Operative service delivery planning and scheduling in Product-Service Systems

Alp, Enes; Pirola, Fabiana; Sala, Roberto; Pezzotta, Giuditta; Kuhlenkötter, Bernd Article — Published Version Operative service delivery planning and scheduling in Product-Service Systems Service Business Provided in Cooperation with: Springer Nature Suggested Citation: Alp, Enes; Pirola, Fabiana; Sala, Roberto; Pezzotta, Giuditta; Kuhlenkötter, Bernd (2024) : Operative service delivery planning and scheduling in Product-Service Systems, Service Business, ISSN 1862-8508, Springer, Berlin, Heidelberg, Vol. 18, Iss. 2, pp. 161-192, https://doi.org/10.1007/s11628-024-00558-y This Version is available at: https://hdl.handle.net/10419/315631 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Service Business (2024) 18:161–192 https://doi.org/10.1007/s11628-024-00558-y 1 3 REVIEW ARTICLE Operative service delivery planning andscheduling inProduct‑Service Systems A systematic literature review EnesAlp1 · FabianaPirola2· RobertoSala2· GiudittaPezzotta2· BerndKuhlenkötter1,3 Received: 23 October 2023 / Accepted: 20 March 2024 / Published online: 21 April 2024 © The Author(s) 2024 Abstract To navigate competition and create higher value for customers, manufacturing companies are more and more adopting the strategy of Servitization by enriching their product offering with services in solutions known as Product-Service Systems (PSS). While the provision of PSS presents numerous advantages for customers and providers, they also pose significant challenges, particularly in the operative service delivery planning and scheduling. This study aims to identify decision-support within this context by conducting a systematic literature review. The analysis uncovers limitations in existing approaches and underscores unaddressed research gaps emphasizing the need for further development of decision-support systems for PSS operation. Keywords Product-Service Systems· Operative service delivery planning· Scheduling 1 Introduction Over the past few decades, manufacturing companies have faced significant market volatility (Pombo and Franco 2023). In response, and to differentiate themselves from competitors, companies have increasingly adopted the strategy of * Enes Alp [email protected]ub.de 1 Chair ofProduction Systems (LPS), Ruhr-Universität Bochum, Industriestraße 38C, 44894Bochum, Germany 2 Department ofManagement, Information andProduction Engineering, University ofBergamo, Viale Marconi 5, Dalmine, BG, Italy 3 Centre fortheEngineering ofSmart Product-Service Systems (ZESS), Ruhr-Universität Bochum, Hans-Dobbertin-Straße 8, 44803Bochum, Germany 162 E.Alp et al. 1 3 Servitization (Le-Dain etal. 2023). Servitization refers to the trend of companies extending their value propositions by offering services associated with their products (Khanra etal. 2021), marketed as Product-Service Systems (PSS). PSS are designed to continuously meet customer needs while reducing environmental impact, making them highly dynamic and complex systems (Gaiardelli etal. 2021). Usually, PSS are provided within innovative business models that differ from traditional ones, solely focused on high sales volumes, as the PSS ones emphasize the use, availability, or benefits of the product (Moro etal. 2022). While PSS offer benefits such as risk avoidance for customers and continuous revenues for providers (Reim etal. 2015), their widespread adoption has not been as rapid as initially anticipated (Brissaud etal. 2022). In fact, there have been instances of deservitization, where companies have scaled back or discontinued their PSS offerings. This is due to numerous challenges associated with providing PSS, requiring companies to undergo a paradigm shift in their thinking to achieve success. Service activities should no longer be treated as reactive additional tasks but rather as key activities for value creation (Kowalkowski etal. 2017). For many manufacturing companies, creating value through services represents a risk, as ineffective or inefficient service delivery can result in high penalties or erode trust between the customer and provider (Reim etal. 2016). Thus, achieving effective, and efficient service delivery is crucial in PSS business models. The effectiveness and efficiency of service delivery are mainly determined in the operative service delivery planning. However, this task is highly complex and requires suitable decision-support systems to make optimal decisions that enhance customer satisfaction and minimize costs (Sala etal. 2019). Accordingly, several approaches have been proposed in the literature to support operative service delivery planning and scheduling. This study aims to explore the state of the art in these approaches developed specifically for the operative service delivery planning and scheduling in the context of PSS. The research questions (RQ) of this study were formulated as follows: RQ1:What is the current state of the art for operative service delivery planning and scheduling approaches in the context of PSS? RQ2:What are the limitations of existing approaches, and what are the requirements for new approaches? RQ3:What is the suitable research agenda to further advance the field? To answer these research questions, a systematic literature review following the methodology outline by vom Brocke etal. (2009) was conducted. The review’s scope is defined using the taxonomy of Cooper (1988). Hence, our literature review focuses on methods and applications with the goal of identifying the central issues related to operative service delivery planning and scheduling approaches in the context of PSS. While we aim to achieve exhaustive coverage, this article is directed to general scholars, thereby adopting a comprehensive approach. The remainder of the article is organized according to the framework of vom Brocke et al. (2009) Sect. 2 serves as the conceptualization of the topic and 163 1 3 Operative service delivery planning andscheduling in… includes the theoretical background to PSS business models, operative service delivery planning and scheduling, and the related challenges. Section3 describes the literature search process in detail. Section4 presents the key findings derived from the literature analysis and synthesis phase. After discussing the results in the research context, a future research agenda is given in Sect.6. 