AUTOMATION TO AUTONOMY:NAVIGATING THE PATH TO SUCCESSAUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

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CONTENTSEXECUTIVE SUMMARY........................................................................................................................ 02
1

INTRODUCTION.......................................................................................................................... 03

2

THE AMBITION........................................................................................................................... 04

3	INDUSTRY INSIGHT – THREE STAGES OF AUTOMATION ADOPTION........................................... 07
3.1

STAGE ONE – EMERGING.........................................................................................................................................08

3.2

STAGE TWO – MATURING........................................................................................................................................09

3.3

STAGE THREE – ADVANCED.....................................................................................................................................10

4	THE RIGHT TECHNOLOGY BUNDLE FOR YOUR AMBITIONS..............................................................11
4.1

DATA AGGREGATION FOR INSIGHT..........................................................................................................................11

4.2	SIMULATED ENVIRONMENTS FOR ACCELERATED DEVELOPMENT..........................................................................125

4.3

SENSOR FUSION......................................................................................................................................................12

4.4

COMMUNICATIONS TECHNOLOGY............................................................................................................................12

4.5

DIGITAL SECURITY..................................................................................................................................................13

CONCLUSION.............................................................................................................................. 14

WHY US?............................................................................................................................................. 15
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REFERENCES...................................................................................................................................... 15

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

WHITEPAPEREXECUTIVE SUMMARY
The pathway from automation to autonomy – and on to commercial success –needn’t be daunting. We are living through a time of unprecedented change, withthe events that began in early 2020 sparking the greatest acceleration of digital
adoption in living memory.
Everyone knows that phenomenal advances in artificial
intelligence, robotics, wireless connectivity and edge
computing are opening up transformative commercial
opportunities. Now is the time to seize them. This whitepaper
sets out to provide a springboard to the future, by offering apractical guide on how to successfully navigate the road toautomation and autonomy.
Our first rule is an important one: there is no straightforward,
one-size-fits-all, off-the-shelf solution. Instead, we advocate abespoke approach focused on a clearly defined business goal.
The paper will explore relevant success stories in the logistics
and automotive industries, reveal parallels to other industries,and examine three levels of emerging, maturing and advanced
adoption. There are plenty of lessons to be learned within
each, but ultimately the journey to successful automation
and autonomy will be yours and yours alone. The overview
of relevant technologies we provide, drawn from our deep
expertise, acts as a foretaste of the game-changing businessbreakthroughs yet to come.

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

1

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INTRODUCTION

For a while, the pandemic hit the world’s pause button, andthe image froze. But a great acceleration simply had to come.
Every recession has always quickened existing trends. Now,
the fast-forward button has been pressed for us. The current
global recession is no different to what we’ve experienced
in the past, just more extreme – with huge falls followed byhuge upticks, all within the space of 15 to 18 months. But
we are riding a pace of change more rapid than anything inliving memory. As momentum quickens, the flexibility andavailability of unskilled labour continues to decline. And evenat a time when money is tight, the desire to innovate to survive
and prosper in the longer term is overwhelming. Something’s
got to give, and greater automation stands ready to come tothe fore.
Since the first industrial revolution, the business case forovercoming a lack of available labour and skills by doing
things more efficiently has always been strong. But it has
never been as irresistible as it is now. At the same time, thebreadth of available technology and the myriad of ways it canboost the bottom line has never been so wide. Yet for all that,many organisations are still holding back. They know that thepathway from automation to autonomy is paved with hugebenefits, but they are still reluctant to move.

A big part of this could be put down to unfamiliarity.
Perhaps that chimes with you. Maybe you are part of anindustry underwhelmed by automation, and which views it ascomplicated, expensive and uncertain in outcome. If that’s
the case, please regard this paper as a roadmap for your routeahead, a touchpoint for your ambitions and a directional aid on
how to take the first steps.
We plan to provide insights and answers along the way.
Digitalisation, for instance, is a trend that has been accelerated
by the pandemic, but what does it actually mean in practice?
The goods you order online, the food you eat and the vehicles
that deliver both have had to evolve rapidly and will continue
to do so over the next decade. How do you compete in thisever-changing world? The answer is to establish precisely how
automation and intelligence can bring unique value to yourbusiness – and deliver on its promise without you having tospend a fortune on R&D activities.

