Tag Archives: AI

4 Reasons To Be Excited About The Future Of Supply Chain Technology

What’s next in supply chain systems? There’s plenty to be excited about


First-generation 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.

These demands are driving progress – which is why I am excited about the future of supply chain technology.

1. We’re actually getting fairly good at applying AI repeatably in supply chains.

In 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 non-negotiable.

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. 

It’s 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.

It’s 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.

One survival 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.

For the 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’.

Before 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.

We’re 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 Trust and ocean-shipping visibility with TradeLens.

These 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.  

4. It’s 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 act on.

Finally, 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. 

From 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.  

Also 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 impacts.


Hear Vijay present in our recent webinar – 4 Supply Chain Capabilities You Need For The Decade That’s Going To Change The World here.

 

Information Hoarders Be Gone

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?

Good news – their days are numbered!

Knowledge is power, but knowledge is now being democratised and made accessible to all, thanks to the development of AI.

A democratisation of data

In 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.

Traditionally, 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 $40 billion 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.

Given the scale and complexity of the IBM supply chain, Ron and his team turned to AI to augment the team’s capabilities.

Ron’s 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 the world.

Now, 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 ahead.

There’s 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. 

IBM Sterling’s AI helps you:

  • Gain visibility into data from across your systems and silos
  • Understand external 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.

Unlocking Collaboration

The final piece of the jigsaw is a concept that’s close to my heart – collaboration. 

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.


How To Stop The Computer Saying ‘No’! Clever Hacks For Getting Hired

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 good impression.

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?

Algorithm Aces

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.

Tips:
  • 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.

Aptitude Hacks

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.

Tips:
  • 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.

Assessment Musts

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.

Tips:
  • 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 else.

Video Tricks

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.

Tips:
  • 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!

Could RPA Make Procurement Jobs More Human? – Best of the Blog 2019

The new “hot” technology generating hype in 2019 is Robotic Process Automation (RPA). Here’s how it can help procurement…

RPA - procurement
Photo by Matan Segev from Pexels

This article was written by Bertrand Maltaverne, and first published in February.

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.”

Source: IEEE Guide for Terms and Concepts in Intelligent Process Automation

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.

Typical Benefits

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.”

Source: IEEE

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.”

The Hackett Group on Procurious

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.

Conclusion

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.

7 Companies Pioneering Artificial Intelligence in Procurement

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
Photo from Pixabay on Pexels

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 conversations.

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.

Purchasing

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.

Spend Analysis

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.

Strategic Sourcing

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.

Contract Management

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.

After A Slow Start, AI Is Starting To Make Its Mark

Procurement has traditionally lagged behind when it comes to technology, but does AI offer an opportunity for things to change?

By GreenCam1/ Shutterstock

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.

Simon 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.

Some 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 supply chain.

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.

Meanwhile, Ireland’s 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.

The 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.

The 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 Ms Firth-Butterfield.

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.

This article, edited by Peter Archer, was taken from the Raconteur Future of Procurement report, as featured in The Times.  


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Is Artificial Intelligence Destroying Your Job?

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.

By Leremy / Shutterstock

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.

When we 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.

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.  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). 

As the 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.

President 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.  

And even 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 killed.

When 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 and management.

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.

Want to get your wheels turning towards a supply chain career one could only dream of? Then don’t miss our upcoming Career Boot Camp with IBM – a free 5-part podcast series with some of the very best of the best. Check it out here: https://www.procurious.com/career-boot-camp-2019

Is AI The New Electricity?

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?

By kung_tom/ Shutterstock


“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.

Webinar speakers

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 Pros: Could AI Save Your Day Job?

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?

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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 risk.

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!) :

  • Expanding 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.

AI and Procurement: Boldly Going Where No Team Has Gone Before?

The battle of “human vs. machine” is raging in Hollywood and, increasingly, in the workplace. What does the future hold for AI?