2 Theoretical background 2.1 Product‑Service Systems Product-Service Systems (PSS) can be defined as “a marketable set of products and services capable of jointly fulfilling a user’s need” (Goedkopp etal. 1999). Manufacturing companies adopt PSS as a strategic approach to achieve various objectives, including revenue growth, customer relationship development, and environmental sustainability improvement (Li etal. 2020). PSS business models are geared toward the long term and can be categorized based on the balance between tangible products and intangible services in the value proposition (Mont 2002). Tukker (2004) outlines three distinct business models, as depicted in Fig.1. Product-oriented business models involve selling the technical product while offering related services like maintenance or end-of-life services. Availability-oriented business models focus on selling the availability of the product, with the provider assuming responsibility for ensuring guaranteed availability and facing penalties if the product is not available. Result-oriented business models shift the focus from the product itself to the desired output such as the pay-per-print concept used by copier manufacturers (Tukker 2004). Customers who engage with PSS can benefit by transferring activities to the provider, allowing them to focus on core competencies, minimizing high-risk investments, avoiding capital lock-up, and gaining access to new technologies (Meier etal. 2011b). The adoption of service-intensive PSS business models, which involve the transfer of activities and responsibilities from customers to providers, introduces Fig. 1 PSS Business Models (Meier etal. 2011b; Tukker 2004) 164 E.Alp et al. 1 3 increased risks – encompassing technical, behavioral, and delivery competence aspects – for PSS providers (Herzog etal. 2014). Technical risks arise from unexpected breakdowns of the technical product, while behavioral risks relate to the possibility of customers treating the product less carefully since they do not own it. Delivery competence risks reflect the provider’s ability and capacity to fulfill the value proposition effectively. Inadequate service delivery not only incurs high penalty costs for the provider but also endangers customer trust and satisfaction (Reim etal. 2016). Service delivery processes encompass all necessary activities to realize the value proposition such as “maintenance procedures, technological upgrades, spare part deliveries” or similar (Meier etal. 2013a). As a consequence of these innovative business models, effective and efficient service delivery across all customers assumes paramount importance for PSS providers. 2.2 Service planning inProduct‑Service Systems Service planning in PSS can be categorized into strategic, tactical, and operative planning (see Fig.2). Strategic planning involves long-term decisions made during the design and development phase of a PSS. Tactical planning focuses on mid-term decisions whereas operative planning deals with short-term decisions made in the operations or use phase and additionally, includes the task of scheduling (Dorka etal. 2014). In this context, the term planning pertains to determining “what and how” while scheduling pertains to “who and when” (Baldwin and Bordoli 2014). The following subsections explain the different planning dimensions in detail. 2.2.1 Strategic service planning The main task of strategic planning is to determine and build up all necessary resources for service delivery in the right quantity and quality. These resources encompass technicians, spare parts, or tools, with technicians being the most critical resources. By employing or training technicians, PSS providers have to ensure the availability of qualified technicians in the long term to achieve effective and efficient service delivery (Meier etal. 2012). In cases where internal resources are insufficient, PSS providers may opt to establish delivery networks (Lagemann etal. 2015), which involve collaboration among different companies to deliver the PSS value proposition or the respective services. To ensure the availability of the required Fig. 2 Service Planning in PSS (Meier etal. 2012) 165 1 3 Operative service delivery planning andscheduling in… resource capacities, the PSS provider can enter partnerships with e.g., component or service suppliers (Meier etal. 2010). Forecasting the required resources presents a significant challenge in strategic planning (Meier etal. 2012). To address this, Lagemann and Meier (2014) introduce a simulation-based capacity planning approach as a decision-support system for strategic planning. This approach enables to determine capacity requirements under the effects of different scenarios. Similarly, Zheng etal. (2017) present an approach based on fuzzy multiple linear regression for an efficient build-up of capacities despite the volatility of service requests. 2.2.2 Tactical service planning The goal of tactical service planning is to ensure the availability of the resources needed in the operation phase. Mid-term decisions within this realm include the management of vacations as well as the organization of training for the technicians. Additionally, appropriate stocks of spare parts need to be planned for each maintenance center (Lagemann 2015). An exemplary approach to tactical service planning was published by Agnihothri and Mishra (2004). The authors focused on the mismatch between the existing skills of the technicians and the required skills of each order. Using a simulation model, the authors analyzed the questions about how many technicians should be trained and when training amortized (Agnihothri and Mishra 2004). A similar approach was introduced by Gutsche (2015). She concentrated on the human factor when mismatches between required and existing competencies occur and analyzed the impact on employee satisfaction (Gutsche 2015). 2.2.3 Operative service delivery planning andscheduling The objective of the operative service delivery planning and scheduling in the context of PSS is to “provide the resources which are needed for the delivery of services during the operation [phase] in the right quality and quantity at the correct time and place” (Meier etal. 2012). For this, a dispatcher matches and assigns appropriate resources to the present delivery processes whether they arise from planned tasks or unexpected machine breakdowns (see Fig.3). He or she can either choose from the internal resources or the resources of the network or subcontract the delivery process to other partners (Meier etal. 2011b). As a result, the dispatcher generates plans that are going to be executed in the short term. For the assessment of plans, Meier etal. (2013c) introduce a list of key performance indicators (KPIs). According to the authors, the most important KPIs for the assessment of the delivery planning and scheduling performance are Mean time to problem solution, costs, revenue, travel time proportion, resource utilization, and rescheduling quota followed by the rates for First time fix and On time delivery (Meier etal. 2013c). There are huge similarities and intersections between Field Service Management (FSM) and especially the field service planning and the operative service delivery planning and scheduling in the context of PSS. Vössing (2017) describes the field 166 E.Alp et al. 1 3 service planning problem as “Spatially distributed customer requests need to be allocated to spatially distributed technicians,” whereby requests can be urgent or less critical tasks. The author classifies the problem as a “unique variant of the vehicle routing problem” (Vössing 2017), which is a problem presented in the late 1950s characterized by finding the optimal routes for a number of vehicles that have to serve customers that are geographically scattered (Dantzig and Ramser 1959). Despite the similarities in the basic structure of problem understanding, there are also some differences and peculiarities in the operative service delivery planning and scheduling in the context of PSS due to the innovative business model logic. In the area of field service planning, it is not clear, whether the company under consideration is an original equipment manufacturer (OEM) providing additional services or a pure service provider that independently from any asset development and ownership offers services. Besides, information on business models and remuneration of services (e.g., remuneration of individual service according to time spent or lump-sum remuneration according to maintenance contracts) as well as potential penalties for tardy service delivery is mostly not specified (Vössing etal. 2018). In contrast, PSS are characterized by the mutual and integral design and development of a value proposition with the consideration of the use phase. Hence, already in the early stages of PSS development, strategic decisions about service delivery are made (Hazée etal. 2020). The technical knowledge about own products in PSS, resulting from the development phases as well as the high customer interaction in the use phase, has a high impact on the effectiveness and efficiency of service delivery. In general, the goal behind providing PSS is to establish a relationship based on a partnership between the provider and the customer (Meier etal. 2011b). There are also approaches to integrate the customers already in the design and engineering processes of PSS (Pezzotta etal. 2017). Since the provision of PSS is motivated by differentiating from the competitors and extending the relationship to the customer over years or decades (Li etal. 2020), the non-fulfillment of the value proposition has a completely different significance. While for companies providing solely services, field service serves as a revenue source, PSS providers, especially Fig. 3 Operative Service Delivery Planning and Scheduling (Dorka etal. 2014) 167 1 3 Operative service delivery planning andscheduling in… in availability-oriented business models, conduct service deliveries to realize the promised value proposition and do not get paid for the individual service processes. Whereas tardy service delivery can cause additional costs due to penalties, losing the trust of the customers is a higher risk that can disturb the long-term relationship (Reim etal. 2016). Besides the increased number and criticality of constraints, there are also peculiarities of PSS business models that can have a positive effect on the operative service delivery planning and scheduling. Due to the high customer proximity and knowledge about their own products, uncertainties regarding the amount and time of requests can be reduced (Wan etal. 2014). With appropriate knowledge management systems and training, the efficiency in fault diagnosis and troubleshooting can be increased, thus resulting in higher predictable service delivery durations. Another characteristic of PSS business models is that not only customers can request delivery processes but the PSS provider is also able to initiate delivery processes, enabling