“many organisations are still holding back. They know that the pathway fromautomation to autonomy is paved with huge benefits, but they are still reluctant
to move.”03

AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

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Image courtesy of Ocado Group2

THE AMBITIONFirst let’s be clear about what we mean by automation andautonomy. The former relates to the employment of machines
and processes to replicate a series of manual tasks. The latter
is an elevation of that, perhaps best defined as automating
a series of complex activities using machine intelligence toachieve the task in hand, with what might be termed selfgovernance. The technologies enabling these solutions canbe truly transformational for ambitious companies. Globally,
there are numerous instances of organisations leapfrogging
their competitors on the back of bold breakthrough innovation.
The Ocado Smart Platform (OSP), for example, is Ocado
Group’s world-leading suite of solutions that has been built totransform online grocery across the world. The OSP combines
Ocado Group’s proprietary end-to-end software systems
with their physical fulfilment assets to deliver superior
economics for grocery retailers and unmatched experiences
for consumers. As a strategic OSP technology partner, we
worked alongside Ocado Technology to develop the uniquecommunication system that sits within the OSP, orchestrating
swarms of bots across highly automated warehouses.
Others have identified the right level of automation andautonomy for their business – and acted accordingly. This isa key but often overlooked point. There is no such thing as
a one-size-fits-all approach to automation. Indeed, the mostpowerful way to avoid the obstacles to greater automation is toinvest time upfront on establishing a bespoke strategy to meetyour needs. Where to start? We advocate beginning by getting
a fix – and perhaps identifying your place – on the automation
to autonomy spectrum.
As the chart here shows, it’s possible to define three levelsof automation and autonomy, broken down into a data stage,a data plus process stage and a data, process, action stage.
The first could be described as ‘semi-intelligent’ in that thesystem collects and logs data about its performance but does
not have the ability to make any decisions. Often, the data will
be used in the future to improve efficiency.
At the second ‘intelligent’ or ‘semi-smart’ level – data plus
process – the system collects data and processes it into realtime actionable information. This is then used by the systemor a human operator to make optimal decisions. The third‘smart’ level adds the action. Here, appropriate actions aretaken in a way that is completely autonomous, dynamic andadaptive to changing operating conditions.

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

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Traditional manual motoring is level zero, with the driver fully
responsible for all aspects of control. At level one, the driver
can delegate one of the driving tasks to the system, while attwo, the driver is monitoring as the system performs several
tasks. At three, the driver can turn their attention away, as
the system controls the vehicle on defined routes. At level
four, the driver can hand over complete control (and take itback whenever they wish) while the system performs all thetasks. At five, no driver is necessary as the system controls thevehicle autonomously at all times.

Despite what some might believe, automation and autonomy
is not about replacing humans, it is about augmenting an
existing labour force or individual with data, insight and tools
which improve efficiency and productivity. This is reflectedin our spectrum, which in the simplest terms can be thoughtof moving from raw data to insight, to response. A good way
to illuminate this further is to consider the Levels of Driving
Automation standard for self-driving vehicles published by
SAE International, the global association of engineers andrelated technical experts in the aerospace, automotive andcommercial vehicle industries.

“There is no such thing as a one-size-fits-all approach to automation. Indeed, themost powerful way to avoid the obstacles to greater automation is to invest time
upfront on establishing a bespoke strategy to meet your needs.”

Data
+

Data

Process

Data

Process

ActionSemi-intelligent
systems

Intelligent /semi-smart systems

Smartsystems

Collects and logs datasuch as its own usage,
environmental conditions
and performance without
any ability to make decisions
based on the acquired data

Collects and processes data
into realtime actionable
information which is thenused by itself or a humanoperator in making optimal
decisionsCollects and processes data
and takes appropriate actions
in a manner that is completely
autonomous, dynamic andmost importantly adaptive tochanging conditions++

Automation

Autonomy

FIGURE 1: Adapted from levels of smart infrastructure by the Royal Academy of Engineering

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

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It probably goes without saying that wherever you are on
the automation to autonomy spectrum, a complex mixof advanced technology is required at each and every
point. There is plenty to be gained from understanding
the necessary sensor technologies, the AI algorithms,
the simulation techniques to speed machine learning, thesoftware and firmware and the sophisticated digital security
required. We’ll explore all these in this report, as we spellout the importance of identifying the right technology
bundle to meet individual automation challenges.

