Digital Value Dependency Framework for Digital Transformation

Suthamathy Sathananthan

, Dennis Gamrad

and Johanna Myrzik

Digital Solutions & Operations, Evonik Technology & Infrastructure GmbH, Essen, Germany

Institute for Automation Technology, University Bremen, Bremen, Germany

Keywords: Digital Transformation, Business Value, Dependency, Digitalization, Benefits, Business Model, Framework,

Value Network.

Abstract: Knowing there is considerable value in digitalization, enterprises have started to transform their operations

utilizing digital technologies. However, current methods used in estimating benefits are methods typically

used in capital budgeting projects which do not consider the value interdependencies or uncertainties from

digitalization into account. Therefore, a standardized yet comprehensive framework and a mathematical

model have been developed to estimate and measure potential from digitalization. The framework and model

together were applied in an industrial digital project where results show overall value of the project based on

economic and qualitatively measured impacts, and value contribution of transformational elements such as

technologies and organizational changes. The results have been used to form value networks which

demonstrate shared values between multiple digital projects with respect to digital capabilities. These results

bring transparency across projects for informed decision making and support in data-driven business model

innovation.

1 INTRODUCTION

According to World Economic Forum’s 2016 report

on digital transformation across industries, upward of

$100 Trillion potential value was estimated for

industries and society combined for the next ten years

(World Economic Forum, 2016). Such large

potentials are believed on a very abstract level to be

conceivable, but many industries have not tapped into

the full potential yet. Due to the uncertainties that

come with fast changing technologies, industries,

especially non-digital industries, which earn majority

of their profit through selling non-digital products or

services, are very slow in actively adapting to these

changes.

Although many companies understand that they

must move quickly to stay in the market and be

competitive, the volatile nature of digital technology

landscape makes it hard for them to decide if certain

technologies can bring the potential they promise to

deliver. For example, Industrial-Internet-of-Things

(IIoT) platforms play an important role in

automatization of production plants and in enabling

flexible digitalization across businesses. In order to

adapt to such platforms, brown-field plants must

change their system landscape completely which

requires a lot of commitment and trust financially.

However, the value potential from such platforms are

not completely transparent so are the costs and risks

associated with it. Therefore, only small-scale

projects are being done to test these technologies so

that the financial commitment is also low.

In order to estimate value from digital projects,

many companies rely on market research reports

which can be very different from industry to industry

therefore susceptible to over or under estimation. The

lack of common and concrete methodology to

estimate potentials makes it even harder for

companies to be agile.

The value from digital products or solutions are

attained through a complex network of value

interdependencies, and these networks must be

described in a formal way to be able to understand

potentials from different projects. Therefore, value

estimation in the digital era should look beyond single

project and give the possibility to see value networks

among different projects such that benefits and costs

sharing can be planned and managed. There exists no

standard methodology to assist business developers

and project managers to formalize particularly

potentials from digitalization in a comprehensive

manner.

Sathananthan, S., Gamrad, D. and Myrzik, J.

Digital Value Dependency Framework for Digital Transformation.

DOI: 10.5220/0009316106430655

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 2, pages 643-655

ISBN: 978-989-758-423-7

643

As described by Okhrimenko, 2019, digitalization

is a complex process where scientific approaches in

terms generalization and systemization are needed to

simplify and understand the process in many

industries and public sectors. As part of this study a

standardized digital value dependency framework has

been developed based on proven methodologies such

as benefit dependency mapping and empirical

findings of digitalization projects where digital

capabilities and organizational changes were

generalized. The framework and the model presented

allow the estimation of benefits from digital projects

in a systematic way and the network results that are

digitally stored therefore giving the possibility to

compare and understand value networks between

different digital projects.

In the following sections, background on existing

methods has been elaborated. Then the redefined

BDN for digitalization called, Digital Value

Dependency framework is introduced where each

column in the framework is described in detail, then

the procedure for using the framework is explained

which is followed by the introduction to mathematical

model. Finally, practical usage of the framework in

industrial setting has been demonstrated with an

example.

2 BACKGROUND

Benefit management has a strategic importance for

companies where the ability to create above-normal

value can lead organizations to sustainable

competitive advantage (Gomes &

Romão, 2014).

