how to succeed in the iot: lessons from the market...the first step in any iot planning process...

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How to Succeed in the IoT:

Lessons from the Market

July 2019

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Strategy Analytics 2019 | www.strategyanalytics.com 2 of 25

Contents

1. Executive Summary 3

2. Introduction 4

2.1 Why should companies deploy IoT? 4

2.2 What are the key benefits that companies seek from IoT? 5

2.3 Deploying IoT: A Reality Check 6

3. How do successful companies implement IoT? 11

4. Case Studies 16

4.1 Lessons from Pacific Gas and Electric Company (PG&E) 16

4.2 Lessons from Deutsche Post DHL 18

4.3 Lessons from E.ON Energy Services 21

5. Summary of key recommendations for enterprises 24

Exhibits

Figure 1: Traditional Maintenance v Predictive Maintenance ....................................................................................... 4

Figure 2: The IoT Value Proposition ............................................................................................................................. 5

Figure 3: The Benefits of IoT ........................................................................................................................................ 6

Figure 4: Indication of the approximate range of spending (in USD) by your company on IoT projects ....................... 7

Figure 5: IoT Budget spend outside Central IT Budget ................................................................................................ 8

Figure 6: What are the main hurdles you expect to face as you build your company’s portfolio of IoT applications? .. 9

Figure 7: What percent of your IoT spending in 2018 was for each of the following categories? ............................... 12

Figure 8: What networks are you using for your IoT Solution? ................................................................................... 13

Figure 9: IoT Operations in a Deutsche Post/DHL Warehouse Environment ............................................................. 19

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1. Executive Summary

The Internet of Things will be the defining technology trend of the next decade and will impact virtually every vertical

industry and permeate the vast majority of business processes. IoT is an integral component of a broader digital

transformation.

Why should companies deploy IoT?

There are multiple objectives to deploying IoT including strengthening customer engagement, in order to increase

customer loyalty and accelerate market penetration, and to streamline operational efficiency to improve

organizational effectiveness and profitability, as well as reduce costs. Moreover, enabling IoT within a business can

also be a way of avoiding obsolescence; connected products generate many more opportunities for additional value

than non-connected products, offering a vital opportunity for competitive differentiation.

With increasing amounts of IoT data stored and managed in company IT systems, companies are starting to see the

value from IoT data. As a result, operational solutions are scaling and IoT is becoming more of a strategic

imperative for companies. However, there is no “one-size-fits-all” solution for something as complex and nuanced

as an IoT deployment. There is no single vendor offering a plug-and-play IoT solution and many companies now

planning to implement IoT are unsure how to go about it.

How do successful companies implement IoT?

As the benefits of IoT are more widely understood, now the more pertinent challenge for many companies is how to

implement IoT and wrestle with questions such as how and where to start, and what skills and partners are needed.

Based on years of IoT industry research as well as in-depth interviews with companies who have successfully

implemented IoT, this report provides hands-on insights into key success factors and mistakes to avoid. A number

of hands-on lessons have emerged from across industry sectors including energy, smart metering, and logistics:

The first step in any IoT planning process should be identification and definition of use cases.

Obtain full C-level buy-in by demonstrating ROI gains to the board at the beginning of the process.

The hardware that forms the vital endpoints in an IoT network is critical to the success of a deployment.

Select a lead software supplier with specific industry sector knowledge

Select the right network topology for the use case, hardware, applications and overall deployment

At the same time recruit at least a data scientist.

Going light on IT, but big on data, avoids unnecessary investment and complexity, whilst promoting easier

scaling of the IoT installation.

A cloud solution is preferable and more cost effective for data storage over on premise or hybrid set-ups.

Adapting legacy technology to build an IoT infrastructure can be a risk in terms of data security.

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2. Introduction

2.1 Why should companies deploy IoT?

The Internet of Things will be the defining technology trend of the next decade and will impact virtually every vertical

industry and permeate the vast majority of business processes. IoT is an integral component of a broader digital

transformation. It is a network of physical objects (such as security systems, vehicles, manufacturing embedded

with smart components, such as microprocessors, data storage, software, sensors, and actuators) all connected to

other devices and systems over the Internet. The “Thing” in the Internet of Things may be a component, such as a

smart meter unit in a commercial office building, or the building itself, as an item in a range of assets that are being

tracked. Underpinning all of this, the system needs to be secure and managed.

Figure 1: Traditional Maintenance v Predictive Maintenance

Source: Strategy Analytics

The true value of the IoT is in data that is collected from these devices and then analyzed to provide insights or

actions, as highlighted by the approach to predictive maintenance in Figure 1. With IoT enablement, additional

revenue streams become possible after the initial product sale, including value-added services, subscriptions, and

applications, which can easily exceed the initial purchase price.

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2.2 What are the key benefits that companies seek from IoT?

There are multiple objectives to deploying IoT. These include strengthening customer engagement, in order to

increase customer loyalty and accelerate market penetration, and to streamline operational efficiency to improve

organizational effectiveness and profitability, as well as reduce costs. Moreover, enabling IoT within a business can

also be a way of avoiding obsolescence; connected products generate many more opportunities for additional value

than non-connected products, offering a vital opportunity for competitive differentiation.

