Modelling the interactions between predictive maintenance, and distributed production and logistics in service management processes.

(1)

D

IPARTIMENTO DI

I

NGEGNERIA DELL

’E

NERGIA DEI

S

ISTEMI

,

DEL

T

ERRITORIO E DELLE

C

OSTRUZIONI

RELAZIONE PER IL CONSEGUIMENTO DELLA LAUREA MAGISTRALE IN INGEGNERIA GESTIONALE

Modelling the Interactions between Predictive

Maintenance, and Distributed Production and

Logistics in Service Management Processes

RELATORI IL CANDIDATO

Prof. Ing. Gino Dini Arianna Montanaro

Dipartimento di Ingegneria Civile e Industriale montanaro.arianna@yahoo.it

Dr Christos Emmanouilidis

Through-Life Engineering Services Centre, Cranfield

Sessione di Laurea del 03/10/2018 Anno Accademico 2017/2018

(2)

Modelling the Interactions between Predictive Maintenance, and Distributed Production and Logistics in Service Management Processes

Arianna Montanaro

SOMMARIO

Questo lavoro di tesi ha lo scopo di sviluppare un approccio di simulazione per investigare le implicazioni derivanti dall’adozione di una strategia predittiva nel servizio di manutenzione e supporto per grandi stampanti industriali. Il modello simulativo cattura le dinamiche caratterizzanti l’attuale processo di gestione del servizio in Xerox. Lo studio ha coinvolto le seguenti attività: la definizione dei requisiti di progetto con il cliente, la revisione della letteratura esistente, la creazione del modello concettuale del processo oggetto di analisi e la sua codifica attraverso l’utilizzo del software simulativo (Anylogic), la definizione e implementazione di scenari di simulazione, e infine l’analisi dei risultati ottenuti dalle simulazioni. I risultati confermano un miglioramento dell’efficacia e dell’efficienza del servizio in seguito all’adozione della strategia predittiva. Inoltre, l’efficace gestione dei ricambi risulta un elemento chiave per garantire elevati livelli di servizio al cliente. Il modello rappresenta un valido strumento di supporto per i managers nel processo decisionale.

ABSTRACT

This thesis work aims to develop a simulation-based approach to investigate the implications resulting from adopting predictive strategy in maintenance service of large industry-grade production printers. The simulation model captures the dynamics characterizing the current service management process in Xerox. The following activities were carried out: the definition of the requirements of the project with the client, the review of the existing literature, the development of the conceptual model of the studied process and its coding using the simulation software (Anylogic), the definition and implementation of simulation scenarios, and finally the analysis of the results obtained from the simulations. The results have showed improved operational efficiency and service performance as consequence of the predictive strategy. Moreover, effective spare part inventory management results a critical element to ensure high service levels to customers. The model value is also

(3)

ACKNOWLEDGEMENTS

The author would like to acknowledge the guidance and support of all who contributed to the accomplishment of this project.

First of all, I would like to express my sincere gratitude to my academic supervisors, Dr Christos Emmanouilidis and Dr Konstantinos Salonitis, for their support and constant supervision throughout the project. Their strong guidance and constructive comments were valuable for completing the study successfully, giving an important contribution to my learning experience. Many thanks also to my Italian supervisor, Professor Gino Dini, for supporting the progress of my work through valuable advice and for being an helpful reference during the whole year in Cranfield. I would like also to thank the industrial supervisor, Richard Glover, who took time from his busy schedule to contribute the results of the study providing his informed insights and knowledge. Finally, many thanks also to Xerox for making possible this invaluable experience in a practical industrial context.

I would like also to extend thanks to all who contributed differently to the accomplishment of this work, giving affection and moral support. First of all, I would like to express my sincere gratitude to my father and my mother for their constant and great support to my education. Their encouragement to pursue my goals was fundamental throughout my educational experience. Many thanks also to my dear siblings, Carmela, Lorenzo and Gabriele, who have always been present throughout my life showing me that neither the distance can divide us. Finally, special recognition should also be made of my closest friends, Carlotta and Sara, and my flatmates, Francesca, Valentina and Federica, for providing me always help and moral support and for the gift of having been like a family for me.

(4)

LIST OF FIGURES

Figure 2 -1. The current state of maintenance strategies. ... 5

Figure 2 -2. Simulation techniques on abstraction scale. ... 12

Figure 2 -3. A framework for field maintenance simulation. ... 13

Figure 3 -1. The outline of the research methodology... 16

Figure 4 -1. The logic flow of the service management process. ... 20

Figure 4 -2. The cost drivers of the service management process. ... 21

Figure 5 -1. The conceptual model. ... 26

Figure 6 -1. Impact of predictive strategy on service performance. ... 37

Figure 6 -2. The impact of predictive strategy on service waiting times. ... 38

Figure 6 -3. The economic impact of predictive strategy. ... 39

Figure 6-4.The economic implications in the spares management. ... 40

Figure 6 -5.The implications of spare management on service performance. ... 40

Figure 6 -6. The outline of the future roadmap. ... 43

Figure A -1. Statechart of printing equipment agent. ... 65

Figure A -2. Statechart of printers OEM agent. ... 66

Figure B -1. Annual average equipment availability in the as-is scenario. ... 70

Figure B -2. Annual average equipment availability in the to-be scenario. ... 70

Figure B-3. Annual cost of service in the as-is scenario. ... 71

Figure B -4. Annual cost of service in the to-be scenario. ... 71

Figure B -5. Annual average equipment availability in Scenario1. ... 72

Figure B -6. Annual average equipment availability in Scenario 2. ... 72

Figure B -7. Annual average equipment availability in Scenario 3. ... 73

Figure B -8. Annual cost of inventory management in Scenario 1. ... 74

Figure B -9. Annual cost of inventory management in Scenario 2. ... 74

(7)

LIST OF TABLES

Table 5 -1. The model scope and level of detail. ... 23

Table 5 -2. The overall structure of the AB model. ... 28

Table 5 -3. The input parameters for the experiments. ... 31

Table 5 -4. The predictive maintenance scenarios. ... 32

Table 5 -5. The scenarios of spares replenishment. ... 33

Table A -1. Parameters of printing equipment agent. ... 58

Table A -2. Variables of printing equipment agent. ... 59

Table A -3. Variables of service crew agent. ... 61

Table A -4. Parameters of printed contents customer agent. ... 62

Table A -5. Parameters of warehouse agent. ... 63

Table A -6. Variables of warehouse agent. ... 63

Table A -7. Parameters for cost estimation. ... 64

Table A -8. Java code for printing equipment agent. ... 67

Table A -9. Java code for printers OEM agent. ... 69

(8)