Systematically planning and managing benefits

especially from new technologies can be a very

demanding task. In order to estimate benefits in

general, organizations use different methods starting

from referring to market research reports to using

tools or techniques. One such tool is Benefit

Dependency Network (BDN) that was first

introduced in 1996 by Ward et al. in order to bring

business objectives, benefits and Information System

(IS)/Information Technology (IT) changes to realize

those benefits together (Ward et al., 1996). Although

there has many adaptation since, the most used BDN

has five steps: IS/IT enablers, enabling changes,

business changes, business benefits and investment

objectives (Ward & Daniel, 2006).

The framework is completed either from right to

left, starting from investment objectives to IS/IT

enablers or from left to right (

Chaves & Pedron, 2015).

The most important aspect of this tool is that it brings

enablers and changes together which guide in

estimating the benefits from those changes. For

example, the organization will operate the same way

when there are no changes and no additional benefits

could be realized, but when changes are introduced or

identified then estimating benefits from those

changes are unpretentious. While utilizing this

mapping tool, project owners brainstorm enablers and

changes specific to their project and put them as the

elements of the steps.

Although this activity supports the planning of

individual projects, it lacks in two ways to make use

of it in digital transformational projects. One is that

every user defines their own enablers, changes and

benefits within the tool to understand their potential

outcomes, therefore results of the mappings from

many projects cannot be compared for strategic

planning of digital transformation because each

mapping’s entries will be completely different from

the other. Second is that although the steps help

logically identify benefits, the network outcome itself

are not stored or measured for better utilization of the

results. In digital transformation however, digital

projects show value interdependencies where benefits

and costs are shared among digital projects and/or

users of the end solution. Also, certain technologies,

capabilities or organizational changes are more

important than others in realizing the value, but

currently there are no measurement system integrated

into the BDN to understand and assess value

contribution of these elements of a resulting network.

Being able to measure how much value each

technology, capability or organizational change

contribute to the projects can help in prioritizing and

managing them effectively. In order to easily

compare, the dependency framework used in digital

projects should have some consistency in its entries,

and the resulting networks should have a

measurement system and model so that the network

elements can be measured and analysed.

Therefore, in this work these two points are

addressed by redefining benefit dependency network

suitable for digital transformation projects through

generalizing elements in the framework that are

specific for digitalization, and a web-tool and a

mathematical model have been developed to store the

estimated dependencies and then the value

contributions of elements are analysed through the

model. This framework not only helps individual

projects to estimate benefits logically, but it also helps

in strategic planning of digital transformation where

resulting value networks from multiple projects can

be compared and analysed.

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3 DIGITAL VALUE

DEPENDENCY FRAMEWORK

The Digital Value Dependency (DigVD) framework

follows traditional BDN and has five steps, starting

from digital technologies, digital capabilities,

organizational changes, specific positive and negative

impacts to high-level business goals/values. The

entries or rows under each of these steps are called

elements. As depicted from the screenshot of the web-

tool in Figure 1, the elements of digital capabilities,

organizational changes and business values are

generalized and fixed, and this as described earlier the

main difference from traditional BDN to DigVD

framework. Elements of digital technologies and

specific impacts are defined as per the project during

the value estimation process.

The DigVD framework is built as a digital web-

tool and every element is given a unique ID. In order

to standardize these elements, digitalization-based

taxonomy findings, and empirical findings from

multiple digital projects in house at the chemical

company were considered. These were then evaluated

with case-studies from other industries. The elements

considered within each step has been described in

detail in the following subsections.

3.1 Digital Technologies

Digital technologies are defined as technologies that

enable digitalization for the intended group of users.

As defined in many papers and reports of recent

times, smart sensors and edge devices, protocols,

network, human-machine interface technologies such

as AR/VR, cloud and platform-based technologies,

big data solutions, machine learning and artificial

intelligence are all considered important technologies

for digitalization (Lee et al., 2015,

Probst, 2018).

Those technologies that are purchased, established

and/or considered necessary to successfully enable

the digital project are the elements in this first step.

In addition to digital technologies in the market,

any internal digital project which becomes a required

or enabling technology for another digital project can

be entered. Furthermore, protocols or connections to

existing IT systems and specific Application

Programming Interfaces (API) are also considered

within this column as enabling technologies along with

existing data sources required to enable the project. For

example, specific API’s to Enterprise Resource

Management systems (ERP) or Process

Instrumentation Measurement Systems (PIMS) which

already contain the necessary data for additional digital

projects/products are considered. This column is kept

open for the framework user, therefore the user can

either enter new technologies or chose suggested

technologies from a drop-down list which shows

technologies entered by previous users of the

framework. Each technology is assigned a unique ID

with DT (Digital Technology) as its prefix.