Figure 2: The IoT Value Proposition

The business benefits are clear and are highlighted in Figure 3. At its route, IoT is a vital component of Digital

Transformation that facilitates a shift from product to service and from Capex to Opex (Figure 1). It changes a

company’s relationship with its assets; helps it better understand the data within the organisation from previously

unconnected objects. This enables a shift in business processes that not only enables faster responsiveness to

customers, but can dramatically improve efficiency and reduce cost significantly. For example, a company that

connects its PLCs (Programmable Logic Controllers) and enables sensors in its manufacturing process can

automatically detect faults before they occur. This could be measuring heat, pressure, vibration, noise or even

alignment.

For example, RS Components1, a company that supplies electronic components, electrical, automation and control,

and test and measurement equipment, and engineering tools and consumables, has large warehouses in the UK

with thousands of meters of conveyor belts. They are shipping more than 45,000 parcels a day, which drop off onto

other belts. These parcels often knock the belts out of alignment from their central position and it is essential these

are monitored and reset, before they are forced too far out of alignment and break, as downtime is incredibly costly.

In these large warehouses they are also monitoring the stratification of air temperature; as warm air rises, workers

1 https://uk.rs-online.com/web/

Improved Data & Asset

Utilization

Improved Productivity

Greater Efficiencies in Supply Chain Management,

Business Processes &

Logistics

Improved Services =

Better Customer

Experience & Satisfaction

Drives Innovation

Reduces Costs, Lowers TCO & Drives

Top-line Revenue

https://uk.rs-online.com/web/

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typically end up being cold all the time. IoT not only enables monitoring of the conveyor belts for automatic

alignment readjustment but also monitors the temperature, so now there are fans that push the hot air downwards

and homogenize the temperature in the warehouse, not only providing a more comfortable working environment,

but also saving a vast amount in energy costs. To facilitate a solution like this requires valuable IoT partnerships, in

this case IBM Watson, Cougar, a UK systems integrator and Actility, providing an IoT connectivity platform and

tools.

Figure 3: The Benefits of IoT


2.3 Deploying IoT: A Reality Check

With increasing amounts of IoT data stored and managed in company IT systems, companies are starting to see the

value from IoT data. As a result, operational solutions are scaling and IoT is becoming more of a strategic

imperative for companies. However, there is no “one-size-fits-all” solution for something as complex and nuanced

as an IoT deployment. There is no single vendor offering a plug-and-play IoT solution. Virtually all deployments, with

any degree of complexity, require partnerships to be truly successful, as highlighted in the RS Components

example.

How can we summarize the pace of adoption and deployment to date in IoT? Although it seems as though every

business lists IoT among its top priorities, how fast are businesses moving?

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Figure 4: Indication of the approximate range of spending (in USD) by your company on IoT projects


Spending on IoT is increasing, as highlighted in Figure 4, but becoming increasingly dependent on line-of-business

(LOB) budgets (Figure 5), with two-thirds of IoT budget supported by areas outside the IT department. This

increases in countries with a strong manufacturing economy, such as Germany and China. Deployments are

continuing to scale, enabled by more efficient platforms, standardization among cloud providers supporting the IoT

and the lower cost of devices. Edge computing is helping companies manage the data tsunami more efficiently and

uptake looks promising, as does adoption of geolocation, providing valuable context and traceability across supply

chains.

However, a deeper look into implementations suggests a cautious, incremental, experimental reality compared to

the daily barrage of hyperbole in the trade press.

The business-benefit arguments are compelling as organizations look to achieve the multiple goals of efficiency,

cost saving, compliance, and the ever attractive promise of new revenue streams. However, the barriers to IoT

adoption are real and significant (Figure 6).

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Figure 5: IoT Budget spend outside Central IT Budget


Despite the increase in knowledge about the IoT, many organizations are still exhibiting caution and have

trouble executing even when they have full buy in. Here are the key issues we continue to see as

companies implement their IoT projects:

Challenges in Changing Legacy Processes or Culture: IoT creates many new challenges. One of these is

connecting previously unconnected assets, which can often enforce a change in work practices. In some cases it

can lead to staff needing to change their roles and how they operate; for example, connecting assets means

engineers now need to understand what to do with the data, as opposed to physically checking if an item of

machinery is working or not. Challenges of an ageing workforce in developing markets and a lack of new, skilled

engineers, mean that IoT is actually becoming an imperative in industrial and manufacturing environments. There

is often a disconnect between IT leaders and business and operations technology (OT) personnel. A successful

project for IT might mean that a system simply works, whereas for business leaders, success is defined by a

project demonstrating clear business value or ROI. An operations manager might define success as it increases

efficiency or reduces downtime. It is critical that all stakeholders involved in a deployment understand the goals

and have clear expectations of how those goals will be accomplished.

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Figure 6: What are the main hurdles you expect to face as you build your company’s portfolio of IoT

applications?


Integration Complexity Challenges: Developing solutions for IoT can be comprehensive and complex. Often it

involves combining IT and OT technology, including hardware compatibility, integrating previously unconnected

assets (OT), as well as software development, which can include embedded software development, mobile apps,

desktop apps, analytics, cloud solutions and reporting, dashboarding and more. Designing for the IoT requires

special considerations, such as a device’s power and battery life requirements, latency requirements, API

compatibility, and other elements. These are all specialized skills that take not only software development

expertise, but also a deep understanding of what it means to develop an IoT solution. At Strategy Analytics, we

believe this is one of the reasons we have seen a pivot towards large enterprise software providers and system

integrators to help deploy an IoT project successfully. Mistakes in a project can be costly.

