usage oriented PSS

PSS Production Systems: a simulation approach for change management Guillaume Marquès, Malik Chalal, Xavier Boucher

Ecole Nationale Supérieure des Mines, FAYOL-EMSE, CNRS-UMR5600, EVS, F-42023 Saint-Etienne – France

{gmarques, chalal, boucher}@emse.fr Abstract. The research presented in this paper is oriented on the transition of the manufacturing industry towards the delivery of product-service system. This servicization challenges is addressed at the level of operation management. The paper presents the first results in the development of a decision support system dedicated to help in configuring service-oriented production systems, notably with an objective of capacity management. A illustrative case study is presented, linked to the production of washing machines. Keywords. Servicization, Product Service Systems, Production System, Modelling, Simulation.

Introduction In an increasing business competition world, where the source of value creation or drastically changing, industrial companies are faced to the necessity to develop strong innovations on their business models and on the way to manage their value-creation chains. One of the area of business innovation is the progressive development of service based added-value. For an industrial company, this leads to a progressive integration of services along the whole life-cycle of industrial offers. Such product-service offers can bring the opportunity of new business models, notably the so-called functional economy business model, where the product in itself is no more sold but only its use is sold through a service contract. Such transition of the manufacturing industry towards the integration of services is known in the literature as servicization [1]. Servicization is not only a new business model, but requires strong organizational changes. The integration of services implies a deep internal transformation of the strategic and operational processes of the enterprise. Servicization induces is a complete re-orientation of the processes. At the heart of this change, production and commercial processes hold a strategic role. Anticipating this transition is becoming crucial for decision makers. The overall orientation of the research work presented below, consists in developing a relevant decision support system to help managing the organizational transition. We consider the cases of traditional manufacturers which evolve towards the integration of new service-oriented activities through the offer of Product Service Systems (PSS). Such industrial transition towards PSS design and delivery has been mostly studied in the literature at the strategic level, notably by studying the influence factors, barriers and motivations of manufacturing companies. Complementary, this paper aims at studying the impact of this transition at the level of operations management. The decision-aid expected from this research is mainly oriented on helping to reconfigure the production system and some of its operation management mechanisms, when confronted to the integration of services activities. To answer this objective, the paper is composed by four main parts. First, we present a background on PSS systems in order to emphasize attendant key decision levers that can have consequence on the enterprise performance. We underlined the necessity to restructure the production process and to change capacity management modes. Then (section 3), we present our proposition, a model to represent and anticipate behaviour of a manufacturing system faced with servitization, which provides the basis of the decision-aid approach. Section 4 proposes an industrial case study to illustrate the use of this model. The last part presents conclusions and future research works related to this approach.

2 Background on PSS Systems PSS is a new concept that has emerged in the research world after the work of [2]. It induces various issues and research questions, particularly at the strategic level, in terms of design [3], motivation and barriers for transition [1] and benefits (economic and environmental) [4]. Services are attractive for traditional manufacturing companies because they are characterized by high margins and stable revenues [5]. Services can establish close and long-term relationships with customers, which at the same time locks-out competitors. Moreover, integrated PSS solutions are less easy to copy, making them a lasting source for differentiation. In order to build a decisionaid model oriented on configurating production modes, we are interested in modeling two main subsystems : the user-oriented subsystem (section 2.1) and the production subsystem (section 2.2).