By Willrow Hood / Shutterstock

2001: a space odyssey… Terminator… The Matrix…

If you were to believe some of the sci-fi blockbusters, you’d think our future as humans is pretty bleak. They all offer a dystopian view of the future where, if the machines don’t kill us, they enslave us.

The battle of “human vs. machine” also seems to be raging outside of Hollywood, and we humans seems to be losing more and more ground to machines each year. Some of this ground has been lost in the world of gaming. Over the past decade, machines have been beating us at increasingly complex games more and more often. Looking back at these “wins” for the machines, we can see some key stages in the evolution of Artificial Intelligence (AI):

•    Deep Blue won against Kasparov at chess in 1997. It was rather dumb but powerful. With brute-force & human-created logic, Deep Blue was able to test and evaluate every possible sequence of moves at every turn and choose the best one.

•    Watson defeated Jeopardy champion, Ken Jennings, in 2011 and was smarter than Deep Blue. It had to understand natural language and find the relevant knowledge from various sources like encyclopedias, dictionaries, thesauri, newswire articles and literary works.

•    Google’s Alpha Go won against Go’s world champion Less Sedol in 2016. To achieve this result, it had to learn from humans from thousands of past games. This is because, unlike chess, which has a limited number of moves, Go is one of the most complex board games in the world, with more possible moves than the number of atoms in the universe. The second generation of Alpha Go learned by itself by playing against itself millions of times to discover what works and what does not.

•    Libratus beat four expert players of Texas Hold ‘Em poker. It also learned by itself and was able to understand behavior because poker is a game of luck, deception, and bluffing!

While very impressive, these victories also show that machines are still dumb when compared to everything that people can do. Machines excel at one thing and have the intelligence of a two-year-old or less for everything else.

What we can learn from sci-fi movies and the battles being waged on the gaming front, is that AI has many faces:

Today, despite all the hype and buzz, computers are still only at the narrow intelligence level. But even at this level, the potential applications of AI are endless.

As far as Procurement is concerned, the same applies: machines are far from being able to replace Procurement teams. Instead, new technologies have another purpose: augment people to achieve better outcomes.  This is a definite shift from the last waves of technologies, which were mostly focused on automation and staff reduction.

Machines in procurement get a promotion: from admins to colleagues and consultants

AI, in short, is all about learning from data to develop new insights and using this new knowledge to make better decisions. It is also about continuous learning and improvement. AI is a master of the “Kaizen” philosophy! This makes it a precious ally for Procurement and AI should therefore be considered as a team member within the broader Procurement ecosystem. Experience shows that “people + machines” get better results than people alone or machines alone.

Of course, in Procurement and in general, it is undeniable and unavoidable that AI will impact the future of work and the future of jobs. Work will continue to exist, despite potentially significant job displacements. While some jobs may disappear, new ones will come to take their place, and most will be transformed by the imperative of cooperation with smarter machines. Procurement jobs will also be impacted and future procurement professionals will require a new set of skills. For example, data analysis and modeling will become a core competency next to more traditional business and relationship management skills. This is because the “data analyst” component in activities will grow due to the collaboration with AI in order to:

•    Train AI and ensure that data is relevant, complete, and unbiased

•    Monitor outputs (recommendations, actions, insights, etc.) of the AI system to ensure relevance, quality, take more contextual / soft aspects into account, and safeguard against AI shortcomings.

Space: the final frontier. These are the voyages of the starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.

To conclude on a more positive and optimistic note than where this article started, I have taken inspiration from another sci-fi classic.  I believe that the future lies in a new type of cooperation between humans and machine.

The duo Dr. Spock and Captain Kirk illustrates, to some extent, how such cooperation is possible and can offer the best of both worlds. By combining Captain Kirk’s instinct and emotional intelligence with Spock’s logic and reasoning skills, they were able to successfully tackle any challenge they encountered.

New developments like explainable AI (XAI) and “caring AI” will make machines of the future even more human and will allow them to take an even more active role in our personal and professional lives. AI will continue to augment us, not replace (or kill or enslave) us.

So, Procurement people, live long and prosper!