services or products able to increase companies' competitive advantage. As a result, the project execution strategy seem more integrated and gives better possibilities for control and improvement. Other advantages are more flexibility for the management of customer change orders and transparency of the practices between project participants. An integrated project execution strategy is also an advantage when ETO companies plan to implement design for manufacturability (DFM) approaches, which is the case for one of the case companies in this study. In order to achieve a better level of DFM, vertically integrated companies consider to start producing own equipment. Among the disadvantages of such approach is that it requires a big organization that is more sensitive to economic downturns. Another disadvantage is that in order to utilize the capacity in a sustainable way, such organizations need a certain number of orders per year [START_REF] Hicks | A Typology of UK Engineer-To-Order Companies[END_REF].

The second category identified in our study is design and engineering type. Companies embracing this strategy own only concept, design and detail engineering in both 2D drawings and 3D modeling. In many of the cases, they outsource also the production drawings to the company producing the steel frame. One advantages of this approach is that the organization is smaller and easy to readjust in times of crisis, which is the case for one of the case companies. Such organizations can also achieve a great deal of flexibility during the design and engineering phases, and less during the production period. Among the disadvantages is the separation from the production facility, which results in less accessibility to lessons learned from this process. Hicks et al., [START_REF] Hicks | A Typology of UK Engineer-To-Order Companies[END_REF] mention also the disadvantage of being dependent upon concurrent procurement, which means viable partnership with the suppliers. One case company in this category decided to reduce such dependency and started to produce some of the strategic equipment.

The third category is production and testing (commissioning) where companies decided to focus on core competencies that were outsourced by other companies. The ETO aspect of these companies lays in their ability to find engineering solutions coming from practical experience. These companies reveal a good understanding and translation of customer requirements into practice. The advantages of such approach seem to be a more effective production process and the ability to implement automation on the appropriate processes. One disadvantage is the difficulty to achieve an effective design for manufacturability approach due to a separate ownership between design engineering and production phases. Another disadvantage could be a high dependency on the design company for modifications that require their permissions and participation. However, according to our study, this type of ETO companies seem to be able readjust quite fast during the economic crises.

Discussion and Limitations

ETO projects need a better management of the involved processes [START_REF] Mello | Supply Chain Management in the Shipbuilding Industry: Challenges and Perspectives[END_REF] and in order to achieve that we argue that project managers need to apply project execution strategies that take into consideration the complexity of their ETO supply chain. The three categories identified through our study can be useful when managers need to establish the strategy for the execution phase of their projects. The competition within the ETO environment challenges the practitioners to continually improve their manufacturing process so that projects can be deliver in a shorter time, at a lower price while maintaining high quality of the final product [START_REF] Kjersem | Implementing Lean in Engineer-to-Order Industry: A Case Study[END_REF][START_REF] Semini | Strategies for Customized Shipbuilding with Different Customer Order Decoupling Points[END_REF]. An efficient and well-adjusted project execution strategy can help ETO companies to achieve such targets. The empirical data suggest that using traditional project management approach is not enough and among the reasons is the number and differences between project participants as well as the iterative nature of design and engineering activities. Our categorization draws attention to the importance of understanding the configuration of the project phases and the necessity to adapt the management strategy to it. A future paper will discuss types of strategies recommended to each of the categories identified in this paper.

The limitations of this research lays in the small number of selected case studies and the fact that most of them are typical Norwegian ETO companies. Similar mapping in other countries might show different results and that would be an interesting research topic. The number of ETO companies grows at a rate of 20 percent per year due to an increasing demand for customized products [START_REF] Grabenstetter | Developing Due Dates in an Engineer-to-order Engineering Environment[END_REF] and so the need for categorization that help us understand which strategies to apply in order to achieve continuous improvement.

Fig. 1 .

 1 Fig. 1. Project phases

Fig. 2 .

 2 Fig. 2. Map of the project execution strategy phases


Peyman Yazdizadeh Shotorbani

Farhad Ameri

Boonserm Kulvatunyou
email: boonserm.kulvatunyou@nist.gov

Nenad Ivezic
email: nenad.ivezic@nist.gov

A Hybrid Method for Manufacturing Text Mining Based on Document Clustering and Topic Modeling Techniques

Keywords: Text mining, Topic modeling, Document clustering, Supplier discovery, Manufacturing service, Knowledge acquisition

As the volume of online manufacturing information grows steadily, the need for developing dedicated computational tools for information organization and mining be-comes more pronounced. This paper proposes a novel approach for facilitating search and organization of textual documents and also extraction of thematic patterns in manufacturing corpora using document clustering and topic modeling techniques. The proposed method adopts K-means and Latent Dirichlet Allocation (LDA) algorithms for document clustering and topic modeling, respectively. Through experimental validation, it is shown that topic modeling, in conjunction with document clustering, facilitates automated annotation and classification of manufacturing webpages as well as extraction of useful patterns, thus improving the intelligence of supplier discovery and knowledge acquisition tools.

Introduction

Manufacturing companies are increasingly enhancing their web presence in order to improve their visibility in the global market and generate high quality leads. Besides using conventional webpages, manufacturing companies publish online white papers, case studies, newsletters, blogs, info-graphics, and webinars to advertise their capabilities and expertise. This has resulted in rapid growth in the volume of online manufacturing information in an unprecedented rate. The online manufacturing information is typically presented in an unstructured format using natural language text. The growth in the size and variety of unstructured information poses both challenges and opportunities. The challenge is related to efficient information search and retrieval when dealing with a large volume of heterogeneous and unstructured information. Traditional search methods, such as keyword search, with their limited semantic capabilities, can no longer meet the information retrieval and organization needs of the cyber manufacturing era. More advanced computational tools and techniques are needed that can facilitate search, organization, and summarization of large bodies of text more ef-fectively. At the same time, the unstructured text available on the Internet contains valuable information that can be extracted and transformed into business intelligence to support knowledge-based systems.

In this paper, a hybrid text mining technique is proposed for processing and categorizing plain-language manufacturing narratives and extracting useful patterns and unseen connections from them. Text mining is the process of deriving new, previously unknown, information from textual resources [START_REF] Manning | Introduction to Information Retrieval[END_REF]. There exist multiple text mining techniques, such as summarization, classification, clustering, topic modeling, and association rule mining that can be applied to the manufacturing documents. Text mining techniques are either supervised or unsupervised. In supervised (also known as predictive) techniques, fully labeled data is used for training machine learning algorithms, whereas in unsupervised (also known as descriptive) techniques, no training dataset is required. Supplier classification using supervised text mining technique was previously proposed and implemented [START_REF] Yazdizadeh | A Text Mining Technique for Manufacturing Supplier Classification[END_REF].