A couple of further points about your ambition. The first
concerns investment. Although it might appear a tempting
option, it can be a false economy to be constrained by offthe-shelf technology. Humans are endlessly adaptable, but wecan’t expect machines to be equally flexible. Your unique needs
demand a unique solution that matches your specifications
exactly. The second point is to pay close attention to value
creation and your new business model. Just because youcollect data doesn’t mean that you automatically have theinsight necessary to pave your route to revenue.

In the early stages of automation planning, the focus should
remain resolutely on the challenge at hand and the practicaloperational and efficiency gains that are to be had. Often,
innovators quickly come to the realisation that full autonomy
is not appropriate. There are sometimes exceptions to thatrule of thumb. Full autonomy, for example, can sometimes bewell suited to a warehouse, which is a well-controlled, wellconnected environment. It is much more challenging to createthat level of autonomy in an unstructured environment such asa building site, theme park or airport.

Once again, this links to the need for the sort of bespoke
solutions that can deliver a return on R&D investments geared
to your sector and disruptive business model. You’ve no doubt
heard plenty of talk around the Fourth Industrial Revolution,
Industry 4.0 and Industrial IoT. Much of it is focused on data,data, data. That’s all very well, of course, but data althoughnecessary is not the end in itself. Data without insight adds
no value, but success will come for those who cherish insight,
and react accordingly. This is the real key to putting distance
between you and your competitors.

Nevertheless, more limited levels of automation can still havegame-changing operational impact. Consider, for example,
how onboard intelligence can help human operators deliver
better results much more quickly (at the data plus process
stage). Bulldozers with precise grade levelling, excavators thatknow precisely how deep to dig, tractor ploughs benefitingfrom precise mapping. These are big gains.

“it can be a false economy to be constrained by off-the-shelf technology. Humans areendlessly adaptable, but we can’t expect machines to be equally flexible.”06

AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

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3	INDUSTRY INSIGHT –THREE STAGES OF AUTOMATION ADOPTIONIn this section we are going to explore three stages ofadoption: emerging, maturing and advanced. In each case
we have chosen indicative industries, but there are of course
many more examples for each. As for our definitions, emerging
relates to an industry still in the relatively early stages ofapplying such solutions. Maturing is where the sector has

developed a number of proven products, but with plenty ofroom for further innovation. At the advanced stage, the sector
has developed successful technologies and can clearly point
to a number of significant benefits that have transformed theirbusiness model and market.

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

3.1 STAGE ONE – EMERGINGSMART INFRASTRUCTURE SECTOR
Smart infrastructure, by which we mean intelligent road, rail
and transportation infrastructure and traffic solutions in andbetween cities, is just starting out on its journey of digitisation
and automation. Construction, a crucial part of the mix ofcourse, has some of the lowest rates of digital and automation
adoption, and has been stuck in a cycle of low productivity
and low profitability for decades.1
Significant economic stimulus would offer the opportunity to
break this cycle, in the shape of considered investment in
an automation to autonomy pathway that links the digital andphysical worlds. The pathway should dictate the prioritisationof the assets to be constructed. For example, the catalyst for
smart road development will be the increasing prevalence ofautonomous vehicles. There will be a pressing demand for theroad infrastructure to deliver information that is not accessibleon the vehicle itself.
Surely then, there is an opportunity for infrastructuredevelopers and operators to add significantly to their value?

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On the surface yes, but the industry is characterised by acomplex ecosystem of suppliers, something that is common
across most of the smart infrastructure space. There are real
challenges to making sure that the areas of the value chainthat require significant investment in automation are also
those that benefit in the long term from that investment.
The potential for gains are significant. Recent McKinsey
reports estimated that digital transformation can result in
productivity gains of 14 to 15 percent and cost reductionsof 4 to 6 percent.1 2 The pathway to those gains, however,is often unclear. Anyone entering this space is looking at avast sea of possibilities and might very probably have had anumber of unsuccessful attempts in the past. Working out
what the best first steps on the roadmap for development
should be – amongst all of the potential options – is achallenge. This is why experience of identifying the mosteffective route is so invaluable.
Ambition is also important. The future of smart infrastructureis big picture stuff and demands broad, bold thinking. The
desire for success must transcend any single-point solution. A
number of steps – like getting all information into a single data
lake – are important, but to really benefit from the power ofdigitisation and automation, system-wide thinking is essential.
The development should be built on technology of course, but
it is also about people and processes. Without these elements
working together seamlessly, any isolated initiative is doomed
to have minimal impact or not scale successfully. Pockets of
excellence usually exist in companies setting out to innovatein this space, but the really successful ones will find a way tonurture that excellence and link it together powerfully.
Identifying short, medium and long-term value opportunities
and placing them within an overarching vision – a common
operating picture – is key to successful engagement andplanning. Immediately, crucial questions can be addressed.
What technologies exist to support the opportunities? Whatmight need to be developed and on what timescales? Creatinga strong vision to motivate your organisation and give clarity
on why actions are being taken is also vital. It is a springboard
to actions that deliver an immediate tangible benefit. In otherwords, the first firm and irrevocable steps on the journey of
automation to autonomy. Smart infrastructure is an emerging
and viable industry in which to make this happen.