2.1

User-oriented system

Use is large research issue addressed through different point of view and mainly by ergonomics and marketing. Both deal with the analysis of the asset in use in order to design product with a maximum of value in use, i.e. products that respond to the larger amount of customers’ expectations [6]. Among the concept studied, the “Service-Dominant Logic” proposed by [7] emphasizes the importance of a complete reorientation of the firm strategy in order to create value; the third step we describe in introduction. [8] shows that most of enterprises stop the progression into service at the second step, settling for adding isolated service to their products. To summarize, these visions induces two main families of analysis of the user system: (i) the user’s behaviour faced with the commercial offer, and, (ii) the user’s behaviour with the asset in use. The first one is linked to the set of PSS for which is ready to pay in order to satisfy an appreciated need. The second one is related to the need in service providing induced by the usage of the product. User faced with commercial offer A lot of studies are focused on the effective usage of the product. This kind of approach have a macroscopic view of the user behaviour faced with commercial offer and only model a global market volume, often timely constant. However, these simulations require a high level of description of the service offer: kind of customer product segmentation (professional, private - [4]), contract term [4], service providing lead time [9]… Different kind of services, called industrial services [8] main be implicated (deliveries, training, repairing…). User faced with the effective product usage Life Cycle Simulation is a current approach to evaluate performance (economic and environmental) of life cycles of products [4]. Discreet event simulation is mainly used in this case. These kind of models are supported by a detailed description of the product and its characteristics [4, 10]: lifespan, maximum of operations per usage period, functionality capacity per operation, breakdown rate, wear-out propensity, reparability, reusability, obsolescence… The user is described through: a functionality need per period or usage intensity. Modelling the appearance of customer needs most often implies stochastic processes in order to characterize this uncertainty. 2.2

PSS oriented production system

To implement a transition towards product-service, a manufacturing company has to develop new production capabilities, covering both new structural characteristics of the system (notably new production processes) as well as new managerial abilities (coordination and management modes). Concerning the structural changes of a manufacturing company from product manufacturing to product-service production and delivery, academic and industrial contributions underline the necessity for the company to manage 2 new production areas: product remanufacturing processes on one side, and service design, production and delivery on the other side. Remanufacturing as well as service production are parts of a full PSS life cycle. As a consequence, the full PSS Production System has to be considered as composed of 3 sub-systems: the manufacturing (1), the remanufacturing (2) and service production (3) sub-systems. Here, we underline below some characteristics of the second and third ones before discussing of a key decision lever of the industrial system: the capacity management. Remanufacturing sub-system Remanufacturing is an industrial process in which worn-out products are restored to like-new condition [11]. It is often associated to the reverse logistics issue [12]. Through a series of industrial processes in a factory environment, a discarded product is partially or completely disassembled. Disassembly may be partial (product is not fully disassembled) or complete (product is fully disassembled). Among disassembled parts, usable ones are cleaned, refurbished, and put into inventory. Then, the new product is reassembled from both old and, where necessary, new parts to produce a unit equivalent and sometimes superior in performance and expected lifetime to the original new product [13]. In comparison to manufacturing, remanufacturing has some general characteristics that complicate the supply chain. There is a major difficulty to assess the number and the timing of the returns. Another complicating issue is that the quality of the used products is usually not known [5]. Service providing sub-system In this study, this subsystem covers any activity included in the life-cycle of the services offered by a company through a PSS. To configure a service production system, a first difficulty consists in identifying service offers which could be consistent with the strategy of the firm. The service opportunities in the context of PSS are distributed along the PSS life-cycle. In [8, 14] authors propose a typology of “industrial services”, i.e. services associated to product life-cycle. This framework emphasizes the combination of service life cycle and product

life cycle: service requirement (co-conception…), service deployment (delivery, training…), service processing (maintenance, upgrade…) and service retirement (take-back…). Capacity management As explained above, a company in transition towards PSS has to manage the change towards a new production system which includes remanufacturing and service production. As a consequence the management modes will be strongly affected. Capacity planning is a key strategic component in designing goods/services production system. The goal of strategic capacity planning is to achieve a match between the long term supply capabilities of an organization and the predicted level of long term demand. Capacity planning is directly affected by the 2 new subsystems. On the one hand, the service production area is submitted to variations of capacity needs much more notable and unpredictable than for traditional manufacturing. The classical optimisation approach to optimise a nominal capacity using a product stock policy, has to evolve towards the possibility to manage stocks of “capacity to deliver service”. This is a strong change for the definition of capacity management policies. On the other hand, capacity management is also affected by the remanufacturing sub-system. Question about capacity planning in context of manufacturing, recovery and remanufacturing systems including economic and environmental issues was presented on [11, 13]. In [15], the authors conclude that the management of capacity is not trivial in PSS context and address the specific question: How should capacity level be managed to support new goods manufacturing as well as after-sales service if they share the same resources. In fact this question could be easily extended to the other services (delivery, maintenance…). According to [16], capacity management may be decomposed in 2 phases : “resource acquisition” then “resource deployment”. This paper is focused on strategic level. Resource acquisition process that is close to the classic Sales & Operations Planning (SOP), is therefore at the heart of our work. In context of services operation, [17] differentiate 2 kind of decisions in SOP : the setting of capacity levels relative demand (lead-track-lag) and the planning strategy given the amount of capacity for the resource we own (chase – level). 2.3

