Shantanoo Desai

Quan Deng

Stefan Wellsandt

Klaus-Dieter Thoben

An Architecture of IoT-based Product Tracking with Blockchain in Multi-sided B2B Platform

Keywords: IoT, Sensors, Traceability, Blockchain, Multi-sided Platforms 1

Business-to-Business (B2B) enterprise systems and services have become increasingly important for Small and Medium Enterprises (SMEs) over the last few years. Their major potential is observable in the massive reduction of business transaction costs and enhanced cross-sectoral collaboration. A successful B2B marketplace should ensure greater transparency and trust while performing business processes. Among different ways to build transparency and trust, IoT-based traceability system can enable partners to track products and monitor the quality of production processes. Partners can principally trust each other and can avoid fraudulent malpractices. It is, however, still insufficient to prevent fraud and fight against counterfeit and inferior products due to data manipulation. Blockchain technology is a promising technology to prevent fraud through its data immutability characteristic. However, the adoption of Blockchain technology in IoT-based traceability system for SMEs entails significant challenges. For example, the transition of a large volume of IoT data from company-owned databases to Blockchain network would require a significant shift in terms of IT infrastructure. This paper discusses the typical challenges and presents an approach to handle them. It can be helpful to pave the way to support SMEs adopt Blockchain technology in IoT-based traceability system.

Introduction

In many industries, the basis of value creation depends on a complex network of organizations that share or exchange information, services, and goods. They use information and communication technologies to manage these processes efficiently with minimum costs. Digital business-to-business (B2B) platforms are one of the recent vital topics in this field. These platforms focus on different domains, such as manufacturing, logistics, smart cities, and e-commerce for data, services, and products [START_REF] Petersen | B2B e-marketplaces: a typology by functionality[END_REF].

NIMBLE [START_REF] Innerbichler | NIMBLE collaborative platform: Microservice architectural approach to federated IoT[END_REF] is a multi-sided, federated, digital platform with a B2B marketplace for products and services. Its source-code is under a permissive license, which allows third parties to use it for any purpose. One of its fundamental business requirements is that buyers want to access recent and precise information about their ordered products. This information includes, for instance, current and past locations, and product-related events. The location information helps the buyer to schedule activities accurately, which can reduce the cost of idle times and re-planning. An example of a relevant event is the exposure to contractually defined environmental conditions, such as temperatures above 0° Celsius. Often such environmental conditions require an Internet of Things (IoT) application where products and locations contain sensors. A benefit of IoT information is that it can support employees in the identification of product quality problems, responsibilities in warranty cases, and plagiarism. Tracking and Tracing (T&T) are software functions that meet the requirements as mentioned earlier.

Typically, a service provider is responsible for the T&T information of the supply chain partners. The latter have to trust the provider that the information is accurate and securely stored. In today's global supply chains, this trust is not always a prerequisite. New data storage technologies, such as Blockchain, address the trust and the security issue. Several activities currently focus on the development and demonstration of T&T solutions with Blockchain technology like OriginTrail [START_REF] Rakic | Origin Trail White Paper[END_REF], Ambrosus [START_REF]Ambrosus Website[END_REF].

This paper presents an architecture for a T&T solution that merges IoT and Blockchain and integrates it in a digital B2B platform. This architecture is the basis of the T&T solution implemented and tested in the NIMBLE platform. Its source code is available on GitHub [START_REF]NIMBLE-Platform GitHub Organization[END_REF].

The paper structure is as follows: Section 2 illustrates concisely a practical Use Case Scenario and some challenges that scenario faces. Section 3 provides a brief overview of the technologies and related works of Blockchain and IoT-related tracing in the research paradigm. Section 4 proposes an approach to the challenges mentioned in Section 2. The paper ends with concluding remarks in Section 5.

Use Case and Challenges

A use case helps researchers to identify problems worth of research to evaluate the results. The latter is especially important if the research focuses on technology application. The authors identified a use case for these purposes. It relies on the production and logistics processes of two real companies from the construction industry. The factories are in northern Europe and experience temperatures below -30° C during winters. The focused product is a composite structure used within construction. Intense changes in temperature and humidity can damage its strength leading to failure during operations and may lead to high warranty claims. The producer of the structure has to monitor the production and storage environment for this particular reason. Fig. 1 illustrates an overview of a production scenario for the producer of the composite structure. These two companies can trust each other. However, adequate trust is not always present between company 1 and other customer companies. Through its immutable and irrevocable properties, a Blockchain solution is increasingly prominent for trust; however, challenges in adopting Blockchain within the IoT-based product traceability system still exist. The authors identify three significant challenges standing in the way:

 Flexible usage of Blockchain technologies: Blockchain represents a significant advancement in technological innovation that facilitates consensus within a trustless environment. However, Blockchain does not always make sense in different application scenarios [START_REF] Wüst | Do you need a Blockchain?[END_REF]. For instance, when all supply chain partners can trust each other, it is not necessary to adopt Blockchain technologies. Traceability system should allow companies to choose whether and which type of Blockchain to use, according to the requirements of specific application scenarios.  Infrastructural transition to Blockchain technologies: The transition from the world of company-owned databases to Blockchain network would require a significant shift in terms of IT infrastructure. Furthermore, companies may have worries, for instance, loss of control of their data, and exposure of sensitive production data during this transition phase.  A large volume of IoT data: Companies can capture an enormous amount of IoT data in the production processes for traceability and quality monitoring. This IoT data include, for instance, EPCIS events, and various sensor data. For quality assurance, interest from customer companies is not only the sensor data at the specific time of the EPCIS event occurrence but also sensor data in the period between EPCIS events. In the use case scenario, temperature and humidity sensors monitor the environment of steps two, three, and four. If one considers a sensor that measures the temperature and humidity every one second, then for a single hour, there will be 2 x 3600 total measurements. When a product stays in "cold storage" in step four for one single day, the relevant number of measurements will range 2 x 24 x 3600 measurements. It is a significant challenge to put this large volume of IoT data into Blockchain at reasonable scalability and cost, taking into account the storage volume in each Blockchain transaction, transaction fee, and transaction rates.

3 Background and Related Works

IoT-based Product Traceability

Increasing competition for similar products in the present day markets requires the manufacturers' attention and awareness regarding each stage a product goes through during the supply chain and manufacturing process. The emphasis lays on the fact that by tracking the events of the products under consideration helps improve the quality and determine any faults or error that might occur. Tracking and Tracing systems widely use the EPCglobal and EPCIS from the GS1 [START_REF]EPC Information Services (EPCIS) Release 1.2 Ratified Standard[END_REF]. Representation of physical products in a digital form is achievable by using EPCglobal. EPCglobal provides a method to identify physical products through unique codes. EPCIS provides a method to acquire the events for the products and allows sharing this event information in an interoperable manner. The identification of the products occurs by scanning the RFID tags via dedicated RFID scanners. An event capturing mechanism provides all other necessary filterings of the data generated from the readers, and finally, a dedicated EPCIS server stores the event-based information. Byun and Kim [START_REF] Byun | Oliot EPCIS: New EPC information service and challenges towards the Internet of Things[END_REF] provide an open source platform, namely Oliot EPCIS, which provides a base for experimentation with sensors along with the integration of EPCIS standard.