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3.2 STAGE TWO – MATURING
AGRITECH SECTOR
Overall, agritech automation is an area that is rapidly maturing,but there remains plenty of scope for further development.
Intelligent automation and closed loop control – where devices
automatically regulate a process without human interaction –are widespread. Examples include self-levelling spray booms
and ‘see and spray’ technology that can differentiate between
cultivated plants and weeds.
On the whole, agritech equipment vendors are adept at
developing devices when the signal emanates from a traditionalsource, such as strain gauges or torque measurement devices.
The story is slightly different when it comes to signals fromless traditional sources like still images or video. This is stilla developing area which requires more sophisticated process
and interpretation intelligence. It explains why robotic fruit
pickers are still very much at the prototype stage. The abilityof the machine to know precisely ‘what good looks like’ is thenext exciting stage of intelligent implement development.
That said, agritech is nothing short of a world leader inautonomous machines. The pioneering US machinery
corporation John Deere has marketed self-steering combine
harvesters for more than a decade. Elsewhere in the industry,
planting machinery guided by real time kinematics GPS
systems is accurate to one to two inches. Here, an operator
only needs to intervene when dusty or wet conditions, or
perhaps a tricky sun angle, cause problems.
Today’s pressing challenge, and indeed opportunity, is toexpand the range of use cases that autonomous vehicles
and machinery is comfortable with. The key to unlocking
further adoption is data, but of course the seasonal natureof agriculture means that obtaining this vital resource is verytime consuming. To overcome this, an increasing number ofinnovators are moving to simulated environments to accelerate
development and reduce R&D costs. Simulated environments
lessen the need for collection of millions of hours of real-world
data, through the ability to allow for synthetic data to be used
to assess and refine autonomous equipment performance.
Recently adopted by the automotive industry in an effort
to reduce the huge cost of developing autonomous cars,
simulation environments are now being adapted for use in
off highway environments like agriculture, construction andmining. You can read more about this subject on page 12.

“an increasing number of innovators
are moving to simulated environments
to accelerate development and reduce
R&D costs.”

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

3.3 STAGE THREE – ADVANCEDLOGISTICS & RETAIL SECTORThis sector is well into its journey to greater digitalisation andautomation. The arc of innovation is widely documented here,with many use cases and numerous examples of companies
making massive strides to transform their business model – andleapfrog their competitors. Some have developed breakthroughcommunication systems to enable swarms of robots in highly
automated facilities. Amazon’s vast warehouse operations area good example, where robotic systems have been put in place
to supercharge its order fulfilment process and make same-day
delivery a widespread reality.
Even before the pandemic, retailers were under pressure tokeep up with rapidly evolving supply chains and customer
habits. Not only have payment methods and support services
changed, but inventory and logistics functions also continue
to shift. These developments are encouraging both brickand-mortar and e-commerce companies to look to artificial
intelligence and robotics in retail.
From supermarkets and consumer electronics to clothes andpharmaceuticals, all retail sectors must continue to drive
innovation to remain competitive. Both online and physicalstores are continually searching to improve efficiency,
customer satisfaction and employee retention. Amazon, alongwith fellow retail giants like Walmart, are using their robots
in warehouses and stores for all sorts of functions, including
inventory scanning, materials handling and even cleaning. To
be useful and effective, automated guided vehicles (AGVs)
and autonomous mobile robots (AMRs) require operating