Security: Security remains the number one concern for companies deploying IoT, despite the fact that other factors

are emerging as challenges to IoT deployments. IoT security can be complex and comprehensive solutions need

to be utilized at the network layer, the hardware layer, application layer and at the datacentre/cloud level, to

protect data. IoT solutions that combine hardware and software from different vendors very often mean that

security is left to the companies deploying IoT. As Strategy Analytics research shows, companies are

increasingly relying on vendors and their technical expertise, to manage the complexity of IoT deployments. With

13%

7%

15%

16%

18%

19%

6%

22%

14%

23%

30%

31%

40%

53%

13%

14%

15%

18%

19%

20%

20%

23%

23%

24%

25%

27%

31%

41%

Adequate/flexible application development tools

Lack of bandwidth to manage

Changing wireless connectivity…

Enabling use of legacy corporate data

Integration (with non-IoT systems) challenges

Scalability

Challenges in changing legacy processes and/or…

Ongoing Data management

Integration of multiple IoT platforms/systems

Changes in technology/standards

Device management

Network management

Costs (support, marketing, management)

Security

2018

2017

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any chain only being as strong as its weakest link, IoT networks are proving to be a target for spoofing and data

theft, DDoS and man-in-the-middle attacks, as already demonstrated by the Mirai Botnet attack and others

. While IT managers understand the importance of protecting their networks, IoT creates new challenges,

including hardware and software, increasing the reliance on suppliers to help secure the network.

Lack of Bandwidth to Manage: With multiple initiatives to undertake and finite resources and budget, IT

departments cannot possibly manage the tsunami of data that is emerging from connected devices via IoT

initiatives. Moreover, operational personnel are not data scientists, which creates new challenges in staffing as

well as managing the information that is being extracted from systems to measure performance data, or drive

changes in the business. As a result of this, companies are becoming increasingly dependent on suppliers via

outsourcing and managed services, which presents a major opportunity for suppliers as well as opening up the

opportunity for new business models.

Cost: Cost remains a key consideration, but is no longer the key factor in IoT deployments. Strategy Analytics end

user research continues to illustrate a cautious level of spending on IoT deployments, with 65% of the

respondent base spending less than $100,000 on IoT deployments. Across all spending categories, no significant

changes were observed in the 2018 survey results, although spending at $1m+ showed a 4 percentage point

increase. There remains a challenge in funding IoT projects, as companies are heavily dependent on

operational/LOB budgets for IoT spending. Central IT budgets remain squeezed and discretionary spending on

IoT is challenged. Cash flows are under huge pressure to maintain the current operation of the business, such

that diversion of funds into a new initiative, such as IoT, is hugely challenging

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3. How do successful companies implement IoT?

The previous section outlines many of the benefits of IoT and understanding of these is now more wide-spread

amongst enterprises. The more pertinent challenge today is how to implement IoT and companies wrestle with

questions such as –

How do I start?

What skills do I need?

What partners should I consider?

Strategy Analytics leveraged several years of IoT buyer journey research as well as in-depth interviews with

companies who have successfully implemented IoT to provide hands-on insights into key success factors and

mistakes to avoid.

Start with a focus on use cases and business benefits.

The first step in any IoT planning process should be identification and definition of use cases or at least one. As the

IoT hype is waning, a focus on developing clear use cases is more critical than ever. At the centre of these uses

cases must be measurable business benefits either in terms of cost savings or – even better – revenue generation.

Building an IoT business investment proposal around a tangible use case vastly improves available funds and long

term corporate support.

Obtain full C-level buy-in

IoT funding will entirely depends on the ability to successfully demonstrate ROI gains to the board and this process

needs to begin at the outset. A common theme we found is insufficient buy-in from the top of the company that will

lead to insufficient funding for the IoT projects which in turn compromises or even jeopardizes its success.

Successful IoT development teams tackle this challenge by lobbying their board from the very beginning. Most

critically this internal promotion must not be based on hype and feeble arguments such as “everyone’s doing it now”

but a convincing story build on demonstrable potential to grow the company’s bottom line – ideally through cost

savings as well as revenue generation.

Selecting hardware vendors

While there may be a perception that hardware is a commodity element in IoT, the reality is that the majority of

spending in IoT continues to be focused on hardware and software (see Figure 7). The physical devices that form

the vital endpoints in an IoT deployment are absolutely critical to the success of a deployment. IoT devices can be

found in applications ranging from commercial networked HVAC systems to industrial power-line communications

(PLC), avionics networking, and network gateway systems, as well as critical energy infrastructure such as smart

meters and power stations. Not only do they need to be robust and suitable for the specific environments in which

they are operating, but they are critical to the security of an IoT deployment. In isolation, software security has

proven inadequate to protect against threats, but now today’s Field Programmable Gate Arrays (FPGA) SoCs

(System-on-Chip) can be used to implement scalable security schemes that extend all the way down to the IC

(integrated circuit) level.