With the rapid growth in the IoT sector, metadata like environmental parameters of the events the product experiences within the processes could be captured via constrained sensor and actuator nodes and provide deep insight on factors that might affect the quality of the product at present or for later purposes. IoT within the supply chain as well as manufacturing processes opens a new paradigm of integrating ICT-based intelligence, in turn opening the prospect of Smart Products. Such additional information enhances the possibility to trace reasons for product failures or defects and help manufacturers pinpoint the root cause. Work presented by Woo et al. [START_REF] Woo | Internet of Things platform and services for connected cars[END_REF] provides an approach to integrate IoT information from cars into the EPCIS structure. They extend the EPCIS structure using the Extension field to add relevant automobile information like RPM, braking, steering wheel data, GPS locations at the particular instance of the event when the car is driven. Based on different events like 'Repairing,' 'Scrapping,' the authors use geo-locations using a GPS module to add extensions to the various Events. The solution from the authors sends information periodically to an EPCIS server in the form of XML documents and use the information to provide tracking and tracing solution for connected cars. Authors of Oliot EPCIS [START_REF] Byun | Oliot EPCIS: New EPC information service and challenges towards the Internet of Things[END_REF] validate the platform by conducting experiments in the healthcare sector. They measure medical sensor values via using Bluetooth (BLE) along with another IoT standard Constrained Application Protocol (CoAP) and generate events based on the sensor data capture. Alfian et al. [START_REF] Alfian | Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system[END_REF] provide a food traceability system for Kimchi supply chain in Korea which amalgamates RFID system for product identification, wireless sensor networks (WSN) for environmental condition monitoring and data mining on the data collection for the estimation of missing sensor data during wireless transmission within the supply chain.

Works presented by authors in [START_REF] Byun | Oliot EPCIS: New EPC information service and challenges towards the Internet of Things[END_REF][START_REF] Woo | Internet of Things platform and services for connected cars[END_REF][START_REF] Alfian | Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system[END_REF] manifest strong efforts of inculcating IoT into standard product traceability within different sectors like healthcare, automobile, and food and emphasis of their work improves upon the overall understanding of product quality within standard supply chains.

Blockchain and Traceability Systems

Blockchain could provide essential features such as assurance of the product genuineness as well as transparency within the supply and manufacturing chains. There are already practical efforts to use EPCIS information from products with Blockchain, as well as IoT information. OriginTrail provides an IT solution to decentralize supply chains using Blockchains. OriginTrail [START_REF] Rakic | Origin Trail White Paper[END_REF] leverages the EPCIS framework for data interoperability among different organization within the supply chains. It also incorporates the WoT (Web of Things) Framework [START_REF] Trifa | Web Thing Model. W3C Member Submission[END_REF] for IoT sensor data by W3C. IoTeX [12] is another solution, which provides a Blockchain powered for IoT information. It leverages the concept of "Blockchains in Blockchains" where multiple private Blockchains termed as "subchain" interact with IoT devices and these "sub-chains" interact to a public Blockchain (rootchain). The concept provides a form of abstraction to the public Blockchain and the IoT devices. These solutions can only guarantee limited flexibility on the use of Blockchain technologies and cannot easily validate the large-volume relevant sensor data in the tracking process. Figorilli et al. [START_REF] Figorilli | A Blockchain implementation prototype for the electronic open source traceability of wood along the whole supply chain[END_REF] provide a prototype Blockchain implementation of chestnut boards traceability within the lumbering industry. The authors use RFID systems alongside QR codes and Android mobile applications for tracing lumber products within the supply chain and use well-known Ethereum [START_REF] Buterin | Ethereum White Paper[END_REF] Blockchain solution for more transparency within supply chains. Tian [START_REF] Tian | A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things[END_REF] illustrates a traceability system in the food supply chain for tracking real-time product information using Blockchain, IoT based on Hazard Analysis and Critical Control Points (HACCP). The work describes the usage of decentralized databases to store information, right from the harvested crop plants via RFID tags all the to the retailers and controlling authorities, subsequently enabling transparency and collaboration via Blockchain based solution. Hepp et al. in their novel approach [START_REF] Hepp | Securing physical assets on the blockchain: Linking a novel object identification concept with distributed ledgers[END_REF] are already addressing issues of securing physical products to Blockchain by using varnishes that produce a unique crack pattern that cannot be replicated or cloned on inferior products. When companies choose to use Blockchain Services, it is only mandatory to send cryptographic-hash rather than raw traceability data into Blockchain. On the generation of cryptographic-hash for traceability data, the authors adapt ideas from Traceable resource unit (TRU). TRU is a unique identification of the resource representation with similar characteristics that must be traceable [START_REF] Kim | An ontology of quality for enterprise modeling[END_REF]. It can have different granularity levels, for instance, single product item, or a batch or product items. In this study, the Traceable Data Unit (TDU) is defined as a unit of data representation that should be traceable and verifiable. Users can define different granularity levels of TDU for EPCIS data and sensor data. For EPCIS data, the authors, take every single EPCIS event as a TDU and generate a cryptographic-hash for each EPCIS event. On the tracking and tracing process, users can retrieve the EPCIS events from data storage of supply chain partners, and then generate cryptographic-hash and verify the validity of the retrieved EPCIS events with Blockchain. For sensor data, users can verify the validity of sensor data as well based on the defined TDU, with the cryptographic-hash stored in the Blockchain. For example, it is possible to define sensor data in one hour starting from every hour exactly (e.g., from 9:00 sharp to 10:00 sharp) as a TDU. Assume a product has been in a storage area for 1 hour from 01:20 to 02:20, users only need to calculate the cryptographic-hash for the batch of sensor data from two TDUs (i.e., from 01:00 to 02:00 and from 02:00 to 03:00). They can then check the existence of calculated cryptographic-hash in Blockchain to verify the correctness of the retrieved sensor data. This kind of validation with Blockchain can ensure that supply chain partners have not manipulated or tampered the traceability data in their local storages.

Approach

Conclusions

In this work, the authors have proposed an architecture that integrates Blockchain technologies as value-added services in IoT-based Product T&T solution. This architecture is the basis of the T&T solution implemented and tested in a digital B2B platform, i.e., NIMBLE platform. It takes Blockchain technology as an optional value-added service to address trust and security issues. Companies, i.e., SMEs, can choose whether to use Blockchain services, according to requirements of their specific application scenarios. This architecture introduces the concept of Traceable Data Unit to handle large-volume of IoT data, SMEs can define different granularity levels on the traceable data unit and sends only cryptographic-hash of each Traceable Data Unit rather than raw IoT data into Blockchain networks. With this approach, SMEs have total control of their data and can benefit from the immutable and irrevocable properties of Blockchain with flexible scalability and cost. Nonetheless, this approach is still a work in progress. As future work, the authors are seeking to address online product quality monitoring and quality assurance with smart contracts in Blockchain. Another limitation of this research is the risk of inaccurate data in Blockchain. Blockchain technology can help to prevent data tampering for the data stored in Blockchain, but cannot avoid the situation that companies send incorrect data, e.g., sensor data into Blockchain.

Fig. 1 .

 1 Fig. 1. Production scenario for a composite structure and logistics overview Company 1 is the supplier of the composite structure, and company 2 is the customer that mounts it in a larger product. The companies have separate factories at different locations. They use trucks to transport the structures from one factory to the other. The test implementation focuses on the first company. Its production has four steps that require tracking. It begins with a robot work area and ends with cold storage outside the factory. RFID readers monitor the entrance/exit of each area. Temperature and humidity sensors monitor the environment of steps two, three, and four.These two companies can trust each other. However, adequate trust is not always present between company 1 and other customer companies. Through its immutable and irrevocable properties, a Blockchain solution is increasingly prominent for trust; however, challenges in adopting Blockchain within the IoT-based product traceability system still exist. The authors identify three significant challenges standing in the way:

Fig. 2

 2 Fig. 2 illustrates the architecture proposed in this study. It integrates Blockchain technologies as a value-added service in the traceability system. The bottom part of the figure is the infrastructure of an example supply chain. Each company in the supply chain has its local infrastructure for the management of traceability relevant IoT data.Besides an EPCIS data repository, it uses a separate time series database for the management sensor data. Based on the time series databases, companies can realize visualization and monitoring of the collected sensor data. When companies have no requirements on the usage of Blockchain for specific production orders, they can keep all traceability data in their infrastructures. In the case of Blockchain, it is necessary for the reason of, for instance, fraud prevention, that companies send traceability data of relevant production orders into Blockchain through additional value-added APIs.

Fig. 2 .