In this research, two unsupervised text mining techniques, namely, clustering based on k-means algorithm and topic modeling based on LDA algorithm, are adopted. Clustering is the process of grouping documents into clusters based on their content similarity, while topic modeling is a method for finding recurring patterns of co-occurring words in large bodies of texts [START_REF] Steyvers | Probabilistic Topic Models[END_REF]. Clustering and toping modeling can be regarded as complementary techniques since the unlabeled clusters, as the output of clustering process, can be characterized and described by their core theme using topic modeling technique. The primary objective of this research is to use document clustering to build clusters of manufacturing suppliers and to use topic modeling to identify the core concepts that form the underlying theme of each cluster. Organization of manufacturing capability narratives into various clusters with known properties will improve the efficiency of the supplier discovery process. Furthermore, extraction of hidden patterns from the capability narratives could lead to generation of useful information and insights about new trends and developments in manufacturing technology.

This paper is organized as follows. The next section discusses the relevant literature in text analytics. The proposed hybrid method is presented in Section 3. This section also provides information about a proof-of-concept experimentation and validation. The paper ends with concluding remarks.

Background and Related Works

Text mining has already been applied in areas ranging from pharmaceutical drug discovery to spam filtering and summarizing and monitoring customer reviews [START_REF] Alghamdi | A Survey of Topic Modeling in Text Mining[END_REF]. In the manufacturing domain, however, it is a relatively new undertaking. Kung et. al [START_REF] Liu | Using Text Mining to Handle Unstructured Data in Semiconductor Manufacturing, Joint e-Manufacturing and Design Collaboration[END_REF] used text classification techniques for identifying quality-related problems in semiconductor manufacturing based on the unstructured data available in hold records. Dong and Liu [START_REF] Dong | Enterprise Website Topic Modeling and Web Resource Searc[END_REF] proposed a tool for manufacturing website classification in based on determined genres for the websites [START_REF] Dong | Enterprise Website Topic Modeling and Web Resource Searc[END_REF]. Their proposed website classifier works based on a hybrid Support Vector Machine (SVM) algorithm. However, SVM is a supervised technique that requires high quality training data. Therefore, in absence of well-prepared training data, the proposed approach will not yield the expected outcome. To address this issue, researchers have adopted unsupervised approaches that eliminated the need for preparation of pre-labeled data. Topic modeling [START_REF] Blei | Probabilistic Topic Models[END_REF] and Clustering [START_REF] Manning | Introduction to Information Retrieval[END_REF] are two prominent unsupervised methods for text classification and mining. While Clustering is a long existing technique, topic modeling is considered to be a relatively new method. Topic modeling techniques are used to discover the underlying patterns of textual data. Probabilistic Latent Semantic Analysis (PLSA) is one of the first topic modeling techniques introduced by Hofmann [START_REF] Hofmann | Probabilistic Latent Semantic Indexing[END_REF]. PLSA is a statistical technique that discovers the underlying semantic structure of data [START_REF] Steyvers | Probabilistic Topic Models[END_REF]. PLSA assumes a document is a combination of various topics. Therefore, by having a small set of latent topics or variables, the model can generate the related words of particular topics in a document. One successful application of PLSA is in the bioinformatics context where it is being applied for prediction of Gene Ontology annotations [START_REF] Masseroli | Probabilistic Latent Semantic Analysis for prediction of Gene Ontology annotations[END_REF]. However, PLSA can suffer from overfitting problems [START_REF] Alghamdi | A Survey of Topic Modeling in Text Mining[END_REF]. Latent Dirichlet Allocation (LDA) [START_REF] Blei | Latent Dirichlet Allocation[END_REF] extends the PLSA generative model. In LDA method, every document is seen as a mixture of different topics. This is similar to the PLSA, except that topic distribution in LDA has a Dirichlet prior which results in having more practical mixtures of topics in a document. LDA, as a method for topic modeling, has been used in different applications. For instance, [START_REF] Alsumait | On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking[END_REF] discusses a LDA-based topic modeling technique that automatically finds the thematic patterns on Reuters dataset. The main distinctive feature of their proposed method is that it incrementally builds and updates a model of emerging topics from text streams as opposed to static text corpora. Some researchers have applied LDA method to public sentiments and opinion mining in product reviews [START_REF] Shulong | Interpreting the Public Sentiment Variations on Twitter[END_REF][START_REF] Zhongwu | Constrained LDA for Grouping Product Features in Opinion Mining[END_REF]. Application of LDA-based topic modeling for exploring offline historical corpora is discussed in [START_REF] Hu | Interactive Topic Modeling[END_REF][START_REF] Yang | Topic Modeling on Historical Newspapers[END_REF].

Most of the existing methods use either clustering or topic modeling techniques to help users categorize existing data and infer new information from unstructured data. This paper proposes a hybrid model based on clustering and topic modeling methods to facilitate online search and organization of manufacturing capability narratives and also extraction of thematic patterns in manufacturing corpora.

Proposed Methodology for Text Mining

The standard method for web-based information search and retrieval is the keywordbased method. For example, in a supplier search scenario, a customer from the medical industry who is looking for precision machining services can simply use precision machining and medical equipment as the search keywords in a generic search engine. Nevertheless, the sheer size of the returned set would undermine the usefulness of the search result. One way to make the results more useful is to present them to the user as chunks or clusters of similar documents and then characterize each cluster using a set of features or themes. In the precision machining example, a cluster characterized by features such as precision machining, medical industry, inspection, and assembly would be of interest for the user if inspection and assembly were the secondary services that the user is looking for. This work proposes a hybrid text mining technique, which facilitates automatic clustering and characterization of the documents available in a large manufacturing corpus. The overall structure of the proposed approach is demonstrated in Fig. 1. As can be seen in this figure, the proposed approach is composed of four major steps as described below.

Fig. 1. The Proposed Hybrid Classifier

Step 1: Building the Corpus

The first step is to create a corpus of manufacturing documents to be used as the test data. The scope of this work was limited to the suppliers of CNC machining and metal casting services. Therefore, to collect relevant websites, a generic web search based on a few keywords such as machining service, contract manufacturing, casting service, milling, turning, and sand casting were used. This keyword-based search is intended to return a set of webpages related to providers of contract manufacturing services. The keywords are selected subjectively based on the requirements of the search scenario and no particular protocol or guideline is used for keyword selection in this work. Each document (i.e., webpages) in the returned set was converted into a text-only document with the XML format. The XML format, due to its generality and simplicity, can be used across different platforms and applications. Fig. 2 illustrates an example of a website that is converted to the XML format with only two tags, namely, type and text. A corpus containing 100 XML documents with13544 terms was created for experimental validation of the pro-posed approach.

