How to use Industry 4.0 technologies to weather the Covid-19 crisis
Industry 4.0 technologies have come into their own in helping combat COVID-19.
China confronted the virus with a futuristic mix of artificial intelligence, machine learning, and robots.
Now that the epicentre has moved to the western world, leaders look to China for clues to stop the spread.
Here’s a look at how China’s use of 4.0 tech is now influencing the way America and Europe identify, treat and track the virus.
A voice of warning
Speed and accuracy of information are everything in a crisis.
The first global warning of the virus didn’t come from the World Health Organization (WHO) or the US government.
No, it came from artificial intelligence. A Canadian company named BlueDot used an algorithm to identify the possible outbreak days before WHO made its announcement.
BlueDot uses AI to analyse news reports and internet data to detect the spread of infectious diseases. The algorithm predicts where diseases will spread, based on millions of flight itineraries. With this information proving invaluable, BlueDot is now working with countries in North America and Southeast Asia to predict virus hotspots.
There are widespread complaints of testing shortages.
On top of that, there are concerns about the long process of taking a sample, analysing it in a lab and reporting the result.
Luckily, necessity remains the mother of invention. Several companies are racing to invent easier, faster ways to test.
Researchers at UK universities are trialling a smartphone app that can give results in just 30 minutes. The app is linked to a small device that analyses a nasal or throat swab. No lab necessary.
It’s no surprise that supply chains are still recovering from the shock of the pandemic.
Hospitals are experiencing a testing swab shortage, owing to supply chain disruptions from suppliers in Italy and China.
Several hospitals are making their own test swabs with the help of 3D printers. One medical provider in New York, called Northwell, is printing 3,000 swabs a day. Side-by-side test results show the 3D-printed swabs are just as reliable as the traditional swabs.
There’s also a swell of companies using 3D printing to make facemasks and other personal protective equipment (PPE).
Authorities in China found a safer way to take temperature: augmented reality (AR) glasses.
Someone wearing the glasses can identify a person with a fever from 10 feet away.
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What’s next in supply chain systems? There’s plenty to be excited about
supply chains were good at automating and optimizing processes. But they were
restricted to functional silos – and that’s not enough for what we need in
supply chains today.
Advances in supply chain technology are needed if procurement teams are to manage supply chains that are dynamic, responsive and interconnected with ecosystems and external processes. The new tech needs the capacity to manage much, much more data (by several orders of magnitude). This in turn will make it possible for an individual procurement manager to make sense of entire supply chain ecosystems in real-time.
demands are driving progress – which is why I am excited about the future of
supply chain technology.
actually getting fairly good at applying AI repeatably in supply chains.
order to continue to maintain the labor ratios and level of service to which
we’ve become accustomed, we need AI within supply chains – this is
The IBM Sterling Supply Chain Suite gives end-to-end
visibility, real-time insights and recommended actions to turn disruptions into
opportunities for customer engagement, growth and profit.
an open, integrated platform that easily connects to a company’s supplier
ecosystem. And that connection and openness provides the data necessary to
build self-correction into supply chains.
2. With blockchain, we finally have a chance to change the way we manage multiparty sharing of supply chain data.
clear that use of AI in supply chains will be essential. But it is important to
start from the understanding that organizations are at different stages of
maturity in this area. Nevertheless, companies can make dramatic improvements
simply by deploying existing tech to digitize and implement an organizational
commitment to information hygiene and managing data effectively. Being able to digitize,
catalogue and normalize supply chain data means having real-time information in
the right place to make decisions quickly.
from the old-tech world of supply chains is the use of enterprise resource
planning (ERP) systems built to manage the data for each individual company.
Each company’s ERP was its view of the world. The procurement team spent their
lives comparing notes with other ERPs to reconcile differences. Everything from
invoices to purchase orders had to be reconciled and supply chain processes
were put in place to facilitate this.
old-world supply chain really to change, we need to recognise that we can’t
each have our own copy of what we believe to be true. We need to have an
accepted, shared view of the truth. This idea of multiparty shared data is a
promising one. And technology such as distributed databases, shared ledgers and
blockchain helps build these common views of the world.
3. We are seeing
the emergence and coordination of specialty
ecosystems and networks that can be integrated in a ‘network of networks’.
hyper-interconnectivity and the opportunity to create ecosystems or a network
of networks, we operated in a limited way – for example, connecting one value-added
network (VAN) to another VAN in a logistics network with practical applications
like document exchange for advance shipping notices and the like.
now seeing that an interconnected ‘network of networks’ really adds value.