 2 Fig. 2. Architecture of IoT-based Product Tracking with Blockchain as a value-added service

Acknowledgments

The European Commission has funded this research within the H2020 project NIMBLE (Collaborative Network for Industry, Manufacturing, Business, and Logistics in Europe), Grant No. 723810, for the period between October 2016 -March 2020.


Daniel G H Sorensen
email: daniel@mp.aau.dk

Hoda A Elmaraghy

Thomas Ditlev Bru- Noe

Kjeld Nielsen

Identification of Platform Candidates through Production System Classification Coding

Keywords: Manufacturing system, Classification code, Platform

Changeable and reconfigurable manufacturing appears as a natural response to a need for improved variety management. Such manufacturing systems are complicated to develop, and it can be advantageous to base or build these systems on product and production platforms. Development of platforms is, however, not a trivial task. Currently, identification and selection of candidates for inclusion in a platform is typically subjective relying on experts and tacit knowledge. The objectivity of this process can be strengthened by collecting data on existing production systems in a company and comparing these systems to each other. To do so, a coherent, consistent and preferably digital representation of multiple production systems is needed. In this research, a production system classification coding (PSCC) scheme is employed to classify and structure data for a number of existing production systems, spanning multiple departments and product families. Candidates for a production platform covering the included production systems are identified based on ranking certain platform drivers, processes and enablers.

Introduction

Manufacturers have long had ambitions of implementing changeable manufacturing. With decreasing product lifecycle and increasing demand for variety, a growing mismatch between product and production lifecycles has appeared [START_REF] Elmaraghy | Product variety management[END_REF]. Manufacturing equipment outlives the products it manufactures, and without the ability to adapt to new product variants or generations, outdated and obsolete equipment will simply be taking up valuable resources. Changeable manufacturing appears as a natural response for this need to accommodate change, but systems enabling economic and efficient change are not trivial to create. One path to developing changeable manufacturing systems and managing variety is through use of platforms, a concept involving standardisation of tangible and intangible assets [START_REF] Simpson | Product platform design and customization: Status and promise[END_REF]. Within product design and development this concept is well-known, and research on production platforms are gaining foothold, often centred on development alongside products, i.e. co-development and co-platforming [START_REF] Michaelis | From dedicated to platform-based co-development of products and manufacturing systems[END_REF][START_REF] Elmaraghy | Products-manufacturing systems co-platforming[END_REF][START_REF] Abbas | Synthesis and optimization of manufacturing systems configuration using co-platforming[END_REF]. Platforms allow companies to develop, manage and use standardised assets for design and develop of new systems, whether these are products or production systems. These platforms and assets can take numerous shapes, both tangible (e.g. a physical component or piece of equipment) or intangible (e.g. a specific process ensuring a good result, or knowledge on a certain subject).

Identification and selection of assets to be developed into or included in a platform is one of the first steps in the process. Successful platform development depends on the selection of appropriate assets. A primary source of platform candidates (i.e. assets with a potential for development into or inclusion in platforms) is expert system stakeholders, who base their selection on a detailed understanding of the systems [START_REF] Sorensen | Challenges in production and manufacturing systems platform development for changeable manufacturing[END_REF]. This understanding is typically tacit knowledge, making it difficult to communicate or grasp the reasoning behind the decisions. Using a more objective method of identifying platform candidates would help system experts and developers explain and back up their decisions, as well as help managers understand these decisions.

In development processes related to product modularisation and platforming, commonality has previously been used as a basis for forming modules and platforms [START_REF] Thevenot | Commonality indices for assessing product families[END_REF][START_REF] Fixson | Modularity and commonality research: Past developments and future opportunities[END_REF]. This commonality, e.g. similar geometries [START_REF] Cardone | A survey of shape similarity assessment algorithms for product design and manufacturing applications[END_REF] or shared assets [START_REF] Kashkoush | Product family formation by matching bill-of-materials trees[END_REF], can be difficult to identify across large complex systems such as factories and manufacturing systems. To alleviate this, progress has been made towards classifying the processes of manufacturing systems [START_REF] Sorensen | A classification scheme for production system processes[END_REF], with the intention of using generic and company-specific ontologies to integrate product and production models [START_REF] Brunoe | Framework for integrating production system models and product family models[END_REF].

This paper presents an effort in identifying potential platform candidates based on data collected from existing manufacturing systems at an industrial partner. The data is a classification of existing manufacturing systems linked to their corresponding cost structure and performance. Focus for this paper is on the classification coding scheme, therefore, the cost and performance data will not be considered in this publication. Platform candidates will be identified as either production processes for which a platform should be developed, or physical equipment which could be developed into a platform. In this paper, physical equipment carrying out or enabling production system operations are generally referred to as "enablers". The use of "enablers" also covers human operators, who can not be considered manufacturing equipment. Based on the above, the following research question has been formulated:

• How can a production system classification coding scheme be used to identify candidates for a production platform?

Firstly, the method is presented with a brief overview of the classification coding scheme. Secondly, the case study is introduced, followed by a description of the results and identified platform candidates. Finally, the paper ends with a discussion of the results, case study and future research.

Method

In order to analyse production systems, a digital representation of them are necessary. For this purpose, a production system classification coding (PSCC) scheme is applied [START_REF] Sorensen | Classification coding of production systems for identification of platform candidates[END_REF]. The scheme is based on ElMaraghy's manufacturing systems complexity, coding and classification system [START_REF] Elmaraghy | A complexity code for manufacturing systems[END_REF] and Sorensen's production process classification scheme [START_REF] Sorensen | A classification scheme for production system processes[END_REF]. It is a hybrid-code considering system design driving requirements, system layout, production processes and enablers. PSCC is essentially used to create a map of a company's production landscape, classifying and documenting manufacturing systems across departments and production locations [START_REF] Sorensen | Classification coding of production systems for identification of platform candidates[END_REF]. A brief overview of the coding scheme and constituent digits is shown on Fig. 1. Digits D1-D9 capture characteristics for the manufacturing system as a whole, specifically the design drivers (or rationale) and physical layout. Digits D10-D25 classify the individual processes performed by the manufacturing system and the enablers that carry them out. Thus, digits D1-D9 are filled out once per manufacturing system, while multiple instances of D10-D25 exist.

Fig. 1. Overview of the 25 digits in the Production System Classification Coding Scheme (PSCC).

To identify platform candidates based on the PSCC, the populated code for a number of manufacturing systems will be analysed according to certain drivers, outlined in the following section. Potential candidates will be ranked according to each driver, giving each candidate multiple scores. A consolidated score is then used to create the final ranking of candidates.

Drivers for Platform Candidate Identification

A drivers is the driving force, or impetus, which sets something in motion towards a certain destination or state. In the case of identification of platform candidates, drivers are the reasons why a certain asset, whether process or enabler, should be considered a potential platform candidate. The following three drivers are examples of such reasons, which can be determined based solely on the data captured by the PSCC.

1. Frequency: Number of instances of a particular process or enabler across all systems.

Frequency does not consider how many times a process or enabler is used e.g. per day. More frequently occurring processes and enablers are ranked higher. 2. Prevalence: Ratio between how many systems a particular process or enabler is used in and the total number of scoped systems. Processes and enablers appearing in more systems are ranked higher. 3. Enabler/process ratio: How many different enablers exist for each process and vice versa. Processes with fewer distinct enablers relative to how often they are carried out are given a higher rank, and similarly for enablers and distinct processes. This driver is also used to determine the number of distinct enablers per type of enabler.

Case Study

A case study is used to illustrate the application of the PSCC and determine which processes or enablers are appropriate for inclusion in a production platform,. This case study was carried out in collaboration with a large Danish manufacturer of discrete products. It covered nine distinct production systems at two different company factories in two different countries. The nine production systems span two departments, assembling products or components for subsequent internal use, others assembling complete products for OEM customers. Automation level, product size and production cycle time vary greatly across the systems. The products manufactured by the systems share the same primary function, but with a variety of supporting and additional functions, aimed at different applications and customer segments.