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E.ON highlighted that there has been a natural tendency in the business to be hardware driven, in terms of decision

making, so it is vital that in order to be included in the consideration set, that hardware vendors:

Understand the complexity and nuances of the industry in which the IoT devices are to be deployed.

Have strong partnerships with channel partners to be considered for project inclusion e.g. Communication

Service Providers (CSPs), Value Added Resellers (VARs) and System Integrators, including smaller

industry specialists.

Have a strong software and application development framework to support secure over-the-air (OTA)

updates and application development for devices, combined with API (Application Programming Interfaces)

for simple integration into Connectivity Platforms, which is important for both security and scaling projects.

Figure 7: What percent of your IoT spending in 2018 was for each of the following categories?


Selecting the right connectivity and network topology for a deployment

Fundamentally, the IoT is about connecting distributed end points, therefore the connectivity network for an IoT

deployment is an important factor in any deployment decision. Companies can underestimate the advantages and

disadvantages of certain radio technologies. According to Strategy Analytics research, Wi-Fi, Ethernet and Cellular

are the leading wide-area network (WAN) technologies used in IoT deployments (see Figure 8).

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Figure 8: What networks are you using for your IoT Solution?


Typically use cases will determine the ideal bearer technology types. Cellular IoT connectivity, for example, can be

easier to set up, provide better reliability, and allow the maker of the device to be in control of the data, as well as

avoiding the challenges of changing SSIDs, passwords or signal interference. In other cases mesh networks have

been selected as the ideal choice, such as the one chosen by PG&E, who used Wi-SUN technology-a mesh

network that allows meters to communicate both outbound and receive communications as well as minimizing the

number of access points required.

Fundamentally the key factors impacting a connectivity decision will be:

Is the bearer network the most suitable for the use case, the applications, hardware and the

deployment overall? Is there sufficient network throughput and bandwidth for current and anticipated

application needs? Will it be possible to do FOTA (Firmware-over-the-air) updates?

What is the existing network topology? Is there a network already present, or access to one? If not,

what are the challenges and costs in creating a new network?

How much will it cost? Current and anticipated data transmission costs as the deployment evolves. Will

more edge devices/gateways be needed to lower the amount of data being sent directly to the cloud, or to

process some data locally?

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Scalability: Does the setup allow for easy expansion of the deployment; can new devices be setup and

connected easily via the network to connectivity management platforms, for example?

Find a lead software supplier that knows your industry well

Few companies including large multinationals begin their IoT journey with the right knowledge and skills in place.

Hence selecting a qualified software and integration provider is one of the most critical steps on the way to a

successful IoT installation. The immediate challenge however will be finding the right software partner which leads

may enterprise to the large and well-known IT vendors. We have found though that many successful IoT

installations have at the heart of the project a more specialist vendor with deep experience of the customer’s

particular industry sector. The providers are often small start-up types and hence more effort will be required on the

customer’s part to scan the provider landscape, identify options, and engage in a sound vetting process. This might

even include preliminary planning workshops focused on use cases with shortlisted vendors. Such an approach

does obviously require significant upfront investment in time and resources but is more likely to lead to success in

the long run.

Recruit at least a data scientist

In addition to working with a specialist vertical industry focused solution provider successful IoT implementers also

invest in internal specialist resources. Whilst companies with limited existing skills may prefer to outsource and not

invest in building sizable internal teams from the start, it is recommended having at least one data scientist,

especially one that understands the business. Nevertheless, finding affordable people with the correct skill sets can

prove a challenge for traditional companies looking to deploy IoT solutions.

Go light on IT but big on data

Many enterprises that are successful with IoT have opted to implement as low touch as possible from an IT

standpoint, at least initially. This approach helps to build momentum for small wins and then turn those small wins

turn into bigger wins. This approach will also help with the change and people management side of the process.

At the same time our research shows that lessons learned by companies includes turning on every data source

possible from the start. Data is cheap and it makes little sense being overly frugal with regards to data capture.

Some companies have realized with hind side and lost efficiency gains by having to go back at a later point and

retrospectively activate further data sources, which can be costly and challenging to implement later on.

Cloud is the answer for data storage

Some companies end up with limitations or charges for access to their own data where it is stored on their

provider’s network, in some cases even on customer premises. With the low cost of cloud storage companies

should maintain control of every data source available, because they are likely to find a usage for that data

eventually. Some companies retrospectively discovered that they could have delivered many more use cases a lot

faster.

In terms of a choice between data storage on premise, in the cloud, or hybrid, the most cost-effective answer is

cloud, with full access control

Adapting legacy technology to build an IoT infrastructure can be a risk

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Many companies start building an IoT network on a foundation of Supervisory control and data acquisition systems

(SCADA). These are older technologies often considered a forerunner to modern industrial IoT systems. Whilst the

line is blurry and it can be tempting not just for cost reasons to leverage legacy infrastructure this can become a

serious security risk.

The protocols used in SCADA systems are often proprietary and, when initially designed, were not linked with the

outside world. Whilst providing ease of integration and convenience for deploying new nodes in existing networks

through backwards compatibility, security was frequently overlooked. When these devices and protocols then

connect to the open Internet, lack of authentication or message repudiation can result in undue exposure to data

breaches. At fault are not just sensors and SCADA devices but also the control systems that control them if and

when these are built from common off-the-shelf hardware and run on top of Windows or Linux. The combination of

these factors can make a system vulnerable for attacks from malware.

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4. Case Studies

4.1 Lessons from Pacific Gas and Electric Company (PG&E)

Collect all the data from the start, partner with multiple suppliers and seek out expertise to minimize disruption.

Pacific Gas and Electric Company, incorporated in California in 1905, is one of the largest combined natural gas

and electric energy companies in the United States. Based in San Francisco, the company is a subsidiary of PG&E

Corporation. There are approximately 24,000 employees who carry out Pacific Gas and Electric Company's primary

business—the transmission and delivery of energy. The company provides natural gas and electric service to

approximately 16 million people throughout a 70,000-square-mile service area in northern and central California.

Collect all the data, figure out additional use cases later! Multiple vendor selection prevents lock-in.

The deployment of smart meters was PG&E’s first foray into the IoT, with the stated purpose of replacing analogue

meters with smart meters, negating the need for company employees to manually go and read meters once a

month, therefore saving on truck rolls and significant cost. As one of the first companies to rollout large smart

metering deployment in the US, PG&E presents fascinating insights into the challenges of deployments and the

management and use of data from end points.

Smart metering deployments face a particular challenge in that end points are highly distributed, so PG&E opted to

use a mesh network operated by Silver Spring networks (since acquired by Itron) using Wi-SUN technology-a mesh

network that allows meters to communicate both outbound and receive communications as well as minimising the

number of access points required. Even during the deployment, there were some meters that could not be read due

to location, or could not reach an access point, whereby a truck with a fitted access point was still needed to collect

data from those smart meters which had been installed (although these were in the minority). This allowed the

company to transition from taking manual readings once a month, to taking automated readings once a week-

reducing truck roll fuel costs, labour costs, improving accuracy and giving far greater visibility to the distribution of

electricity on PG&E’s network.

The vendor also split vendors on the hardware side to prevent any lock-in or any one vendor becoming too

dominant, using GE Electric Meters (acquired by Aclara) and Landis and Gyr. Platform developments were built

internally with support from SpaceTime Insight (acquired by Nokia) to assist with its Machine Learning and Analytics

on its data collected from the network.

The R&D phase which followed the rollout, in order to establish how the collected data could be used for additional

business cases, cost the company more money, than had additional use cases been established prior to the

deployment. In PG&E’s case, the rollout was accelerated by the industry regulator, which left less time to plan

around holistic use cases in advance.

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What emerged is that it is better to capture and store as much network data as possible from connected assets, as

new business cases are not always obvious at the outset and retrofitting additional capabilities to the network can

be expensive!

Build internal R&D capabilities to gain maximum value from connected assets.

Following the implementation of a relatively simple business case, PG&E discovered a number of additional use

cases that emerged from the data that was being collected from the original smart metering roll out. The company

invested in a period of exploration to establish what other insights these connected IoT smart meters could share

about the business and created an internal R&D group which it called the “Grid of Things”. This consisted of running

additional pilots on the existing smart meter dataset, as well as ideating and establishing new use cases, before

testing those and pushing updates to the network of smart meters. This extended to the broader company business

and assets, incorporating areas such as wind and solar power as well as the broader energy distribution network.

Low Touch IT is better. Small wins lead to bigger wins. Partner with an industry specialist to maintain momentum!

With the creation of an R&D group, PG&E worked with a utility specialist consulting firm Veregy to minimize the

impact on the existing IT environment. This meant that PG&E was supported in terms of thought leadership and

implementation as well as new ideas, while not having to slow down as it recruited its own data scientists to the

team behind the support of a consulting and system integration specialist. This enabled PG&E to work with flat files

of existing data to determine future use cases without the need for additional IT build-out, while also not spending

months on building out an IT backend infrastructure only to discover that it didn’t meet business needs, offer

business value or doesn’t meet the value in tandem with business processes.

Through internal capabilities and the support of specialist expertise, PG&E was able to maintain momentum and

build out its own internal expertise at the same time. This meant that the company started to establish small wins

that were not always optimal in terms of efficiency, but helped to scale projects, enabling small wins to turn into

bigger wins, allowing for more recognition within the company.

Eyes on the prize: Less data and lower accuracy can be better, if it means faster response times and more complete integration

The tendency with data scientists is to use as much data as possible to produce as accurate a model as possible.

Sometimes it can be just as effective to scale back modelling and analytics in order to deliver a technically inferior

model but one that can be more easily and quickly integrated into existing processes or existing systems. This may

mean leaving out a data source that is really useful to the modelling but creates additional complexity or developing

a model that offers the ability to detect an issue in two days rather than four days. The time lost in a more complex

integration can potentially offset the value of detection two days earlier, for example!

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Mighty oaks from little acorns grow: Data from simple business cases can transform a network!

The business case behind capturing data from smart meters was clear. However, multiple additional business cases

emerged from the initial deployment that had a transformative effect on PG&E’s electricity network:

Revenue Protection: Data captured from the network allows for the matching of data that is coming from

specific electricity substations to be cross-referenced with data that is coming from smart meters. This

allows the company to identify where there are losses happening in the system and where there are

anomalies, which reduces the amount of losses and protects revenue.