People are using technology and data to work together to solve domain-specific
issues like fresh food provenance with Food
ocean-shipping visibility with TradeLens.
specialized ecosystems can be seamlessly integrated into existing business
networks to provide a wealth of information about previously opaque areas of
the supply chain – where things went dark at critical moments.
possible to have personalized ‘control towers’ that can track the essential
elements of global ecosystems but are tuned to what we each want to measure and
we’re able to see the world the way we want to – from each of our perspectives
– bringing together actionable recommendations from real-time intelligence to
act on supply chain implications.
a simple example of inventory management that can have downstream supply
implications for a logistics analyst, to the same information tracking
financial implications and payment terms for a financial analyst, the varying
views, insights and interrelated metrics stemming from core supply chain
activity helps everyone across the organisation.
knowing that no two supply chains are the same means the ability to quickly
configure and personalize ‘control towers’ is twice as useful as simply having
the static data.
So just when the need for a strong supply
chain has never been greater, technology is increasingly proving itself up to
the challenge of meeting this need. And what’s more, small changes can have big
Knowledge is power, but knowledge is now being democratised and made accessible to all, thanks to the development of AI.
Long live the democratisation of data
Is there someone in your work life who is hoarding information? Holding the data cards very close to their chest? Making it difficult for you to succeed because they have vital information and know-how shackled up close to their desk?
news – their days are numbered!
is power, but knowledge is now being democratised and made accessible to all,
thanks to the development of AI.
A democratisation of data
supply chain, data plays a very critical role; data about suppliers, shortages,
shipping and shelf life, the list goes on. And supply chain professionals are
inundated with making sense of all this data.
to unlock the value from this data we’ve needed a group of people with deep
technical skills in our teams to gather, manage and query. Exhausting and time-consuming work, leaving
little space or brain power for problem solving and decision making. The need for these skills has concentrated
the power of data in the hands of a few, rather than the wider team.
Nobody knows this better than the supply chain team at IBM. With thousands of supply chain employees, over $40billion in spend and millions of SKUs to manage from over thirteen thousand suppliers in their supply chain across 175 markets, there is a lot of data to keep track of. There is a real need to ensure every supply chain professional has all the information to make the right decisions at the right time.
I reached out to IBM’s Chief Supply Chain Officer Ron Castro – firstly to congratulate him on his Manufacturing Leader of the Year by the National Association of Manufacturers. However, I also asked him to participate in our Supply Chain Career Boot Camp and then went on to quiz him on the detail behind why Gartner had been recognised by the IBM Supply Chain team as a Finalist in their Chainnovator Awards.
the scale and complexity of the IBM supply chain, Ron and his team turned to AI
to augment the team’s capabilities.
experience leading teams across the globe resulted in a really pragmatic
approach. AI was used to upskill supply
chain talent and engage with subject matter experts. The analytics and tools
developed gave wider access to data insights for their supply chain pros around
everyone in IBM’s supply chain can make better decisions and be creative –
which is just the kind of capability needed in this new and challenging decade
no more tedious data capture and formatting for the IBM team. No more worrying that they’ve missed something in the
never-ending news stream or even the weather forecast.
The Human + Machine Personas
For many years, the IBM Supply Chain team has known that one type of tech solution couldn’t fit all the needs of their team. Everyone has different data needs according to their role – some are forecasting, others are planning and many are executing or delivering.
IBM’s approach is simple – it’s people-centred. Data personas were created to map each supply chain team member’s requirements. Now AI serves up data in the format and time that suits their needs.
Sterling’s AI helps you:
Gain visibility into
data from across your systems and silos
events and their impact on your supply chain
Get ahead of events
and buy yourself time with predictive insights
Capture and share
knowledge and best practices with digital playbooks
By creating these personas, IBM Sterling uses AI to provide just what the forecaster needs to augment their brain and make the decision to keep those supply chains flowing.
final piece of the jigsaw is a concept that’s close to my heart –
IBM Sterling’s AI reviews unstructured data in its many and varied forms. Whether it’s emails, discussion threads or reports, AI now has the power to find insights from previously inaccessible data sources such as team conversations, social media and news feeds, and weather reports… and serves it back to the person who needs it, when they need it. AI makes key suggestions like:
Why don’t you consider this? – “They used it in the UK when
weather conditions were similar”
Is this a change in risk level? – “The last time this supplier’s lead times
dropped to this level there was an underlying shortage issue”
It’s exciting thinking about the improvements in supply chain from the introduction of AI Augmentation. I think we’ve only scratched the surface and can’t wait to see what happens as the power of IBM Sterling’s AI is unleashed on our supply chain brains.
AI is increasingly involved in recruitment. But how do you get on the right side of a computer that is reading your CV, running an aptitude test or assessing you in an online interview?
It’s impossible to argue with a computer, which is why the famous Little Britain TV comedy skit – ‘The computer says “No”!’ – is so memorable. However, there are ways to get around recruitment algorithms and perform better in an AI video interview.
You have just a few seconds (between 5 and 7)
to impress someone with your CV. Hiring managers will quickly scan your résumé to decide
whether or not to reject your application.