to flexibly conduct preventive measures (Meier etal. 2011b). Since the value proposition in PSS is not specified on certain services or products but focuses on fulfilling customer needs through a variable combination of products and services, the provider gets a higher flexibility in decision-making while planning the service delivery. To leverage this flexibility and optimize service delivery plans, Meier etal. (2011a) introduced a set of variance options, which are summarized in Table 1. These variance options enlarge the solution space for plans and provide opportunities for generating optimized plans. In summary, despite there are great similarities and intersections between field service planning and operative service delivery planning and scheduling in the context of PSS, the business model logic in PSS gives additional constraints and flexibilities when making decisions. Operative service delivery in the context of PSS can be understood as a specialized form of field service planning where not only economic logic influences the decisions but also social values regarding the relationship with customers and partners. Thus, matching the appropriate resources with the right delivery processes to generate an operative plan is a complex process. The variance option inherent in PSS Table 1 Variance options in delivery planning and scheduling 168 E.Alp et al. 1 3 makes this task a large-scale optimization problem (Meier etal. 2011b). To structure the decision-making process, Sala etal. (2021a) introduced the D3Mframework to improve the decision-making based on real-time and historical data. According to the D3Mframework data that arises from the services, resources, customers, and products can be as a basis to make delivery decisions. Additionally, data that emerge from delivering the process itself can help to improve decision-making (Sala etal. 2021a). Because of the large solution space as well as the huge data basis that could be considered, generating optimal plans remains a huge challenge. With the aim of minimizing costs and maximizing profits as well as customer satisfaction, PSS providers are in need of suitable decision-support systems for operative service delivery (Sala etal. 2019). 2.3 Solving operative planning andscheduling problems The huge solution space makes the operative planning and scheduling an NP-hard problem. This means that using exact methods to solve the problem is only possible for small problem sizes. Therefore, heuristic methods are typically used to solve such problems (Vössing 2017). Heuristics resemble “rules of thumb” for a particular domain application and can find good (near-optimal) solutions within a short computational time. While heuristics do not guarantee optimality and may converge to local optima (Burke and Kendall 2014), they align with the fact that “Real-world scheduling often does not require optimal solutions, but reasonable good solutions in reasonable time” (Vössing 2017). A further development of heuristics is metaheuristics which operate on a higher level and can find optimal solutions even for large problem sets (Dokeroglu etal. 2019), by employing search strategies based on phenomena in the nature, physical laws, or human behavior (Abualigah etal. 2022). Typical metaheuristics are the Genetic Algorithm (García-Martínez etal. 2018), Simulated Annealing (Aarts etal. 2014), or Tabu Search (Laguna 2018). Recent advancements in the field of heuristics led to the development of hyperheuristics. Hyperheuristics offer a higher-level search methodology that does not operate on the problem domain itself but on heuristics that solve the problem which increases the generality of the algorithms (Drake etal. 2020). When solving operative planning and scheduling problems, two categories (online or offline) of problem settings can be distinguished. In offline problems, all the relevant information regarding requests, requirements, process times, etc. is given before the planning and scheduling. Thus, the entire plan can be generated at time zero. In contrast, in an online setting, not all information is known in the beginning but becomes available during the execution. The decision-maker does not know how many delivery processes will be requested and what their attributes will be (Pinedo 2022). In the past, research has focused on solving operative planning and scheduling problems in general. This study aims to conduct a systematic literature review to identify and analyze the existing approaches for generating optimized operative service delivery plans in the context of PSS. The objective is to gain insights into the methodologies, main characteristics, and limitations of these approaches. 175 1 3 Operative service delivery planning andscheduling in… is consistent throughout the subsequent sections. Notably, the literature exhibits considerable heterogeneity in terms of terminology and information availability. The authors do not clarify all aspects of their approach, leaving room for assumptions. The majority of publications describe product-oriented PSS business models, wherein the PSS provider is responsible for scheduling after-sales services such as maintenance or repairs. In (Petrakis etal. 2012), (Antunes etal. 2018), (Yumbe etal. 2019), and (Sala etal. 2021b), the provider is obligated to deliver services within a specific point in time or face penalties, indicating an availability-based business model. Only in the work by Ding etal. (2017), a result-oriented business model is discussed. For the remaining publications, the available information is insufficient to definitively assign a specific business model type. Regarding the classification of the operative service delivery planning and scheduling problem, significant variations can be observed across publications. Each