Phase Mapping: PG&E’s network runs three voltage phases-for supplying electricity from the network grid.

Often imbalances can occur, especially after power outages, such as storms or other natural disasters

causing an imbalance in the mapping to buildings and other endpoints. Using voltage data and movements

of that voltage data it is possible to create a cluster analysis to identify which phases are being distributed

where, allowing much greater visibility and control of the network.

Meter to Transformer Mismapping: Fixing meter-to-transformer mapping errors is important. Identifying

and correcting mapping errors is critical for transformer load management and will also un-mask diversions

and other faulty connections.

Volt/VAR Optimization: VVO is an advanced application that runs periodically or in response to operator

demand, at the control centre for distribution systems or in substation automation systems. Combined with

two-way communication infrastructure and remote control capability for capacitor banks and voltage

regulating transformers, VVO makes it possible to optimize the energy delivery efficiency on distribution

systems using real-time information. Benefits of VVO are: Improved energy efficiency leading to reduced

greenhouse gas emissions and reduced peak demand cost for utilities.

4.2 Lessons from Deutsche Post DHL

Placing Use Cases and Integration on the Ground at the Heart of the Deployment. Go at a pace that works and get Board Backing!

As a leading global logistics company Deutsche Post DHL Group not surprisingly started looking into IoT much

earlier than many others. Given its vast and global logistics infrastructure there were existing process automation

experience and a decent internal base of technology skills. Nevertheless the company’s German-based Research

& Innovation team underwent a challenging process of successfully implementing IoT solutions that deliver clear

business benefits and thus provide other companies in the wider transport and logistics space with valuable lessons

as how to go about it.

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Deutsche Post DHL Group’s key objectives for IoT were improved efficiency in operations, customer service, and

also predictive maintenance. The aim was to connect and control the entire parcel sorting process including the

level of lighting needed, door opening and closing, regulating staff access and achieving an overall higher level of

control. In addition the company wanted to facilitate predictive maintenance for lighting such as manage the

replacement of light bulbs when needed. In connection with the warehouse fulfilment process, Deutsche Post DHL

Group intended to automate access and temperature control, both of which are important for storage of higher value

items. DHL installed smart sensors for inventory control, i.e. to know which product is stored where and on which

shelves, compartments, or boxes are occupied. Finally, while the IoT development team intended to connect parcel

lockers/stations for improved utilisation, a legacy network of some 3000 around Europe proved too much of an

upgrade effort.

Figure 9: IoT Operations in a Deutsche Post/DHL Warehouse Environment

Source: Deutsche Post DHL Group

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Get Board Buy-in!

The first major challenge for DHL was one shared by many companies which is making an EBIT driven CEO

understand that an R&D investment makes business sense. In typical corporate fashion there was a lot of

PowerPoint developed and presented to make the case and achieve board-level buy in. With hindsight it may have

been more effective to try and take the CEO on site for a more hands-on illustration and demonstration of the

business benefit. See and feel is always more effective than words and diagrams.

Whilst the ideal approach presents a logistical challenge and may not have even been possible at Deutsche Post

DHL, what is clear is that internal education efforts are almost always necessary and must start as early as

possible. What is also clear is that the days when the mere mention of IoT generated corporate excitement and

determination to get on board are over and the only way to sell IoT certainly to the people who write investment

cheques is by presenting them with a clear ROI case.

Focus on clear, demonstrable use cases. Prove it works “on the ground” to handle change management challenges

Deutsche Post DHL Group’s first major development activity was the identification and definition of clear use cases

that were presented to the board for buy-in. The starting assumption was very much not to do IoT because others

are doing it, but to focus on benefits, such as cost savings, improved security and better customer service.

The vendor selection process then formed the second step. The company did have an advantage through its own

logistics solutions subsidiary, Agheera, which started around 2010 with smart tracking devices to monitor the

condition of parcels (e.g. temperature, humidity, location). Hence DHL did not have to rely on an external integrator

but did need to bring hardware vendors into the mix.

The vendor selection process started by identifying potentially suitable suppliers and Deutsche Post DHL then

shortlisted five based on what they could offer in terms of tech capabilities, applicability and understanding of

concrete use cases. A key factor was the focus on use cases. Deutsche Post DHL then invited the most promising

two companies to onsite workshops to carve out concrete use cases in great detail and to more precisely define the

business benefits. Importantly, this was conducted not only at a theoretical level but also on site, including pilot

builds for real-life environment testing. Only then did Deutsche Post DHL finally select the vendor to proceed with

for the full implementation. Moreover, once local site management had the process explained and clarified (i.e. as

not a lofty HQ initiative), it assisted greatly with operational buy-in at a local level.