It’s easy to spot ones that will be
instantly dismissed: too short or too long (2 pages max), too unusual (the
rejection rate for those with photos is around 88%), badly presented and
littered with spelling mistakes . . . with barely a glance, these will all be
filed away (or binned).
It doesn’t give you much time to make a
However, if you think that someone in HR is
hard to please, try impressing a computer algorithm.
A human being might, at least, see your potential if you write a convincing personal statement and a powerful cover letter showing that you have the ability and determination to succeed in a role for which you don’t quite have the right qualifications or experience.
When the process is automated, whether or
not you get past the first few stages of the hiring process is all down to
data. If you fail to score highly, you’ll never get hired – however brilliant
you are. So what are the clever hacks?
Always include everything asked for in the
job spec in your CV . . . and use exactly the same words.
So if the candidate requirements say ‘Must
be proficient in Excel’, say ‘proficient in Excel’ rather than ‘Have experience
of using spreadsheets’.
Yes, you might not quite have the required level of expertise, but you can then explain that. The main thing is to pass the first hurdle. You could, for example, say ‘Proficient in Excel: with a relevant qualification’ – then go online to sites such as reed.co.uk or udemy.com and sign up for an online course. For £10 or so and 4–16 hours of online study you could have a qualification.
The other advantage is that you can then add this to your LinkedIn profile and other job applications.
At the very least make sure you include all the ‘musts’ and as many of the ‘desirables’ as possible.
Tailor your CV to each job. You
won’t know in advance which applications are screened by algorithms and which
by a human being . . . so play safe.
Don’t lie – but be creative. If
the job spec requires ‘At least 5 years in a leadership role’ you could add in
leading a team (even if that was only 2 of you) or leading a project, to
stretch your years of experience to 5.
Remember your aim is to get to
the interview stage – most firms are struggling to find candidates that tick
all the boxes, so don’t be afraid of applying for jobs where you don’t quite
have all the qualifications and experience that is required. As long as you
pass the initial screening, you can then elaborate on your answers in person .
. . and hopefully impress the interviewer so much that you land the job.
Increasingly often employers are posting online assessment tests to pre-screen applicants.
If possible, set up a dummy account, so that you can go through the process and familiarize yourself with it before doing it for real. Also see if there are any similar aptitude tests online.
If the test is timed or a stretch,
you might want to do a test run several times. However, if you find the test a
real struggle perhaps this isn’t the job for you.
If the employer leaves the
assessment until the day of the interview, prepare – you might be asked to
prove your proficiency in a particular program, so go online and do a quick
refresher course to get up to speed.
Some employers also undertake personality
profiling to make sure you have the right characteristics for the role.
The key with this is to be totally honest.
Relax and complete the assessment truthfully – using the first thing that comes
to mind as your answer, rather than overthinking each question.
If you lie in a personality test, it can be
easily spotted. Often assessments take this into account – as they know that
people tend to answer with what they think they should say, rather than what
they honestly feel in the first 10 or 20 answers. After that they tend to relax
and tell the truth.
Being honest is important – if
you are the wrong fit for the job, it will not work out and you could find
yourself out of work and with little or no severance (remember, you have
virtually no rights in the first 2 years of employment).
If the assessment is in a group
situation or you are asked to perform a mock sales pitch/presentation etc. at
the interview, be the best version of yourself rather than trying to be someone
Unconscious bias is a problem in recruitment and is the reason for a lack of diversity within organizations.
Interviewers tend to have preconceptions
about individuals and often look for similarities – leading to them hiring a ‘mini
me’. This can leave organizations open to discrimination claims.
This – along with the need to reduce costs –
has led to the introduction of AI as an interviewing tool.
However, it is very disconcerting to find
yourself talking to a computer screen rather than a real human being.
Practise, practise, practise.
You will often be given a set time limit to answer each question. Umming and ahhing
or lengthy pauses will impact on your score.
Video yourself answering
questions – some AI programs look at your body language, which can give away
tell-tale signs of lying (such as looking away or to one side).
Treat a video interview as a
real interview – get a good night’s sleep, dress to impress, don’t drink too
much coffee and try to relax.
Stick a photo of someone you
like and want to impress (even a celebrity) next to your screen camera. Visualize
yourself talking to this real person and your conversation will be more natural
– your eyes will also be looking towards the camera, rather than down, and this
can make you appear more professional and confident.
So be prepared for AI when you’re applying for your next position. Remember these few tips and behavioural tweaks to handle selection and assessment algorithms and give yourself the best chance of having a happy ending to your job-search story.
Think you could use a little career motivation for the new year and new decade? Join our upcoming webinar – Don’t Quit Your Day Job!
Procurement is, by nature, in the business of relationships. Whether it’s managing suppliers or stakeholders, the success of any procurement organisation relies heavily on building relationships between people.