author tends to present their own classification scheme for the underlying problem, resulting in a lack of consensus or standardized categorization. The majority of the approaches in the analyzed literature can be categorized as offline planning approaches, where schedules are generated in advance based on available information. Only Petrakis etal. (2012), Castane etal. (2019), and Yumbe etal. (2019) address the aspect of online planning, where plans are generated in real-time when a new service request is added. The objectives pursued by the various approaches are largely aligned and revolve around common themes. The primary objectives commonly observed in the analyzed publications are minimizing costs and maximizing the number of in-time service deliveries in order to achieve high customer satisfaction and avoid penalties. Additionally, some publications consider worker utilization as an additional objective to be balanced. Table 3 Main characteristics of the approaches Maturity Stage Objectives Online vs. Offline Problem ClassificationPSS-Type 2MAX (Punctuality), MIN (Costs), Even (Workload)OfflineMultidimensional and multiobjective optimization problem -Meier et Funke (2010) 5MAX (Punctuality), MIN (Costs), Even (Workload)Offline Traveling Salesman Problem with Time Windows (TSPTW) as basis -Dorka et al. (2015) 4 MIN (Transportation Costs, Deadline Penalties, Overtime Costs) Online & OfflineField Service Scheduling with Priorities (FSSP) Availability -basedPetrakis et al. (2012) 3 MIN (Mileage Costs, Deploy Costs, Invalid time Costs, Satisfaction loss Costs) OfflineMultiple Traveling Salesman Problem Product-orientedZhao et al. (2014) 3 MIN (Total Cost of Service --> Mileage Costs, Time Charge, Downtime Loss, Waiting Costs) OfflineTechnician Scheduling Problem Product-orientedLi et al. (2015) 4MIN (Total Cost of Maintenance Service Delivery)OfflineMaintenance field service delivery problem Product-orientedZhou et al. (2016) 4 MIN (Time, Costs)OfflineProduction and Installation Planning Product-orientedAlexopoulos et al. (2017) 4 MAX (Customer Satisfaction Degree, Resource Utility Efficiency), MIN (Product -Service Costs) Offline Environmental and Economic sustainability-aware resource service scheduling problem (RSSP) Result-orientedDing et al. (2017) 3MAX (Revenue)OfflineIntegrated Order Acceptance and Scheduling (OAS) Product-orientedDan et al. (2018) 3MIN (Storage Cost, TardinessCosts)Offline PSS order scheduling Problem with Time Windows (PSS-OSPTW) Product-orientedZhang et al. (2019) 4MIN (Earliness, Lateness, Penalty Costs)OfflineMobile Workforce Scheduling Problem Availability -basedAntunes et al. (2018) 4 MIN (Travel time, Idle Time Costs), MAX (Percentage of on - time Task Completion by Task Priority) OnlineField Service Problem Product-orientedCastane et al. (2019) 5 MIN(Total Labor Costs) by equalizing the work amount for each date and worker OnlineField Service Technicians Scheduling Problem Availability -basedYumbe et al. (2019) 3MIN (Total Tardiness)OfflineParallel Machine Scheduling Problem -Sala et al. (2020) 4MIN (Number of Tardy Interventions)OfflineAvailability -basedSala et al. (2021) 1MAX (Punctuality), MIN (Costs), Even (Workload)OfflineVehicle Routing Problem with Time Windows (VRPTW) -Alp et al. (2022) 3MIN (Costs, Energy, Risk Level, or Time)OfflineModified Travel Salesperson Problem Availability -basedYi et al. (2023) Literature PSS FSM Literature 176 E.Alp et al. 1 3 To assess the level of comprehensiveness of the approaches presented in each publication, a 5-stage maturity model was developed following Poeppelbuss and Roeglinger (2011). The model, visualized in Fig. 10, categorizes the approaches based on their level of development. Stage1 represents approaches that introduce descriptive and/or visual concepts of an operative planning and scheduling approach. In Stage 2, the mathematical formulations of objective functions and further formalized constraints regarding the problem are provided. Stage 3 encompasses approaches that were tested and verified using synthetic data in conceptual scenarios. Typically, publications in this stage focus on showing the general applicability of their developed algorithms and methodologies in the context of PSS operative planning and scheduling. Stage4 presents approaches that underwent validation in real-world use cases using company data and thus prove their suitability for reality. Stage5 describes approaches that are applied in real-world use cases and whose performances are compared to the existing methods and approaches in the respective use cases, e.g., manual planners. The analysis reveals that the majority of the publications present at least a verification of their approaches within conceptual scenarios. Furthermore, nine of the 17 publications evaluated their approaches using real-world data within specific use cases. In two of these publications, Dorka etal. (2015) and Yumbe etal. (2019) compared the performance of their approaches to existing methods used in the respective companies. In both cases, the proposed approaches outperformed the existing methods, highlighting their effectiveness in practical settings. 