A deep understanding of customer needs and integration into existing systems are key differentiators in a commoditized market

In terms of partner selection, Deutsche Post DHL highlighted the lack of differentiation on hardware itself, focusing

instead on the ability of partners to help with use cases. For example, automating the opening and closing of doors

or basic asset tracking and geo-fencing are fairly standard procedures, but understanding why Deutsche Post DHL

wanted to do this, in order to deliver measurable benefits, helping the company demonstrate clear value, requires a

more involved approach and understanding of needs. Deutsche Post DHL leveraged its own internal resources for

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much of the project, engaging smaller players to provide platform elements (Kore2 and Kaa

3 as well as Agheera and

others, but has since moved to using Microsoft’s IoT Azure cloud platform as its standard platform for managing IoT

data. Integration with existing systems is a critical element that will determine the success of any IoT installation

and whether the desired business benefits can be achieved. Deutsche Post DHL was able to leverage its own

internal capabilities, but most companies will need to find an external provider such as Agheera, Kore, and Kaa.

”Do it right” beats “Do it quickly”

DHL was engaged in a 3-year project from the concept stage to implementation. The vendor selection process took

18 months, which was considered reasonable given the multi-step development including workshops and trials with

vendor candidates even prior to selection and roll-out.

Some would consider this longer than desirable and smaller organisations may be able to do it faster, but Deutsche

Post DHL found that by not focusing on time, the team ensured that all aspects and details were thoroughly

investigated and processes defined. Some elements of IoT are reasonably straight forward and based on many

years of field experience, but a well-designed and functioning system entails many moving parts and a fair deal of

complexity. In particular, the critical use case definition and implementation can be challenging once planners get

into the weeds of what exactly the sensors need to measure, what data needs to be shared, with what parts of the

systems, and how to transport the data.

A more rapid implementation should not come at the expense of placing the use case at the centre of the process.

4.3 Lessons from E.ON Energy Services

The right partnerships and C-level buy-in, are essential to success in an industry with cultural change management

challenges!

It’s all about maximizing the commercial management of energy assets

E.ON is a leader in the North American renewables market in wind, solar, storage and services. E.ON Energy

Services (EES) is a full service provider of operations & maintenance, and asset management services in North

America. The company currently manages some 3,000 MWs of wind capacity for its customers. EES also services

about 3,700 wind turbines for E.ON and more than 25 utility customers.

The required flexibility and efficiency of energy capture, storage, dispatch is only possible with IoT

E.ON’s wholesale operations for wind, solar, energy storage (batteries) are part of the smart grid – an IoT

infrastructure connected to the grid designed to deliver enhanced efficiency and economics. In essence it’s all

2 https://www.korewireless.com/

3 https://www.kaaproject.org/

https://www.korewireless.com/

https://www.kaaproject.org/

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about management of energy and how to capture and distribute energy across a smart grid in an optimized fashion.

The energy is first captured in huge 50mW or even larger batteries and from there the focus is on how efficiently

these batteries charge and discharge. IoT enablement allows the company to automate storage and access energy

assets by charging and discharging batteries at the right time, ensuring that assets are managed in an optimum way

to meet set economic goals. In turn E.ON helps its utility customers to achieve optimum efficiency, for example by

avoiding the build of additional substations across their network.

Some system elements are engineering-led, for example “if the state of charge reaches a certain level do

something”, others are driven by economics such as maintaining contractual supply levels. Most importantly

though, without IoT it would not be possible to control this vast amount of data, or at least not in the time required

for achieving economic improvements for E.ON and its utility customers.

The set up – involve industry experts from the start

The hardware is composed of an array of containers comprising batteries, power electronics, and devices to extract

data – for example how much energy is in the battery. In addition there are many devices related to safety which is

a critical consideration given the presence of large amounts of high-powered energy in dense spaces. A master

controller then pushes data to the cloud via a cellular network provided by partner firm Xdata. A wide-area network

is required to serve a lot of very remote locations in sparsely populated places like Texas or Oklahoma.

SCADA (Supervisory control and data acquisition) systems are an older technology that many consider a forerunner

to modern industrial IoT systems formed the foundation in the IoT build and as is often the case the line between

SCADA and IoT is blurred. If incorrectly setup and managed this approach can pose a security risk as the protocols

used in SCADA systems are often proprietary and, when initially designed, were not linked with the outside world.

Most SCADA and data acquisition protocols were layered on top of IP with minimal changes. With no

authentication, message repudiation, or confidentiality, these systems can be open and exposed.

Integration with legacy systems is always a challenge though and E.ON mitigated this risk by bringing in a partner

with deep sector knowledge from the very beginning.

The ideal partner – small specialist wins over large generalist

E.ON decided against attempting to build its own proprietary IoT platform and instead select a specialist partner in a

central role of the planning and implementation process. At the time Greensmith was a start-up part-owned by

E.ON but later acquired by Finnish maritime equipment manufacturer Wartsila. The route chosen here could be

considered typical of the energy sector where deep sector understanding is most critical with big specialisation

factors at play. So whilst energy companies use global IT firms such as IBM and SAP to support general business

processes these vendors are less seen as partners for an energy company’s core operations.

Focus your goals on revenues as well as cost savings

E.ON considers its IoT systems to be in good shape for the defined task of predictive maintenance which is a prime

IoT application across all industry sectors. The core business, however is to generate revenue from generating

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power and currently there is a gap in the IoT platform in dispatching of assets4, for example under certain or defined

conditions, in a technical sense, and an effective energy management system for making economic decisions. This

means the ability of the system to promote, enable, and optimize the monetization of each megawatt hour that is

introduced to the grid.