All nine systems have previously been surveyed for various purposes. Using the data collected on this occasion, each system was classified in accordance with the PSCC. The classified systems were then linked to their respective cost and performance data, which were available in an online data hub. Cost and performance data is not, however, considered in this study as the quality of this data has not been sufficiently ensured. A sample of the available data is illustrated in Error! Reference source not found..

Results

The ranking of process subclasses and enabler types, digits D15 and D20 in the PSCC respectively, are available in Tables 1 and2. In total, 21 different process subclasses and 12 different enabler types are considered. Allocate, with a final rank of 1, is an obvious candidate. It is defined as the process of creating a partial quantity of parts or components of a certain size, and the movement of said quantity to a target location [START_REF] Sorensen | A classification scheme for production system processes[END_REF]. Thus, allocate is a material handling process, where new parts or components are assigned a manufacturing system or process. Considering its low enabler variety combined with the high frequency and prevalence, it is likely that a platform already exists for allocate, or that there is simply an agreed upon way to carry out this process. If a platform does exist, it needs to be documented, and if it is merely an agreed upon way to carry out the process, it should be developed into an actual platform module and documented as such.

The highest ranked process in the manufacturing category is screwing. While it only ranks 7 in prevalence, it has a high frequency and a low variety, with only two different enabler types. With only one boring machine present across the nine systems, the remaining 29 instances of the processes are carried out manually, meaning this process may already be standardized, and fit for formal development into a platform.

For enablers, the manual enabler, i.e. an operator, clearly comes out on top in all four measures, with the conveyors and product/component pallets coming in second and third. All three have a high prevalence (1.000), low process variety (0.067, 0.182 and 0.100) and 3 or more distinct enablers, making them good candidates for standardization of enablers. The tester, based on its high prevalence (1.000) and relatively high process and enabler variety (0.333 for both) should also be considered for inclusion in a production platform, with the goal of lowering the variety and standardizing testing equipment and process. Finally, the robot enabler is a potential candidate, as it comes in second in frequency and carries out four different processes with six distinct enablers.

Discussion and Conclusions

A production system classification coding scheme has been used in a case study to classify nine surveyed manufacturing systems. The resulting data is a digital representation of each manufacturing system, its characteristics, structure and functions. Based on three generic drivers for platform identification, the classified manufacturing systems are analysed to identify platform candidates. Candidate processes or enablers are rated and ranked according to these drivers with the highest ranked candidates being recommended for further development into platforms. The PSCC classification coding scheme and subsequent comparison of processes and enablers act as a decision support tool for manufacturers looking to develop production platforms based on their existing production systems, i.e. brownfield platform development.

Additional platform candidate drivers should be considered and added to the rating and ranking process, depending on the requirements of the specific manufacturer. For example, the ratio of instances of a given process subclass defined as a core process (D10 in the coding scheme, Fig. 1) i.e. used for all product variants on the system. Adding to the list of other factors useful in selecting individual instances of enablers, certain qualitative drivers can be considered. Examples of such are whether individual enablers have: (1) been a bottleneck in their respective system, (2) had a long calibration period, (3) frequently broken down or required extra maintenance or (4) been deemed especially important by system experts.

Implementation of a classification coding scheme such as the one employed in this study is not a trivial task. An appropriate level of detail must be found, if implementation is to be successful, and it is advisable to take a gradual approach, selecting one segment of the production at a time in order to ensure the quality of the gathered data and resulting code.

To improve and strengthen the recommendations made through the identification process, the classified systems should be linked to their related performance and cost data. This would allow companies to correlate system characteristics, performance and cost, thus determining the system characteristics usually resulting in high performance and low cost. As digitalisation and big data are areas of interest for increasing number of manufacturers, this data is becoming more readily available for analysis.

Besides the drivers in Section 2.1, other factors captured in the PSCC can affect the identification and ranking of potential platform candidates. These factors can modify the scores and rankings of individual instances of processes and enablers to improve the confidence in selection of candidates for further development. Their effect on the scores are decided based on the company's requirements. The purpose of this is to determine the individual instances of already identified processes and enablers which should be prioritized for inclusion in the platform. Selecting the instances, upon which the standard for future systems should be based, is an important step in further platform development. This step will be detailed in future research. Three such factors captured by the PSCC are: (1) enabler structure, (2) sourcing and (3) product variety. Enablers with a modular structure are rated higher than enablers with a fixed structure, and those with a changeable structure are rated even higher. Enablers designed and developed inhouse are given a higher rating as it is often a long and costly process, benefiting significantly from standardisation. Finally, processes and enablers in systems handling a higher degree of variety are given a higher rating.

While not captured in the PSCC, the year in which a system was constructed plays a role in selection of candidates. Enablers in newer systems will take priority over enablers in older systems when it comes to inclusion in a production platform.

As previously stated, identification and selection of platform candidates is typically a matter of tacit knowledge held by system experts. When deciding on which platforms to develop, such knowledge can be difficult to back up and communicate. Using a method for platform candidate identification, like the one presented in this paper, would ease the process of making such decisions and backing them up, while improving transparency and standardisation by classifying manufacturing systems throughout the company. It can also lead to the identification of less obvious platform candidates, or platforms that already exist, but have yet to be documented. By identifying platform candidates and developing them further, the development of new systems can be eased through reuse and modification of existing solutions.

Fig. 2 .

 2 Fig. 2. Graphs of frequency and prevalence for processes (top) and enablers (bottom).

Table 1 .

 1 Process subclasses ranked according to the three drivers. Sorted by total rank.

	Process	Frequency	Rank	Frequency	Prevalence	Rank	Prevalence	ratio	Enabler	ratio rank	Enabler	Total Score	Total Rank
	Allocate	56		1 1.000		1 0.089		2	4	1
	Screwing	30		2 0.556		7 0.067		1	10	2
	Guide	10		4 1.000		1 0.300		7	12	3
	Hold	10		4 1.000		1 0.300		7	12	3
	Milling & routing	7		7 0.778		4 0.286		6	17	5
	Non-reactive adhesive bonding	9		6 0.778		4 0.444		11	21	6
	Lay & put on	12		3 0.667		6 0.667		16	25	7
	Laser carbonization	4		13 0.444		10 0.250		4	27	8
	Position	5		10 0.222		14 0.200		3	27	8
	Press fit	4		13 0.444		10 0.25		4	27	8
	Spring & snap fit	7		7 0.444		10 0.429		10	27	8
	Check position	6		9 0.556		7 0.500		12	28	12
	Dispense	5		10 0.556		7 0.600		15	32	13
	Pump system test	5		10 0.556		7 0.800		17	34	14
	HEB Welding	3		15 0.333		13 0.333		9	37	15
	AC load test	2		16 0.222		14 0.500		12	42	16
	Check orientation	2		16 0.222		14 0.500		12	42	16
	DC generator test	2		16 0.222		14 1.000		18	48	18
	Check identity	1		19 0.111		18 1.000		18	55	19
	Check presence	1		19 0.111		18 1.000		18	55	19
	Slew	1		19 0.111		18 1.000		18	55	19

Table 2 .

 2 Enabler types ranked by the three drivers, sorted by total score.

	Enabler	Frequency	Rank	Frequency	Prevalence	Rank	Prevalence	ratio	Process	ratio rank	Process	Ratio	Enabler	Ratio Rank	Enabler	Total Score	Total Rank
	Manual	89		1 1.000		1 0.067		1 0.067		1	4	1
	Conveyor	11		3 1.000		1 0.182		5 0.273		4	13	2
	Pallet	10		4 1.000		1 0.100		2 0.300		7	14	3
	Robot	23		2 0.667		6 0.174		4 0.261		3	15	4
	Mill	7		6 0.778		5 0.143		3 0.286		5	19	5
	Tester	9		5 1.000		1 0.333		9 0.333		8	23	6
	Manipulator	5		8 0.222		10 0.200		6 0.200		2	26	7
	Laser	7		6 0.444		8 0.286		8 0.286		5	27	8

Acknowledgements

The research presented in this study was conducted at the Mass Customization research group at Aalborg University in Denmark, in collaboration with the Intelligent Manufacturing Systems (IMS) Centre at University of Windsor in Canada.