3.2 Digital Capabilities

The abilities of a digital product user or of the

company that are improved or developed using digital

technologies are defined as digital capabilities.

Elements under this second step are fixed on the

framework. Many studies have come up with

Figure 1: Screenshot of the web-tool for Digital Value Dependency Framework.

Digital Value Dependency Framework for Digital Transformation

645

taxonomies or classifications of digital capabilities

based on current and commonly considered digital

technologies (

Freitas Junior et al., 2016; Lenka et al.,

2017; Cenamor et al., 2017; Rizk et al., 2018). These

classifications from various findings were

consolidated, and taxonomy-based research process

was applied to classify them into appropriate

categories and verified with their fit to existing

projects.

According to this analysis and as illustrated on

Figure 1, Sensing & capturing, Connectivity of data

& information, Data contextualization & analytics,

Information sharing & collaboration, Visualization &

decisiveness, and Monitoring & control are

capabilities distinctively defined as abilities

developed or improved with current digital

technologies. ‘Sensing & capturing’ is the ability to

sense and capture the digital information where

technologies like sensors and edge devices are used

in realizing this capability. Secondly, ‘connectivity’

is defined as the ability to connect various data and

information where the source of the data, interfaces,

platforms, and cloud technologies are all considered

in enabling this capability. ‘Data contextualization &

analytics’ is defined as the capability to make sense

of the connected data and information by structuring

and analysing them, with the use of digital

technologies such as data engineering tools, data

modelling, databases, basic analytics and big data

analytics.

The ‘Information sharing & collaboration’ is the

ability to share information enabling the ability to

work together using technologies such as mail clients,

notifications, social media or other network related

technologies specifically established for the product

in consideration. Next is ‘Visualization & decision-

making support’ and this capability allows the digital

product user to visualize the data, information and

knowledge with the use of dashboard technologies

and events-based suggestions and alerts are integrated

where decision making ability of the user has been

made easy. Finally, the ‘Monitoring & control’

capability is defined as the ability to monitor and

directly control the outcome through the product itself

and alter the outcome, where the control function can

be either fully automatically or semi-automatically

embedded into product. These predefined elements

on the digital capability step are given unique ID’s

with DC as their prefixes.

3.3 Organizational Changes

Organizational changes are listed on the third step,

where elements are fixed and categorized based on

empirical findings from in house digital projects,

which were categorized based on taxonomy research

methods and verified with findings in other studies

(

Mikusz, 2014; Cenamor et al., 2017; Gerber et al.,

2017;

Westermann & Dumitrescu, 2018). What changes

will be influenced by the capabilities of the product

to the organization on the frontend and on the

backend during and after the implementation are

defined as the elements as shown in

Figure 1.

From these findings, it was evident that processes

or workflows in an organization are changed

primarily by digitalization in two ways. Firstly,

certain processes are changed due to user’s direct

interaction with data on the physical level using

digital devices. Within this interaction-based changes

there are three elements. 1. Changes in terms of

consuming data and information, which means the

user can now consume data from different sources

easily therefore the current frontend process has to be

changed. 2. Changes with regards to feeding in raw

data and information. This means, user is now able to

input data directly, for example instead of writing on

a paper the user inputs the data into a digitally

readable format which changes the current workflow.

3. Changes related to completing a workflow that

does not have to do with consuming or feeding in

data, but conducting the overall process with the use

of digital devices. This means, with a digital

tool/application extended work processes are

changed and/or simplified.

Secondly, within the process related changes, the

new knowledge and insights that are derived from

raw data change the existing processes, where those

insights from data help the user on an intellectual

level. The elements within this category are separated

by the type of insights possible with data. As

mentioned in number of taxonomy-based studies,

there are three types of insights possible and they are

descriptive, predictive and prescriptive (

Rizk et al.,

2018)

. Descriptive insights are related to historical

knowledge and reveals to the user what has happened

in the past, changing the workflow to accommodate

this new knowledge. Predictive insights are

knowledge related to what will happen in the future,

which can also change the existing process to

accommodate the new predictive knowledge. Lastly,

the prescriptive insights are knowledge about what

should be done about something where it can also

change the ways the current workflow is completed.

When these types of insights are given to the user the

current front-end processes must account for some

changes and therefore considered within the frontend

process related changes.