Despite this, many procurement professionals do not have the time to focus on the human side of their job. Data collection, reporting, transactional activities, urgencies, etc. are all tasks that eat up their precious time. They prevent them from focusing on relationships that could generate more value and better outcomes.
This problem isn’t new. It’s the main driver behind the constant, growing interest in procurement technologies that automate processes and increase efficiencies.
What is new, though, is the pace of innovation and the hype around some of the latest technologies.
Emerging technologies have begun to dominate discussions in the procurement space, and it has become impossible to avoid debates, articles, publications, etc. on artificial intelligence (AI) or blockchain. The new “hot” technology that has been generating a lot of hype in 2019 is Robotic Process Automation (RPA).
Before jumping on the RPA bandwagon, it is critical to look beyond the features to understand the bigger picture. In the case of the latest RPA technology that has integrated AI, it is about making procurement jobs more human by offloading even more mundane, robotic tasks to… robots!
The goal is to augment, not replace, people by combining the best qualities and capabilities of both human and machine to achieve better outcomes.
RPA: Copy/paste on steroids…
“[RPA is] a preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.”
This technical definition of what RPA is and how it works can be summed up with a simple analogy. Imagine that you have to repeatedly copy data from one Excel file to another to produce a monthly report.
One way to eliminate these mundane, low-value, tedious tasks would be to create a macro that would do all the copy/paste for you. In addition to saving hours of your precious time over the course of the year, it would also reduce the risk of errors. This is, essentially, a simplified definition of what RPA is about.
It’s a way to automate repetitive and scripted actions that are usually performed manually by users (not just copy/paste!). It is a form of business process automation.
The typical benefits of RPA are:
efficiencies to free-up resources usually spent on manual tasks and re-focus them on core business (efficiency fuels effectiveness)
better consistency and compliance in data entries by reducing errors
from a system/IT perspective, RPA is a valuable workaround to break data silos. It avoids the costs (investment, change mgmt.) and risks associated with replacing an existing system or creating interfaces. RPA solutions sit on top of the existing infrastructure and simply simulate user actions to take data from system ‘A’ and put it in system ‘B’.
RPA has limitations and it is important to be aware of them and consider if the trade-offs are worth it. Some of them are:
RPA can do one thing and only one thing. If there are changes in the source or in the destination systems, then it will stop to work correctly
It requires extensive programming to ensure that the RPA solution takes all cases into account. If not, it will not work or, even worse, it will create even more issues as it is very consistent in executing rules. If something is off, the same error(s) will be consistently repeated
For the same reason, it is vital to ensure that processes are running well before implementing RPA
If RPA only had a Brain…
There’s no getting around it: RPA is a very dumb technology. It does exactly what it’s told, blindly executing whatever set of rules it’s given. Such technology has been in use for years but on a limited scale.
However, with the advancement of other, smarter technologies opening up new opportunities to make RPA more useful and less “dumb,” it is experiencing a revival. AI is one of the emerging technologies revitalising RPA, and stirring up hype. These days, it’s rare to see RPA without an AI component, which has also lead to a lot of confusion between RPA and AI.
“[AI is] the combination of cognitive automation, machine learning (ML), reasoning, hypothesis generation and analysis, natural language processing and intentional algorithm mutation producing insights and analytics at or above human capability.”
By nature, RPA and AI are very different technologies:
Because most business processes require a combination of “DO” and “THINK,” newer generations of RPA solutions integrate AI components to:
Understand input via natural language processing, data extracting and mining, etc.
Learn from mistakes and exceptions
Develop/enrich rules based on experience
It is this new, smarter generation of “RPA+AI” solutions that has broader applications as a valuable tool for Procurement.
RPA Applications for Procurement
“It is not the type of business process that makes for a good candidate for RPA, but rather the characteristics of the process, such as the need for data extraction, enrichment and validation.”
RPA is particularly well-suited for operational and transactional Procurement because these areas are characteriSed by countless manual activities. Here are some examples:
Automation & elimination of mundane tasks
Invoice processing: It is possible to drastically reduce efforts and cycle times to extract essential information from an invoice and perform an m-way match by using a combination of RPA and AI (Optical Character Recognition + Natural Language Processing)
RFx preparation: Tasks related to data collection (quantities from ERPs, specifications from PLMs or other file sharing systems, etc.) and even the drafting of RFXs can be streamlined by using RPA.
Data compliance and quality
Supplier onboarding: RPA can automatically get more supplier data or data needed to verify registrations or certifications by crawling the web or other data sources.
Data mappings and deduplication: RPA can be a great support in Master data Management (MDM) by normalising data (typos, formatting, etc.) and by ensuring that naming/typing conventions are respected.