I Roda

S Arena

M Macchi

P F Orrù

Total Cost Of Ownership Driven Methodology For Predictive Maintenance Implementation In Industrial Plants

Keywords: Predictive maintenance, Total Cost of Ownership, Condition monitoring, decision-making

This paper proposes a methodology to drive from a strategic point of view the implementation of a predictive maintenance policy within an industrial plant. The methodology integrates the evaluation of system performances, used to identify the critical components, with simulation and cost analysis. The goal is to evaluate predictive maintenance implementation scenarios based on alternative condition monitoring (CM) solutions, under the lenses of Total Cost of Ownership (TCO). This allows guiding the decision on where in the industrial system to install diagnostic solutions for monitoring of asset health, by keeping a systemic and life cycle-oriented perspective. Technical systemic performances are evaluated through Monte Carlo simulation based on the Reliability Block Diagram (RBD) model of the system. To validate the methodology, an application case study focused on a production line of a relevant Italian company in the food sector is presented.

Introduction & State of the Art

This work presents a methodology aimed at supporting the implementation of predictive maintenance in industrial applications by providing a strategic guideline. The final aim of the methodology is to support industrial engineers in defining where in the industrial system to install technologies for collecting monitoring data and which type of solution to select. The concept of predictive maintenance, is widely analysed in the scientific literature and it is more and more recognized as a potential area for getting benefits for manufacturing companies thanks to the possibilities provided by the new technologies. However, in the literature some limitations for the development of industrial applications in the real world are highlighted, and they include:

-lack of frameworks that can be used to guide decisions from a strategic point of view [START_REF] Faccio | Industrial maintenance policy development: A quantitative framework[END_REF][START_REF] Sgarbossa | Impacts of weibull parameters estimation on preventive maintenance cost[END_REF][START_REF] Bukhsh | Predictive maintenance using treebased classification techniques: A case of railway switches[END_REF][START_REF] Matyas | A procedural approach for realizing prescriptive maintenance planning in manufacturing industries[END_REF]. In fact, many authors introduce specific application cases of predictive maintenance implementation without providing general procedures that can be taken as a reference and generalized; -lack of simulation approach [START_REF]ISO 17359, Condition monitoring and diagnostics of machines -General guidelines[END_REF][START_REF] Al-Najjar | On establishing cost-effective condition-based maintenance[END_REF]. In fact, there is a lack of methodologies in the literature that exploit the possibility of implementing and analysing scenarios a priori through simulation, overcoming the problem of absence of historical data;

-lack of integration between economic evaluation and technical performance analysis for predictive maintenance implementation [START_REF] Al-Najjar | On establishing cost-effective condition-based maintenance[END_REF][START_REF] Zachariassen | Exploring a differentiated approach to total cost of ownership[END_REF][START_REF] Afrinaldi | Minimizing economic and environmental impacts through an optimal preventive replacement schedule: Model and application[END_REF][START_REF] Gogu | A cost driven predictive maintenance policy for structural airframe maintenance[END_REF]. In fact, many authors have proposed an economic assessment only as simple review and feedback of performed maintenance activities to correct maintenance tasks without considering relationship between predictive maintenance and impact of Total Cost of Ownership (TCO).

Moreover, companies nowadays are facing a vast offer of solutions for predictive maintenance by technology providers and are expressing needs for having formal guidelines to understand where to address their investments.

The proposed methodology has the purpose of overcoming these criticalities with the aim to combine technical performance analysis with economic evaluation, representing a structured approach that supports the implementation of predictive maintenance activities in industrial applications. In the following Section 2 the main steps of the proposed methodology are described and in Section 3 an application case within the food sector is described.

Methodology description

The complete iterative procedure is reported in figure 1, and is described in this section, step by step.

Step 1 -Context definition. The first step of the methodology consists in the clear identification of the context in which predictive maintenance activities should be introduced, defining the asset system (production plant) to be analysed and modelled. In this step it is important to clearly identify the type of production process involved; the reference objectives of the company and any industrial specific constraints (production capacity, budget, quality of products, performance indicators, layout configuration). All the elements defined in this step are key points for the guidelines definition in the elaboration of predictive maintenance approach in the industrial reality.

Step 2 -Cost Breakdown Structure (CBS) and Targets definition. The second step is aimed to build the cost model structure for the TCO of the reference asset system. In particular, the Cost Breakdown Structure (CBS) has to be defined at this step, including the relevant cost items along asset life-cycle [START_REF] Woodward | Life cycle costing -Theory, information acquisition and application[END_REF][START_REF] Enparantza | A Life Cycle Cost Calculation and Management System of Machine Tools[END_REF]. Moreover, criteria and required parameters to estimate each item must be identified. The defined TCO model will be used to evaluate alternative scenarios for predictive maintenance implementation. Selection of adequate target levels in terms of net present value of TCO or payback time are also defined at this step.

Step 3 -System modelling. This step consists in system modelling which has two aims. On one side, a Failure Mode and Effect and Criticalities Analysis (FMECA) must be implemented in order to identify the asset system components (machines and functional sub-groups) and provide information on asset failure modes/causes and effects. This activity enables getting information on asset components degradation and its detectability. On the other side, the use of Reliability Block Diagram (RBD) is addressed in order to model the entire system including the impact of each component failure at system level [START_REF] Birolini | Reliability Engineering[END_REF]. Step 4 -Data collection. This step is dedicated to data collection. In particular, two types of input data should be considered, i.e. technical and economic data. Regarding the needed technical data to perform the performance analysis, for each component of the system, the probability density functions of TBF (time between failures) and TTR (time to repair) should be identified. Strategies to collect the data to define these distributions can refer to historical data register and fitting, in case collected data are available and reliable in terms of data quality, use of experts' opinion or pre-defined distributions/benchmark data from similar assets in case of no quality data of in case of no available data due to assets at their beginning of life stage (greenfield projects). Regarding the economic data, they are the ones needed to quantify the cost items in the CBS and to estimate the TCO of the asset. If they are not available, experts' opinion and benchmark data from similar asset/system could be used.

Step 5 -Simulation. Innovative step is the introduction of simulation to evaluate and compare several scenarios derived from different condition monitoring (CM) systems, exploiting Monte-Carlo technique, based on the RBD model of the plant. For each END scenario, a set of independent histories that collect events on plant life as failures is obtained to generate samples from probability distributions, representing the expected future evolution of system status from initial to final time event [START_REF] Zio | The Monte Carlo Simulation Method for System Reliability and Risk Analysis[END_REF]. The final result is a statistical estimate value of the performance of the complete system, expressed through a performance indicator such as Operational Availability, and of any of its sub-systems, under the specific simulated scenario conditions. This step is run for the case base scenario, enabling identified critical components within the system, and for any alternative scenarios defined in the following step, through and iterative procedure, enabling evaluating alternative CM solutions.

Step 6 -Diagnostic methods: type and quality. Based on criticality analysis of system components carried out in step 5, this step allows defining several scenarios (derived from different CM techniques on critical equipment) with the aim to select the best solution among proposed alternatives. This innovative step is directly connected to step 5 through a logical connector "New Scenario?", in order to regulate the generation of diagnostic methods scenarios, that bring modifications on system with respect to the AS-IS context already analysed in the previous step.

In particular, it enables to consider alternative types and installation locations of tools to monitor asset health and different expected level of quality of capability of the diagnosis and prognosis process. Moreover, it enables considering the economic impact of the solution. These elements are input for running again step 5 (simulation) for each alternative scenario and evaluating the impact of condition monitoring measurement systems on the system performance during its lifecycle.

Step 7 -TCO calculation. This step allows to evaluate each single scenario, predefined in the previous step, through TCO evaluation [START_REF] Garetti | TCO Evaluation in Physical Asset Management: Benefits and Limitations for Industrial Adoption[END_REF]. In this step, the CBS cost items are estimated (dependent/independent on the system technical performances) and cost flows discounted, quantifying the net present value for each scenario.

