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# Designing Value-Robust Product-Service Systems by Incorporating Changeability: A Reference Framework

Raj Jiten Machchhar, Alessandro Bertoni

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Raj Jiten Machchhar, Alessandro Bertoni. Designing Value-Robust Product-Service Systems by Incorporating Changeability: A Reference Framework. 23th Working Conference on Virtual Enterprises (PRO-VE), Sep 2022, Lisbon, Portugal. pp.623-630, 10.1007/978-3-031-14844-6_50 . hal-04642022

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# Designing Value-robust Product-Service Systems by Incorporating Changeability: A Reference Framework

Raj Jiten Machchhar* and Alessandro Bertoni

Department of Mechanical Engineering, Blekinge Institute of Technology,
37179 Karlskrona, Sweden
{raj.jiten.machchhar,alessandro.bertoni}@bth.se

**Abstract.** When a Product-Service System (PSS) has a longer lifecycle, it is subjected to several internal and external changes along its path that may deteriorate its value. A PSS capable of delivering value despite the circumstances is called a value robust PSS, and one way of achieving value robustness is by incorporating changeability. A lot of uncertainties prevail in decision-making concerning changeability as change is a phenomenon not clearly understood in PSS design. To bridge this gap, this paper presents a reference framework to support the definition and quantification of changeability in PSS design to enable the delivery of a value robust PSS. The paper is mainly focused mainly on the realization of the concept of changeability in PSS design, crossing the boundary between rich literature in the field of PSS and Systems Engineering.

**Keywords:** Product-Service Systems, Uncertainty, Risk, Changeability, Value robustness, Value-driven design.

## 1 Introduction

It is nowadays widely acknowledged that manufacturing industries are transitioning to a servitized business model, where the perspective changes from a pure product-based offering to a Product-Service Systems offering (PSS). These systems have the potential to generate consistent revenue over time and foster a stronger customer relationship [1]. Such a business model accentuates the need to capture, measure, and enhance value throughout the lifecycle as the value expectation must be measured at various instances along time as against one-time during delivery [2]. Thus, inculcating capabilities for value sustainment of the PSS along the lifecycle becomes one of the key decision-making aspects of PSS design [3]. Along similar lines, Systems engineering (SE) literature stresses that complex systems with a longer lifecycle go through a lot of internal and external changes along the path, namely, change in the requirements, change in the system itself, and change in the context [4, 5]. In such a case, a system capable of achieving a higher value despite the circumstances is called a value robust system [5]. One way of achieving value robustness is by developing a “changeable” system that can be suitably modified to counter value deteriorating events [5, 6]. A lot of uncertainty exists in decision-making during the design phase as the internal and external changes that may arise during the lifecycle of the system
are not clearly understood [7]. This viewpoint is shared in PSS literature, where uncer-
tainties are seen as one of the major barriers to forecasting a phenomenon [8].

The theme of changeable PSS and how to deal with the multiple uncertainties, al-
ready since the beginning of the engineering design of PSS, is still poorly discussed in
the literature. To bridge this knowledge gap, the paper aims to provide a comprehen-
sive outlook on various levels of complexities, and thus uncertainties, framing the
theoretical discussion into a reference framework highlighting what are the aspects
and drivers that shall be considered in the efforts of quantifying the changeability of a
PSS to support the development of value robust PSS. To achieve such an aim, the
paper presents the results of a research effort based on the combination of literature
review in the field of PSS and SE combined with the analysis of qualitative empirical
data mainly collected in collaboration with company partners, following a diagnosis,
invention, and reflective learning cycle (as elaborated by [9]), that has finally led to
the formulation of the proposed reference framework for the designing value-robust
PSS by incorporating changeability.