4.2.2 Considered data As discussed in Sect.2.2.3, operative service delivery planning and scheduling can draw upon data from four sources (Sala etal. 2021a). Table4 provides an overview of the publications and their consideration of data during the planning process. All publications consider data and information about the delivery processes Compared to Stage 5 EvaluatedApproach Applied on real -world use case Validated Approach Stage 4 Applied on conceptual scenarios Verified Approach Stage 3 Formalized Approach Stage 2 Conceptual Approach Stage 1 Fig. 10 Maturity model of operative planning and scheduling approaches 177 1 3 Operative service delivery planning andscheduling in… and resources. A detailed analysis of the considered attributes of delivery processes and resources can be found in the subsequent section. Only two out of the 17 publications consider data from the installed machines. Li etal. (2015) and Sala etal. (2021b) utilize machine condition data to determine the remaining lifespan before potential breakdowns, which supports scheduling the processes timely. It is noteworthy that none of the approaches incorporate additional information about the customer, such as their history or their significance to the PSS provider. Similarly, data that could be collected from the service execution processes, such as feedback or performance metrics, are not utilized in any of the analyzed approaches. 4.2.3 Delivery process andresource attributes As mentioned earlier in the foundational section, the main logic behind the approaches for operative service delivery planning and scheduling is to match delivery processes with appropriate resources. In the analyzed publications, the term “delivery process” is referred to as e.g., Customers’ requirement in (Ding et al. 2017), orders in (Dan etal. 2018), and tasks in (Yumbe etal. 2019). While the majority of publications primarily focus on planning maintenance-related delivery Table 4 Considered data Data from or about Customer Machine/ Equipment Resource Delivery Process ○○●● Meier et Funke (2010) ○○●● Dorka et al. (2015) ○○●● Petrakis et al. (2012) ○○●● Zhao et al. (2014) ○●●● Li et al. (2015) ○○●● Zhou et al. (2016) ○○●● Alexopoulos et al. (2017) ○○●● Ding et al. (2017) ○○●● Dan et al. (2018) ○○●● Zhang et al. (2019) ○○●● Antunes et al. (2018) ○○●● Castane et al. (2019) ○○●● Yumbeet al. (2019) ○○●● Sala et al. (2020) ○●●● Sala et al. (2021) ○○●● Alp et al.(2022) ○○●● Yi et al. (2023) ● = incorporated ○ = not incorporated PSS Literature FSM Literature 178 E.Alp et al. 1 3 processes, there are a few exceptions. For instance, Alexopoulos etal. (2017), Dan etal. (2018), and Zhang etal. (2019) specifically address the scheduling of installation services for technical products within a PSS, including the coordination of their preceding production. Throughout these diverse approaches, the key resource consistently considered is the human element responsible for executing the delivery processes, referred to as e.g., technicians in Meier and Funke (2010), engineers (Petrakis etal. 2012), or operators in Sala etal. (2020). An overview of the considered attributes of delivery processes and resources across the investigated publications is found in Table5. Analyzing the attributes of the delivery processes, it becomes evident that in nearly every approach, the location of a delivery process is used to calculate traveling time and costs. A special case is presented in the approach of Li etal. (2015), where it is mentioned that locations are changing dynamically. Notably, Ding et al. (2017), Dan etal. (2018), Zhang etal. (2019), and Yi etal. (2023) do not incorporate location-based information in Table 5 Considered attributes of delivery processes and resources 179 1 3 Operative service delivery planning andscheduling in… their respective approaches. Time windows, specifying allowable delivery times, are included in the majority of publications, whereby three publications solely focus on deadlines, e.g., resulting from the remaining lifetime of the equipment like in (Li etal. 2015), (Yumbe etal. 2019), or (Sala etal. 2020). In most cases, penalties are associated with delayed delivery of the processes. Skill requirements for effective process delivery are taken into account in ten out of the 17 publications and process duration is typically represented as a fixed value or calculated deterministically. Only Petrakis etal. (2012) and Castane etal. (2019) incorporate stochastic times, considering the inherent uncertainties in service delivery durations. Additionally, Petrakis etal. (2012), Zhou etal. (2016), and Castane etal. (2019) introduce priorities for delivery processes. In general, in the investigated approaches, only one technician is required to deliver the processes. However, Meier and Funke (2010), Dorka etal. (2015), and Ding etal. (2017) introduce the possibility of having the requirement of multiple technicians operating as a team to deliver certain service processes. In terms of the attributes related to resources, there are bigger differences between the approaches. The working times of technicians are taken into account in seven out of the 17 approaches, restricting their availability for allocation. Six approaches consider the option of technicians working overtime to meet increased service demands. Skills and qualifications of technicians are considered in ten approaches, while two of them introduce the aspect of familiarity. Here, familiarity refers to the experiences and nearness of technicians to individual customers and sites. Meier and Funke (2010) mention the familiarity of the technicians with the requested process as a duration-effecting factor, while Antunes etal. (2018) assume that familiarity enhances delivery quality. The authors use the aspect of familiarity as a decision criterion for technician allocation and aim to always send the same technician to the customers. The technical, allocable resources receive less consideration. Spare parts are taken into account in four approaches, tools are incorporated or mentioned in two. In cases where internal resources are insufficient, Meier and Funke (2010) and Alexopoulos etal. (2017) address the option of contracting additional service or resource suppliers in order to meet service demands effectively. It is noteworthy that throughout the analyzed publications, the problem descriptions often include detailed discussions of multiple attributes