LIST OF ABBREVIATIONS

AB Agent-Based

CBM Condition Based Maintenance CM Corrective Maintenance CPS Cyber-Physical Systems

CPSS Cyber-Physical Product-Service Systems DES Discrete Event Simulation

ICT Information and Communication Technology OEM Original Equipment Manufacturer

PdM Predictive Maintenance PM Preventive Maintenance PSS Product-Service Systems

(9)

1

INTRODUCTION

1.1 Background

Maintenance plays an important role in ensuring and enhancing production assets availability. Because of expanded global competition and increasingly demanding markets, industries are exploring new opportunities for playing a key role in providing customers with the necessary support for maintenance operations. This has led to the introduction of Product Service Systems (PSS) as promising business models describing the evolution from product-based offers to integrated product-service solutions in order to increase the value for customers and securing constant revenue streams (Baines et al., 2007).

However, offering a PSS requires the need to develop new functionalities to deliver production systems with new intelligent behaviors and communicating capabilities. From the technological perspective, Cyber-Physical Systems (CPS) are becoming relevant enablers of additional innovative product-service bundles (Wiesner and Thoben, 2017). However, the combination of CPS functionalities with PSS business models, describing the Cyber-physical Product-Service Systems (CPSS) paradigm, creates new challenges for engineering CPS-based PSSs, since an integrated cross-domain approach is required to align a large number of different cyber, physical and service components (Wiesner and Thoben, 2017).

Furthermore, with the introduction of CPS, the provider has to support more and more all phases of the production system lifecycle, leading to the need of interacting with a distributed network of different partners, including suppliers and logistic service providers (Baines et al., 2009). Consequently, the implications for moving towards this new business model should be taken into account with specific regard to the linkage between service management, distributed logistics and manufacturing. In the case of maintenance services, the whole maintenance operations lifecycle system needs to be aligned with operations related to manufacturing and supply chain to deliver the expected value effectively and efficiently.

(10)

1.2 Project motivation

Xerox Ltd is one of the largest manufacturer and service provider of production printing equipment worldwide. Among the wide range of products, Xerox offers large industry-grade printing machines, able to produce large-volume of personalized printed content. Such printers challenge significantly the maintenance service process offered by Xerox to its customers. Indeed, the expected machine availability needs to be ensured in an environment characterized by high-volume orders with short customer lead-time.

To improve the service management process, Xerox is looking at the opportunity of introducing data-driven strategies for predictive maintenance in order to increase machine uptimes thanks to a proactive approach that enables to anticipate machine failures and reduce equipment downtime cost. This change will have an impact on the current maintenance service management, leading to the need of re-engineering the process in order to deliver the desired functionalities and benefits.

1.3 Project scope

The scope of the project is limited to the following elements:

 Focus on modelling the service management process of large industry-grade production printers.

 Investigate the impact of predictive strategy for maintenance in service management processes.

 Analyse the interactions between the service provider, the supplier and the customers in a limited number of scenarios.

 Develop a simulation approach to address the problem rather than to conduct analysis on accurate process data.

Therefore, the following elements are beyond the scope of the project:

 Any aspect that is not relevant to the maintenance service process.

 Any aspect that is related to the development of predicting capabilities.

 Any scenario of interaction between suppliers, service provider and customers different from those included in the project scope.

(11)

 Implementation of the future maintenance service model.

1.4 Project aim and objectives

The overall aim of this project is to study the interactions between predictive maintenance, logistics and manufacturing in the maintenance service management process of large industry-grade production printers OEMs.

In order to achieve the project aim, the research objectives are to:

1. Conduct literature review in order to underline key findings related to modelling field maintenance systems and the impact of predictive maintenance in service management processes.

2. Model the interactions in the current service management process of production printers using a simulation-based approach.

3. Run experiments and analyse results in order to assess the impact of predictive maintenance strategy on system performance.

4. Verify and validate the model.

5. Propose an appropriate future roadmap for guiding the shift towards the new business model.

(12)

2 LITERATURE REVIEW

This chapter provides an overview of the state-of-the-art and the main challenges related to the research field. Firstly, the review methodology is described; thereafter several aspects relevant to the study are reviewed: (1) the state-of-the-art of maintenance strategies, (3) the concept of Product-Service Systems and (4) their key challenges, (5) the concept of Cyber-Physical Product Service Systems and its related implications, (7) the impact of data-driven strategies on industrial maintenance services, (8) the relationship between maintenance and spare part inventory as well as related issues, and finally (9) an overview on simulation approaches for PSS contexts.

2.1 Review methodology

A systematic research was carried out by using ScienceDirect and ProQuest, two of the largest databases of peer-reviewed literature, as main sources of information. The targeted papers were obtained searching the following keywords: (predictive maintenance performance), (maintenance and spare parts), (spare parts logistics), (Product-Service Systems), (Cyber-Physical Product Service Systems) and (field maintenance modelling). Further papers were driven from the referenced citations of the early research articles. First priority was given to more recent papers as well as the most cited in the literature.

2.2 Overview on maintenance strategies

Effective maintenance of production assets is becoming increasingly important, as higher asset availability is a key driver for successful competition. Gits (Gits, 1992) defines maintenance as “the total of activity required to retain the systems in, or restore them to the state necessary for fulfilment of the production function”. As illustrated in Figure 2-1, two types of maintenance can be performed: Corrective Maintenance (CM) and Preventive Maintenance (PM) (BS EN 13306:2017). CM is carried out to react to asset faults, whereas, PM can be categorised into Predetermine

(13)

Maintenance or Condition Based Maintenance (CBM). The first one is conducted in accordance with a given schedule resulting from the application of appropriate policies, such as age-based (where PM actions are planned every x unit of operating time) or time-based (where PM actions takes place every x unit of time) (Barlow and Hunter, 1960). Finally, although CBM applications are becoming increasing popular in particular in the context of Product Service Systems (PSS), only limited effort was found in the literature towards investigating the implications of CBM strategies. Specifically, many models of maintenance systems investigate predetermined maintenance policies in manufacturing systems (Alrabghi and Tiwari, 2015), whereas predictive maintenance is poorly covered in the literature.