Support to gain better insights
Supplier score-carding: This is an activity that requires thorough data collection. RPA can be leveraged to collect data from various sources and integrate the information into one system either for internal purposes and/or for the preparation of a negotiation or business review
Contract analysis: RPA can crawl file sharing systems, network disks, and even emails to collect and gather contracts in one central location. Then, it can extract key terms and store them as metadata in a contract management solution.
RPA, combined with other technologies, is an efficient way to connect data silos to win back valuable time. It can remove the “robot” work from the desk of procurement teams so they can focus on the human side of their job.
On top of that, procurement organisations can gain tremendous insights from implementing RPA because it can make new data digitally accessible and more visible.
However, it is important to keep in mind that RPA is only a workaround; it does not break silos like an end-to-end procurement platform would do.
With so much written on Artificial Intelligence it’s hard to know where to look. However, there are companies from whom we can take our lead.
Artificial Intelligence (AI) is one of the hottest topics in business right now. It’s also a bit like teenagers and sex. Everyone seems obsessed with it, everyone feels left out, few actually know what they are doing, so everyone claims they are doing it.
There is so much hype about AI we recently
collaborated with Procurious on a quick AI challenge for CPOs at the Big Ideas
Summit in Chicago. From their savvy answers you’ll see that many procurement
leaders understand the value of AI. What we need as a community is transparency
on how it affects us here and now.
The new book AI in Procurement explores many realistic use-cases for
artificial intelligence within procurement. The authors Sammeli Sammalkorpi and
Johan-Peter Teppala were among the first to pilot AI solutions in procurement
software and scoured much of the literature available today on the topic to
write their book.
Don’t worry. We won’t get in to too many
details about the mechanics and jargon of AI. Before we go through the examples
from procurement, there is just one thing to understand.
Artificial Intelligence in Procurement
Many people have a somewhat distorted view
of AI. They may remember futuristic movies where chrome-plated androids
interact in human-like ways, or computer systems that have natural language
In reality, most AI applications today are a lot more boring and inconspicuous. You’re likely to interact with AI when you search for address details on Google Maps, or look up a playlist of music on Spotify. It’s already a part of the software you use every day, but you rarely see it.
This is much the same in business. Most of the applications of AI we see in procurement come as solutions to existing problems humans have a hard time solving. They are enablers, rather than replacements to human expertise.
AI in Procurement presents the concept of “human machine collaboration” to explain how AI builds on the strengths of both humans and machines.
7 Examples of
Artificial Intelligence in Procurement in 2019
Now that we’ve covered
the background, let’s dive into those fresh AI examples across seven different
areas of the procurement cycle.
Supplier risk management
AI can be used to monitor and identify potential risk positions across the supply chain. For example, RiskMethods identifies new and emerging supply chain risk events by handling data gathered from different sources, helping to identify emerging risks faster.
AI can be used to automatically review and approve purchase orders. For example, it allows employees to order office supplies without requests for approval, making the process leaner and more efficient.
To state an example, in Tradeshift’s platform a chatbot called Ada can be used to check the status of purchases or automatically approve virtual card payments, regardless of the user’s location.
Accounts Payable Automation – Machine learning is increasingly used in accounts payable automation. ML assists in identifying errors and potential fraud in large amounts of automated payments. An example of this is Stampli, which leverages machine learning to speed up payment workflows and automate fraud detection.
At Sievo, machine learning algorithms are widely used in spend analysis to improve and speed up a number of processes, including automatic spend classification and vendor matching.
For example, if you have DHL, DHL Freight, Deutschland DHL, and DHL Express in your data, the machine learning algorithms are easily able to consolidate these together as DHL for increased visibility and data coherence.
Supplier Information Management
Big data techniques enable new ways to identify, manage and utilise supplier data across public and private databases. Tealbook is one platform that applies machine learning to supplier data in order to create and maintain accurate supplier records across all systems and areas of the business.
AI can also be used to manage, guide, and automate sourcing processes. Keelvar’s sourcing automation software uses machine learning for the recognition The reality of AI in procurement 59 of bid sheets and specialises in category-specific eSourcing bots such as raw materials, maintenance and repair.
AI has many potential use-cases in contract management. Seal Software uses optical character recognition (OCR) and advanced text analytics to clean up and consolidate information contained in contracts.
We’re likely to see many more successful examples
of AI shared across procurement functions in the coming years. The more we
share as a community, the better we get.
If you would like to dive deeper into the topic, you can get early access to AI in Procurement as a free download before the printed book comes on sale on Amazon in 2020.
Procurement has traditionally lagged behind when it comes to technology, but does AI offer an opportunity for things to change?
Artificial intelligence (AI) is going to make business better, at least that is what the solutions providers would have us believe. Businesses will be more agile, more efficient and, importantly, more profitable. Yet it still feels procurement is behind the curve when it comes to AI adoption, despite those that have implemented things, such as machine-learning and AI-driven data analysis, seeing the benefits.
