den einkauf intelligenter machen durch volle …...digital assistant for demand management, contract...

1 © 2018 Ivalua, Inc. © 2018 Ivalua, Inc.

Den Einkauf intelligenter machen – durch volle Ausschöpfung des Potenzials von KI

BME Webinar / Oliver Oehmke / Senior Account Executive


AI is everywhere! Is it? Last week, while finishing preparing my presentation…


AI & Procurement…

• AI, myths & legends…

• AI in procurement • what?

• why?

• how?

• Ivalua’s approach to AI…


AI myths & legends (& hype…)

A dystopian future where machines enslave or kill us? (based on blockbuster movies, the answers looks like “yes”…)

Humans vs. Machines…


Humans vs. Machines…


Serious games…

1997

IBM’s Watson wins at Jeopardy agains champion Ken Jennings

2011

IBM’s Deep Blue wins over Kasparov at chess

2016

Google’s Alpha Go beats world Go champion Lee Sedol

2017

Libratus, an AI system beats four expert players of Texas Hold ‘Em poker


Serious games… The evolution of AI is constant and rapid.

• Deep Blue was dumb but powerful. It was powered by brute-force human-created logic that would test and evaluate every possible sequence of moves at every turn and choose the best one.

• Watson had to be able to understand natural language questions and find the knowledge associated with them through encyclopedic recalls. But those questions are often nuanced and contain hidden, convoluted meanings that are hard to find, which made it even harder.

• Alpha Go had to learn from humans from thousands of past games because Go has more possible moves than the number of atoms in the universe, making it the most sophisticated board game in the world.

• 2nd generation of Alpha Go learnt by itself by playing against itself million of times to discover what works and what does not.

• Libratus also learnt by itself and also was equipped to understand other players’ behavior because poker is a game of luck, deception, and bluffing!


Despite all of that, AI is still relatively dumb when compared to ALL what humans can do.

If it is all what you can do.

DEPTH

BREADTH



If it is all what you can do.

It is what AI can do… Narrow AI = can do 1 thing extremely well. Fails at everything else. DEPTH

BREADTH


https://sloanreview.mit.edu/article/just-how-smart-are-smart-machines/



Demystifying AI = understand where we are, where we go! (to get the right level of expectations)

AI = Machine ability to understand, learn, and act on information and events designed to augment, provide assistance to or perform tasks independently from humans

AI stages Artificial Narrow Intelligence (ANI)

Execute specific

focused tasks without ability to self-expand

functionality

Artificial General Intelligence (AGI)

Perform broad tasks, reason,

and improve capabilities comparable to humans

Artificial Super Intelligence (ASI)

Demonstrate inteligence

beyond human capabilities

Timing Today In 20 yrs? Soon after AGI

Implications Outperform humans in specific repetitive

functions like driving, medical diagnosis,…

Compete with humans across all endeavors, such as earning

university degrees, and convincing humans that it is

human (Turing test).

Outperform humans, helping to achieve societal objectives or threatening

the human race.


AI in procurement, what?


AI is definitely on top of the agenda of most CPOs...

50% of mature supply

chains will use AI and advanced analytics for

planning.

By 2025, the artificial intelligence market will

surpass $100 billion.

AI will be a top five investment priority for more

than 30% of CIOs.

IDC Constellation Research

Gartner

45% of CPOs using or

piloting AI.

Deloitte


Support for humans Repetitive task automation Context & learn

Analyze & understand structured data

Scenario for Sourcing decision/vendor mgmt/nego

Scoring model/algo for supplier perf.

ML for spend categorization and analysis/new knowledge/opportunity identifications, contract/transaction analysis for risk/fraud/opportunities…, Recommendations for CatMan, Sourcing, SRM,…

Analyze & understand unstructured data

NLP/chabots for basic query/request management

NLP/OCR for invoice processing, contract intelligence

Digital assistant for demand management, contract mgmt/mining

Perform digital tasks Smart workflows for vendor mgmt/onboarding, disputes/exceptions + older technology like P2P…

Blockchain/smart contracts for supply chain (& +) automation RPA for MDM and data cleansing/normalizing, PO/Invoice/Payment processing

Some examples…


AI in procurement, why?


“Automation ++”

Automation and gains in efficiencies are one of the key reasons why Procurement has been looking at technology since years.

So far, the focus was on tactical, manual, and repetitive tasks.

Smarter systems (AI,…) open new horizons for automation by making more complex tasks “automatable.”

They are in the cognitive space (analyze/decide/act)!

Smart Tech. penetration


But there is more…

Simulation:

You are eating breakfast at home…

You have the radio on…

You hear that there was an earthquake in a region where you have several suppliers…

What do you do?


Simulation…

You are eating breakfast at home…

You have the radio on…

You hear that there was an earthquake in a region where you have several suppliers…

What do you do?

Describe tasks/activities and give context on timing/systems used/people contacted…


My own mock-up when exploring possibilities of Chatbots. Not related to IVALUA.

Now, imagine…


Risk characteristics:

• Multiple sources (internal, external, geopolitics, nature,…)

• Real-time and always changing

VUCA: Volatility, Uncertainty, Complexity & Ambiguity

Too much data to apprehend in real time for us, humans!

Risk/incident management is a great example of where AI can be your best ally.

Characteritics of smart tech.:

• Watch (always on)

• Analyze massive sets of data (computational power)

• Develop insights (hidden correlations, new knowledge)

• