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Customized E2E network slices for industrial use-cases. Outcome-based methodology for estimation of target economic and technical parameters of industrial E2E slices.

October 2021

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Customized E2E network slices for industrial use-cases. Outcome-based methodology for estimation of target economic and technical parameters of industrial E2E slices.
Customized E2E network slices for industrial use-cases. Outcome-based methodology for estimation of target economic and technical parameters of industrial E2E slices.
October 2021

Customized E2E network slices for industrial use-cases. Outcome-based methodology for estimation of target economic and technical parameters of industrial E2E slices.

October 2021

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Telcos need new revenue streams for monetization of 5G, MEC and other elements of next generation ICT-infrastructure, especially in regions characterized by extremely low ARPU in consumer segment of telecom market. The key and probably the only source of extra revenue able to improve return on investments (ROI) into deployment of next generation ICT-infrastructure are industrial (vertical) use-cases.

 

Software-defined virtualized ICT-infrastructure, including 5G, allows telcos to shift from provisioning of narrow range of universal services to almost unlimited number of customers to provisioning of almost unlimited range of customized services to limited number of customers, as depicted on Fig. 1. Diversity of these customized services includes industry-specific end-to-end (E2E) network slices. In order to design these slices correctly, telcos need methodology for estimation of target parameters and ranges of their values of a specific E2E network slice, i.e. telcos must be able to design slices in accordance with requirements of narrow market segments.

 

Achievement of commercial success of industrial digitalization use-cases, which includes industrial-specific E2E network slices for deployment of digital applications, is possible only if outcomes from digitalization exceed digitalization-related expenses. This is exactly what our methodology developed for. Based on estimation of outcomes from digitalization within a specific industry or even an enterprise, it allows to define acceptable ranges of values of specific E2E slice parameters meeting both technical requirements from applications used for digitalization and economic requirements defined by comparison of achievable outcomes from digitalization to expenses on digital applications, ICT-infrastructure and internal digitalization-related expenses.

 

Ability to design E2E network slices in accordance to specific requirements of different industrial use cases is the key challenge for telcos deploying industrial services based on 5G and MEC, while the proper functionality is still absent in OSS/BSS stack of telecom operators. Our methodology is aiming to partially fill this gap.

The methodology is universal – it may be applied to any region and any industry. It shall be used for initial phase of design of industry-specific E2E networks slices allowing to roughly estimate acceptable ranges of values of economic and technical parameters of E2E slice. For now, we have successfully implemented the methodology to some regions with extremely low ARPU, where monetization of 5G and other elements of next generation ICT-infrastructure is the most challenging issue, as well as for some regions with high ARPU, including USA.

 

 

Outcome-based monetization of ICT-infrastructure for digital industrial applications

 


Economic outcome due to value chains digitalization is the only source of additional revenue enabling successful monetization of next generation ICT-infrastructure deployment. Each vertical and even each enterprise have to use specific set of industrial and business-applications in order to implement digital transformation. This diversity of applications calls for diversity of network slices optimized to requirements of specific sets of applications (Fig. 1). 

 

 

Volume of value chains digitalization outcome and conditions of its achievement are the key inputs for initial evaluation of requirements to network slices – both technical and economic ones (Fig. 2). 

 

 

 Assumptions of our methodology are:

 

- economic outcomes due to replacement or deep upgrade of existing assets are not considered – we only consider effects due to more optimal management of assets;

 

- volume of economic effect depends on scale/depth of optimization management:
- fragmented optimization mostly by on-premise applications,
- end-to-end optimization of the whole value chain mostly by cloud applications;

 

- optimization management is provided by specific digital applications, these applications imply specific requirements on networking infrastructure which are to be met by specific network slices;

 

- digital applications and ICT-infrastructure are been provisioned under aaS model allowing to eliminate lag between spending on digitalization and receiving of outcome;

 

- volume of internal digitalization-related expenses, which are primary labor and R&D expenses, is calculated using typical structure of a corporate IT-project, where internal labor-related expenses may reach 70% of budget, i.e. of economic outcome.