Geale, vice president of client solutions at transformation procurement
services provider Proxima, says: “It is early days. On the procurement side of things, we are
seduced by the hype over practicality. Most of
what we are seeing is either aggregating data or speeding up a process, so far.”
That is not
to say that businesses are shunning AI. A recent survey by McKinsey found 47
per cent of companies have embedded at least one AI function in their business
processes, up from 20 per cent in 2017.
McKinsey’s research showed that while most companies were adopting AI in areas
such as service operations, marketing and product development, a significant
number have started to use the technology in managing their supply chains.
sectors, such as retail, are adopting the technology far more rapidly in supply
chain management than others.
It may be time for those businesses on the long tail of adoption to speed things up. Of those that have adopted AI in supply chain management, McKinsey reports 76 per cent have seen moderate or significant benefits.
AI Focus on Efficiencies and Productivity
So how are companies using AI? A survey by RELX Group late last year shows a focus on using AI and machine-learning principally to increase efficiencies or worker productivity (51 per cent), to inform future business decisions (41 per cent) and to streamline processes (39 per cent).
There are those in
procurement who believe AI will destroy their jobs. Yet not all are convinced
of this nightmare scenario.
Trudy Salandiak of the
Chartered Institute of Procurement & Supply says: “Unlike
many professionals, we think procurement will be future-proofed from being
completely taken over by technology due to the human interaction and
relationship management required.
“What it will do is
provide much more visibility over supply chains to manage risk and seek out
opportunities for innovation. It will also take away the process back-office
side of the role to allow procurement teams to focus on more strategic areas.”
Ms Salandiak sees a role
for AI in quicker and more accurate fraud detection, intelligent invoice
matching and categorising vendors to rank their strategic importance in the
Chatbots for Procurement?
AI chatbots have started to be used to help businesses articulate their needs with procurement, instead of completing lengthy requests on enterprise resource planning (ERP) systems. This echoes the voice experience consumers get through the likes of Amazon Alexa and Google Assistant.
Turkish telecoms company
Turkcell has implemented a procurement chatbot, which learns continuously and
simulates interactive procurement professionals’ conversations
with business partners and vendors by using key pre-calculated user phrases and
auditory or text-based signals. The chatbot interfaces with the company’s
ERP system and it has enabled procurement professionals to cut out
non-value-added activities and allocate their time to more strategic topics.
Moyee Coffee has been working on a project in Ethiopia where farmers, roasters
and consumers can access data as beans are moved from farm to cup. Consumers
are able to use QR codes on the back of coffee packs to see where the beans
have been sourced and how much the farmers have been paid, bringing
unprecedented transparency to the supply chain. The project uses Bext360’s Bext-to-Brew
platform with AI, blockchain and internet of things technology.
AI Procurement Policy
As consumers demand more authenticity and transparency, this trend is likely to continue.
forecast value of AI to the global economy is being recognised by the World
Economic Forum (WEF). In September, the WEF’s Centre for the Fourth Industrial Revolution unveiled a plan to develop
the first AI procurement policy.
The work is being done in
conjunction with the UK government’s Department for Digital,
Culture, Media and Sport. A pilot starts in July and it is hoped it will be
rolled out in December. This will include high-level guidelines as well as an
explanatory workbook for procurement professionals. A further eight countries
have expressed interest in extending the pilot globally.
reason for putting together a policy now is that “regulation
tends to be too slow”, says Kay
Firth-Butterfield, WEF’s head of AI.
“From the procurement
perspective, it’s drawing a line in the sand, saying this is how we expect AI
to be produced in our country and we will not accept AI products that do not
meet these criteria. It is agile governance,” says
Reorganising Time for Strategic Tasks
The technology will also allow public sector employees to do more strategic work. “In government, there are back-office gains to be had to free up civil servants to do more,” she says, adding that work on AI procurement in the public sector is expected to transfer to the private sector.
“Governments want their
citizens to be at forefront of developing and using this
tech, and benefiting from the economic gains,” says
Ms Firth-Butterfield. “Governments’ significant
buying power can drive private sector adoption of these standards, even for
products that are sold beyond government.”
The 53 per cent of
companies that have not started implementing AI may like to start thinking
about it now.
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Just because a machine can learn from mistakes doesn’t mean it is self-aware and about to deploy robots to destroy humanity throughout time and space. But it does mean that increasingly, machines can take on more and more human work.