2 Product-Service System Value and Changeability

The value-driven paradigm can provide methodological guidance for making PSS
design decisions [10]. Thus, value and its assessment have a vital role in the PSS de-
velopment process, although a consistent understanding of what value means is miss-
ing [11]. For the scope of this paper, the definition adopted is the one proposed by
[12], where value is measured in terms of benefits for multiple concerning stakehold-
ers under the applicable constraints such as cost. [11] argue that a distinction is neces-
sary to clarify the roles of each actor in the value creation process. Such a perspective
remains beyond the scope of this paper. In the realm of PSS, the perspective of value
shall abandon the one-time delivery perspective typical of traditional products, to
embrace the through-life creation perspective [13]. As highlighted by [2], several
value creation opportunities exist in the operational stage of the PSS by accordant
support from the provider in the form of maintenance, exchange services, updates,
etc. Assuming that the purpose of any system is to maximize the value for the associ-
ated stakeholders, these systems need to withstand the adverse effects of all the inter-
nal and external changes that might occur along their lifecycle. To ensure sustained
value delivery, "functional" and "non-functional" requirements are often defined
during the design of a system, where functional requirements govern "what" the sys-
tem must achieve and non-functional requirements govern "how" the system should
achieve the functional requirements [14]. The non-functional requirements are usually
the properties of the system that determine how successful the system will be in de-
livering the intended value, and are typically bundled together as "ilities" [5].

One such "ility" is changeability, which is a property of a system that enables the
system to reach various conceivable states under the implied constraints, allowing the
system to meet the value expectation during the operational stage [5]. Some research-
ers propose changeability as an over-arching property, comprising many change-
related properties such as adaptability, flexibility, scalability, etc., however, at a fun-
damental level, changeability is the ability of the system to either change the “form”
or “function”, thus bring about a change in its operation as a response change in the
requirement, system or context [5]. Concerning PSS, many researchers have acknowl-
edged that a PSS comprises three elements: 1) product, 2) service and 3) infrastruc-
ture [15, 16]. In this respect, changeability is not limited to the tangible counterpart,
but rather to the entire domain of components within each PSS element, respectively.
Such a perspective is also acknowledged in PSS, where changeability is attributed to
the system’s ability to respond to unexpected perturbations cumulatively [3].

3 The Need for Quantifying Changeability in PSS Design

Changeability is seen as a potential approach for achieving value robustness, and thus,
there is an evident need to develop more changeable PSS solutions. However, to what
extent must the PSS be changeable is a difficult question as changeability comes at an
additional penalty in the form of investment cost, change cost, and change time [5, 6].
Thus, from an engineering design perspective, the need is to rather quantify changea-
bility that can enable the tradeoffs between changeability and cost for value robust-
ness. Some of the known issues of changeability quantification and perspectives on
the implication of those on PSS are as follows. To quantify change, one must quantify
system states, and the issue concerning complex systems is that discretely quantifying
the states is a challenging task [6]. This challenge aggravates as a PSS is an amalgam
of products, services, and infrastructure, and all three elements are acknowledged to
be changeable concerning the form and function. The next issue is particularly regard-
ing the function or operation of the PSS. A complex system is subjected to a higher
degree of unbounded control variables [4]. This may require an educated guess
bounding the conceivable states of the PSS for confining the design space. The final
issue is concerning digitalization, where changes in the form of software and digital
services are relatively easier and quicker compared to traditional hardware changes,
[16]. Thus, design decisions must now consider the impact of small and incremental
changes in the expected value of the PSS. To summarize, concerning PSS, the prob-
lem first transcends to comprehending what are the possibilities of change within each
PSS element and then finding the right balance of change to maximize value dynami-
cally. Thus, the tradeoffs become more complex and multi-dimensional. Towards this
end, while researchers have argued the need for a better support system for defining
and managing uncertain variables [17], changeability quantification is not widely
exploited for value robustness in PSS. If the changeability of a PSS plays a relevant
role to assure its value robustness along the lifecycle, then it emerges as an intrinsic
requirement to be able to define and quantify such changeability since the early stage
of PSS design.

As per [18], to rationalize a changeable PSS, one must understand the complexities
that are a potential source of uncertainties in the PSS lifecycle. In such a case, five
essential aspects for engineering a complex system were proposed by [19], namely,
“structural”, “behavioral”, “contextual”, “temporal”, and “perceptual”. Referred to as complexity aspects in this work, they can be described as follows. Structural complexity relates to the physical manifestation of the system components and their relationships. Behavioral complexity relates to the operation of the system to deliver the intended value. Contextual complexity relates to the external factors that can influence the behavioral aspects of the system. Temporal complexity relates to understanding the impact of contextual factors as a function of time. And, perceptual complexity relates to the value perceived by each stakeholder along with their cognitive biases. [20] state that these aspects of complexity are directly associated with a certain level of uncertainty, arguing that from structural to perceptual, the uncertainty grows exponentially. As the uncertainty in decision-making increases, so does the risk, emphasizing the need to build systems capable of withstanding the associated risks [18].