Step 2: Customized preprocessing of the corpus

Corpus documents need to be noise-free before they can be analyzed and mined efficiently. Corpus preprocessing entails removing the redundant and less informative terms in order to create a clean corpus. The first preprocessing step is to remove numbers, punctuations, and symbols. The next step is to remove the stop words that do not contain significant manufacturing information. The words such as "quote", "inquire", "call", "type", "request", "contact", and "address" that frequently appear in manufacturing websites, but has marginal information about the manufacturing capability, belong to this category. After the removal of numbers, punctuations, symbols, and stop words, the number of words in the corpus is reduced to 10357. Word stemming is the next step in preprocessing which deals with reducing the derived words into their word stem. For ex-ample, terms such as "casted" and "casting" are stemmed to "cast". This step is necessary for reducing the dimensionality of data and improving the computational efficiency of the text analytics algorithms. Stemming reduces the number of words to 7470.

Fig. 2. XML-based representation of a document in the corpus

The last preprocessing step is to generate the Document-Term Matrix (DTM) for the manufacturing corpus. DTM is a matrix containing the frequency of the terms in the manufacturing documents. In the DTM, documents are denoted by rows and the terms are represented by columns. If a term is repeated n times in a specific document, the value of its corresponding cell in the matrix is n. The DTM represents the vector model of the corpus and is used as the input to the next step, document clustering.

3.3

Step 3: Document Clustering

This step involves creating groups of similar documents in the corpus. In this work, a K-Means clustering algorithm is implemented which automatically clusters the documents of the corpus such that documents in a cluster are more similar to each other than the documents in other clusters. In K-Means clustering technique, the user needs to specify the number of clusters (K) in advance [START_REF] Kodinariya | Review on Determining Number of Cluster in K-Means Clustering[END_REF]. Then the algorithm defines K centroids, one for each cluster. The next step is to assign each document to the nearest centroid. The distance from a document to the centroids of the clusters is calculated based on the projection of multidimensional DTM on Euclidean planes. The objective function of the k-means algorithms is to minimize the sum of square of distances from the data points (i.e., documents) to the clusters. Therefore, multiple iterations are required until the convergence condition is met. The main steps of the clustering algorithm are listed below: 1. Randomly distribute the documents among the K predefined clusters; 2. Calculate the position of the centroid of each cluster; 3. Calculate the distance between each document and each centroid; 4. Assign each document to the closets centroid; 5. Iterate over steps 1 to 4 until each document is assigned to at least one cluster, no document is relocated to a new cluster, and the convergence condition is met.

To estimate the proper number of clusters in the dataset, the Sum of Squared Error (SSE) method is used in this work. SSE refers to the sum of the squared distance between each document of a cluster and the centroid of the cluster. The corpus holds 100 documents. Therefore, the value of K ranges from 2 to 99. The challenge is to select the proper number of clusters through investigating the SSE corresponding to each cluster. Generally, as it is depicted in Figure 3, when the number of clusters increases from 2 to 99, the SSE decreases since the clusters become smaller in size.

Fig. 3. SSE curve for different values of k

Fig.4. Result of clustering

Based on the SSE plot, the suggested number of clusters is determined by the point where the sharp drop in SSE ends [START_REF] Kodinariya | Review on Determining Number of Cluster in K-Means Clustering[END_REF]. This points is referred to as the elbow point. As can be seen in Fig. 4, the elbow point occurs where the number of clusters is equal to 3. Therefore, three clusters were generated for this particular dataset. These three clusters with their assigned manufacturing documents are illustrated in Fig. 4.

The plot of clustering result, shown in Fig. 4, is obtained based on a dimension reduction technique called Principle Component Analysis (PCA). The proposed clustering algorithm is based on the number of words in the corpus (7470 words or dimensions) which makes it impossible to visualize the documents of the clusters. To overcome this problem, PCA is used to enable the projection all data points (i.e., documents) on a 2D plane.

As it can be seen in the plot, the two upper right clusters (clusters A and B) have partial overlap, while the third cluster (Cluster C) in the lower left corner is clearly distinct from the other two. After inspecting the clusters, it was revealed that the members of the over-lapped clusters (A and B) were the websites of contract manufacturers who offer machining and casting services. The distinctive feature of the overlapping clusters is the depth of information provided by the member websites. The websites in cluster A contain general and high-level information about the type of process and services the suppliers offer while the websites in cluster B provide more detailed information about the type of processes, sub-processes, secondary services, and materials offered by the company. Cluster C mainly contained trade websites, blogs, or technical white papers. This experiment demonstrated that the clustering algorithm can successfully build meaningful clusters based on the type and nature of documents and also the level of detail incorporated in them. However, the clustering algorithm did not make a distinction between machining and casting websites. Also, it is not possible to learn about the characteristics of each cluster without exploring each cluster and investigating its contents. To further analyze and explore each cluster automatically, topic modeling technique is used in the next step. Cluster B, which contains 50 documents, is selected as the input to the topic modeling process.

3.4

Step 4: Topic Modeling Document clustering results in partitioning a heterogeneous dataset into multiple clusters with more similar members. However, it doesn't provide any description or characterization for the generated clusters. Topic Modeling is a text mining technique for analyzing large volumes of unlabeled text. Latent Dirichlet Allocation (LDA) is used as the underlying algorithm for topic modeling. LDA technique can be used for automatically discovering abstract topics in a group of unlabeled documents. A topic is a recurring pattern of words that frequently appear together. For example, in a collection of documents that are related to banking, the terms such as interest, credit, saving, checking, statement, and APR define a topic as they co-occur frequently in the documents. LDA technique assumes that each document in the dataset is randomly composed of a combination of all available topics with different probabilities for each. The basic steps of the LDA technique are listed below. The reader is referred to [START_REF] Blei | Probabilistic Topic Models[END_REF] for more detailed discussion of the LDA technique:

1. For each document d, randomly allocate each word in the document to one of the t topics. This random allocation provides topic representations of all the documents and also distributions of words of all the topics. 2. For each document d, calculate two values. a. p (topic t | document d), which is the proportion of words in document d which are currently assigned to topic t. b. p (word w | topic t), which is the proportion of allocations to topic t over all available documents that are using word w. 3. Reassign the word w to a new topic. 4. Repeat the steps 1 through 3 until a steady state is achieved where the word-to-topic assignments sound meaningful.

As the last stage of the experiment, the application is run to find a predetermined number of topics in the dataset. The number of topics depends on the diversity of the documents in the dataset. More diverse documents discuss more topics, whereas more focused documents are centered around only a few themes. In this experiment, the desirable number of topics was set to four after studying the documents and their themes. Table 1 shows these four topics and their 10 most frequent words.