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The second part of organizational changes are the

backend architectural changes within its current

infrastructure and system level which act as the

backbone for frontend processes to take place. Under

this category, three levels are identified. Network

based changes, hardware-based changes and finally

software-based changes. Within network-based

changes, any changes in firewalls, proxy, and WLAN

are considered together as one element. Under the

hardware-based changes category, it has been split

into three parts based on data gathering, data handling

and data storing hardware. Changes in data gathering

hardware components would be sensors and

actuators, changes in data handling devices would be

mobile devices, and changes in data storing devices

would be server related hardware components.

Within the software-based changes category, there

are three elements, which are grouped based on the

level of coding/programming would be required.

First, changes with respect to software-as-a-service

(SAAS) components or applications, platforms and

operating system are grouped into one element as

they all belong to system related software. Second

element is software interfaces or protocols which are

required to connect other data sources or systems.

Third element is changes relevant to software within

data storing servers. These fixed elements of the

organizational changes step are given unique ID’s

starting with OC as their prefixes.

3.4 Specific Impacts

The fourth step is to identify specific impacts or

effects from the project due to the organizational

changes identified in previous step. These impacts are

estimated for the company through the product user

perspective that is being considered. Impacts in this

case are defined as any influence on a business’s

values. These impacts could be positively or

negatively influencing the business values.

Therefore, this step is separated into positive impacts

and negative impacts. Within positive impacts all

financial or economically measurable benefits as well

as soft or qualitative benefits and opportunities which

are not measurable in economic terms are considered.

Within the negative impacts, all financial or

economically measurable costs and soft or qualitative

costs and risks that are not economically defined are

considered. This step is kept open therefore the

framework user only enters specific benefits and costs

of the digital product being evaluated. Every element

in the impact step is also stored by unique IDs, with

prefixes SI1 and SI2 for specific benefits and costs

respectively.

3.5 Business Goals/Values

Business Goals/Values are defined as the high-level

value categories that are important for the business.

Based on the various benefits and costs from digital

projects, five categories of business values are

derived for this framework. These goals could be

uniquely defined for each company’s own set of

goals, but the goals here are more generically defined

to be able to classify highly relevant impacts of

digitalization. Mostly considered business value

categories in BDN networks such as profitability,

productivity and customer value were examined but

certain impacts which could not be directly linked to

these three major categories but have been rated as

equally important by businesses, were grouped under

two additional categories.

As part of the framework, profitability, producti-

vity and customer value are listed as Financial Value,

Internal Process Value and External Stakeholder

Value respectively. Any specific impact that affects

the return on investment, profit, revenue or costs and

can be measurable in economic terms are linked to the

Financial Value element. All other impacts that are

not measurable in economic terms are connected to

one of the other four business value categories and a

qualitative impact level is chosen. Specific impacts

relating to internal processes such as operations and

administration processes or productivity

improvement and efficiency gains or loss in the

processes that are not measurable in economic terms

are connected to Internal Process Value element.

External stakeholder’s value which are not yet

measurable in economic terms such as customer

satisfaction is connected to External Stakeholder

Value element.

Although every impact in a business

hypothetically linked to Financial, Internal Process or

External Stakeholder there are certain benefits from

digitalization that can be classified into other

categories due to uncertainty in when they will start

impacting economically and some impacts’

qualitative nature in general. For example, safety is

paramount in many industries. Digitalization plays an

important role in enhancing safety value by allowing

real-time sensing/monitoring of unsafe conditions

and allowing businesses to act fast to avoid

catastrophic incidents from occurring altogether.

Such values are indirectly related to avoidance of

losing productivity therefore profit, from which the

safety impact can be economically measured.

However, there is uncertainty to account for in this

case, where ‘if and when’ the risk event is avoided

should be considered in the benefit calculation and

Digital Value Dependency Framework for Digital Transformation

647

can be hard to estimate. Another way to look at this

would be, it improves the safe operation of plants,

therefore provides safe working conditions for

employees. Therefore, such impact with a qualitative

nature should be estimated as well. Similarly,

employee satisfaction is one of the main benefits of

digitalization where workflows are improved through

digital products and such benefits are significantly

important for businesses’ sustainability.

Although these impacts can indirectly influence

the productivity of the business, it would not always

be possible to translate it into an economic value due

to its inherent qualitative nature. Such specific

impacts have significant importance to the business

and directly related to internal stakeholders, but when

they cannot be economically defined just yet they

should be connected to a category that relates to the

internal stakeholders. Therefore, Internal Stakeholder

Value has been introduced.

Furthermore, digitalization offers opportunities

for growth and be innovative, which in the long run

improves financial value. It is however not possible

at early stages to predict with accuracy how much

benefit such innovation related impacts will bring.