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systems and data collection and analysis tools that are robust
and compatible with existing retail systems.
In our view, autonomous service and delivery robots are
not just there to better serve customers, they are there tosave retailers from possible demise. Not so much a gamechanger as a game-saver. The sector is advanced in terms ofautomation, but challenges to further development remain. A
big upcoming need, for example, is a multi-vendor, multi-site
open-orchestration platform running different autonomous
systems in a warehouse. Wireless communication, digital
security, interoperability will all be key enablers for this. (Seethe following section 4 for more details.) Another big questionis how shipment companies can reduce shipping times andcost. Large operators have anything from 20 to 40 manual
touchpoints from shopping cart to the customer’s door. Data
aggregation and sensor fusion will be among the enablers here.
Some industry analysts have predicted that robotics
in will be instrumental in more than three-quarters ofretail logistics operations, with McKinsey making a ratherbold estimate that autonomous vehicles will make up 85%
of deliveries by 2025. That seems a stretch, but the trend
is clear. For many logistics organisations, most of the costis carried in providing last-mile service. This is driving an
increased attention on super-efficient, real-time route planningand automated drones, vehicles and pods of all descriptions.
A good example here is the FedEx ‘same day bot’ Roxo, whichhas passed legislative hurdles to operate as a last-mile delivery
service in five US states. Further testing is under way to target
more consumer use cases, from pizza delivery to groceries.

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COMMUNICATIONSTECHNOLOGYDATA
AGGREGATIONFOR INSIGHTSIMULATEDENVIRONMENTS
FOR ACCELERATEDDEVELOPMENTSENSORFUSION

DIGITAL
SECURITY

4	THE RIGHT TECHNOLOGY BUNDLE FOR YOUR AMBITIONS
Automation to autonomy is always a unique journey. Eachorganisation will embark and jump off at a different point,depending on the level and exact nature of corporate
ambition, as well as the level of investment ambition. There
are multiple roads to follow, influenced by the overall goalthat is to be achieved. As a tech agnostic organisation with amultidisciplinary breadth of expertise, we invest a lot of timeworking with our clients to guide them through a complex andever evolving technological maze.
Your ideal package would reflect this and would almost certainly
include elements from some particularly effective approaches,
techniques and technologies. Here is an overview of some of
the most potent components of successful automation.

4.1 DATA AGGREGATION FOR INSIGHT
As we’ve made clear already, data is nothing without insight.
New techniques are emerging to ensure that data that is derived
from sensors can be interpreted rapidly and acted upon locally.
Imagine, for example, an autonomous agricultural machine
operating in the field. It can’t rely on intelligent decisions
being made in the cloud – the latency and delayed dataprocessing involved as well as connectivity challenges would

negate efficient operation. But if the intelligent computation
power, in other words the AI processing capability, is situatedon the device itself, the data can be processed immediately,
and the action can happen straightway.
This ‘AI at the edge’ approach is well illustrated by Fafaza,a breakthrough crop spraying concept developed here at
Cambridge Consultants. It performs both plant recognitions
and precise, individualised treatment in the field, in realtime. The system comprises off-the-shelf components andan AI compute platform costing less than $100. A low-cost
camera identifies the target by texture, and machine vision
probabilistic programming tracks the target and determines
where to aim and when to fire. Everything is identified,classified and applied in real time, at the speed of the tractor.
This is the power and cost-effectiveness of edge AI.
Another good example from the Cambridge Consultants stable
is a fruit-picking robot that tackles the complex picking andsorting tasks involving irregular organic items. Unlike withrepetitive production line tasks, fruit-picking robots must beable to work around people, cope with irregular items, andadapt to a changing environment. Designing a robot thatis able to it requires many tasks to be performed – fromrecognising the correct objects and calculating what order to11

AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

pick them in, to planning the grip, and the lifting and placingof the items. The solution comes in the form of world-classindustrial sensing and control that combines high-powered
image-processing algorithms with low-cost sensors andcommodity hardware to allow ‘soft’ control of robots when thetask is not rigidly defined.