From Table 1, it can be inferred that Topic 2 is mainly about casting processes while Topic 3 corresponds to the turning and milling processes. However, as mentioned earlier, each document can address more than one topic. The LDA addresses this issue by returning topic probabilities associated with each document. From Table 2, it can be concluded that the first document belongs to topic 3 which is mainly about CNC machining services. Also, the calculated probabilities suggest that the fourth document belongs to topic 2, which corresponds to the casting process and services. Furthermore, document 5 equally discusses topics 1 through 5 which implies that the supplier pertaining to this document is not specialized in only one manufacturing process. The performance of the proposed technique can be improved in time by adding more terms to the list of stop words that will be filtered out at the preprocessing stage. For example, the terms component and format under topic 1 are not as informative as the other terms in the group and can be eliminated from the vector model.

Conclusions

This paper presents a hybrid text mining method based on document clustering and topic modeling techniques. The objective of the proposed method is to build clusters of manufacturing websites and discover the hidden patterns and themes in the identified clusters. Furthermore, it harvests the key manufacturing concepts that can be imported into manufacturing thesauri and ontologies. Given the unsupervised nature of the algorithms used in this work, there is no need to prepare training data. This significantly reduces the initial setup cost and time. The results provided in this paper are only based on a single run of the mining process. The performance of the proposed method can be further improved through multiple iterations and subsequent elimination of less informative words under each topic. When highly informative terms are clustered together under a topic, the likelihood of discovering useful patterns in data increases. The corpus used in this proof-of-concept implementation contains only 100 documents. To reap the true benefits of text mining in manufacturing, the size of the corpus has to be significantly larger.

There are multiple areas that can be further explored in the future. One future task is to evaluate the performance of different topic modeling algorithms that can be used in the proposed framework. In the current implementation, the number of topics is determined upfront by the user, but there is a need for calculating the optimum number of topics in the corpus automatically.

Table 1 .

 1 Table 2 lists these probabilities for five example documents in the dataset. Top 10 stemmed terms in Topic 1 through Topic 4

		Topic 1	Topic 2	Topic 3	Topic 4
	1	turn	cast	cnc	machine
	2	service	die	turn	custom
	3	steel	mold	part	process
	4	industry	aluminum	tool	product
	5	component	sand	equip	quality
	6	alloy	housing	mill	manufacture
	7	format	iron	material	high
	8	stainless	rang	product	engine
	9	standard	test	chuck	grind
	10	aerospace	system	precision	provide

Table 2 .

 2 Documents and their topic probabilities

	Document	Topic 1	Topic 2	Topic 3	Topic 4
	1.xml	0.091	0.166	0.554	0.187
	2.xml	0.609	0.053	0.223	0.113
	4.xml	0.149	0.659	0.085	0.105
	5.xml	0.215	0.203	0.226	0.354

Acknowledgement: The work described in this paper was funded in part by NIST cooperative agreement with Texas State University No. 70NANB14H255.


Vitor Mendes Caldana

Márcia Terra Da Silva

Sustainable Economic Development and High Quality Engineering Education: Correlating Factors in Brazil's Macro Regions

Keywords: Economic development, Regional development, Engineering Education

GDP development over time is one method of measuring the economic evolution of a country. According to the OECD, there are several factors that can influence GDP, one of which is Engineering Education. A possible way to determine the potential for economic development would be assessing the performance of high-level education, especially if the GDP-Engineering correlation is taken into account. Studies were overtaken to study the lack of engineering in Brazil as a structural problem. However, there is no analysis about the regional factor and the engineering education performance. The objective of this paper is to assess if there is a correlation between the regional GDP and good performance Engineering Education in Brazil. The study is based on official data provided by governmental organizations. A conclusion is reached were the regional disparity in GDP is similar to the disparity on High Quality Engineering Education.

Introduction

Brazil is the seventh biggest economy in the world; however due to the continental dimensions of the country (8,515,767 km 2 ) there is a great challenge to equally develop all the regions. Global Brazil's Gross Domestic Product (GDP) has had an increase of a 5.27% on average in the last 20 years. In this period, the annual increase has not been consistent. This shows that Brazil's GDP growth results are inconsistent facing big fluctuations when compared to the average growth [START_REF] Do Brasil | Time Series Management System[END_REF].

The GDP results also showed that the regional disparity is still significant, as shown in Table 1.

The GDP percentage participation of the regions, aligned with the GDP and GDP per capita retraction scenario shows the challenge to equally develop regions. For that, according to Lins [START_REF] Lins | Escassez de Engenheiros no Brasil? Uma Proposta de Sistematização do Debate[END_REF], there is a direct correlation observed by the Organization for Economic Co-operation and Development (OECD) in terms of Human Resources in Science and Technology (HRST) and GDP. The discussion of sustainable regional development is a pressing issue [START_REF] Oliveira | Regiões Inteligentes como Estratégia de Desenvolvimento Local[END_REF][START_REF] Quandt | Inovação e Território: Elementos para a Formulação de Políticas de Capacitação Tecnológica e Desenvolvimento Regional[END_REF], and is met with similar concern on the development of expertise [START_REF] Quandt | Inovação e Território: Elementos para a Formulação de Políticas de Capacitação Tecnológica e Desenvolvimento Regional[END_REF]6].

This study correlates the regional factor on the GDP percentage participation and the performance of reginal engineering education. The objective of this work is to compare the recent results in GDP with the current engineering education performance and try to verify if there is a correlation between both.

Methodology

This work is an analysis of bibliographical references and governmental data. It is a research to better understand the impact of Higher Education Institutions (HEIs) in engineering on regional GDP.

Recent studies conducted to analyze the lack of engineers were influenced by parameters suggested by Butz [START_REF] Butz | Is There a Shortage of Scientists and Engineers? How Would We Know[END_REF]. Butz proposed five parameters to measure the shortage of products. This can be adapted to be understood as "production of high performance engineers" if necessary considerations are made. The parameters suggested by Butz are:

-Production is lower than in recent past. -Leader's market share has been increasing over time.

-Production is lower than what expected by the suppliers.

-Production is lower than what expected by society.

-Production does not meet market needs, which can be demonstrated by ascending prices.

Another study made by Nascimento [START_REF] Nascimento | Há Escassez Generalizada de Profissionais de Carreiras Técnico-Científicas no Brasil? : Uma Análise a Partir de Dados do CAGED[END_REF] included specific parameters for understanding the lack of engineers. He understood that the parameters suggested by Butz were not specific to deal with the issue of skill shortage and proposed the following adaptations:

-Low unemployment rates.

-Rising proportion of graduates taking typical occupations on the area.

-High job rotation between engineers, what suggests they receive job offers from the competition or different business with better employment conditions.

-New job positions are difficult to be filled.

-Increase in working hours.

-High competition between companies for the top professional.

-Reduction in hiring demands.