Step 8 -Final reports analysis. This step is based on the analysis of the results obtained from the estimation of the TCO in each scenario. In order to get to the final choice of the best alternative solution, an adequate documentation that summarizes main features of each conducted analysis should be provided through reports, diagrams and graphs based on both technical performance parameters and economic evaluation. The solution that ensures minimum TCO has to be selected while, if more than one solution satisfies this criterion and different scenarios have equal or similar TCO value, alternatives should be compared using other criteria, that have to be defined initially and that have a relevant meaning in performed analysis

Step 9 -Predictive maintenance implementation. This step refers to the planning and the implementation of the predictive maintenance activities based on the CM systems as chosen in the previous steps. Company should integrate CM systems with scheduling and planning of entire maintenance strategy. This step is relevant and not trivial because a correct integration between company strategy and defined maintenance activities, on critical machines in the production system, facilitates the exploitation of predictive approach, reducing costs and increasing efficiency of production.

Step 10 -Feedback and corrective actions. Last step identifies feedback and review on performed maintenance activities and corrective actions through two main subphases. The first one is based on audit result and comparison with pre-defined objectives to provide information about technical and economic impact of predictive maintenance activities on the system performances. The second one is based on improvements implementation, if needed, which allow correction of maintenance activities, based on achieved results. If company decides to make changes because it is necessary to correct maintenance tasks or to introduce new CM techniques, it is identified by "YES" path in the proposed methodology. Thus, it is necessary to come back and define a new scenario, that represent changes required by company. This step completes the entire proposed procedure and it represents the key to obtain a dynamic methodology oriented to continuous improvement.

Application case

The proposed methodology is implemented in a numerical application carried out in collaboration with a relevant Italian food company with the aim to validate it. The case study is focused on an industrial plant, recently installed. The company expressed the need for a methodology that can be used to guide the definition of the maintenance policies for managing the plant and in particular, to focus the investments in predictive maintenance solutions. Through the methodology, the implementation of the first five steps, was done by the use of a Reliability Engineering software (R-MES ©) which supports Reliability Block Diagram modelling and Monte Carlo Simulation. The application is implemented only as a numerical experimentation thus, steps 9 and 10 are not included because they consist of practical activities only based on company cost-effective decisions and implement actions.

Based on the criticality analysis developed in phase 5, two scenarios were selected in step 6 to be compared and evaluated. The scenarios address the installation of different diagnostic systems for monitoring wear on different critical components within the system. Moreover, each scenario is analysed considering two levels of diagnostic capability quality: a. Best case: it is based on the assumption of perfect functioning of installed CM system and relative achieved benefits on system availability:

(𝑇𝐵𝐹 𝑎𝑠𝑠𝑒𝑡𝑠 = 𝑇𝐵𝐹 𝑎𝑠𝑠𝑒𝑠𝑡 𝐴𝑆-𝐼𝑆 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜 and 𝑇𝑇𝑅 𝑎𝑠𝑠𝑒𝑡𝑠 = 0) b. Worst case: it is based on the assumption that condition monitoring systems are not perfect in detecting failures and also, delays could affect restoration activities: (𝑇𝐵𝐹 𝑎𝑠𝑠𝑒𝑡𝑠 < 𝑇𝐵𝐹 𝑎𝑠𝑠𝑒𝑠𝑡 𝐴𝑆-𝐼𝑆 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜 and 𝑇𝑇𝑅 𝑎𝑠𝑠𝑒𝑡𝑠 = 50% 𝑇𝑇𝑅 𝐴𝑆-𝐼𝑆 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜 ) Table 1 summarizes cost items as calculated in step 6 after running simulation (step 5) for each alternative scenario. It can be noticed that investment costs of scenario 2 are higher than investment cost of scenario 1 due to higher cost of adopted sensors. The time losses for installation of CM systems are identical while, the disposal costs in scenarios 2 are higher due to higher number of sensors installed. Energy consumption costs, associated to installed systems, in scenarios 2 are higher due to higher number of sensors, while production losses costs are reduced in scenario 2 due to higher overall system availability with respect to scenario 1. An important parameter considered is the availability of the production process since the introduction of sensors allows to reduce inefficiency associated to production losses. The availability comparison between base case (A = 81.57%) and different proposed scenarios, as well as total annual equivalent cost (CTAE) and the payback time (discounting rate of 5%, as used by the company for investments evaluation) are assessed in table 2. All scenarios provide an increment of system availability thus, all proposed solutions are cost-effective respect case base, even if each of them has a different impact on overall system availability. Payback time is less than 1 year in best condition while investment costs are recovered in 2 years in worst operating condition. Best solution is scenario 2 that provides a higher increment of technical system availability despite a higher investment due to higher investment cost. Moreover, scenario 2 shows the lowest TCO value representing best investment during entire plant lifecycle since it provides a high reduction of production losses costs.

Discussion and conclusions

This work concerns the definition of an innovative methodology that provides support for investment and management decisions for predictive maintenance implementation in industrial plants. In detail, key strength points are:

-the definition of practical methodology to implement predictive maintenance activities through a significant integration between technical performance evaluation (RAM analysis) and economic assessment (TCO evaluation);

-the combination between RBD model with discrete events simulation based on Monte Carlo technique, through exploitation of R-MES software, for estimation of technical system performances along its lifecycle under different CM scenarios;

-the introduction of an innovative step for the generation of the alternative CM scenarios for evaluating the impact of different types of monitoring systems and quality of diagnostic capabilities, through simulation and TCO estimation;

-finally, the validation of proposed methodology within an important Italian food company.

The methodology was actually positively valued by the managers of the company which recognize, as its main potentialities the easiness of application, satisfying company requirements; the overcoming of the criticality associated to absence of historical data, through the introduction of the simulation phase and the possibility of exploiting several data collection strategy; and, the integration of TCO estimation as principal tool for the evaluation of generated scenarios, providing the possibility of considering whole plant life-cycle. After the application case implementation, the company decided to develop a pilot project to evaluate the investment in the solution provided by the proposed decision-making methodology.

Overall, the defined methodology represents a good decision-making support to identify critical components, on which predictive maintenance activities should be implemented. In fact, it allows to manage a better utilisation of resources because it avoids the installation of CM systems on machines and components, that in reality, are not the most critical ones.

Fig. 1 .

 1 Fig. 1. Proposed iterative methodology.

Table 1 .

 1 Summary of economic cost items for proposed scenarios.

	Cost item		Economic value		Description
	Una-tantum costs	Scenario 1a Scenario 1b Scenario 2a Scenario 2b
	Investment cost	28000 € (year t=0)	28000 € (year t=0)	32500 € (year t=0)	32500 € (year t=0)	Acquisition of sensors and software
	Cost due to plant down-time for in-stallation	28080 € (year t=0)	28080 € (year t=0)	28080 € (year t=0)	28080 € (year t=0)	Production losses for CM systems
	Disposal cost	400 € (year t=20)	400 € (year t=20)	500 € (year t=20)	500 € (year t=20)	Recovery or dismiss CM techniques
	Yearly costs
	Energy con-sump.	1200 €/year 1200 €/year 1500 €/year 1500 €/year	Energy con-sumption by CM system
	Production losses costs	-64228.32 €/year	-21864.96 €/year	-79260.48 €/year	-25964.64 €/year	Savings costs for availability increase

Table 2 .