related to delivery processes and resources. However, when it comes to presenting the actual approach, authors tend to make simplifications and do not incorporate all of the mentioned attributes. To give an example, Dorka etal. (2015) explain the relevance of technician preferences regarding travel times and durations. A technician could prefer to come home daily or to stay near the customer over the weekend. The incorporation of this attribute, however, is neither shown nor mentioned in the approach description. This could be due to the inherent challenges associated with developing comprehensive solutions that encompass all important aspects. As with all models, the challenge lies in striking the balance between the complexity and feasibility of implementing practical solutions. These simplifications, while necessary, also open up opportunities for further research and the development of more sophisticated approaches that can effectively address the complexities and nuances of operative service delivery planning and scheduling. In Sect.4.2.7, the limitations and further research opportunities stated by the authors are explained. 180 E.Alp et al. 1 3 4.2.4 Solving methodologies To solve the optimization problem of matching delivery processes with appropriate resources, various methods are employed across the analyzed approaches. Table6 provides an overview of the mathematical formulations, solving methods, and software utilized in each approach. The majority of publications include a mathematical objective function to evaluate plans, with the exception of Dorka etal. (2015) and Alexopoulos etal. (2017) who mention its use without explicitly providing a function. Besides, Castane etal. (2019) utilize a simulation model for plan evaluation. Alp etal. (2022) do not introduce an objective function in their approach, as they are in the early stages of development. Furthermore, most approaches with an objective function also include mathematically formalized constraints that ensure the feasibility of the generated plans. The solving method in each approach resembles the cores for operative service delivery planning and scheduling. Analyzing the 17 publications, it becomes evident that the majority of the approaches employ (meta-) heuristic optimization methods to generate and optimize plans. Modified versions of the Genetic Algorithm and Table 6 Solving methods 181 1 3 Operative service delivery planning andscheduling in… Simulated Annealing are particularly common. Meier and Funke (2010), for example, use a sequential combination of Evolutionary Algorithms, Simulated Annealing, and Brute Force Search methods to generate optimized schedules by leveraging the variance options (see Sect.2.2.3) in service delivery. Petrakis etal. (2012) and Zhang etal. (2019) compare the results and performance of different (meta-)heuristics. Some authors introduce their own heuristics specific to their approach, such as Dan etal. (2018) or Yumbe etal. (2019). Besides the heuristic approaches, mathematical programming (Sala etal. 2021b) and quantum annealing (Yi etal. 2023) are also used. Although few authors indicate the software used in their approach, there is evidence of increased use of the Cplex software in the analyzed literature. 4.2.5 Functionalities Subsequent to the analysis of solution methods, the individual mechanisms incorporated in each approach are examined in this subsection, as summarized in Table7. While most approaches assume that technicians do not have preassigned tasks or appointments, Alexopoulos et al. (2017), Antunes et al. (2018), and Sala et al. (2021b) deviate from this by planning with technicians who already have a partly Table 7 Functionalities 182 E.Alp et al. 1 3 filled schedule with appointments, such as delivery processes, training, or holidays. In four other approaches, preassigned delivery processes are partly considered, as these approaches conduct rescheduling when unexpected tasks occur or assigned tasks cannot be executed as planned. Some approaches make skill requirement matching a prerequisite for allocation. In (Petrakis etal. 2012; Dorka etal. 2015; Sala etal. 2021b), technicians must have the necessary skills in order to be assigned to the respective delivery process. In other approaches, such as in (Castane etal. 2019; Li etal. 2015; Sala etal. 2020), all technicians have the skills to deliver all tasks; however, the duration of the delivery process is adjusted based on their skills, and qualifications. Regarding delivery process durations, Dorka etal. (2015) stand out since their approach incorporates longer-duration delivery processes, e.g., 16h, by distributing the process across consecutive days. In terms of decision-making flexibility, three approaches stand out. Meier and Funke (2010), Sala etal. (2021b), and Yi etal. (2023) provide the PSS provider with the ability to choose alternative delivery processes. For instance, Meier and Funke (2010) explore the effects of replacements instead of repairs on the overall plan, while Sala etal. (2021b) consider options such as remote support or sending spare parts instead of deploying a technician. Another particularity of the approach presented by Meier and Funke (2010) is the inclusion of planning with different vehicles, such as cars or trains, which allows for potential cost reduction or time-saving measures in service delivery. Table 8 Application scenarios 183 1 3 Operative service delivery planning andscheduling in… 4.2.6 Application scenarios This subsection focuses on the verifications, validations, and evaluations conducted in the analyzed publications, as summarized in Table8. The table gives an overview of the main characteristics related to the application scenarios. Among the 17 approaches, 15 present their application scenarios, with ten