Based on both the parameters from Butz [START_REF] Butz | Is There a Shortage of Scientists and Engineers? How Would We Know[END_REF] and Nascimento [START_REF] Nascimento | Há Escassez Generalizada de Profissionais de Carreiras Técnico-Científicas no Brasil? : Uma Análise a Partir de Dados do CAGED[END_REF], Lins et al. [START_REF] Lins | Escassez de Engenheiros no Brasil? Uma Proposta de Sistematização do Debate[END_REF] conducted a study of lack of engineers. The parameters selected by Lins et al [START_REF] Lins | Escassez de Engenheiros no Brasil? Uma Proposta de Sistematização do Debate[END_REF] for the study, properly adapted to the case study, were:

1. The number of engineering graduates is lower than in the past. 2. The number of engineering graduates is lower than expected. 3. The number of engineering graduates is lower than what would be needed by the market, which would reflect on an escalation of salary. 4. Low unemployment percentages among engineers than in the past. 5. Increase in number of engineers working among the typical professions from the past. 6. High job rotation between engineers, what suggests they receive job offers from the competition or different business with better employment conditions.

Using those parameters, the conclusion was that Brazil doesn't have a structural lack of engineers. Even though the consensus is that the lack of engineers is not a structural issue, there are variables on the analysis of the study that showed a positive indication towards it (parameters 3, 4 and 5). The studies also do not investigate the regional factor, in which different regions and different clusters may have a very specific need for technical background, as well as do not take into consideration the quality factor of the degrees -although mentioned -and the curriculum design. The studies also do not reach a conclusion as to why, according to Maciente and Araújo [START_REF] Maciente | A Demanda por Engenheiros e Profissionais Afins no Mercado de Trabalho Atual[END_REF], only 38% of graduates remain with their main occupation as engineers.

This paper will focus on the discussion of better understanding if the results of regional GDP and HEI's point to a regional lack of engineers, if the regional GDP and HEI's results are similar and if Engineering Education can affect a sustainable development scenario.

To continue the discussion of this paper, we will take a closer look on the factors of high-level education engineering schools performance, curriculum design, the roles of academia in regional development and the development of expertise.

Data Analysis and Bibliographical Review

High Level Education Engineering Performance

Brazil has a performance exam of high education named ENADE (Nacional Exam of Student Performance). The result from ENADE evaluates graduation students on a grade from 1 to 5, with 5 being the highest score. There is also a SC concept that is given to HEIs that do not have at least two graduates taking the exam in that term, which is common on new approved universities. The exam is annual however, due to the number of different careers and HEIs, each one is evaluated every three years. ENADE takes into consideration both specific and general performance. The general graduation curriculum represents 25% of the total grade and the specific represents 75%. After calculating the average grade and standard deviation for each particular HEIs, the method than calculates the average and standard deviation of all HEIs in that specific area that took the test to standardize the grade in all Brazil [START_REF]INEP: Relatório Síntese[END_REF].

According to ENADE the number of engineer graduates who took the test rose from 25,657 in 2008 to 46,675 in 2011. Only 20% of the graduates received grades 4 and 5 in 2011, with a significant decrease from 2008 when the result was 29%. The increase of number of participants does not indicate necessarily an increase of number of graduates, as the exam has been gaining importance as the years pass. Good performance evaluation is considered on grades 4 and 5. With that parameter, we can see that southeast has the biggest amount of schools (127 or 49%). The values shows a decrease in HEIs with grade 1, that can be explained by both evolution of grade and also closure of some institutions, as consecutive 1 results will cause the HEI to close, according to ENADE policy. The evolution of quantity of HEIs with grade 2 (2.28%), 3 (25.61%) and 4 (33.10%) shows not only new institutions; a deeper look at the database shows an increase in institutions quality, with HEIs being able to increase their test scores over the three evaluations. The biggest warning comes from the significant reduction of 5 grade: 25.29%. This value can only be explained by the decrease in education performance [11].

The low percentage of high performance HEIs (22.07%) can be a determinant factor as to why engineer graduates can't keep jobs in their typical applications as they lack the necessary specific knowledge to perform properly.

Curriculum Design

The standard curriculum design is still not what the market expects according to Lansu. He proposes a method of developing the design based on an interactive workshop, divided in two rounds in which the key actors of each region would unite to formally discuss and reach a consensus on the necessary design for that region. The study is consistent with the idea that graduates must have the qualifications desired by their region. Bringing the industry as a consultant to properly design the curriculum of the career, especially on cases such as engineering, will bring a better understanding of the needs for development [START_REF] Lansu | Changing professional demands in sustainable regional development: a curriculum design process to meet transboundary competence[END_REF].

Another positive factor is the possibility of keeping the graduate in the region through local employment. This supports the regional scenario without the need of migration to seek job opportunities, allowing continuous and sustainable development. Through the continuous "upgrade" of the curriculum design and the necessary adaptation of the industries demands each region will get the necessary professionals to proper develop such region. That being said, it is important to notice that the needs from regions can be significantly different from each other, as described by the Central-west example of high participation of agroindustry or the Northeast tourism industry.

There are also other examples of adaptive curriculum, in cases as analyzed by Avard and Zenios in the case study of the Polytechnic of Namibia where core competences such as Networked Learning, Innovation and Problem solved based learning were introduced in the curriculum. Even though not utilizing the process proposed by Lansu, it generates a result from an established analysis of the market and the needs for specific graduation curriculum [START_REF] Avard | Curriculum Framework Considerations for Introducing Networked Learning within a Career-Focused Higher Education Institution[END_REF].

Curriculum Design is also a factor that would remove engineers from the typical applications jobs, as even though the student might even have a good performance on the course, the curriculum itself is not what is expected by the market and thus the candidate still is unable to develop properly in the profession due to an unmatched skill set.

Roles of Academia in Regional Development

Following the creation by OECD of the "Supporting the Contribution of High-Level Educational Institutions in Regional Development" program, studies were implemented to establish what roles and what contributions academia could give to regional development.

There are several roles that the academia can play according to the study made by Devine-Wright to support regional development [START_REF] Devine-Wright | Role of Social Capital in Advancing Regional Sustainable Development[END_REF]. According to Stephens et al, the main contribution is educational and training activities. Universities can also contribute in various forms to help the regional development [START_REF] Stephens | Higher Education as a Change Agent for Sustainability in Different Cultures and Contexts[END_REF].

According to the study made by Zilahy and Huisingh, some significant obstacles are present. The major obstacles are lack of understanding of concepts of Sustainable Development, lack of clear vision and objectives; lack of commitment; conflicting interests; lack of information about the regional activities; lack of leadership; high organizational costs and lack of sufficient funding. Those obstacles justify as to why the majority perception of academics, 70%, still believe that the participation of academia in regional sustainable initiatives is still not sufficient [START_REF] Zilahy | The Roles of Academia in Regional Sustainability Initiatives[END_REF].