Conclusion from background

The characteristics and specificities of PSS have to be considered, before addressing the dimensioning and configuration of PSS production systems. Several authors like [1] have contributed to characterize PSS and the scientific literature has converged towards a typology which differentiates Product-oriented, Use-oriented and Result-Oriented PSS [1]. When a manufacturing firm is confronted to the change from product towards PSS offer, it is confronted to several kinds of limits and barriers [1] (financial, political, social and cultural), but also methodological barriers (lack of tools, methods and design patterns, modeling and evaluation of PSS). Furthermore, [17] emphasize the necessity to consider carefully servitization due to example that show non or limited profitable servitization experience. Consequently, the current contribution addresses these methodological limitations, with a specific focus on the production systems configuration. We intend to build decision aid method and tool, to support the dimensioning and evaluation of alternative configurations of a PSS production system. To implement a transition towards PSS, a manufacturing company has to develop new production capabilities, covering both new structural characteristics of the system (notably new production processes) as well as new managerial abilities. To study this transition of production system, this paper proposes a modelling approach dedicated to PSS Production Systems. This approach aims at implementing event driven simulation of various sub-systems of the full PSS Production System. Simulation is used as a decision support to build comparative analyses of alternative transition scenarios.

3 The modelling approach proposed for PSS Production Systems The approach proposed has the ambition to provide a simulated model of industrial production system, supporting the performance analysis of the transition process. We propose, at first, a general conceptual model which considers two subsystems: the user-oriented subsystem and the production subsystem. The user-oriented subsystem represents the usage environment which generates the various demands of products and services. It is dedicated to model and simulate user behaviours which impact the production system. The production-oriented subsystem represents a set of services providing, manufacturing and remanufacturing processes. This second subsystem is dedicated to model and analyse the internal organisation, planning mechanisms, interactions and performances of both manufacturing-oriented and service-oriented processes. The 2 subsystems are further presented in sections 3.1 & 3.2. Decoupling two subsystems allows a twofold objective. On the one hand, both production and user oriented systems can be modelled individually with a lot of details. On the other hand, a particular interest may be taken in their interactions. The two subsystems (user-oriented and production-oriented) are in constant interaction

through several business events (demands, services providing...). This coupling makes possible investigating different transition scenarios and analyse their impacts on the sustainable performance of production subsystem, decomposed in economic, environmental and societal externalities. Performance analysis has to emphasize the existence (or not) of balancing mechanisms between decision levers associated to each subsystem (capacity management and PSS offer engineering). 3.1

User-oriented subsystem

This subsystem represents behavioural aspects of the customers. Two main types of behaviours are modelled: the commercial behaviours leading to PSS purchase decisions of the client population; and utilization behaviours which notably generate strong variations in needs of services triggered along the product lifecycle. In this submodel, we adopt stochastic representation to model market and utilization behaviours. The commercial behaviours may be characterized by different kinds/families of markets and/or customers (professional or not, exigent or not, intense use or not…). Thus, this subsystem represents the global behaviour of the market, and the commercial behaviour of the customer with regards to the PSS market offer. The model variables represent the decision levers associated to the PSS offer engineering: price, contract term, service-mix, service quality (service provision lead time)… Then the utilization behaviours depend on different characteristics, also represented by modelling variables: characteristics of the product, characteristics of the user and characteristics of the usage processes. During the PSS lifecycle, all these characteristics (i.e. variables) influence the level of product solicitations and, as a consequence, the needs of service providing. Offer