On 11 February this year, President Trump signed an executive order directing US government agencies to prioritise investments in Artificial Intelligence (AI) research and development. There isn’t any detail on how the AI Leadership executive order will be paid for, but as a statement of intent right from the top, it’s pretty powerful. So, is this something you need to worry about? Will robots be taking your job next Tuesday? Probably not, but the answer is not as reassuring as it sounds.
think of AI, we probably think of Skynet (the evil computer that hunts humans
in the Terminator films) or the
similar tricked-up calculator that is the meanie in the Matrix films. But real AI is
a little more mundane. It is more likely
to be making sure your car headlights are on when you need them (and not on
when you don’t), sending a nuisance spam call to your voice-mail or suggesting
the next thing to watch on Netflix. AI
is the catchall term for software that can solve problems based on rules rather
than a linear set of fixed instructions.
Really advanced AI can modify the rules based on how things turned out
the last time or patterns that it detects in the environment.
because a machine can learn from mistakes doesn’t mean it is self-aware and
about to deploy robots to destroy humanity throughout time and space. But it does mean that increasingly, machines
can take on more and more human work. In
recent decades we have seen this kind of automation steadily eat away at
assembly line jobs as increasingly AI driven robots replace workers performing
limited and repetitive functions. A
robot can sort big apples from small oranges more efficiently than a human and
it never needs to take a break (or be paid).
technology advances, it’s starting to creep into areas we might have thought of
as immune from automation. Medical
diagnosis is increasingly the target for deep learning AI, the kind that
recognises patterns and makes predictions based on those patterns. During their career a doctor might see a few
thousand x-rays or MRI images and get better at noticing patterns. But AI software can review every x-ray ever
made before the doctor has finished her morning coffee.
A recent study, for example, compared the diagnostic
precision of AI software with that of teams of specialist doctors from all over
China. The AI software was 87 per cent
accurate in diagnosing brain tumours in 15 minutes. The doctors could only diagnose 67 per cent
and needed twice as much time to do it.
The AI increased precision and saved time because it was able to learn
from a much larger base of experience than any individual doctor or team of
doctors ever could. It uses like this that are why AI is predicted to add $15 trillion to the global
economy by 2030.
Trump joined the 18 other countries that have announced AI strategies since
March 2017, because he wants the US to be a leader in AI rather than a follower. And it is why investment in AI based startups jumped 72 per
cent to almost $10 billion in 2018 alone.
though some analysts are predicting 1.8 million jobs will be lost
to AI in 2019 alone, those same analysts are predicting that the AI industry
will create 2.3 million jobs in the same timeframe. You can’t buy buggy whips now because the
industry that created them was destroyed by Henry Ford, but there are many more
jobs in the automobile industry he created than there ever were in the one he
analysts from McKinsey looked at the employment impact of AI in five
sectors last year, they concluded that jobs which use basic cognitive skills,
such as data input, manipulation and processing will likely decline, while
demand for higher cognitive, social and emotional, and advanced technological
skills should grow, as will the number of jobs that require customer and staff interaction
If your job
could be classified as administrative support then the future does not look
bright. And even if it requires you to
do years of training so you can manipulate or recognise patterns in data, like
those Chinese doctors, a financial analyst or a military strategist then AI
will be coming to a workstation near you within the foreseeable future. Humans are still a little too messy and
unpredictable for the average AI bot.
So, if your job needs you to interact with humans and please them, such
as in direct sales, management or counselling, then you are probably safe, for
now. And of course, if you are writing
the programs that drive the AI then your career is assured.
AI is rapidly changing the face of the modern workplace. And while nothing much will change by the end of the year, by the end of the decade, most jobs will be unrecognisable. You’ve been warned. It’s time to transform yourself from a data geek to a people-person, before your computer takes your job.
For supply chain professionals, the drive to use AI is there. But how do organisations get to the point when AI-enabled supply chain management is the norm?
“Electricity changed how the world operated. It upended transportation, manufacturing, agriculture, health care. AI is poised to have a similar impact. Artificial Intelligence already powers many of our interactions today. When you ask Siri for directions, peruse Netflix’s recommendations, or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs.”
Andrew Ng – Stanford University – March 2018
According to the results of our latest survey, Procurement 2030, supply chain pros are well aware of how impactful AI could be for their profession. Indeed, 92 per cent of professionals believe the profession will transform by 2030 as a direct result of new technological innovations. And 51 per cent predict that, with the help of AI, supply chain professionals will become an agile group of strategic advisors.
The intention to utilise technology is there. But how do organisations get to the point when AI-enabled supply chain management is the norm?
Getting started, and knowing where to start, is tough going
– as with anything new and unknown. We know that many supply chain pros are
concerned that implementing AI into their supply chains is a complex step. In
fact, our survey takers ranked it as the technology they feared most difficult
to adopt. But are their fears unfounded?
We want procurement pros to be pushing the limits on Industry 4.0, and the first to adopt new technologies.
And so, in our latest webinar – How AI Saved My Day Job: Confessions from a Supply Chain Pro we’ll be demonstrating that AI is the real deal by giving you the insider information, the low-down, on what it is delivering right now for supply chain teams.