Oliveira also contributes to this idea, specially taking into consideration what he proposes as "learning region". It would be the role of academia to implement, with help from local and political actors, an environment that would allow research and development to evolve regions from an exploratory context to a development context. With an innovation scenario present, with help from academia to produce the necessary expertise, the region would enter in a potent development self-sufficient circle [START_REF] Oliveira | Regiões Inteligentes como Estratégia de Desenvolvimento Local[END_REF].

Development of Expertise

The development of expertise is fundamental as a method of training and continuous upgrading workforce capacity to deal with new and unseen difficulties. It is a corner stone for regional development as it allows regions to innovate to achieve goals [START_REF] Quandt | Inovação e Território: Elementos para a Formulação de Políticas de Capacitação Tecnológica e Desenvolvimento Regional[END_REF].

Sgobbi and Cainarca demonstrated on their research, conducted by 1,800 interviews, that the high-performance work practices -HPWPs -are conducted by high-performance professional [START_REF] Sgobbi | High-Performance Work Practices and Core Employee Wages: Evidence from Italian Manufacturing Plants[END_REF]. However, the growth in core wage is inconclusive unless other aspects are accounted for. Not only high performance but a series of other skills that are growing in importance in Engineering Education to supplement this finding, such as the factor described by Avard and Zenios [START_REF] Avard | Curriculum Framework Considerations for Introducing Networked Learning within a Career-Focused Higher Education Institution[END_REF].

Litzinger reached a conclusion that is fundamental to align the curriculum and the teaching skill/methods [6]. This can be done in several ways; however, the expected outcome will be the development of the necessary expertise to properly prepare the graduate to be able to produce in the region, raising his wage and thus the GDP per capita.

Ultimately, the generation of expertise will be responsible for several enhancements not only in better HEIs, but also in a networking standpoint between academia and industry. Through this enhanced expertise it is possible to introduce the "learning regions" proposed by de Oliveira [START_REF] Oliveira | Regiões Inteligentes como Estratégia de Desenvolvimento Local[END_REF] and achieve a solid and virtuous cycle of development.

Results

It is possible to notice in Figure 1 that the difference in regional GDP is significant. It also shows a disparity that is consistent with the relation between economic development and quantity of graduates from HEIs with good performance, thus showing a challenging scenario to achieve a sustainable and constant growth of Brazil's regions. The conclusion by Litzinger also point to generation of expertise as a mutual process between HEIs and the industry that can profit both [6].

Conclusions and Outlook

Analyzing the difference in regional contribution of the national GDP, as shown in Table 1, it is clear that there is a significant disparity and the challenge is even bigger considering the difference between the industrialization level and the quantity and quality of the universities from the different regions as shown in Table 2.

Concerning the quantity of engineering degrees, there's been an increase in number of universities, which will lead to an increase in total number of graduates. The quality factor however has declined as shown especially at the 25.29% decrease of grade 5 ("State of the Art") Engineering Education Universities.

It is important to bring forth mechanisms to further include the academia in regional development. Several policies and actions can be taken by both sides (academia and regional actors) to enhance their participation and further increase the bond between them.

It is clear that the lack of engineers on the regions and the low occupation rate have a direct impact on the regions as shown by the proximity of the GDP percentage and the quantity of quality of degrees. It is also possible to asses from Figure 1 that the High Quality Engineering HEI's and the GDP have very similar behavior between the macro regions.

To finalize, the sources quoted enforce the need for innovation and technical background as fundamental to regional development, especially if the cluster mode and academia fomented research is to be installed as an alternative for sustainable development.

Fig. 1 .

 1 Fig. 1. GDP -HEI Performance

Table 1 .

 1 Macro-Regions GDP Participation. Source:[START_REF]Contas Nacionais Trimestrais[END_REF]

	Macro Region GDP Percentage
	Central-West	9,60%
	Northeast	13,40%
	North	5,40%
	Southeast	65,20%
	South	16,20%

Table 2 .

 2 Number of Engineering HEIs per region and ENADE results in 2011

	Region	Engineering Schools 1 2 3 4 5 SC Total
	Central-West 6 13 17 12 3 8 59
	Northeast 11 41 50 34 4 21 161
	North	5 19 19 4 0 25 72
	Southeast 36 197 176 84 43 82 618
	South	7 44 96 59 15 38 259


Muztoba Ahmad Khan
email: mdkhan@mix.wvu.edu

Gabriela Lais Rozati
email: garozati@mix.wvu.edu

Thorsten Wuest
email: thwuest@mail.wvu.edu

Sensor Triggered Replacement of Spare Parts: Customer Service Process Innovation

Keywords: Supply chain, Sensor, Intelligent products, Service automation, Internet of things, Predictive maintenance, Spare parts replacement

Customer services hold an increasingly dynamic and crucial role in today's highly competitive world. It is also one of the important factors that enable companies to sustain their competitive advantage. The purpose of this study is to develop a basic model of customer service process automation, which will support the spare parts replacement procedure. The idea is to use sensors for identifying the condition of spare parts in real time and initiate an automated replacement process at the right time. A conceptual model of an integrated customer service process automation, derived from a case study, is illustrated, which emphasizes the significance of information exchange between business processes and communication between supply chain collaborators. The suggested changes for the model aim to improve the service level, machine and part life, design of the part and also to reduce human errors during ordering process, overall cost and inventory level.

Introduction

To thrive in today's highly competitive world, business organizations must constantly look for innovative strategies to improve their competitiveness. The advancement of Information and Communication Technology (ICT) and its creative use in business has compelled industries to revolutionize their business proposition and the way they operate [START_REF] Rozendal | A Change for the Better: Realizing Business-IT Alignment Through Organizational Change[END_REF]. Competition also has driven industries to transform into customer-focused, service-oriented organizations, irrespective of the products and services they offer. Even manufacturing firms need to be aware of the service aspects because the service component of the product-service mix, e.g. Industrial Product Service Systems, offers the best opportunity of achieving sustainable competitive advantage and revenue streams along the whole product and/or service lifecycle [START_REF] Gebauer | Customer-focused and service-focused orientation in organizational structures[END_REF]. Among different processes in an organization, the customer service process, such as availability and supply of spare parts, after-sales service or repair service on products, affects customer satisfaction about products and purchases to a great extent [START_REF] Paparoidamis | Service Quality, Customer Satisfaction, Value and Loyalty an Empirical Investigation in A Service Failure Context[END_REF]. The essence of today's business requires that firms interact with their customers and business partners using technology to provide these services instantaneously and globally. To do so, businesses can foster innovation to improve their service performance and efficiency that will ideally benefit both business and customer [START_REF] Visnjic | Only the Brave: Product Innovation, Service Business Model Innovation, and Their Impact on Performance[END_REF].