Figure 2-1. The current state of maintenance strategies (BS EN 13306:2017).

2.3 Servitization of business 2.3.1 Product Service Systems

Increasingly companies are relying on service providers for conducting maintenance operations on complex production systems in order to benefit from lower-costs and higher maintenance performance. Within this context, maintenance service providers

(14)

customers. Among those, Product-Service Systems are increasingly becoming popular, in particular to support companies with a high installed product base, including aircraft, elevator, industrial printing equipment, agricultural machinery and more (Baines et al., 2009). Baines at al. (Baines et al., 2007) define a PSS as “an integrated product and service offering that delivers value in the use”. The servitization of business have moved the focus from physical products to integrated product-service solutions (Wiesner and Thoben, 2017) enabling to increase the value in use for the customer and to secure constant revenue streams. Generally, the concept of PPSs is referred to strategies which enable to add value by adding services (Vandermerwe and Rada, 1988). Three different perspectives of PSSs are reported in the literature: (1) Product-Oriented, (2) Use-Oriented and (3) Result-Oriented, promoting respectively functionality, availability and results of the product (Tukker, 2004).

2.3.2 Servitization process model

A general outline of the servitization process model is proposed by Baines (Baines et al., 2009). Different actors interact throughout the whole lifecycle system: the customer, the OEM and their partners. The OEM offering is composed by both the asset and a set of related services. Mathieu (Mathieu, 2001) identifies between services supporting the product (SSP) and services supporting the customer (SSC). In the first case, effective maintenance, repairs, spares provisioning and upgrades are triggered from remote monitoring of asset in use; whereas asset performance data, training as well as advices on effective and efficient asset use are key activities for supporting the customer.

2.3.3 Key challenges in PSS

Although many benefits may be achieved delivering product-service bundles, including increased business competitiveness and customer satisfaction (Aurich et al., 2010); the introduction of PSS business models pose significant challenges in the engineering process of integrated product and services. Moving towards PSS business models

(15)

entails facing higher complexity due to the need of aligning tangible (product) and intangible (service) components of the PSS (Wiesner and Thoben, 2017), as well as of effective inter-organization coordination achieved integrating the different subsystems composing the product-service structure, being manufacturing, maintenance, spare parts supply and logistics (Baines et al., 2009). Baines et al. (Baines et al., 2009) identifies the following key challenges in integrated PSSs:

 Product and service requirements need to be considered simultaneously during the design process in order to ensure effective and efficient through-life performance of the overall product-service offering.

 Effective coordination and integration in the supply network by sharing resources and knowledge.

 Organizational changes are required in the shift towards servitized business models.

2.4 Intelligent devices

2.4.1 Cyber-Physical Product Service Systems

The ongoing advancements in technology are leading manufacturing industries to explore new ways for leveraging the capabilities provided from new intelligent devices to enhance their business. New opportunities derive from the integration of Information and Communication technologies (ICT) and predictive analytics with the physical assets, describing Cyber-Physical Systems as promising technologies in the rapid advancement of industries (Shi et al., 2011). The key capabilities delivered from CPS involve real-time data streaming from the physical asset to the cyber space, as well as intelligent data analytics. Many industrial applications for the CPS concept have been explored in the literature (Herterich et al., 2015), including in the field of asset management where improved production systems availability could be achieved by leveraging CPS capabilities of condition monitoring and failures prediction (Lee et al., 2015). Wiesner et al. (Wiesner and Thoben, 2017) outline further insights from integrating the concepts of CPS and PSS, giving the definition of Cyber-physical

(16)

Product-service systems (CPSS) that describes integrated PPSs fostered by CPS capabilities. Although such integration enables to deliver higher value to customers (Wiesner and Thoben, 2017), some challenges and implications should be considered during the through-life of such complex systems. These challenges concern the engineering phase as well as the operational phase, in which the issues related to data management need to be addressed to ensure the effectiveness of the service.

2.4.2 Impact of CPSs on industrial service business

Herterich et al. (Herterich et al., 2015) discussed the value of CPSs in the context of industrial PPSs identifying the following implications:

1. Improved product engineering by leveraging performance data of the current installed base.

2. Optimized industrial asset operations by anticipating breakdowns based on historical failure patterns.

3. Equipment management and control can be performed via remote service centers.

4. Effective and efficient schedule of maintenance activities based on real-time data collection of usage and wear.

5. Maintenance and even repair can be performed remotely by leveraging comprehensive knowledge base and experienced staff.

In other words, digitalization and CPSs offer substantial potential to enhance service delivery in industrial contexts. Firstly, remote service capabilities are enhanced, since remote service centers can be supported by CPSs data for errors and faults diagnosis. This leads to increased service efficiency, as cost savings results from reduced need of travels to the customer’s site (Herterich et al., 2015) since the equipment can be remotely controlled and managed. Furthermore, benefits from exploiting CPSs emerge also in field service activities, as issues are solved faster and more effectively: based on the results of the remote diagnosis, field service staff is sent to the customer’s site with all the necessary resources, including spares, materials and tools, improving the first time fix rate (Herterich et al., 2015). Furthermore, significant implications emerge

(17)

from leveraging data analytics for predictive maintenance. In this way, it is possible to minimize equipment downtime by preventing breakdowns through industry-specific algorithms that enable to predict failures based on real-time data of wear and usage. Consequently, maintenance operations are conducted only when it is required in response to a real equipment condition (Schmidt and Wang, 2015). The decrease of unexpected breakdowns has a significant impact on customer satisfaction: breakdowns of such production machines are very expensive as both mechanical and electrical components are involved, leading to high-cost and time-consuming repairing (Herterich et al., 2015), as well as a loss of earnings due to machine downtime.

