how to succeed in the iot: lessons from the market...the first step in any iot planning process...
TRANSCRIPT
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How to Succeed in the IoT:
Lessons from the Market
July 2019
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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
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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
Source: Strategy Analytics
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
Source: Strategy Analytics
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
Source: Strategy Analytics
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?
Source: Strategy Analytics
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?
Source: Strategy Analytics
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?
Source: Strategy Analytics
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/
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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.