For example, initiatives to deliver new digital tools,

new ways of working or providing training using

digital technologies such as AR, increase the learning

and put the organization in the growth path.

Such futuristic impacts can be qualitatively

described but they would not fit into the currently

defined categories of business goals. Therefore, in

order to classify innovation related impacts, Learning

and Growth Value has been introduced. Each

element in this step has a unique ID, indicated by FV,

IPV, ESV, ISV and LGV for Financial Value, Internal

Process Value, External Stakeholder Value, Internal

Stakeholder Value, and Learning and Growth Value

respectively as in

Figure 1.

3.6 Procedure

The owner of a digital product/project and its

stakeholders are the intended users of the DigVD

framework which helps them systematically

determine the impacts of their digital product before

development starts. A facilitator who is trained on the

framework can conduct the estimation process in a

one-on-one interview basis, when the product owner

can estimate benefits and costs from all stakeholders’

perspectives. Otherwise, it can also be conducted as a

facilitated workshop where product owner and

stakeholders of the digital product/project are

involved. The framework has been developed as a

digital web-tool and this tool has been directly used

in this estimation process. The results are saved in the

database of the tool, such that the dependencies and

elements can be queried and analyzed later.

At the beginning, the product owner identifies

unique user groups in order to capture impacts from

all user perspectives. User-groups here are the type of

users of the digital product being developed. First,

one user-perspective is chosen, then the framework

elements are connected from left to right according to

Figure 1, and the steps described in Figure 2 are

followed.

In this process, mutually exclusive positive and

negative impacts are considered and described, and

these impacts are then connected to most fitting

business goals. When this connection is established,

the estimation of the impact must be entered. When

an impact can be economically measurable, it will be

connected to the Financial Value category and a

calculated monetary value is entered together with an

estimated probability percentage. When it is not

possible to estimate an economic value the product

owner estimates the impact qualitatively from a 5

level ratings from ‘Insignificant’ to ‘Extremely

significant’, and the selected qualitative impact level

is then converted to a score as described in section

3.7.1.

These steps are then repeated for every user

perspective defined earlier for the digital project in

question, and the results are saved and analyzed.

When every element on the left-side step is connected

to an element on the right-side step, user has

the

option to enter the influence weightage (defined

Figure 2: Dependency mapping procedure.

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in Section 3.7.3) on the edge, otherwise it will be

assumed to be equally influencing the adjacent

element during analysis.

3.7 Mathematical Model

In order to measure and estimate the value

contribution of every element in a network, a

mathematical model has been developed and the

terms, variables and equations used in the

measurement system are explained in this section.

Although the specific impact is estimated, this value

gets passed onto and distributed to all elements that

are connected on its left. Figure 6 summarizes the

variables and equations used when the value of a

successive element on the right side gets passed onto

the predecessor element.

3.7.1 Value Weight of Specific Impact

Value Weight (VW) is defined as the measurable

quantity of the impact. There are two types of value

weights based on whether the impact’s economic

value is measurable or can only be defined

qualitatively. Economically measurable impacts are

estimated for its corresponding economic value in

euros and a probability (%) of occurrence is also

entered. The qualitative impacts are based on ordinal

levels where the significance of impact is selected

along with a probability of occurrence. Impact levels

are insignificant, minor, moderate, major and

significant which range from 1 to 5 for positive

impacts and -1 to -5 for negative impacts. Probability

of occurrence starts from rare to very likely with 10%

to 90% rating. Then using impact-probability matrix

an impact score is given for every impact.

Therefore, an economic value or qualitative score,

represented by € and Q respectively is stored for each

impact. The positive and negative impacts are stored

separately as well. Therefore, there are four types of

value weights are possible, where positive impacts

are stored as €+ and Q+, and negative impacts are

stored as €- and Q-. Value Weights are specified by

impact IDs on the subscript (i and j indicating

elements within categories) and value weight types on

superscript as below:

𝑉𝑊



€

> 0 , 𝑉𝑊



€

< 0 Economic

Impacts

𝑉𝑊





> 0 , 𝑉𝑊





< 0

Qualitative Impacts

3.7.2 Value Weight of Business Value

Since only the economically measured impacts are

connected to Financial Value (FV) category, all

economic benefits (€+) and costs (€-) are summed up

and positive and negative impacts are saved

separately. On the other hand, those impacts that are

not measured economically, gets a qualitative score

as explained in previous section and these qualitative

impact scores are connected to one of the qualitative

business value categories (IPV, LGV, ISV, ESV)

which are then averaged to estimate the overall

qualitative impact of every business value category,

as shown for IPV in (2).