2.5 Maintenance and spare part inventory 2.5.1 Maintenance inventory related issues

A considerable amount of literature investigates aspects related to maintenance inventories demonstrating its importance in the industrial practice. Spares availability is becoming increasingly important in maintenance service management, where providing the right part at the right moment is one of the main constraints of maintenance performance (Alabdulkarim et al., 2011). Kennedy et al. (Kennedy et al., 2002) discussed the main aspects related to spare parts inventory, including the followings:

 Spare parts provision is driven by maintenance policies rather than equipment usage.

 Poor reliability information concerning failure rates affect the accuracy of spare parts inventory.

 Problems related to obsolescence of spare parts make difficult to estimate how many units to stock.

With regard to obsolescence related problems, determining the stock level is a significant challenge, in particular with regard to slow-moving parts which demand is difficult to forecast. In this regard, the trade-off between the risk of asset downtime and the risk of scrapping spares due to obsolescence is an important issue, which has

(18)

an impact on maintenance performance. However, Kennedy et al. (Kenned et al., 2002) highlighted, in their review on spare parts inventory, the lack of obsolescence related components of cost in many models for maintenance inventories. Such costs are significant when long lifecycle products are involved, since spare parts evolve throughout the lifecycle due to design and engineering issues as well as technological advancements.

2.5.2 Maintenance inventories modelling

Modelling maintenance operations requires facing the complexity resulting from integrating different subsystems, including production, maintenance and spare parts inventory management. Investigating performance of maintenance service provision, Van Horenbeek et al. (Van Horenbeek et al., 2013) demonstrated that improving asset monitoring through sensing technologies does not deliver the expected service performance level if aspects associated with spare parts inventory management are not considered at the same time. However, a considerable amount of literature models the different maintenance subsystems in isolation (Alabdulkarim et al., 2011) or assuming ready available resources (Lin et al., 2002), preventing from fully investigating the impact of maintenance strategies on the whole system. Although interesting conceptual models describing the interactions between such subsystems have been developed (Duffuaa et al., 2001), practical applications are still poorly covered in the literature.

2.6 Modelling of PSS 2.6.1 Value of simulation

Modelling PPSs is a significant challenge in the design process of integrated product-service since multifaceted, interactive and dynamic structures are involved (Legnani et al., 2010). This requires the need of a tool that is able to support managers in evaluating impacts and implications resulting from moving towards the new business model and in assessing systems performance both from the tactical and operational

(19)

point of view (Phumbua and Tjahjono, 2009). Simulation models has been widely used to support decisions in the field of manufacturing system design and operation (Smith, 2003), as they are powerful tools that enable to visualize system dynamics and experiment with different scenarios; and now they are becoming popular also to handle the greater complexity involved in the design process of PSSs. A considerable amount of literature related to PSSs is of a conceptual nature, and there is a need for verifying and validating results in order to make them functional for practice in industry (Roy et al., 2013).

2.6.2 Simulation techniques

The literature reports Discrete Event Simulation (DES), Agent-based (AB) Simulation and Systems Dynamics (SD) as the most used simulation techniques. However, their applications within PPS contexts are poorly covered in the literature, and few examples (Lin et al., 2002; Legnani et al., 2010; Panteleev et al., 2014) enables to understand the value of business process simulation in specific case studies. Among the three approaches, AB simulation delivers an advantage over the other two (Borshchev and Filippov, 2004) since it enable to model complex individual behaviours and to derive the dynamics of whole systems as consequence of agents interactions. The AB technique is able to address many types of problems, including operational, tactical and strategic, since it allows to move easily from high to low levels of abstractions (Borshchev and Filippov, 2004). In addition, active objects can be modelled with the AB approach, defining logics to describe individual behaviours, and deriving the whole system dynamics from direct and indirect interaction. By contrast, DE and SD deliver less flexibility since the first approach enables to model only passive objects and process-oriented dynamics, while the second one handles with Stock-and-Flow systems using an high level of aggregation (Borshchev and Filippov, 2004). Figure 2-2 summarizes the aforementioned findings.

(20)

Figure 2-2. Simulation techniques on abstraction scale (Borshchev and Filippov, 2004).

2.6.3 Field maintenance simulating

In order to address the problem using a systematic approach, a review on existing frameworks for maintenance systems modelling was carried out. A considerable amount of literature covers simulation models for maintenance systems proposing useful frameworks for supporting decision making for maintenance modelling. Duffuaa (Duffuaa et al., 2001) captured the complexity involved in maintenance systems developing a generic conceptual model, in which seven modules interact to deliver effective maintenance. The key maintenance support functions are included, namely materials and spare parts provisioning as well as tools and equipment availability checking. Furthermore, other existing literature proposes generic frameworks to support and guide decision making when optimization problems need to be addressed in complex maintenance systems. Interesting contributions are the maintenance optimisation classification scheme proposed by Horenbeek et al. (Van Horenbeek et al., 2010), and the framework for simulation-based optimization of maintenance systems developed by Alrabghi et al. (Alrabghi and Tiwari, 2016). Finally other papers cover the same issue for field maintenance system. Alabdulkarim et al.

(21)

(A. A. Alabdulkarim et al, 2011) proposed a general framework, highlighting for each maintenance sub-system, including equipment, labor, inventory of spares, ordering systems, the appropriate model parameters. The described framework for field maintenance systems simulation model is shown in Figure 2-3.

Figure 2-3. A framework for field maintenance simulation (A. A. Alabdulkarim et al., 2011).

(22)

2.7 Key findings and research gaps

The prior literature review allowed highlighting interesting insights into the research field. Firstly, predictive maintenance strategy is becoming popular in the field of asset management and the literature demonstrates its beneficial impact on system performance against the traditional maintenance strategies. Secondly, the new trend from product-oriented value propositions to integrated product-services bundles is becoming increasingly common among manufacturing industries, and rapid advancements in Information and Communication Technologies (ICT) are leading to leverage CPS capabilities for delivering higher value to customers. Many applications of CPS-based PSSs concern the field of asset management, and increasingly production assets are equipped with sensing technologies in order to enable condition monitoring and predictive analytics. However, this transformation requires reviewing the entire business structure, as greater complexity results from aligning the intangible and tangible elements of the PSS, including the product, the service, the customer, the provider and the infrastructure. Introducing PSSs pose further challenges, as the coordination between different subsystems is required to deliver the expected value to customers: in the case of maintenance service, the whole maintenance operation lifecycle system need to be aligned with operation related to spare part manufacturing and supply chain. Therefore a crucial phase to assist companies in this transition involves the design and the assessment of the desired PSS through appropriate tools and methods. The effectiveness of simulation tool to support integrated product-service engineering has been extensively highlighted, although practical applications in PSS contexts have been poorly covered, in particular in the case of distributed network of partners. Investigating the impact of predictive maintenance on the service management process may be beneficial to the PSS research field, and further insights could be derived from taking into account the whole manufacturing ecosystem, including customers, suppliers and logistics service providers. Although a considerable amount of literature highlights the importance of effective spare parts inventory management to maintenance operations, little effort is directed towards investigating the impact of spares management on maintenance performance since many models