However, the dynamics of the PSS in a changing context are often difficult to capture, rendering sub-optimal solutions driven by the challenges is incorporating uncertainty in the requirement definition for PSS design [17]. Additionally, literature shows how the PSS value proposition hinges on through-life value creation for both the provider and the customer [2, 12]. Thus, a time perspective is inherently entangled with PSS, yet temporal aspects seem to be largely neglected in the early PSS design. Some approaches to estimating the cost implications of changes in product and service configurations have been proposed (e.g. [21]). However, a systematic evaluation of how time-dependent changes in the context can lead to value deterioration of the PSS is still missing. Additionally incorporating contextual awareness is being largely discussed for PSS from an operational perspective, but a majority of demonstrative adaptive changes in the PSS are focused on digital services [22]. Uncertainty is ubiquitous in the engineering design of such solutions from a time perspective, and a reliable decision comes by the consideration of system-context interactions to be stochastic and not deterministic. As [8] argues, completely removing the uncertainties can lead to higher risk as various possibilities were not thoroughly evaluated. However, if the uncertainties are balanced, risks can be negotiated to be within acceptable limits.

# 4 A Reference Framework for Changeability Quantification in PSS Design

To achieve a viable solution to these uncertainties, this paper outlines a reference framework for changeability quantification in PSS design (refer to Fig. 1). The framework positions the five complexity aspects (structural, behavioral, contextual, temporal, and perceptual) from a PSS perspective, where each aspect imposes inherent uncertainties in decision-making. It is assumed that perceptual uncertainty does not have an independent stance, but rather is a function of all the previously accumulated uncertainties. To address perceptual uncertainty, all the former uncertainties need to be addressed systematically. In a broad sense, a system is subjected to three types of changes [4, 5, 7]: 1) change in the requirement, where stakeholders' expectation of the system changes with time. For example, the system is subjected to capabil-
ity change, inclusion in SoS, etc. 2) change in the system with time, meaning a system experiences capability degradation, physical damage, or similar value deterioration avenues. 3) change in the context, where the externalities change with time such as the price of fuel, the operational environment, the users' preferences, the cost of electricity, technological advancement, etc. These are regarded as change drivers in Fig. 1 as they enforce the PSS to change for resisting value deterioration.

Also, Fig. 1 details three essential facets for a system to change [5]: 1) the change “agent”, 2) the change “mechanism”, and 3) the change “effect”. The agent is the enabler of a change in the system. An emphasis on external enablers makes the system flexible while an emphasis on internal enablers makes the system adaptable. However, both cater to making the system more changeable. The mechanism is the path that the system takes to reach the desired state under the applicable constraints. These paths have been indicated by dashed lines in Fig. 1. The effect is the change in the initial and final state of the system. The effect can be a change in the form, function, or both of a system and it is indicated by fluctuating blue and yellow bars in Fig. 1, respectively. The accumulation of underlying structural, behavioral, contextual, and temporal uncertainties leads to a risk that the PSS might fail in achieving the desired value. Thus, the perceived value is subjected to an ever-expanding variation concerning time due to the cumulative effect of uncertainties (grey triangle in Fig. 1) **Error! Reference source not found.**

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** A reference framework for quantifying changeability in PSS design</figcaption></figure>