Economic:

𝑉𝑊



€

∑

(𝑉𝑊





→

€

)

(1)

Qualitative Impact:

(2)

3.7.3 Influence Weightage

Influence weightage (µ) is defined as how much a

predecessor element influences the successive

element’s value. This weightage is either defined by

the user by estimating the percentage of influence

directly when edges are drawn, or calculated

afterwards based on how many edges connect to a

successive element, where every undefined edge that

connects to the successor element is assumed equally

influential as follows:

(3)

pi->sj

= VW

* µ

pi->sj

(4)

The value weights of predecessor-elements

(VW

) are calculated by summing up every edge

weight going from the predecessor element as follow.

∑

𝐸𝑊







(j: successive elements

dependent on pi) (5)

3.7.4 Edge Weight

Edge Weight (EW) is defined as the value weight of

the edge from a predecessor element to a successive

element. Using µ and VW of successive element, the

Edge Weight is calculated as below. Since the VW of

impacts are the starting point, VW

=VW

Digital Value Dependency Framework for Digital Transformation

649

3.7.5 Value Weights of Step 3, 2, 1’s

Elements

Value Weights of the specific impact elements are

distributed backwards to other elements in each

predecessor step according to edge weights that

originated from the predecessor element. This is

based on the logic that if predecessor element is

influencing a successive element, the value weight of

the successive element is distributed to the influencer.

The basic operations have been shown in Figure

3. The equation is applied to each value type

separately. For qualitative value types, when scores

are summed up to more than 4.5, then the maximum

score of 4.5 is assigned to the element, since the score

has a limit and that predecessor element has the

maximum possible score to influence the successive

element.

3.7.6 Value Contribution Fraction

Value Contribution Fraction (VCF) is defined as an

element’s contribution percentage to the total value of

the project and calculated as follows:

VCF

= VWpi/(Total VW of Project per value

type) (6)

Similar to value weights, there will be four value

contribution fractions to each element representing

each value types of €+, €-, Q+, and Q-.

3.7.7 Value Share

Value Share (δ) is defined as how much importance

the product owner estimates the economic impacts vs.

qualitative impacts towards the project, where δ

€+

Q+

= 100 and δ

€-

+ δ

Q-

=100. If both economic and

qualitative value types are equally important then it

will be 50:50. Additionally, if total value weight of an

impact type is zero then the other value type’s share

becomes 100. (ie:

= 0), then, δ

Q-

= 0 and δ

€-

= 100.

3.7.8 Normalized Contribution

Normalized contribution (η) is the contribution of

both economic value and qualitative score for every

element per positive and negative impact. Since

economic value and qualitative scores are on two

different scales, they are both combined using the

Value Share and Value Contribution Fractions for

positive and negative impacts.







𝑉𝐶𝐹



€

∗



€

𝑉𝐶𝐹





∗





(7)







𝑉𝐶𝐹



€

∗



€

𝑉𝐶𝐹





∗





(8)

4 APPLICATION

4.1 Digital Project

In order to illustrate the applicability of the

framework, a typical industrial digital project has

been chosen. This is a test digital project within

energy management for industrial plants. Among

many user groups, a specific group of managers are

expected to benefit the most from such a solution,

therefore the results are only presented for this user

group’s perspective in this paper. The network

outcome from the framework mapping is shown in

Figure 4

as a Sankey diagram

Digital Capabilities, Org. Changes and Business

Values had fixed elements as described in section 3.2,

3.3 and 3.5 respectively. The Digital technologies and

Specific Impacts steps were left open and 13 digital

technologies, 9 benefits and 3 costs were identified by

following the process described in Section 3.6. The

thickness of the links indicates the influence

weightage % (µ) used in the calculation of edge

weights (EW) of each link and therefore the value

weights (VW) of elements.

Figure 3: Value Weight calculation of elements in steps OC, DC and DT.

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Figure 4: DigVD network result for applied project.