Input : Market (volume, temporal evolution)

PSS offers

Input : Market (volume, temporal evolution)

Commercial behaviour

User-oriented subsystem

Commercial behaviour

Customer commercial behaviour subsystem

User behaviour

Output : Business Events frequency

User behaviour

Output : Market repartition on the PSS offer

Product characteristics

Usage processes

Consumer usage behaviour subsystem

Output : Business Events frequency

Fig. 1. Global view of the user-oriented subsystem model 3.2

Production subsystem

The production-oriented subsystem represents a set of services providing, manufacturing and remanufacturing processes. This second subsystem is dedicated to model and analyse the internal organisation, planning mechanisms, interactions and performances of both manufacturing-oriented and service-oriented processes. The model is built on a discreet-event simulation approach distinguishing two classical horizons: strategic and operational decisions. A company in transition towards PSS has to manage the change towards a new production system which includes remanufacturing and service production. As a consequence the management modes will be strongly affected. Notably capacity planning is a key strategic component in designing goods/services production system. The goal of capacity requirement planning is to achieve a match between the long term supply and services providing capabilities of an organization and the predicted level of long term demand. We model the Production System with two decision levels: strategic and operational levels.

Fig. 2. Global view of the production subsystem modeling approach

Strategic decision level From a strategic decision point of view, the model is focused on the decision-makers’s behavior with regards to the capacity balance strategy (for manufacturing, remanufacturing and service providing subsystems). Different decision-makers’s planning strategies have been identified: (i) Chasing strategy (where the capacity is adapted to the demand), (ii) Stable strategy (where the capacity level is kept constant and proportional to the average demand), and (iii) Smoothing strategy (where demand variation may be anticipated from a given amount of periods). This last strategy is dedicated to manufacturing and remanufacturing process. The 3 strategies may be added to stock-out acceptation or subcontracting strategies, in case of demand superior to capacity. Operational decision level From an operational system monitoring perspective, the objective is to represent the daily decision-makers’s behavior towards the resources balancing. In a context where the global capacity level is fixed (by strategic decision), managers have to establish resources – jobs affectations. Resources are defined by a set of competencies (different operations in manufacturing, remanufacturing, several services providing for example). Resources balancing strategies are essential to ensure high customer service level with good efficiency. A first level of strategy concerns the priority between production and services providing operations. Then, different behaviors may be modeled in term of resources affectation (% of utilization, competency level, cost per hour…).

4 Case study A part of this paper will be dedicated to an industrial case study build from ENVIE, a French enterprise. ENVIE’s mission is to contribute to progress in the remanufacturing of Waste Electrical and Electronic Equipment (WEEE) through different activities as collecting, (re)manufacturing, selling and other logistics activities dedicated to elimination. ENVIE is committed to conducting every aspect of its business in a responsible and sustainable manner. This includes being a work-integration social enterprise. ENVIE covers whole French territory through a local Small and Medium Enterprises (SME) network. This study particularly focuses on “ENVIE Loire” that represents ENVIE in a French region (Loire) and employs 17 persons. Choosing this company to support this feasibility study presents a twofold interest: (i) a strategic question relative to PSS adoption by ENVIE Loire, and (ii) the sustainable orientation of the enterprise (mainly on environmental and social aspects). In this study we aim to propose a decision support system that allows the performance of washing machine activities to be analyzed. The target is to emphasize the interest (or not) for the PSS transition (selling washing functionality instead of selling washing machine), through a simulation and analysis of the performance of the production system.This case study will be used in the paper aims to demonstrate: (i) the feasibility and the relevance of the modelling approach, and (ii) the kind of results, analyses and decision support provided by the model. 4.1