(23)

assume ready available spare parts. Therefore this study aims to close the described gaps in the literature, firstly investigating the impact of predictive maintenance in PSS contexts looking at the interactions between customers, suppliers and service providers; thereafter, studying the implications of different spare parts inventory replenishment policies in the predictive maintenance performance.

(24)

3 RESEARCH METHODOLOGY

3.1 Introduction

In order to achieve the stated objectives, the research was conducted following the key processes involved in a simulation study (Robinson, 2004). The simulation approach proposed by Robison has been used as guideline to address the problem, but other useful steps have been included in the methodology in order to fulfil the requirements of the client. The main phases of the research methodology are: (1) Problem Definition, (2) Model Building, (3) Simulation Experiments, and (4) Study Completion. These steps enable to deliver the five objectives of the project.

Figure 3-1 illustrates the outline of the research methodology used in the study, breaking down the four phases into a number of separated activities.

Figure 3-1. The outline of the research methodology.

Problem

Definition

Project Brief

Literature Review

Gathering requirements

Model Building

Conceptual _Model Simulation _Model Validation

Simulation

Experiments

Set up experiments Run experiments Analyse results

Study

Completion

Future

(25)

3.2 Description of the research phases

The key steps of the research methodology were carried out as described follows:

1. Problem Definition

The first step involved developing an understanding of the problem and determining the aim of the project in collaboration with Xerox Ltd. A project brief was created in order to agree upon the aim, objectives, work plan and expected results of the project. Simultaneously, the literature review was conducted in order to understand the state-of-the-art and identify research gaps with regard to the research field. The review methodology is described in section 2.1. The understanding of the current service management process, as well as relevant information to the modelling purpose were gathered through structured questionnaires and interviews to the persons in charge of the technical support at Xerox Ltd. These steps enabled to deliver the first objective of the project by establishing its baseline.

2. Model Building

Based on the requirements of the problem, a decision was made as per which simulation approach and commercial software tool to use. The decisions was driven from the literature, after understanding the key features and applications of the different simulation techniques (Borshchev and Filippov, 2004) and evaluating alternative software tools based on their features and specifications (Abar et al., 2017). Thus, the first step of the model building involved developing a conceptual model setting the modelling purpose and determining the inputs, the outputs and the model content. Such decisions result from taking into account different aspects: (1) the requirements of the problem, (2) the current as-is process, and (3) the best practice in field maintenance modelling. The understanding of the first two points was establish as described in the previous step, whereas the literature gave insights into the common practice for field maintenance systems modelling, as described in Appendix A.2. Thereafter, the phase of model coding involved developing the computer model by using the selected simulation software, and finally the validation phase enabled to

(26)

verify the model behaviour represents the dynamics of the real world. The verification and validation consisted of testing the model using different methods proposed by the literature (Robinson, 2004), since opinions from experts could not be collected. In this way the second objective of the project, namely the model of the service management process, was met.

3. Simulation Experiments

Alternative experimenting scenarios were set and experiments were performed in order to analyse the impact of predictive strategy on the service network. Decisions on input and output factors as well as parameters were made considering which scenarios are representative of the case study, as well as the key findings from the literature review. The results were collected, providing the model with the functionality of extracting data set in Excel sheets, and thereafter they were analysed in order to meet the third objective of the project. The results from the experiments enabled to complete the validation of the model, established as fourth objective.

4. Study Completion

Based on the simulation results, a future roadmap was developed in order to provide recommendations to boost the value of predictive maintenance strategy, and future works were proposed to expand the research. In this way also the last objective of the project was met. The last step of the project was to formally report on the performed work.

(27)

4 SERVICE MANAGEMENT PROCESS

This chapter aims to provide the description of the current service management process of Xerox Ltd; firstly describing the as-is scenario, and thereafter outlining the relevant cost drivers in the process.

4.1 The current process

Although the interest is directed towards the whole International Technical Support department of Xerox Ltd, for practical reason the scope of the project is limited to the UK geographical area. This is organised as a distributed service network made up of 11 districts, each one covering 30/40 miles distance. Each district has available a crew of service engineers that delivers maintenance and support to customers.

The trigger of the process is a service request sent from a customer to the corresponding service district for either scheduled maintenance or machine failure. In the latter case, first the assigned service call assistant tries to solve the issue remotely. If all attempts fail, a service engineer moves to the customer’s site to diagnose the fault and to decide for example if a component needs to be repaired or replaced. In the latter case, three different scenarios might occur: (1) the needed spare part is readily available in the engineer kit and consequently the replacement is conducted immediately; (2) the needed spare part is not available on-site but it needs to be delivered from a central warehouse; (3) the needed spare part is not available in the central warehouse, consequently an emergency order needs to be placed to a spare part supplier and the job is suspended until the ordered part is received in the warehouse. The operating logic to schedule service engineers’ activities is the followings: first priority is given to suspended jobs due to spares stock-out, then to new service requests and finally to scheduled maintenance tasks. This is a simplified working assumption since variations may exist in practice depending on criticality of job requests.

Figure 4-1 illustrates the logic flow diagram describing the dynamics characterizing the service management process.

(28)

(29)

4.2 Cost drivers

Depending on the scenario, the service incurs various types of cost. The more a call is escalated, the higher the cost of the service, thus the lowest cost is when issues are solved remotely, since only the cost of labour is incurred. The second level of escalation involves also the cost of travel to the customer’s site, since on-site support is needed; the third scenario involves additional logistics costs incurred in moving a needed spare from the warehouse to the customer. Finally, the last level of escalation has the highest economic impact on the service since high costs are incurred for emergency ordering and job resuming after the ordered spare part is received at the warehouse.