 

 

According to our methodology, primary parameters for estimation of economic outcome and digitalization-related expenses are (Fig. 2): 

 

- number of key production assets within an industry or an enterprise;

 

- potential volume of per-asset economic outcome due to more optimal use of asset;

 

- per-asset cost of use of digital applications needed to gain economic outcome from more optimal use of an asset;

 

- per-asset volume of data generated by these applications;

 

- QoS requirements of applications needed to gain economic outcome from more optimal use of an asset – usually QoS requirements of applications needed for digitalization of an industry or an enterprise vary significantly, so an industry/enterprise needs more than one E2E slice.

 

By means of these inputs it is possible to roughly estimate:

 

- possible per-asset and total volume of gross economic outcome due to digitalization;

 

- per-unite and total volume of applications consumption needed to achieve estimated economic effect;

 

- possible per-unite and total volume of payments for specific network slices been provisioned under specific SLA complying with QoS requirements from applications;

 

- total volume of internal digitalization-related expenses;

 

- total volume of net economic outcome due to digitalization.

 

It is worth to mention that increase of QoS-requirements and volumes of industrial M2M data due to transition from fragmented and mostly manual (left side of Fig. 4) to closed loop automated E2E optimization of value chains (right side of Fig. 4) outpaces growth of economic outcome: more than x1000 increase of traffic in combination with tightening QoS-requirements generates merely x10 growth of economic outcome. Furthermore, for all industries except transport and logistics maximum of achievable optimization effect due to more optimal management of assets, i.e. without assets replacement or deep upgrade, is estimated within range of several percent compared to revenue, which make the task of finding balance between volume of economic outcome and expenses quite challenging.

 

 

General requirements to industrial E2E network slices

 

Estimations implemented by J’son & Partners Consulting with use of described above methodology shows that along with industry-specific requirements to network slices there are some general requirements identical throughout all verticals. As depicted on Fig. 4, all industries need high cyber-security and availability (up to 99,99%) of their applications used in “what if” simulation and closed loop automation cases, in combination with call for radical decrease of per-unite cost of traffic. For regions already characterized by extremely low ARPU, as of 2030 per unite cost of traffic shall be eight times lower than achieved by routine scaling of capacity of access, transport and long-haul networks.

 

It means that general (throughout all industries) net profitability of industrial digitalization is achieved only if two contradictory requirements are met:

 

- customized connectivity services for industrial cases shall be provided under premium SLA, which guarantee high level of security and availability of industrial applications;

 

- for major part of industrial cases per-unite cost of traffic with premium SLA shall be lower than actual per-unite cost of “best effort” traffic, i.e. industrial cases’ revenue per connected asset will be lower than actual ARPU of universal “best effort” services for the same volume of traffic;

 

Profitable provisioning of services under these two contradictory requirements is possible only in case of use of extremely effective specific optimization logic for specific industrial slices. In fact, these unprecedentedly strict requirements to networks slices are requirements to management applications of slices, both intra and cross-domain, been tailored to manage specific set of parameters and ranges of their values of a customized network slice.

 

After preliminary estimation of economic and technical requirements, the next step of industrial E2E network design phase is prototyping and piloting of intra- and cross-domain orchestration/optimization applications needed to manage E2E network slice.

 

We are focusing on provisioning of full set of consultancy services for all phases of industrial network slices lifecycle. Along with development and implementation of methodology for preliminary outcome-based estimation of industry-specific requirements to network slices and their optimization management applications, experts at JP Innovative – daughter company of J’son&Partners Consulting, have experience of participation in pilot projects for cross-domain orchestration. Both of these two groups of services are available to order.

 

 

 

Customized E2E network slices for industrial use-cases. Outcome-based methodology for estimation of target economic and technical parameters of industrial E2E slices.

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