To operationalize the framework and provide an example of application, Fig. 1 depicts the value of the PSS along with the operational phase, and some crucial states of the PSS that depict the value of the PSS as a function of time have been indicated numerically. State 1 indicates the original value of the PSS, i.e., the expected value of the PSS at the commencement of operation as agreed upon by various stakeholders.
The PSS will continue delivering the desired value if no change drivers are acting as shown from states 1 to 2. Essentially, these are the same states of the PSS because no change has been incurred, although depicted via different numbers for illustrative purposes. The dashed black line shows the path taken by the changeable PSS. At state 2, it is assumed that one of the change drivers is acting that deteriorates the value of the PSS. For example, the PSS may have encountered a change in context enforcing a change in at least one of the PSS elements to recover part of its value. The dashed red line shows the path taken by the non-changeable PSS to reach a hypothetical new state 3'. But since the PSS is changeable, it can be suitably modified to achieve the best possible value in the circumstances. Thus, state 3 represented by the changeable PSS has a value in this case and the arrow connecting the changeable PSS state and the non-changeable PSS state is the value appreciated. A PSS may change its state regardless of a change in any of the PSS elements, and thus there may be a change in its value. For example, the value deterioration from states 3 to 4 can be due to system degradation. Here, the change driver is the system itself, but none of the elements of the PSS have changed. A value recovery may happen via actions such as maintenance, replacement, etc. Notably, the y-axis is not a utility function but rather a value function that is adopted as a ratio of benefits and costs. Thus, there could be instances where the value of the non-changeable PSS is higher than the changeable PSS. As an example, consider the requirement of taking scenery photographs as well as portraits. Artificial intelligence and software advancements have enabled smartphone cameras to generate reasonable bokeh effects on photographs. A digital camera capable of swapping lenses may not be a viable option from an economic stance for many customers. State 5 indicates that the value of the changeable PSS is rather lower in this case. The arrow connecting state 5 and state 5' is the value depreciated. As the PSS operates, these cycles of value appreciation and depreciation are repeated along its path as shown in Fig. 1. Also, while the transition from one state to another is depicted to be linear, these transitions can be instantaneous or highly non-linear as well. Value robustness is then the minimization of the variance of value as a function of time, where variance is the measure of the dispersion of value from the expectation.

# 5 Conclusion and Delimitations

The paper has presented a reference framework to support the definition and quantification of changeability in PSS design to enable the delivery of value robust PSS. The contribution of the paper is focused mainly on the realization of the concept of changeability in PSS design, crossing the boundary between rich literature in the field of PSS and SE. Additionally, it shall be regarded as an effort toward nurturing the academic discussion about changeability quantification in PSS design. Such work is part of a broader research initiative aiming to develop a simulation-based support framework that will enable decision-making about value robustness for a PSS. Ultimately the goal is to mitigate the risks rather than eliminate the uncertainties when designing PSS [8]. One way to mitigate the risks is to shorten the period of the opera-
tional phase [23]. But complex systems are typically associated with a longer lifecy-
cle, leading to a higher probability of encountering unfavorable circumstances. In
such a case, a changeable PSS is proposed as a measure for countering these circum-
stances, thus achieving value robustness. PSS elements consist of the product, service,
and infrastructure. In this light, changeability is implied to the entire domain of PSS
components which makes changeability quantification a crucial aspect of PSS design.

As a delimitation, it must be said that a typical lifecycle of a PSS consists of vari-
ous stages, right from design and manufacturing to the retirement of the system.
Along this path, there are several opportunities for value creation that inherit many
challenges from economic, environmental, and social stances [22]. To delimit the area
of contribution, this paper primarily focused on the operational stage of the PSS.
Based on [15], the operational stage of a PSS is proposed to comprise the following
three stages: 1) the "use" and "reuse" stage where the PSS is operational to achieve
the desired value, 2) the "maintenance" of the PSS, consisting of all activities to en-
sure that the desired level of value is nurtured, and 3) "reconfiguration" of the PSS,
consisting of all activities to ensure value sustainment of the PSS within the opera-
tional stage. This viewpoint enables the authors to exclude change-related ilities out-
side the operational stage and delimit the scope for this paper. For example, reproduc-
ibility in the form of "remanufacturing" or "recycling" [22] can be an ability of a
system to enhance the value in the next cycle but falls beyond the scope of this paper.

**Acknowledgments.** The work was performed in the frame of the Strategic Innovation Program Swedish Mining Innovation, concurrently funded by the Swedish Innovation Agency (VINNOVA), the Swedish Research Council for Sustainable Development, (FORMAS) & the Swedish Energy Agency (Energimyndigheten). The research also received financial support from the Swedish Knowledge & Competence Development Foundation through the MD3S research profile at Blekinge Institute of Technology.

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