User-oriented sub-system

As specified on the Fig. 1, the User-oriented subsystem is composed by customer commercial behaviors subsystem and consumer usage behaviors subsystem. The customer commercial behaviors subsystem is modeled by the characterization of the market and customers, and also by characterizing the company's offer. We model the market through the evolution of a specified product family (VPF,t). In our case study, the product family is “washing machines”. The market will be decomposed into two market segments, the classical selling market, represented by the variable (VC,t) , and PSS market represented by the variable(VPSS,t) that represent the temporal evolution of the PSS or classic customer volumes. For PSS customer’s, we define commercial profile as PSS, this variable allows different commercial profiles to be distinguished through a specific commercial behavior. The distribution of all PSS customers on different commercial profiles is represented by a discrete probability distribution αi between 0 and 1 and associated with each commercial profile Pcomi as Σ αi = 1. The PSS contract offer is defined by a set of contracts, each contract offer (OC)i is defined by: (i) a type of product TPi,(ii) a bundle of services {Sk} k=1,p and (iii) a service quality SQ. The company brings to market a portfolio of contracts offers, denoted: {(OC)j} j=1,n / (OC)j=(TPi, {Sk} k=1,p, SQ). Commercial profiles ({Pcomi}j=1,q) describe users’ behavior when selecting a specific contract offer among {(OC)j}. The purchasing decision process is represented stochastically where each commercial profile is associated to a discrete random variable denoted Disc(µq,n). This last one traduces the attraction of a commercial profile (q) to a type of offer (n). To simulate consumer usage behaviors, this feasibility study focuses on the washing machine utilization process. This process affects the demand for maintenance services. To represent this process, we have also implemented defined a set of variables, to represent stochastically various user utilization profiles.

The simulation of this user-oriented sub-system provides a set of demand signals, which can be utilized afterwards as input to simulate the production sub-system. By simulating commercial as well as usage behaviors , we finally generate stochastically various demand signals for service operations : notably for ‘delivery and installation’, ‘maintenance and repair’ and ‘take back’ services. 4.2

Production subsystem

Using the results of user-oriented subsystem simulations, the production subsystem model make possible to study the impact commercial behaviors (selection of a given offer and contract duration proposed by the company) on the internal industrial performances. In this feasibility study we focus on analyzing internal logistic regulation parameters to manage the variability generated by service demands: the impact of affectations rules and resources competencies on the industrial performance. Manufacturing and Remanufacturing Processes We consider two types of product called Pf1 and Pf2 respectively characterized by high and medium quality levels. Pf2 is a remanufactured product. Product was assembled with three parts (A, B and C). For Pf1, only new parts are used. For Pf2, reused parts called A’, B’, C’ are used. In case of inventory stock out, new parts (A or B or C) can be used to assembly Pf2. The manufacturing system is composed by 3 workstations (Fig. 3). The two first are assembling operations. The last one allows a transformation operation to be made (testing and packaging for example). According to the routing sequence of each end-item, the 3 operations have to be made in sequence. B and C

A and SE

Station 1

Station 2

B’ and C’

A’ and SE’

P1

Pf1

Station 3 P2

Fig. 3 : Production routing sequence

Pf2

B’

SE’

Pu

Station 5

Station 4 A‘

C’

Fig. 4 : Remanufacturing routing sequence

Products are remanufactured through disassembly, test, repair, refreshing, reassembly, and packing operations. We consider that the remanufacturing can be decomposed on the two sub-processes (Fig. 4). The first one is disassembly line (disassembly, test, repair and refreshing operations). The second one is assembly line already described with fig 5 for Pf2. The disassembly line is composed by two stations. The disassembly sequence is the reverse of the assembly routing sequence. This assumption is justified by [22] more than half of all disassembly sequences are generally the reverse of the assembly sequence. Services providing According to [18], we consider in this paper a list of potential services: Selling, Product Delivery (including installation), Maintenance (including repairing) and product Take-Back. All the processes necessary to deliver these services are represented through a simulation model. Each service is modeled as an operational process which uses resources (during a given time). Each resource is characterized by a flexibility level which authorize or not the resource to be used to provide a service. 4.3