Several digital technologies being considered for

this project but on the network only their IDs are

presented with respect to how it is saved in the tool’s

database. These technologies ranged from smart

devices, an application development environment,

connectors to existing IT systems, interface protocols,

an integration platform, databases, to data

engineering and analytics technologies. The nine

benefits are also specified on the network by their IDs

and ranged from reducing process times, saving costs

due to having quick access to data, better efficiency

management, to employees and external stakeholder

satisfactions due to the consolidation of data. Among

the nine identified benefits, 3 are economic benefits

(€+) and 6 are qualitative benefits (Q+). The user

group not only makes use of the product, it also pays

for the solution. Therefore, in terms of negative

impacts 3 costs elements were identified which were

all economic costs (€-) related to technology

purchases and maintenance of the solution.

Furthermore, other industrial test projects from

supply chain and site management are brought in to

illustrate user group specific results and multi projects

analysis possibilities of common elements.

This approach has helped in estimating actual

impacts in terms of economic and qualitative values

from the intended projects. However, on this paper,

for publication consideration instead of their true

economic values, the impacts and VW’s are

represented as low to very high levels so that results

from the framework can be illustrated.

4.2 Results and Discussion

From the estimated impacts, business values that are

influenced with positive and negative impacts are

identified according to their VWs

(Figure 5).

becomes evident that in terms of economic impacts

close to a very high-level benefits are estimated, and

less than 25% of the estimated benefits has been

estimated as costs. Furthermore, the estimation

process with the tool enabled identification and

recording of impacts which cannot be readily

measurable in economic terms. In terms of such

qualitative impacts, External Stakeholder Value,

Internal Process Value, and Learning & Growth

Value categories will be expected to have positive

impacts, where the averages for each value has been

calculated to be 1.9, 2.40, and 2.15 respectively out

of maximum score of 4.5 that is possible with highest

impact and highest probability. There were no

negative qualitative impacts identified. Although the

estimation is only for one year, when positive and

negative value weights of Financial Value are added,

the net is positive, indicating a positive turnover for

the project. In addition, overall positive qualitative

impact scores in the other three business values also

hint that the project has an overall benefit to the

business.

Now the importance of other elements such as

organizational changes and digital capabilities of the

dependency network with respect to the measured

impacts have been calculated using the formulas in

section 3.7. The economic value distribution across

all organizational changes has been presented in

Figure

, and each change element’s benefit and cost

Digital Value Dependency Framework for Digital Transformation

651

Figure 5: Economic and qualitative impacts of the project.

contributions are displayed. As seen on this figure,

OC20 relating to the change within the software

systems contribute to most of the cost. In terms of

economic benefits, OC20, OC10 and OC13 changes

contribute to a lot of benefits. These results can be

used in change management.

Additionally, using a Value Share of 50:50,

meaning economic and qualitative impacts are

equally valued for this project, among the positive

impacts it is evident that OC_13 has been rated with

most normalized contribution, considering its large

contribution to both positive qualitative impact and

economic impact. This means that this particular

change relating to having descriptive knowledge

access, contributes to benefits the most. Being able to

access the knowledge about historic data brings

highest value to the project compared to all other

changes. In this example, front-end process related

changes are contributing to high benefits which mean

that these changes should be prioritized during

transformation. Furthermore, identification of which

changes influence the most benefits can help in

measuring the benefit after the product has been

implemented.

It is often the case that product owners find it

difficult to decide economic impacts from new

technology-based projects and therefore qualitative

impacts have been given as an option when they

estimate the impacts. Now with the Value Contribution

Fraction and the Value Share, the Normalized

Contribution is calculated which brings two different

value types together so that total positive impact and

total negative impact of an element can be calculated.

On the overall, applying such a measurement model

helps in understanding the dependencies better, and

product owners are able to prioritize changes,

technologies and capabilities more effectively.

Similarly, within Digital Capabilities and Digital

Technologies the most value contributing elements

are identified. As such Connectivity (DC_4) and

Visualization

(DC_3)

are

contributing

most

value,

Figure 6: Organizational changes’ economic impact

distribution.

and DT_65 which is an application service and a

technology influencing most of the capabilities

contribute to highest benefit when compared to all

other technologies.

Change management in transformational

undertakings like digitalization can be difficult

especially when potentials from a new digital project

is not transparent to all involved stakeholders. Since

the framework is done for every user perspective of a

project as presented for a site management project in

Figure 7, it allows users to have transparency on how

much value is being contributed by different user

groups within the same project. In this example, the

financial value of the project completely comes from

User 2’s perspective. However, qualitative values

such as external stakeholder value, internal

stakeholder value, internal process value and learning

& growth value are being influenced through impacts

that are estimated from User 1, User 2 and User 3

perspectives. Such results make the benefits shared

by all user groups more transparent and enable the

measurement and realization of benefits more viable.