4.2	SIMULATED ENVIRONMENTS FOR
ACCELERATED DEVELOPMENT
Developments shaped around automation and autonomy rely
on inherent intelligence from AI, machine learning and deeplearning. They, in turn, depend on feeding in vast volumes ofreal-world data. But the task of generating it is time consuming
and extremely expensive. It is for this reason that innovators
are turning to synthetic data and simulation to drasticallyreduce the cost and timescales of development.
Let’s imagine a delicate and precise robotic task – picking
strawberries in a field, say. It would be prohibitively slow and
expensive to train the robot exclusively in real life conditions.
But complementing a certain amount of in-field training withsynthetic data allows the machine to be tested in thousandsupon-thousands of simulations. The development proceeds
more rapidly and the innovator gets to market before theircompetitors. When it comes to simulated environments, aninteresting example can be seen in the automotive industry,where mature gaming engine platforms such as Unity andUnreal have been used to speed the development of algorithms
and scene understanding on the road to autonomous driving.
The algorithms can be rigorously tested for obstacle detection,
location precision and path planning without the need for
expensive hardware and time-consuming real-world trials.
Further, simulation environments allow for testing or responseto rare or usual operating conditions. These ‘edge cases’ arevery difficult to obtain in the real world because they require
specific combinations of weather and obstacles. Simulation
environments offer the opportunity to recreate those dusty,
low sunlight, wintry conditions that could overwise lead to asignificant failure and determine interesting combinations of
sensor suites to cope with such eventualities.

4.3 SENSOR FUSION
Essentially, sensor fusion is the process of combining datafrom disparate sensor sources to provide insight that is morecertain than it would have been from the individual sources.
To illustrate its efficacy, let’s return to the autonomous

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driving. Currently, reliable perception solutions are extremely
expensive. This makes it tough for industry players trying toestablish a viable industry model to push autonomy behindthe luxury segment and into mid-range vehicles. Creating an
accurate depth picture is one of the biggest obstacles.
The problem is exacerbated by one of our key themes – thebarrier to innovation caused by the need to capture massive
amounts of real-world data to train the system. In response to
the challenge, a team here at Cambridge Consultants developed
a concept called EnfuseNet,3 which fuses data from extremely
low-cost sensors and cameras to generate high-resolution
depth data, the reference point that autonomous systems
need. The system was created within a modular and flexible
AV simulation environment which enables faster and cheaper
development of the model. Consequently, the complexity of
sensor fusion calibration, real-world interference, noise andother effects can be dealt with at the earliest stages.

4.4 COMMUNICATIONS TECHNOLOGYAs this paper is illustrating, the post-pandemic world is
witnessing an accelerated democratisation of access to AI
techniques, enabled by faster and cheaper compute, betterdata and significant advances in machine learning techniques.
AI and edge computing have reached a level of maturity needed
for widespread and effective implementation which is being
boosted by a third key component – wireless connectivity.
Communications technology, in the form of wireless networks,
satellite communication networks, cellular networks andmany more, continue to transform our personal and businesslives. The availability of radio spectrum for 5G is opening
new opportunities across the world for enterprises to own
and operate their own private networks, for example Citizens
Band Radio Service (CBRS) in the United States. Ambitious
enterprises, for example, are seizing on the advantages of
newly released spectrum that allows them to create private
5G networks. This new connectivity opens the way to a hostof new industrial use cases – not just in relatively easily
controlled environments like warehouses but in outdoor areas
such as farmyards and travel hubs. Let’s take a busy shipping
port as an example. Previously, without usage data provided
by wireless connectivity, maintenance of a crane would
follow a fixed schedule or happen when it breaks down. But
streaming data enables the operator to predict the breakdown,scheduling maintenance accordingly. Both the use of theequipment and general efficiency are improved significantly.

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AUTOMATION TO AUTONOMY: NAVIGATING THE PATH TO SUCCESS

4.5 DIGITAL SECURITY
The technologies concerned with digital security are taking
an ever more influential role in the automation to autonomy
journey. It is clear that higher levels of connectivity andautomation bring with them increased risk from hostile
players. Such systems are likely to be a target from not justcriminals but also nation states. This is true across sectors
and even in agriculture, which is part of the Critical National
Infrastructure, as designated by the CPNI (Centre for theProtection of National Infrastructure) in the UK.
Our advice is always to build security in from the start of any
automation or autonomy based development. There are many
examples of solutions developed for a particular industry
then being used in another, with security compromised asa result. It is important to work from the ground up, with afocus on robust system design. Formal design processes will

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ensure that the risks and vulnerabilities of the product orservice are addressed. This means establishing the security
requirements to set the overall strategy, define the security
architecture and system design, and test to verify regulatory
and legal compliance. A final word here: threats in the far
future also need to be considered. At Cambridge Consultants,
our experts are developing solutions to meet the quantum
computing challenge. The coming quantum revolution willbring a magnitude of gains over today’s classical computing
approach, as well as greater security risks. Work is alreadyunder way to understand the challenges and develop new,secure architectures to protect assets that will be deployed for
decade-long timeframes, such as infrastructure.