We’ll be speaking with supply chain professionals who are already implementing AI in their organisations and have discovered that AI does provide a demonstrable bottom-line impact across all supply chains structures. Speakers include:
Rob Allan – Program Director, Supply Chain Insights Offering Management – IBM
Tania Seary Founder – Procurious
Connie Rekau – EDI Manager – The Master Lock Company
Nickolas Bonivento – EDI Manager – Anheuser-Busch InBev
When is the How AI Saved My Day Job webinar?
The webinar takes place on 15th May 10am ET / 3pm BST. Sign up or log in via the form above and we’ll be in touch ahead of the event to provide details on how to join the webinar live.
How do I listen to the How AI Saved My Day Job webinar?
Simply sign up here and you’ll be re-directed to the Supply Chain Pros group where you can access heaps of related content. You will also join the webinar mailing list, so we can provide you with details on how to access the webinar before it goes live.
Help! I can’t make it to the live-stream of the How AI Saved My Day Job webinar?
No problem! If you can’t make the live-stream you can catch up whenever it suits you. We’ll be making it available on Procurious soon after the event (and will be sure to send you a link) so you can listen at your leisure!
Do I have to be a member of Procurious to access the How AI Saved My Day Job webinar?
Yes. To access the webinar you’ll need to sign up to Procurious. You’ll be joining a community of 30,000 like-minded procurement and supply chain peers and gain access to all Procurious’ free resources. You’ll be joining a community of 30,000 like-minded procurement and supply chain peers and gain access to all Procurious’ free resources.
Could AI revolutionise your supply chain and save your day job – allowing you to make better decisions, more efficiently and in a more repeatable way over time? Let’s find out!
Supply chain leaders know AI is a game-changer, a technology that will allow them to optimise their supply chain for competitive advantage. But just how much will it impact your profession?
Today, business leaders are looking to their supply chains to create differentiation and they recognise that data is a key driver. Having said that, only a small fraction of supply chain data is effectively used, and most companies are virtually blind to data that is unstructured – for instance, from social, weather and IoT sources. With limited visibility it’s difficult to optimize supply chain operations, leaving the business exposed to unnecessary disruptions, delays and risks, as well as increased costs. In fact, 87 per cent of Chief Supply Chain Officers say it is extremely difficult to predict and manage disruptions.
Supply chain leaders know AI is a
game-changer, a technology that will allow them to optimise their supply chain
for competitive advantage. They understand and have relied on descriptive
analytics – using massive volumes of data within the enterprise to
understand better what has happened in the past and what is happening today.
They’re now ready to explore how to use AI to see beyond the four walls of
their business; understand how potential disruptions in the environment could
impact the supply chain; and act quickly to seize opportunities or mitigate
A new era of AI in the supply chain
Already, AI capabilities in IBM Watson Supply
Chain Solutions are moving from descriptive analytics to predictive
insights. We’re helping clients look ahead of supply chain events and see
likely delays, demand spikes, supply changes and stockouts with new
capabilities, such as anomaly detection in supply chain processes and
leveraging conversational analytics for response management. Going even
further, we are showing clients the power of prescriptive analytics, where
Watson evaluates several dynamic parameters associated with a supply chain
scenario and in near real time suggests the best actions and can even
automatically create supply chain playbooks.
But this is not the end of the journey. We
are also creating a plan where Watson adapts on its own, learning what matters
to you and developing the capability to show you where to focus your attention
to mitigate disruptions and take advantage of opportunities.
Here are some new capabilities available
today (and some that are still to come!) :
data sources for Watson – IBM Supply Chain Insights allows us to add
new data sources specific to each client’s challenges in as little as five
weeks, accelerating the content that Watson draws from to gain
intelligence, from basic ontology and supply chain terminology to weather
and now many more external data sources.
Anomaly detection – This new capability in IBM Business Transaction Intelligence for Supply Chain Business Network tracks supply chain transactions, spots anomalies and provides early warning signals so you can discover potential problems and take corrective action sooner.
Optimising order and response management – IBM Order Management software uses AI to select the best location to fulfill an order, adjust availability promises and safety stock levels, and empower customer service reps to make more informed decisions and answer questions with greater accuracy and speed.
What’s next for AI – In the future, Watson Supply Chain capabilities will include predicting supply chain cycle times, to new frontiers where Watson adapts to your supply chain and users and learns about trends, issues, actions and behaviors to make recommendations.
Could AI save your day job?
On 30th April I’ll be taking part
in a new Procurious webinar: “How AI Saved My Day Job – Confessions from a
Supply Chain Pro.” We’ll be exploring the real-life applications of AI in
workplaces today and the problems it can solve for supply chain professionals.
How AI Saved My Day Job – Confessions from a Supply Chain Pro will go-live on 30th April 2019. Sign up here (it’s free) to join the Supply Chain Pros group on Procurious and gain access to this webinar.