Today this process is still managed manually by a human operator and E.ON plans to add an additional Energy

Management System (EMS) layer to the overall IoT system. There is a need across the energy industry for a single

system to manage both technical and economic objectives. The highly fragmented supplier landscape that includes

a sea of highly specialised small providers and often start-ups poses a structural barrier in this regard. Without this

single system the required aggregation of different platforms creates friction and results in mistakes in dispatching

the asset, and at minimum results in a suboptimal environment for extract maximum value from energy assets.

Human Skills are critical yet often not recognized at C-level

The development of E.ON’s IoT infrastructure is being held back by a lack of commitment at the company’s top

level. Not unlike many other companies trying to implement IoT specialist human resource requirements, such as

data scientists, were filled by existing internal resources or through outsourcing. A common approach towards the

needs of the responsible IT department might be described as “you know the business, you figure it out”, with little

willingness to recruit personnel with the requisite new skills.

In reality it is not possible to acquire the necessary knowledge and skills in a rapidly evolving technology space

through staff training and hiring subcontractors. Energy companies can be very hardware driven with software

considered secondary, with a tendency to look for suitable software solutions that fit and work with existing

hardware. Even just monitoring the market and having awareness of what possibilities exist poses a challenge to a

lean IT function.

This situation is not helped by the external reality that the energy industry is not front of mind at enterprise software

solution providers. This is due to the sector being perceived as operating at a slow pace. For an IoT vendor this

could mean the difference between six-month decision windows in this sector versus a mere six weeks in other

industries. Hence energy companies can face a lack of understanding and willingness to go deeper when dealing

with large generalist technology providers.

Promoting the revenue generating potential of IoT could be key

The first prerequisite to a successful IoT project is top executives that recognize the value of technology. This in

turn will be an easier sell by internal technology promoters if the focus is not merely placed on cost savings but also

on the revenue generating potential of IoT. A well-thought-out and researched internal development plan is

essential to obtaining the necessary C-level buy in which will provide the foundation for a successful implementation

starting with the necessary skills acquisition to ensure a sufficient software focus and solution awareness. As

outlined earlier, partnering with a proven specialist firm is also instrumental but to ensure healthy scaling and

longevity of the IoT system a balanced mix of external and internal resources is required.

4 An energy industry term for service delivery, in this case releasing energy from storage for a customer

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5. Summary of key recommendations for enterprises

1. Focus on use cases

As the IoT hype is waning, a focus on developing clear use cases is more critical than ever!

Placing use cases that can deliver measurable business benefits at the centre of the all IoT

planning and the entire development process is a must.

2. Obtain full C-level buy-in

IoT funding will entirely depends on the ability to successfully demonstrate ROI gains to the board.

This process needs to begin at the outset!

3. Do it as “IT lite” as possible

Following the first step of establishing IoT business value, focus on how to implement as low touch

as possible from an IT standpoint, at least initially.

It helps to build momentum for small wins and then turn those small wins turn into bigger wins.

Think big, start small, grow fast!

This approach will also help with the change and people management side of the process.

4. Turn on every data source

Data is cheap and it makes little sense being overly frugal with regards to data capture.

Some companies have learned this lesson and lost efficiency gains by having to go back at a later

point and retrospectively activate further data sources, which can be costly and challenging to

implement in hindsight

5. Choose the right connectivity network topology

Is the bearer network the most suitable for the use case, the applications, hardware and the

deployment overall? Is there sufficient network throughput and bandwidth for current and

anticipated application needs? Will it be possible to do FOTA (Firmware-over-the-air) updates?

What is the existing network topology? Is there a network already present, or access to one? If not,

what are the challenges and costs in creating a new network?

How much will it cost? Current and anticipated data transmission costs as the deployment evolves.

Will more edge devices/gateways be needed to lower the amount of data being sent directly to the

cloud, or to process some data locally?

Scalability: Does the setup allow for easy expansion of the deployment; can new devices be setup

and connected easily via the network to connectivity management platforms, for example?

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6. Consider a lead software supplier that knows your industry well

Many companies benefit from vendors with deep experience of their sector even if they are often

small start-up types.

Bigger is not always better – large global partners are more easy to find and may be considered a

safe pair of hands but they may lack specialisation and are less likely to go the extra mile and

deeper into the customer’s requirements. Specialist consulting firms can cover significant ground

for enterprises, provided they have core knowledge of the industry.

7. Recruit at least a data scientist

Whilst companies with limited existing skills may prefer to outsource and not invest in building

sizable internal teams from the start, it is recommended having at least one data scientist,

especially one that understands the business.

8. Cloud is the answer for data storage

Some companies end up with limitations or charges for access to their own data where it is stored

on their provider’s network, in some cases even on customer premises.

With the low cost of cloud storage companies should maintain control of every data source

available, because they are likely to find a usage for that data eventually. Some companies

retrospectively discovered that they could have delivered many more use cases a lot faster.

In terms of a choice between data storage on premise, in the cloud, or hybrid, the most cost-

effective answer is cloud, with full access control

9. Avoid adapting legacy technology to build an IoT infrastructure

Supervisory control and data acquisition systems (SCADA) are older technologies that many

consider a forerunner to modern industrial IoT systems. The line is blurry and many IoT rollouts

seek to build IoT on a legacy foundation. But this can become a serious security risk.

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