Figure 4-2 provides the outline of the costs involved in the service management process. Such scenarios of costs are simplified working assumptions, but variations may be introduced to map details of actual practice, which have not been included. Moreover, even though such cost drivers derive from a specific case study, they can be representative of other service management processes that entail the same general features.

Figure 4-2. The cost drivers of the service management process.

Issue solved remotely Cost of labour On-site support, readily available spare part Cost of labour Cost of travel to customer site On-site support, not readily available spare part Cost of labour Cost of travel to customer site Cost of spare logistics On-site support, spare part stock-out Cost of labour Cost of travel to customer site Cost of emergency order Cost of job resuming

(30)

5 AGENT-BASED MODEL

This chapter aims to describe the agent-based model created in order to capture the interactions characterizing the service management process. This model was personally developed using Anylogic as simulation software. Therefore, the model is presented by (1) stating its aim and (2) boundaries, (3) describing the conceptual model, and (4) formulating significant experimentations.

5.1 The purpose of the model

The overall aim of the model is to compare two alternative maintenance strategies, namely preventive maintenance and predictive maintenance, investigating operational efficiency over time. Specifically, the main interest is to analyse the system performance using the following metrics:

 Annual Average Cost of Maintenance, keeping track of all the types of costs associated with the maintenance service process (including service cost as well as logistics cost).

 Annual Average Availability of Equipment, describing the annual average percentage of printing machines in operational state.

Furthermore, alternative strategies for spare part inventory management are evaluated, in order to assess their impact on system performance in the predictive maintenance scenario.

Accordingly, maintenance strategy related questions to be answered are:

1. How two different maintenance strategies, namely preventive and predictive strategy, affect service performance in term of cost and equipment availability? 2. Focusing on the predictive scenario, how alternative strategies for spare part inventory replenishment affect service performance in terms of cost and equipment availability?

(31)

5.2 The boundaries of the model

Table 5-1 illustrates the scope and level of detail of the model, describing which maintenance service related elements are included, as well as to which extent they are taken into account. Such decisions represent working assumptions, which can be easily modified to adapt the simulation model to more specific requirements.

Table 5-1. The model scope and level of detail.

Decision element Level of detail

Equipment A fleet of printing machines is geographically

distributed in the UK area. Each customer has only a machine.

Degradation pattern Printing machines might fail according to three alternative failure modes, each one having a different failure rate. Exponential distribution is used to describe the component reliability during its lifetime. Failures rates are not constant but depend on three factors: (1) equipment age, (2) equipment usage, and (3)

timeliness of maintenance. The failure function is described in the section 5.4.

Maintenance actions Technical support is provided both remotely and on-site. Decisions on which maintenance actions are required are driven by probability functions.

Maintenance policy The maintenance policy consists of a combination of corrective and preventive strategy. The service deals with scheduled maintenance as well as unexpected machine breakdowns.

Maintenance resources and capacity

The service crew is made up of a deterministic number of engineers. There are no constraints on the number of working hours per day.

(32)

Spare parts inventory management

Spare parts are kept both in the engineer kit (ready available for on-site replacement) and in the central warehouse. Replenishment policies are set defining appropriate values of spare parts arrival rate and lot size. If a stock-out occurs, an emergency order is placed to the supplier. The supplier lead time is set to be constant.

Maintenance equipment Maintenance tools and equipment are not included in the model. There are not requirements on workforce specialization but only the resource availability determines whether it is able or not to perform a maintenance action.

Service level There are no requirements on the service level.

5.3 The conceptual model

Four agents operate in the environment: (1) the printers OEM providing technical support to (2) the printing equipment of its customers, (3) the central warehouse storing spares for replacing failed components, and (4) the printed contents clients placing orders to the owners of printing equipment.

With regards to the equipment’s behaviour, each machine iterates cyclically between three main states: (1) operational, (2) not-operational and (3) under-service. During the operational state the equipment processes orders of printed contents according to its production rate. When the machine fails, it enters the not-operational state and a service request is sent to the printers OEM. Differently from the operational state, in which the machine is able to fulfil the customers’ requests, in the not-operational state the printer is inoperable. In other words, the failed machine needs to wait until the failure is solved by the repair operation of a service engineer. The different dynamics involved in the process lead to many types of waiting times for the printers, as described below:

(33)

 Waiting for engineer arrival, that represents the time the service engineer takes to reach the customer’s site.

 Waiting for collecting spare, that represents the time needed for logistics operations to deliver the required spare part to the customer’s site.

 Waiting for ordering spare, that represents the supplier lead-time in case of emergency spare part orders.

Several delays might affect the machine downtime due to the following reasons:

 Limited service crew capacity,

 Need to collect the required spare part from the central warehouse,

 Need to place an emergency spare part order to supplier.

Consequently, the customer service incurs unprofitable waiting times due to either engineer arrival, spare collection or emergency lead-time.

With regard to the under-service state, four different types of service are provided: (1) remote service, (2) on-site repair, (3) on-site replacement, and (4) scheduled maintenance.

(34)

(35)

5.4 The failure function

A failure function is defined in order to describe the rate to which each machine transits from operating to failed state. Machines’ degradation is modelled with exponentially distributed times to failures. However, the probability of failure is not assumed to be constant over time, but it changes according to three different factors: (1) equipment age, (2) number of working hours, and (3) late maintenance. In other words, the older the equipment, the more number of pages are processed, as well as the more maintenance is overdue, the higher the component’s failure rate. The formula of the failure rate is defined by:

failureRate=BaseFailureRate*agefactor*usagefactor*mtcefactor where

failureRate = probability of failure,

BaseFailureRate = average component’s failure rate, agefactor = coefficient expressing equipment age, usagefactor = coefficient describing equipment usage,

mtcefactor = coefficient representing timeliness of maintenance.

In the computer model, the aforementioned factors alter the machine base failure rate according to the logics described through the java codes in Appendix A.3.