 2 Summary of availability and TCO calculation for proposed scenarios.

		Scenario 1a	Scenario 1b	Scenario 2a	Scenario 2b
	Availability*	83.45%	82.21%,	83.89%	82.33%
	Δ Availability	+ 1.88	+ 0.64	+ 2,32 (2.77	+ 0.76 (0.92
		(2.25%)	(1.5%)	%)	%)
	Payback Time	< 1 year	< 2 years	< 1 year	< 2 years
	CTAE	-58851.39 €	-16488.03 €	-73246.48 €	-19950.64 €
	*Availability is estimated through Monte Carlo simulation running 10.000 iterations


R M Chandima Ratnayake
email: chandima.ratnayake@uis.no

Arvind Keprate

Roman Wdowik

Architecture for Digital Spare-Parts Library: Use of Additive Layer Manufacturing in the Petroleum Industry

Keywords: Spare Parts Management, Petroleum Industry, Three-dimensional Printing, Digital Spare Parts Library

Spare parts management (SPM) is a typical challenge in the offshore and onshore petroleum industry. SPM consumes a significant amount of operational expenses that are attributed to maintaining the integrity of production and process facilities (P&PFs) at the anticipated level. Productive SPM approaches are essential for maintaining integrity; in particular, ageing P&PFs and legacy assets exacerbate the challenge of maintaining integrity at the anticipated level. This manuscript firstly provides an overview of how additive layer manufacturing/three-dimensional (3D) printing has been effectively utilized by different manufacturers to produce equipment, which resembles the equipment used in the petroleum industry. Then, it discusses the potential use of a digital spare parts library and the optimal strategy to mitigate current challenges pertaining to SPM. Finally, it proposes an architecture for a digital spare-parts library.

Introduction

Additive layer manufacturing (ALM) {or the buzz term 'three-dimensional printing' (3DP)} was originally used for prototyping; however, it is now more and more applied to manufacturing end products [START_REF] Janssen | TNO: The impact of 3D printing on supply chain management[END_REF][START_REF] Campbell | Could 3D printing change the world? Technologies, and implications of additive manufacturing[END_REF]. It has been estimated that ALM has the potential to generate an economic impact of $230 billion to $550 billion per year by 2025, through direct manufacturing and the use of 3DP to create tools and molds [START_REF]EU: Working Paper: Digital economy facts & figures[END_REF]. In some cases, ALM has been considered a supplement to conventional production technologies. In other cases, it is the only means through which complex products can be fabricated [START_REF] Janssen | TNO: The impact of 3D printing on supply chain management[END_REF]. A further distinguishing feature of ALM is its distribution nature. On-site manufacturing for maintenance becomes an important application of ALM. While traditional manufacturing mostly takes place at a centralized facility, with the resulting parts distributed to end users, ALM has the potential for implementation at the point-of-use. It enables innovations in manufacturing value chains, many of which are still being realized [START_REF]Additive manufacturingqualification and certification process for materials and components[END_REF]. In particular, the petroleum industry seems to have benefits when it comes to preserving the integrity of legacy assets. Especially, unprecedented levels of mass customization, smaller and cheaper supply chains, and the 'democratization' of manufacturing, enable the integrity of legacy assets to be maintained (i.e. particularly by allowing clients to print their own spare parts) in the petroleum industry. Hence, it is essential to investigate how to integrate ALM into petroleum-industry-related activities, to transform the effectiveness of upstream supply chains, as well as to bring new markets and sources of revenue to the downstream businesses. The technology for ALM has been advanced through a coordinated development focusing on three areas (Fig. 1): 3D printers and printing methods [START_REF] Earls | The road ahead for 3-D printers[END_REF], software for product design and print [START_REF] Berman | 3-D printing: The new industrial revolution[END_REF], and the development of materials for ALM [START_REF]Project management practices for asset maintenance: A Guide for power, energy & process industries[END_REF]. The use of ALM in the digitalized and automated process chains of spare parts in petroleum industry seems to be one of the most significant opportunities (Fig. 2). This paper focuses on investigating how the petroleum industry should plan for the widespread adaptation of ALM, focusing on spare parts. In this context, it discusses whether to go for in-house manufacturing or to outsource it to ALM service providers, as well as examining the impact of ALM on the traditional supply chains vs. the potential for having digital and/or more refined supply chains. It also proposes a framework to reinforce decision-making related to the use of ALM in the petroleum industry.

Challenges Facing Application of ALM in Petroleum Industry

It is a significant challenge to manage all assets effectively in a predictive manner within the petroleum (i.e. oil and gas), power, energy and process industries, including utilities, petrochemicals and metals and mining [START_REF]Project management practices for asset maintenance: A Guide for power, energy & process industries[END_REF]. A recent survey reveals that, out of 83 respondents, 53% report that it is necessary to reduce operational expenses and 40% state that dealing with ageing assets and infrastructures poses a challenge [START_REF]Project management practices for asset maintenance: A Guide for power, energy & process industries[END_REF]. The aforementioned survey also reveals that it is important to invest in technology to enhance the management of maintenance projects [START_REF]Project management practices for asset maintenance: A Guide for power, energy & process industries[END_REF]. The primary objective of a maintenance project is to enhance asset reliability and performance and to minimize the non-productive downtime. In this context, project performance plays a key role, as better project performance reduces the duration of the shutdown of equipment for complex and routine work [START_REF]Project management practices for asset maintenance: A Guide for power, energy & process industries[END_REF]. It has also been revealed that there is a 6% increase in the shutdown duration in complex maintenance projects, whilst budgetary restrictions prevent asset owners spending on the upgrade or repair/replacement of equipment. The situation has been further exacerbated in the context of maintaining ageing equipment.

In particular, the parts related to legacy assets cause a significant challenge when the manufacturers are no longer in business. In this context, ALM offers the opportunity to print parts that are required to maintain and/or replace equipment as it becomes legacy [START_REF] Boman | What kind of potential does 3D printing hold for oil, gas?[END_REF]. Furthermore, the inherent characteristics of ALM provide significant benefits to the petroleum industry, especially when it is necessary to manufacture complex parts that use expensive materials and are required to be made in small batch sizes [START_REF] Boman | What kind of potential does 3D printing hold for oil, gas?[END_REF]. Moreover, ALM has an interesting potential for the offshore petroleum industry, as it mitigate some of the challenges pertaining to the remoteness of offshore from machine shops and skilled labor. For instance, one of the long-term holy grails of ALM is the potential to manufacture parts on site as needed, instead of having to order and wait for delivery when equipment breaks, and spare parts are not available (refer to Fig. 2).

Spare part management and holding inventory increase operational expenses; as 3DP/ALM requires no complex tooling, it is possible to bring the manufacturing nearer to the ultimate consumer, something which is referred to as 'distributed manufacture' (i.e. "migration of manufacturing from the centralized factory to more localized distributed factories") [START_REF] Reeves | Additive manufacturing-A supply chain wide response to economic uncertainty and environmental sustainability[END_REF]. Moreover, 3DP/ALM reduces many stages of the traditional supply chain, making it leaner via a reduction in the need for haulage, warehousing, logistics and unnecessary expenses on disposable packaging [START_REF] Reeves | Additive manufacturing-A supply chain wide response to economic uncertainty and environmental sustainability[END_REF].

ALM: Offshore Petroleum Industry Opportunities

Table 1 illustrates the pros and cons of ALM in industrial applications [9; 10].

Pros Cons Economical product customization:

 Permits cost-effective production of very small batches.  Allows every single product to be tailor-made to the customer's exacting specifications (i.e. significantly difficult with traditional production processes, as it is necessary to have expensive tooling and molds, whilst redesign becomes expensive).

Limited product dimensions:

 Limits the maximum dimensions of the final product due to relatively small 3D print bed sizes.  Need to manufacture large products by other technologies.

Freedom of design:

 Allows for freedom of design because standard CAD software can be used to design and redesign products.  Allows a CAD model to be printed directly into a physical part, whilst allowing redesigns to be implemented with ease.  Permits designs to be created by anyone skilled in computeraided design.

Reduced choice of materials:

 The limited number of raw materials that can be used for 3D printing limits the choice of materials, colors, and surface finish, compared to subtractive technologies.

Pros Cons Complex product manufacturing:

 Enables relatively easy manufacturing of products that were hard or even impossible to produce in the traditional way.  Allows printing complex products as a single entity at once, resulting not only in less waste and in lighter products but also in time savings with improved performance.  Reduces overall manufacturing time for complex products, due to the ability to consolidate several machining steps into a single manufacturing step.