Most talked about topic when it comes to change

management is people and culture. If certain group of

users are resistant to change, it is important to be open

about which benefits are expected and communicate

them

effectively

and

early

possible. From the

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Figure 7: Impacts from different user perspectives.

results of DigVD such transparency is achievable.

Although any benefit-cost analysis must consider

user perspective in general, what is special about

applying DigVD approach is that it brings more

transparency onto the type of changes, capabilities

and technologies utilized by the different user groups

so that decisions can be made to maximize benefits

based on the dependency results. When the kind of

changes required for highest value realization is

identified the right training for the specific user group

can be decided to realize those dependent benefits in

timely manner. Likewise, if benefits are not high from

one user group’s perspective compared to other user

groups, it can be explained why this is the case from

the dependency mapping. Untapped organizational

changes that can influence high benefit contribution

can be initiated so that additional positive impacts for

that user group become possible.

These results show the kind of analysis possible

for single projects so that product owners can build

business cases and manage project and changes

effectively. Additionally, when benefit dependency

framework’s results from multiple projects are put

together, it brings additional transparency to the

overall digital transformation of the business, where

the results help in decision making. In this regard,

digital capabilities being developed within the

business can be brought together like in Figure 8,

where benefit and cost from four projects that are

influenced by the six digital capabilities’ of a business

are displayed. From this, Visualization capability is

contributing to high economic value and to high

positive qualitative score as well. Following this,

connectivity is the next digital capability contributing

to high positive impact overall. On the other hand,

when these four projects are considered together

information sharing capability accounts for the

highest cost followed by connectivity capability.

From this result, it is also evident that monitoring and

control capability has not been associated with values

of the four projects yet, where this capability’s

contribution to benefits and costs are very low.

Although this representation shows only four projects

and not the right representation of the current capabi-

lities mapping of the business, such results can tell

which capabilities they can potentially invest in future

to have a balanced digitalization effort in all areas.

Therefore, the DigVD framework and the model

behind it take benefit dependency network to where

the estimated outcome can be measured and

compared for further understanding of value

contribution and the development of business cases

driven by data. These results demonstrate project-

based analysis is possible where the results can help

prioritize transformational changes. Being able to

relate the projects values to other elements through

value weights, value contribution fraction and

normalized contribution open better understanding of

how other elements are influencing the project. The

framework applicability has been tested with four

process industry projects with outcomes showing

more impact estimations when it was done with the

DigVD than it was done before for similar projects.

As future work, further tests on consistency of the

resulting networks between similar projects are to be

evaluated. Additionally, influence weightage has

been assumed to be equal based on the number of

lines connecting to an element so that the

methodology can be studied. Since the methodology

has been studied with positive outcomes, the actual

influence weightages could be included but deciding

on these influence weightages is a separate process on

itself and must be evaluated together with product

owners more extensively. One of the main advantages

of this tools is that size of the project is not a

limitation. Project that is expected to have a small

return to large size projects with expected return in

millions of euros can be evaluated. However, the way

the tool is currently defined only one-year estimation

of impacts is possible. However, how the project

performs in the following years are important for

product owners and therefore by extending this work

and using the results from the current tool, calculation

of values over number of years using real option

valuation are being investigated.

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Figure 8: Values of Digital Capabilities of a business according to the four sample projects assessed.

5 CONCLUSION

Transparency on potential benefits from digital

projects is strategically important for companies. In

this paper, a redefined BDN for digital projects has

been presented where dependency results can be

saved, measured and compared. The elements within

the framework are generalized and a mathematical

model has been developed such that results from

multiple projects can be consolidated which then help

in implementing and better manage digital

transformation in a company.

The methodology and model presented here

are foundational for understanding value networks

within digitalization. Filling out the dependency

network for every project enables not only the

transparency of value potential in one project, but it

also aids in digital transformational activities, starting

from prioritizing changes to understanding

dependencies and value shared between multiple

projects and user groups. This allows for the creation

of value networks where relationships between digital

technologies, capabilities and organizational changes

and their contribution to value are made transparent.

The results from the applied projects show specific

identification of impacts, contribution of value from

different user perspectives, as well as reduce the

complexity in understanding the role of digital

technologies, capabilities and organizational changes

in brining value to the business. When multiple

projects use the same framework to assess their

projects’ benefits and costs, investment decision

making for digital transformation in a company

becomes more standardized, and changes or

capabilities the organization must enable for realizing

highest value are identified with this approach.

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