5.5 The overall structure of the model

As in AB modelling the behaviour of the whole system results from interactions between different agents characterized by their own logics and dynamics, a description of the key concepts characterizing the model might serve the purpose of clarifying the model’s structure. Table 5-2 illustrates the architecture of the model by defining the following elements: (1) the goal that the agent wants to achieve, (2) the possible states among which the agent can iterate, (3) the actions performed by the agent, (4) the information that the agent can access, (5) the communication with the other agents, (6) the space and the scale of the model.

(36)

Table 5-2. The overall structure of the AB model.

Agent Printers OEM Printing Equipment Central Warehouse Printed Contents Client Goal To respond service requests. To fulfil orders of printed contents. To manage spares inventory. No goal State - Available - Remote Service call - On-site support - Moving to customer - Moving to warehouse - Idle - Working - Failed - Repair - Replacement - Maintenance - Waiting No changes in state No changes in state Action Check service requests and perform required tasks. Check printed contents orders and process them.

Replenish spares inventory. Generate orders for printing equipment. Information - Service requests. - Spares stock level. - Printed contents orders. - Spare parts requests. No access to any information

(37)

Interaction Communicate with printing equipment agent to manage service request, and with the warehouse agent to ask for spare parts.

Communicate with Printers OEM agent to notify the need of service, and with the printed contents client agent to receive orders. Communicate with Printers OEM agent to respond to spare part request. Communicate with Printing equipment agent to place orders.

Space & Scale

The environment is modelled through a GIS map. The model time unit is one day and the simulation length is 20 years.

5.6 Experiments

According to the two research questions, experiments involved two different phases: 1. Maintenance strategy comparison, aimed to compare the current and future

service management process.

2. Inventory strategy analysis, aimed to investigate the impact of alternative inventory management strategies on system performance in the predictive scenario.

5.6.1 Setting up the model

Before running up the model, some issues need to be addressed. On one hand, any initialization bias should be avoided by introducing a warm-up period; on the other hand, sufficient output data should be collected by performing the simulation as long as needed. With regard to the initial state, some randomness was introduced in the model by initiating parameters, such as “Time Last Maintenance”, “Time Last Replacement” and “Printed Pages”, using probability distributions. However, the starting point is still affected by the following conditions:

(38)

 All printing machines are in operating state.

 All service engineers are idle and at Xerox site.

 There are not requests for service or maintenance.

Therefore it is appropriate to set a warm-up period in order to let the system develop its dynamics and to analyse its performance during the steady state. In the matter of run length, Banks et al. (Banks et al., 2001) suggest a simulation duration equal to at least 10 times the warm-up period. Accordingly, considering the nature of the problem, the warm-up period is set to 1 year, and the duration of an experiment to 20 years. Performing time-series inspections, the assumed warm-up period results consistent with the time point in which the model seams to settle into a regular state, but other settings are also possible.

5.6.2 Definition of input parameters

Alternative scenarios were designed in order to address the two maintenance strategy related questions. Both the experiments were carried out considering the following assumptions and setting up the parameters as shown in Table 5-3. Since variations of these scenarios are easy to define and implement, generalizability can be supported by the study.

(39)

Table 5-3. The input parameters for the experiments.

Inputs Value

Number of service engineers 10 engineers Number of printing equipment 30 machines

Number of warehouses 1 warehouse

Base failure rate {0.03,0.05,0.06}* per day

Maintenance period 90 days

Remote service time 15 minutes

Repair time {120,80,50}* minutes

Replacement time {160,120,90}* minutes

Maintenance time 180 minutes

Supplier Lead Time 3 days

* It is assumed that values changes depending on the failure mode.

5.6.2.1 Maintenance strategy comparison

In order to compare the two maintenance strategies, the to-be scenario was generated altering the following input parameters:

Pa = Probability that on-site support is needed to solve the issue.

Pb = Probability that the needed spare part is available in the engineer kit.

Pc = Probability that the needed spare part is available in the central warehouse.

Table 5-4 describes the as-is and to-be scenarios, providing values for the different probabilities and justifying the choices. All the probability values are define over a day period of time. Variations and extensions of these scenarios can be easily defined and implemented.

(40)

Table 5-4. The predictive maintenance scenarios. Scenario Input parameter Pa Pb Pc As-is 0.75 0.70 0.80 To-be 0.50 0.95 0.95

The as-is scenario describes the current maintenance service, in which predictive maintenance is not carried out. By contrast, the to-be scenario represents a situation in which predictive strategy is carried out, and probability values changes due to the following reasons:

1. Better diagnosis of errors and faults enables to decrease the need of on-site support, increasing the number of issues successfully solved remotely.

2. More accurate diagnosis of failures as well as better understanding of fault patterns enable to send the service engineer to the customer’s site equipped with the right spare part, increasing the probability of ready available spare part on-site.

3. Improved predicting capabilities enables to forecast spare parts demand more accurately, leading to better spares inventory management.

5.6.2.2 Inventory strategy analysis

With regard to inventory management, others scenarios are generated to assess the impact of alternative replenishment strategies on system performance. The following assumptions are made:

 Predictive maintenance strategy is carried out (Pa = 0.5).

 Not ready available spare parts on-site (Pb = 0). This assumption is made to

(41)

 The spare part availability in the warehouse is no longer modelled through probability distributions, but calling a function that returns true if there are spares (see Appendix A.3 for codes).

 The spare part inventory is replenished according to the following parameters: (1) Lot Size, representing the amount of units delivered per order; (2) Inter-arrival Time, representing the time between two consecutive orders.

Table 5-5 describes the different scenarios, providing values for the input parameters and justifying those. Variations and extensions of these scenarios can be easily defined and implemented. The three scenarios are set in order to investigate the impact of different frequencies of spare part inventory replenishment, as describe above:

 Scenario 1 describes a situation in which spares provisioning occurs with high frequency.

 Scenario 2 describes a situation in which spares provisioning occurs with medium frequency.

 Scenario 3 describes a situation in which spares provisioning occurs with low frequency.

Table 5-5. The scenarios of spares replenishment. Scenario Lot Size Inter-arrival

Time

Justification

1 {3,4,5} units 25 days Low inventory level. 2 {3,4,5} units 32 days Medium inventory level. 3 {3,4,5} units 45 days High inventory level.

5.6.3 Definition of output parameters

In order to assess the performance of the system either for the maintenance strategy comparison and the inventory strategy analysis, two output metrics were defined as follows:

(42)

1. Annual Average Cost of Maintenance, keeping track of all the types of costs associated with the maintenance service process (including service cost as well as logistics cost).