utilizing conceptual use cases and eight employing real-world use cases for validation. Notably, Dorka etal. (2015) and Yumbe etal. (2019) conducted comparisons with existing methods in their respective companies. With their approach, Dorka etal. (2015) generated a plan, that demonstrated approximately 35% less technician utilization and 30% less travel times compared to the plan generated by the operative planner of the company. However, it is worth noting that these results were obtained after nearly two days of computing time. Similarly, Yumbe etal. (2019) compared their approach to conventional planning methods used in a Japanese IT company. They were able to reduce the number of required technicians by 25% and travel distance by approximately 22% when generating an initial plan. Furthermore, with dynamic rescheduling, their approach achieved a reduction of approximately 13% in required technicians and approximately 21% in travel distance, while also significantly reducing the number of tardy tasks. Notably, their approach delivered the best results in less than 10s. The analyzed approaches were primarily applied in the field of the mechanical engineering industry. The use cases varied significantly in scale and complexity. For example, Sala etal. (2021b) considered scenarios with a range of seven, ten, or twelve delivery processes to be scheduled with five technicians in a single depot setting. On the other hand, Petrakis etal. (2012) employed a larger use case involving 177 technicians, with each technician having four to five delivery processes assigned to them. This use case was designed based on real-world data and implemented in a multi-depo scenario. Yi etal. (2023) test their approach on large-scale problems with 1281 alternative service processes. It is worth noting that the majority of the approaches utilized single depot setting as the standard scenario, while only a few explored multi-depot scenarios. 5 Discussion The literature search conducted in this study yielded a total of 17 publications relevant to the topic of operative service delivery planning and scheduling in the context of PSS. Through analysis of these publications, several key findings emerged. Firstly, a limited correlation was observed across different author groups. Several author groups published their approaches in several stages and presented adjustments, modifications, or evaluations of the contents in the previous publication. Thus, eleven distinguishable clusters across the 17 publications could be identified. Most of the approaches were still in the early stages of development, lacking realworld testing and comparison with existing methods employed by companies. Furthermore, the majority of the approaches were offline in nature, working with static data despite the dynamic nature of service delivery. There is a lack of a decentralized 184 E.Alp et al. 1 3 view of the problem as proposed by Avraham etal. (2017). Data utilization in the approaches also showed room for improvement. Machine data, despite its potential in prognostics and condition monitoring (Teixeira etal. 2013), was rarely considered but could help schedule necessary services more efficiently. Customer or process execution data, which could provide valuable insights for decision-making, were not incorporated into any of the approaches. None of the approaches encompassed all analyzed attributes, leading to an overly simplified consideration of the problem. Additionally, most approaches assumed deterministic travel and service durations, which may not reflect the uncertainties present in practices. In general, service delivery is seen as a centralized problem, in which the OEM oversees the execution of all service processes. Regarding solving methodologies, heuristic and metaheuristic algorithms were commonly employed. However, the lack of benchmark instances prevented meaningful performance comparisons among these algorithms. Although some approaches were applied to different use cases and demonstrated superior performance compared to existing methods, none of the authors conducted comparisons between the generated plans and their actual execution, or calculated costs and actual expenses. This highlights a need for further evaluation and validation of the approaches in real-world settings. 6 Conclusion andresearch agenda Operative service delivery planning and scheduling in the context of PSS is not merely an academic problem. It holds practical significance for business, as effective service delivery is essential for customer satisfaction and operational success. To address the challenges and complexities inherent in service delivery, it is imperative to develop suitable approaches. Thus, a comprehensive research agenda has been synthesized to guide future endeavors. Research in the following seven areas could lead to the development of improved decision-support systems within the context of PSS, thereby facilitating optimized service delivery planning and scheduling and ultimately enhancing the overall performance and success of PSS business models. 6.1 Realistic attributes andconstraints To gain a more comprehensive and realistic understanding of the factors influencing decision-making in operative serviced planning and scheduling, further research could involve conducting field studies and engaging with industry practitioners. Case studies and close collaboration with companies can provide valuable insights into the attributes that are considered in real-world scenarios. Additionally, taking a human-centric approach, it would be beneficial to interview besides the dispatcher also technicians to gather their perspectives and input, which can contribute to the development of more effective and practical approaches. 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