The aim of this paper is to (i) analyze the spare part replacement process within a company case study, (ii) identify the current shortcomings and issues and (iii) develop an innovative model of spare parts replacement process that will help to address the identified shortcomings and issues. The main idea is to use sensors to identify the current condition of spare parts and initiate an automated replacement process considering the lead time, inventory level, etc. The suggested changes for the model aim to improve the service level, machine and part life, design of the part and also to reduce human errors during ordering process, overall cost and inventory level.

This paper is organized as follows: Section 2 discusses the states of the art of sensor based predictive maintenance and business process reengineering. In Section 3, we first describe and then analyze the problems of the current spare parts replacement process within the boundary of our case study and then we propose our new customer service model. Section 4 discusses and concludes this paper.

2

State of the Art

In recent years, the utilization of sensors combined with condition monitoring is becoming more popular in many industries. Its application can be found in a variety of areas, e.g., on rotating equipment in order to measure vibration [START_REF] Hashemian | Wireless Sensors For Predictive Maintenance of Rotating Equipment in Research Reactors[END_REF]; on military vehicles, mortar systems and other weapons to monitor wear-and-tear [8]; on bridges to record vibration, humidity, strain, inclination, load, cracks, etc. [START_REF] Casas | Fiber optic sensors for bridge monitoring[END_REF]; on diesel engines to verify the permittivity and viscosity of the fluids [START_REF] Scherer | Fluid Condition Monitoring Sensors for Diesel Engine Control[END_REF], on cars to measure tire pressure [START_REF] Pohl | Monitoring the tire pressure at cars using passive SAW sensors[END_REF], [START_REF] Brady | Advanced Sensors and Applications: Commercial Motor Vehicle Tire Pressure Monitoring and Maintenance[END_REF], etc. Sensor technology is rapidly improving and the availability of low-cost sensor is continuously growing. This enables applications and application scenarios that were not feasible until today. Condition-based monitoring, using sensors, algorithms and wireless communication, allows to identify when a part or equipment is in the process of failing and subsequently allows to take appropriate measures, e.g., repair, prior to the failure. This prevents machine downtime due to unexpected failure.

Commonly employed Preventive Maintenance (PM) schedules repairs, lubrication, adjustments, and machine rebuilds for all critical plant machinery at regular intervals, regardless of the performance of the equipment or machine. The main difference to Predictive Maintenance (PrM) is that the maintenance activities are performed at the time they are required. Based on findings from the US Department of Energy, the implementation of a functional predictive maintenance programs in the oil and gas industry has been shown, on average, 25 to 30% reduction in maintenance cost [START_REF]Predictive maintenance: Is the timing right for predictive maintenance in the manufacturing sector?[END_REF].

The application in the focus of this publication is the sensor based monitoring of spare parts to obtain information on current condition and identify potential issues and replacement needs at an early stage. Based on the sensor data, it is possible to predict the equipment's Residual Useful Life (RUL). The application of PrM can bring gains to all stakeholders of the supply chain, such as providing accurate lead time and preparing and executing the repair or the replacement of the part [START_REF] Compare | Predictive Maintenance by Risk Sensitive Particle Filtering[END_REF]. The focus of this research is not on the technical implementation of sensor-based monitoring but the combination of PrM with customer service processes and business process reengineering (BPR) of those to improve efficiency.

Case Study

In this section, a case study is presented to motivate the development of a new spare part replacement process. First, the case study and its background are illustrated before the current spare part replacement process, currently in use, is analyzed. Based on this analysis, existing issues of the process are identified and elaborated. Following, a new process is developed, aimed to address the previously identified issues.

Background of Case Study

The studied company is a manufacturer and service provider of construction equipment with a long history of manufacturing capital goods involving state of the art technology. This company will be referred to in the following as the Construction Equipment Manufacturer (CEM). The CEM operates globally with subsidiaries in all major markets while being headquartered in the United States. The CEM has been offering spare parts and technical field service for its equipment for a long time and has gradually added more services to its total offering. They pride themselves of superior customer service, making sure their customers will receive the best possible support to ensure their operations to run smoothly. This allowed them to build a reputation of high quality products and services, which in turn justifies their premium prices. CEM's customers include construction, agriculture, resource, energy and transportation industries. Thousands of small, medium and large organizations from these industries around the world utilize CEM's products and services. The CEM currently offers thousands of spare parts for its equipment. In this study, the focus is on frequently replaceable parts that require minimal technical knowledge to replace (i.e. parts that can be easily diagnosed and replaced by end users). This is because a part that needs in depth technical knowledge to being serviced or repaired, will most likely require a different customer service process. Therefore, in order to being able to generalize the findings of the study, the scope is limited. Some of the most frequently replaced parts offered by CEM include, air filters, oil filters, batteries, radiators etc. Fig. 1 presents the company's simplified spare parts distribution chain. We see that these subsystems are typical for a classic distribution chain and hence the case which is presented here can be understood as being generalizable to some extent.

Analyzing the spare part replacement process

In this sub-section, the current spare part replacement process employed by the CEM is analyzed in detail with the goal of identifying potential issues and potential for improvement. The current spare part replacement process is initiated by customer demand.

It can be triggered in different ways; for example periodic replacement, sensor based recommendation, failure of the machine etc. Next, the customer communicates his/her order to the CEM authorized dealer (e.g., via email or phone). The placed order arrives at the dealer with the following attributes: customer information, part identity number, required quantity, due date of the order and customer tolerance for tardy job. The customer tolerance for tardy job translates into different priority levels defined by the CEM; for example, priority level 1 indicates that the due date must definitely be met with no room for delays. If the inventory level at the dealer is sufficient to fulfill the order, the parts are sent to the customer from the dealer's inventory. Otherwise, the dealer reorders the required parts from the distribution center (DC). Then the DC determines a shipping date based on the order attributes, inventory level and lead-time.

The DC may ship the part from its own facility or may ask the supplier to send it to the dealer. Based on feasibility, the DC may decide to send the parts directly to the customer, avoiding shipping them to dealer's location first. Fig. 2 illustrates the current spare part replacement process.

Fig. 2. Current spare part replacement process

Shortcomings and issues of existing service process

The current practice of the customer service process for frequently replaceable parts is successfully providing value to the customers. However, there is still potential for improvement and certain issues that may be addressed by a process redesign. Some of the challenges were indicated by the CEM personnel themselves as they previously emerged during operation. Other issues were identified in a comparative analysis. The identified issues are labeled as: suboptimal part and machine life, human errors during the process (especially critical was the ordering process), higher shipping cost and higher inventory level. Next, each category is explained separately, although some of them are interconnected.