2. Annual Average Equipment Availability, describing the annual average percentage of printing machines in operational state.

The logics used to calculate such parameters in the model are described in the following subparagraph.

5.6.3.1 Annual Average Cost of Maintenance

Two major groups of costs were identified: the cost of service and the cost of inventory management.

The cost of service includes all the costs incurred during the delivering of maintenance and support to the customers. The two main types of costs are: (1) the remote service cost, and (2) the on-site service cost. The remote service cost is assumed to be lower than the on-site service cost, which includes not only the cost of performing the required action on the systems (either repair or replacement) but also the cost of travel (either to the customer’s site or to the warehouse).

The cost of inventory management includes three major parts: (1) the inventory cost, (2) the emergency order cost and (3) the scrappage cost. The inventory cost takes into account two different components: the holding cost, which expresses the annual cost for stocking one unit (including manpower, space, equipment, insurance); and the ordering cost, which is the cost for placing a regular order to the supplier. The emergency order cost brings the high cost related to emergency replenishment of spare parts, whereas the scrappage cost is an estimation of the potential loss in the value of the unused spare components due to obsolescence.

Based on the key findings from the literature and the dynamics involved in the process, a decision was made as how to estimate such costs in the model.

With regard to the cost of service, the two major components of cost were calculated as follows:

(43)

 The remote service cost increases by the average cost per call any time the issue is successfully solved via remote, according to the following java code: RemoteServiceCost+= RemoteServiceCosperCall;

 The on-site service cost increments by the corresponding average cost per visit any time either repair or replacement is completed. In addition, the cost of travel is included in the on-site service cost by incrementing it by the average cost per travel any time an engineer move to either the customer site or the warehouse. Those constructs are modelled using the following java codes: OnSiteServiceCost+= RepairCostperVisit;

OnSiteServiceCost+= ReplacementCostperVisit; OnSiteServiceCost+= TravelCostperTravel;

With regard to the cost of inventory management, the various types of cost were estimated as described below:

 The inventory cost increases by the average cost per order any time a regular order is placed to the supplier, as described by the following java code:

InventoryCost+= OrderingCostPerOrder;

Instead, the holding cost is estimated at the end of the year as function of the order lot size. It is assumed that the annual average inventory level is equal to one-half of the ordered quantity. Since the order lot size is not the same for each type of component, and the annual holding cost per unit depends on the component value, the construct for calculating the holding cost includes three parts, as shown below:

InventoryCost+= HoldingCostperUnitperYear[0]* LotSize[0]/2; InventoryCost+= HoldingCostperUnitperYear[1]* LotSize[1]/2; InventoryCost+= HoldingCostperUnitperYear[2]* LotSize[2]/2;

The numeric index (from zero to one) is a reference to the parameters characterizing the specific component.

(44)

 The emergency order cost increments by the average cost per order any time an emergency replenishment occurs, as shown by the following java code: EmergencyOrderCost+= EmergencyCostPerOrder;

 The scrappage cost is estimated at the end of the year as function of the inventory level: it is assumed that the remaining components have a loss in value by the 10% of their initial value. Since different components have different initial values of price, the construct for calculating the holding cost includes three parts, as shown below:

ScrappageCost+= InventoryLevel[0]* ComponentValue[0]*0.1; ScrappageCost+= InventoryLevel[1]* ComponentValue[1]*0.1; ScrappageCost+= InventoryLevel[2]* ComponentValue[2]*0.1;

The numeric index (from zero to one) is a reference to the parameters characterizing the specific component.

5.6.3.2 Annual Average Equipment Availability

The second metrics used for assessing system performance is the annual average equipment availability. During the simulation runs three statistics related to the fleet of printing machines are collected:

 Number of printers that are operational, that means they are able to process orders of printed contents.

 Number of printers that are under service, that means the required maintenance task is being performed.

 Number of printers that are not operational, that means they are inoperable because they are waiting for service or repair work.

Such statistics are collected every hour during the simulation run, and the annual average value is obtained by calculating the mean of the result gathered throughout the year.

(45)

6 RESULTS

This chapter aims to provide the results obtained running the model according to the scenarios defined in the subparagraph 5.6.2. The cost parameters used in all the experiments are specified in Appendix A.1. The results are visualized and their implications are discussed.

6.1 Maintenance strategy comparison

Figure 6-1 illustrates the impact of predictive maintenance strategy on equipment availability, highlighting the average percentage of machines that are operational (that means they are able to process customers’ orders), not-operational (that means they are inoperable because they are waiting for service or repair work), and under-service (that means the required maintenance task is being performed). The to-be scenario demonstrates an improved service level against the as-is scenario, since the average percentage of machines that are in not-operational state is significantly reduced. The shift towards predictive strategy leads to an increase of the annual average level of equipment availability from 86% to 98%, demonstrating its positive impact on customer satisfaction.

Figure 6-1. Impact of predictive strategy on service performance. 75% 80% 85% 90% 95% 100% As-is To-be

Annual Average Equipment Availability

NotOperational UnderService Operational

(46)

In Figure 6-2 the average number of not-operational machines is split up into the different types of customer waiting times. Moving from the current to the future strategy the most significant decrease is in the number of machines waiting for ordering spare parts since increased probability of spares availability at the warehouse enables to reduce the need of emergency replacements.

Figure 6-2. The impact of predictive strategy on service waiting times.

In the matter of system efficiency, Figure 6-3 shows the annual average cost of service, splitting it up into the cost of remote service, the cost of on-site service and the cost of emergency order. Moving towards the to-be scenario, lower emergency order costs are involved as consequence of better spares management. Furthermore, the on-site service cost shows a considerable decrease against a slight increase of the remote service cost, since effective fault diagnosis and resolution via remote entail a situation in which travel is no longer necessary and the first time fix rate of the service increases. However, the increase of the remote service cost is well compensated by the reduction of the on-site service cost and the near elimination of the emergency order cost, since the cost per call is much lower than other types of costs.

0% 20% 40% 60% 80% 100% As-is to-be

Not-Operational Machines

Waiting for ordering spare

Waiting for collecting spare

Waiting for engineer arrival