Lower accuracy level:  3DP/ALM technologies have not yet reached the same level of parts' accuracy associated with other technologies.

Decentralized manufacturing:

 Allows the products designed in standard CAD software environments to be produced anywhere around the world, provided that compatible 3D printers are available (AMplatform, 2014).  Brings the manufacturing process physically closer to clients, as 3D printers are spreading around the world.  Reduces time-to-market and/or lead times, as it is possible to outsource manufacturing for globally dispersed 3-D printing service providers.

Limited strength:

 Due to the layered additive process, products have limited strength, restrained resistance to heat and moisture, and compromised color stability.

In the petroleum industry, there are unique challenges, especially in relation to maintenance and spare parts' management. Figure 3 illustrates different aspects of spare parts in relation to ALM and the corresponding pros and cons. Besides maintenance and spare parts' management challenges, another significant challenge is the dearth of additive manufacturing standards, due to which companies conduct their own testing to ensure the integrity of equipment, processes and products [START_REF]Additive manufacturingqualification and certification process for materials and components[END_REF]. Costly and time-consuming testing deters the wider application of additive manufacturing, underscoring the need to develop standards from design to part build to operation. Hence, one of the most serious hurdles to the broad adoption of additive manufacturing of materials in the petroleum industry is the qualification and certification guidelines for additively manufactured parts. Some of the challenges related to qualification and certification of ALM processes and components are [START_REF]Additive manufacturingqualification and certification process for materials and components[END_REF]:  There is a lack of information related to material properties, and we have much less experience and scientific knowledge of ALM processes than of conventional manufacturing.  Risk assessment based on the statistics of large-volume history data does not apply to ALM production as it does for conventional manufacturing.  The ALM process has a more disintegrated processing route, compared to conventional manufacturing. Hence, a global traceability solution, enclosing multiple ALM and supply chain locations, will be needed more than ever.  Secrecy about technology and software/algorithm sources is an obstacle. Since software plays an important role in product quality, it is a subject of concern for certification.  Directionality and heterogeneity of ALM products can bring challenges for certification and testing.

 Lack of product reproducibility and uncertainty of quality control still exist.

ALM vs. Technology Qualification

renewal and/or extension of existing technologies and the ability to take advantage of new technologies play a major role in remaining competitive and improving system efficiencies in the petroleum industry [11; 12]. In this context, it is a challenging task to confirm that new technological solutions are fit for purpose and that a system will perform as intended during its service life without potential failures [START_REF] Samarakoon | The IPPC directive and technique qualification at offshore oil and gas installations[END_REF]. In such situations, the concept of technology qualification identifies opportunities to improve system design, minimize schedule risk, reduce cost risks during operations by reducing uncertainties and increasing reliability, and identify failure modes and necessary design changes at an early stage [14; 15; 16]. Therefore, it is of prime importance to implement a technology qualification process when there is: limited experience about new technology; limited records of accomplishment of novel technologies; absence of classification rules, statutory requirements and industrial standards; and challenges with new and harsh environments [14; 16]. The core idea of the technology qualification process is to identify potential failure modes and the risk of potential failures at an early stage, prior to utilizing a new technology. Hence, it is possible to use recommended practice -DNV-RP-A203for qualifying ALM applications in the petroleum industry [17; 15].

Framework for Petroleum Industry: A Digital Spare Parts Library

The competitiveness of the petroleum industry is directly or indirectly affected by the productivity of the maintenance strategy that has been employed. In this context, having a preventive maintenance program (PM) is vital to minimize major repairs and abrupt stoppages of production and to sustain the continuous workflow [START_REF] Almomani | Preventive maintenance planning using group technology: A case study at Arab Potash Company, Jordan[END_REF]. Spare parts play a significant role in implementing a PM program, and the previous sections in this manuscript have revealed that ALM has a higher potential to cater for spare-parts-related challenges. However, it is also essential to investigate the manufacturing operational challenges such as 'parameter design' [START_REF] Ratnayake | Use of robust design technique in job shop manufacturing: A case study of die-sinking electro discharge machining[END_REF]. In this context, the 'parameter design' involves investigating the opti-mal parameter combination that provides the required output performance in the ALM process. The aforementioned is crucial for ALM, due to its inherent suitability, such as the manufacturing of complex parts that use expensive materials with small batch sizes together with a high level of customization. It is impossible for the ALM machine manufacturer to develop a user manual, which caters for different product manufacturing demands. For instance, [START_REF] Kechagias | Dimensional accuracy optimization of prototypes produced by PolyJet direct 3D printing technology[END_REF] demonstrated an investigation of the process parameter effects on the dimensional accuracy of parts produced by the Polyjet Direct 3D printing process. Hence, it is possible to use an engineering robust design approach, as indicated in [START_REF] Ratnayake | Use of robust design technique in job shop manufacturing: A case study of die-sinking electro discharge machining[END_REF], for 3DP/ALM 'parameter design', in order to align with lean manufacturing philosophies (i.e. minimization of waste in relation to time and other resources). It is possible to make the transition from a physical spare parts inventory to a digital spare parts library with 3DP/ALM capabilities [START_REF] Ratnayake | Making sense of 3D printing/additive layer manufacturing in offshore petroleum industry: State of the art[END_REF]. In this context, it is vital to develop a group technology (GT) based coding system to retrieve spare-parts-related information from a digital library for performing ALM work just-in-time [20; 18; 21]. Fig. 4 illustrates an architecture for a spare parts library, focusing on 'prioritization of spare parts', 'parameter designing', and 'group technology' (GT), for effective exploitation of ALM in the petroleum industry. For instance, [START_REF] Almomani | Preventive maintenance planning using group technology: A case study at Arab Potash Company, Jordan[END_REF] have proposed a GT-based approach to optimize the inventory of spare parts, using a "failure similarity coefficient (FSC)", which "defines the relationship between pairs of equipment/machines with respect to their needs for PM based on the type of failures they encounter". In addition, [START_REF] Li | The demand forecast of spare parts based on group technology[END_REF] demonstrated the use of GT-based classification and grouping according to the principle and structure of the new equipment and new spare parts. Moreover, [START_REF] Samatova | A vector perturbation approach to the generalized aircraft spare parts grouping problem[END_REF] have demonstrated the use of the "Vector Perturbation Approach" for addressing the aircraft spare parts' grouping problem.

Conclusion

The study presented in this manuscript has demonstrated the role of ALM and its potential for use in the offshore petroleum industry. It showed the importance of the development of three areas: 3D printers and printing methods, materials for ALM, and software for product design and print. It also presented the need to invest in technologies to better manage maintenance projects and the role of ageing equipment (requiring additional maintenance). In addition, it explained how ALM fits within advanced manufacturing performance, as well as focusing on mitigating maintenance and spare parts' challenges. Furthermore, the potential use of a technology qualification process for assuring the use of additive layer manufactured parts has been explained. Finally, it proposed an architecture for a 'spare parts library', demonstrating the interrelationship among: 'determination of critical spare parts', 'prioritization of spare parts', 'parameters involved in 3DP/ALM', 'determination of need and potential for 3DP/ALM', 'GT-based classification and coding system', and 'multi-criteria analysis for decision-making accompanied by parameter design' for enhancing the potential use of ALM in the petroleum industry. Future research shall be carried out to investigate the effective use of risk-based prioritization of spare parts, accompanied by artificial intelligence/machine learning supported group technology, focusing on developing methodologies for effective parameter designing, to enhance the use of ALM, mitigating spare-parts-related challenges.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Three areas of 3DP/ALM. Fig. 2. On-site implementation of ALM.

Fig. 3 .

 3 Fig. 3. ALM vs. maintenance and spare parts.

Fig. 4 .

 4 Fig. 4. Architecture for digital spare parts library.

Table 1 .

 1 Pros and cons of ALM.

Acknowledgement:

The study was partly carried out within the project, "Science internship for the investigations of digitalization in manufacturing", financed by the Polish National Agency for Academic Exchange in the Bekker Programme.