Suboptimal part and machine life:

The replacement process starts with customer demand, which is often triggered by the customer's judgment of the condition of that part. For some parts they depend on periodic replacement. But here, any kind of misjudgment by the customer can result in too early or too late replacement of the part. For example, air filters can sustain more than its average lifespan if it works in a clean environment compared to a dusty environment. If the filter gets replaced earlier than it is supposed to, we lose the span of the RUL of the filter. Fig. 3 illustrates this phenomenon. This is a waste of resources both environmentally and monetarily. If the part is overused (beyond its capability to fulfill its intended function), it may jeopardize the performance of the machine or even put other expensive parts or even the machine itself at risk.

Fig. 3. Lifespan of air filters in different environments

Human errors during ordering process: As most of the orders are communicated manually through emails or phone calls, there is always a risk of human errors involved. For example, the current ordering process requires the customer to provide a unique part number to the dealer. Any mistake by the customer or dealer in communicating this number correctly may result in a faulty shipment or even the production of a wrong part.

Higher shipping cost: Sometimes customers wait until the machine breaks down, before ordering a required spare part; this is because they do not have sufficient information about the current condition of the parts. As a consequence, to minimize the idle time of the machine, customers ask the dealer to provide the spare parts within the shortest possible time. This kind of rush translates often directly into higher shipping cost. Whether these higher shipping cost are covered by the supplier or the customer, they are still an unnecessary waste of resources.

Higher inventory cost: Currently the dealers need to maintain relatively high inventory levels in order to being able to react to the volatile customer demand. This is because they do not have any information about the current condition of machines used by their customers. Maintaining high inventory levels is very costly as it not only binds capital through the stored material but also as it increases the requirements towards the storage facility and workers. Besides, some of the parts may start degenerating if they are kept waiting on the shelf for a long period of time and/or under certain conditions.

Besides these four main issues presented, there are several other ones that can potentially benefit from a redesign of the process. Those include for example part design. With the implied access to real-life usage data within the new process, the part design can be improved based on a cross reference of the condition and cross-referenced environmental data. Leading to an improved design of parts for certain conditions, like application in desert (Hot, dry and dusty) environments.

Proposed customer service model

In this sub-section, the previously identified issues are taken as a basis for a reengineering of the current business process. Creating an new customer service model is initiated by the need to the issues and challenges presented in the previous section. Because the future model handles process innovation at a conceptual and theoretical level, it is called the basic model of customer service process innovation.

The main idea of the proposed model adaptations is to automate the spare parts ordering process by means of real-time, sensor-based condition monitoring of spare parts. Sensor data along with customer data, such as inventory level, location, preferred shipping method and reordering quantity etc., will be stored in the cloud. Any of the stakeholders with proper authority will have access to this data in real-time. In the new model, customer demands will be triggered automatically based on the integration of customer and sensor data stored in the cloud in the supplier specific ordering systems. Depending on predetermined individual customer preference, the system may ask the customer to approve the order before processing or may automatically start processing the order at the DC. Real-time customer and sensor data will allow the system to find best possible solution in terms of cost, accuracy, lead time, shipping, inventory and quality without the additional effort of the currently employed system. Fig. 4 illustrates the new customer service process. The proposed changes in the process will help customers to utilize the full RUL of a part. Additionally, it will prevent 'overuse' of a part beyond its RUL, which in turn will increase the overall machine life by avoiding failure during employment. The automated ordering process will also help to reduce potential human errors during the ordering and shipping process. The instantly available data will enable the system to plan a shipment before it will actually be needed at customer's inventory, which means that a slower but cheaper mode of transportation could be utilized while still meeting the customer demands on lead-time. When the CEM will have access to all of its customers' (relevant) data, it will be able to forecast demand more precisely, which in turn will Demand triggered by customer and sensor data Customer may need to approve the order Order processed automatically Supplier, DC or dealer ships the part help to reduce overall inventory across the whole supply chain (at both, the supplier and customer level). Better forecast will also allow to reduce the bullwhip effect.

The proposed model will be able to facilitate additional business-to-business (B2B) transactions such as invoicing and payment, reduction of procurement cost, and shortened lead-times better and more efficient than the traditional one. It may also have the potential to improve coordination and collaboration, both within and across companies due to the instantly available real-time data in the cloud. However, this has to be carefully assessed in further study and was not within the scope of this paper.

Discussion and Conclusion

After-sales customer services in manufacturing firms that are selling durable products have a strategic relevance in its potential contribution to a company's profitability, customer retention and product development [START_REF] Saccani | Configuring the After-Sales Service Supply Chain: A Multiple Case Study[END_REF]. Since customer service is a major source of competitive differentiation in today's business environment, many firms are trying to gain a competitive edge by innovating their customer service process. In this paper we have illustrated a case study of a leading Construction Equipment Manufacturer's spare part replacement process. We identified existing issues and scopes for improvement by analyzing the current process and proposed an improved conceptual model targeting these issues. The main idea of this proposed model is to automate the spare parts ordering process by means of real-time, sensor-based condition monitoring of spare parts making the data available in a cloud-based solution to all relevant stakeholders.

For small manufacturing firms this model may not be applicable because if the overall demand of spare parts remains relatively low, the proposed model might not be feasible from a financial perspective. However, some aspects, especially the direct connection to the customer's operation and access to use data might make it worthwhile.

The proposed model depends strongly on the reliability of the employed sensors. If a sensor fails to detect the current condition of spare parts with the required accuracy, then an incorrect order may be issued. However, this should not be an issue because sensor reliability is generally very high today.

Furthermore, the proposed model requires the customers to share information and data with the manufacturer. This might not be acceptable to some companies due to privacy issues. In this case it is important to ensure transparency of who has access and what data is exchanged for what application. Also there might be customers who may find this model too complicated for their operation and/or that want to remain in full control of their process and feel the automated variant is not providing that (felt) level of control.

In this paper we proposed a basic model of a customer service process innovation utilizing sensor technology and cloud infrastructure. The case study and the original spare part replacement process were analyzed and the process innovation was aimed to target the identified shortcomings. However, the proposed model only provides and outlook on the potential impact in full-scale industrial application. Further study needs to aim at operationalizing the proposed model and actually designing and implementing the technical solution, incl. sensors and cloud-connectivity to compliment the new business processes. Furthermore, additional use of the acquired data through the sensorbased condition monitoring is worthwhile to be explored. Potential areas are new services for Product Service Systems (PSS), innovations on the product design level and cross referenced analysis of the data for, e.g., environmental impact or worker safety solutions.

Fig. 1 .

 1 Fig. 1. CEM's simplified spare parts distribution chain.

Fig. 4 .

 4 Fig. 4. New customer service process.

Acknowledgement. This work was partly funded by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) through the Science without Borders (CSF) fellowship program. The authors gratefully acknowledge the support of the CAPES. Finally, the authors would like to thank the reviewers for their helpful comments.