Simulation

The results presented are the average results of 20 replications and the Simulation duration is 4 years. In our simulation, we consider that company provide four different contract offers (OC) j=1..4. For this paper, we consider 3 performance indicators: (i) Service Level (SL), assessed by the average time of response maintenance and average time of service delivery, (ii) Inventory Level, and (iii) Rate of use of resources. We consider three commercial profiles ({Pcomi}j=1,2,3) with two scenario of the distribution of PSS customers on different commercial profiles α1i and α2i . In construction of our scenarios, we consider also two contract duration, two affectation rules and two situations of resources skills. We therefore have six scenarios, the six scenarios are analyzed in the two situations (distribution of commercial profiles), which gives us a total of 12 scenarios. The different scenarios are summarized in the table 1:

Scenarios

αi

Contract duration

Scenario1. (α1i) / Scenario1. (α2i)

α1i α11=0.2 α12=0.4 α13=0.4

Scenario2. (α1i) / Scenario2. (α2i) 1

2

Scenario3. (α i) / Scenario3. (α i)

α2i α21=0.4 α22=0.4 α23=0.2

Scenario4. (α1i) / Scenario4. (α2i) Scenario5. (α1i) / Scenario5. (α2i) Scenario6. (α1i) / Scenario6. (α2i)

1 year

Resources Competencies Flexibility

Affectation rules Production-Remanufacturing-Service

2 years

Flexibility

Production-Remanufacturing-Service

1 year

Flexibility

Service-Remanufacturing-Production

2 years

Flexibility

Service-Remanufacturing-Production

1 year

specialization

----

2 years

specialization

----

Table 1 : scenarios simulated Results and interpretations The following Fig. 5 and Fig. 6 give the influence of commercial profile distribution and contract duration on service performance using the indicators described below for each one. 4.4

I.1

i  1...6,

SLscenario   SLscenario   100 SLscenario   1

2

i

i

1

i

I.2

i  1;3;5,

SLscenario i   SLscenarioi 1  100 SLscenario i 

k  1;2, i, j   5,1; 5,2; 6,2; 6,3,

I.3

SLscenario   SLscenario   100 SLscenario   k

k

i

j

k

Fig. 5 : Influence of profile commercial repartition

j

We note that the distribution of commercial profiles affects the services performance (Fig. 5) for all scenarios, we note that in the case of a specialization of resources, service performance are better in the case of α2 (we have 13% of gain for maintenance reaction times and25% for the delivery time). For the influence of contract duration on service performance, we note that the duration contract of 2 years allow the company to reduce the response time for delivery and maintenance reaction (Fig. 6). The (Fig. 7) gives the influence of resources skills on service performance using the indicator. We note that in both cases (α2, α1), giving the priority to service, allows company to reduce the delivery time and maintenance reaction time.

Fig. 6 : Influence of duration contract

Fig. 7 : Influence of resources skills

The (Fig. 8) gives the average resource utilization rate. We note that in both cases, the average rate is substantially identical. In scenario 3 and 4, resources are less utilized, because priority is given to the services, and therefore the resources are allocated to services, the execution time and demand are less important than the production process and remanufacturing. To overcome this problem of unavailability of certain resources for the production process and remanufacturing when priority is given to service, the system draws the finished product stock as shown in (Fig. 9). The stock is used to allow some resources to support service activities.

Fig. 8 : Average Utilization rate of resources

Fig. 9 : Average Stock (Pf1+Pf2)

5 Conclusion and perspectives The study conducted in this paper shows the impact of the commercial offer of the company on the one hand, and on the other hand the impact of the competence based rules for task assignation on business performance. We have to extending further the coupling between the User-oriented subsystem and the productions subsystem to find rules of resource management and capacity strategy management for a given commercial offer situation.

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