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CONCLUSION

There are several clear strands that inform effective automatedand autonomous systems. Moving successfully from data
capture to deep insight will not only reduce development
time and costs, but it offers up the potential for new revenue
streams. Intelligent automation will improve efficiencies
for you in manufacturing and operations and also for yourcustomers by optimising their ‘jobs to be done’, saving timeand resources. World-first autonomy will deliver product andservice differentiation capable of putting clear water between
you and your competitors, increasing market share in theprocess. At the heart of successful implementation sits abundle of technology options:
DATA AGGREGATION FOR INSIGHT – new techniques are emerging
to ensure that data that is derived from sensors can beinterpreted rapidly and acted upon locally

SENSOR FUSION – the game-changing ability to combine datafrom disparate sensor sources to provide insight that is morecertain than it would have been from the individual sources
COMMUNICATIONS TECHNOLOGY – wireless networks, satellite
communication networks, cellular networks and many more,are ready to help transform industry
DIGITAL SECURITY – a vital component of a successful automation
strategy, to combat the heightened risk
The secret is to pursue your ambitions with the optimal mix oftechnology tailored to your unique needs, and with a partner
capable of providing clear, impartial and objective advice. The
Cambridge Consultants team has a proven record of successin automation and autonomy – and would be delighted tocontinue the conversation.

SIMULATED ENVIRONMENTS FOR ACCELERATED DEVELOPMENT –synthetic data and simulation is now able to drastically reducethe cost and timescales of development

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WHY US?
We can draw on deep expertise to offer a uniquely broad
response to your automation to autonomy challenge. Our skills
and knowledge combine with real-world application experiencethat has been earned by multidisciplinary in-house teams.
Above all, we love to create collaborative partnerships withour clients, and are proud of our record of developing andmaintaining long-lasting, mutually beneficial relationships.
The team is ready to help get you where you want to be – with
reduced development risk, time and cost.
Learn more:
www.cambridgeconsultants.com/automation-autonomy
Please get in touch to discuss your ambitions

AUTHOR
Sajith Wimalaratne, Head of Automation to Autonomy
sajith.wimalaratne@cambridgeconsultants.com

CONTRIBUTORS
Niall Mottram, Head of Industrial and Energy
Kieran Reynolds, Head of Smart Infrastructure

REFERENCES
1

McKinsey & Company [online] 2016, Decoding
digital transformation in construction. Available athttps://mck.co/3makkAA [Accessed Aug. 2021]

2

Mckinsey.com 2017, Reinventing construction:
A route to higher productivity. Available athttps://mck.co/2W3fzxt [Accessed Aug. 2021]

3

Cambridge Consultants 2020, https://enfusenet.
com/ [Accessed Aug 2021]15

About Cambridge Consultants
Cambridge Consultants has an exceptional combination of people,
processes, facilities and track record. Brought together, thisenables innovative product and services development and insightful
technology consulting. We work with companies globally to help them
manage the business impact of the changing technology landscape.
We’re not content to deliver business strategy based on target
specifications, published reports or hype. We pride ourselves on
creating real value for clients by combining commercial insight withengineering rigor. We work with some of the world’s largest blue-chipcompanies as well as with innovative start-ups that want to change
the status quo fast.
With a team of more than 900 staff in Cambridge (UK), Boston, San
Francisco and Seattle (USA), Singapore and Tokyo, we have all the inhouse skills needed to help you – from creating innovative concepts
right the way through to taking your product into manufacturing.
Most of our projects deliver prototype hardware or software and trial
production batches. Equally, our technology strategy consultants
can help you to optimize your product portfolio and technology
roadmap, investigate new opportunities or refine your operations.

For more information, or to discuss your requirements,
please contact:

UK  USA  SINGAPORE  JAPANwww.CambridgeConsultants.com
Cambridge Consultants is part of Capgemini Invent, the innovation,
consulting and transformation brand of the Capgemini Group.
www.capgemini.com

Lit-WHI-054_v0.6 '2021_A2A II_Whitepaper'

Sajith Wimalaratne
sajith.wimalaratne@cambridgeconsultants.com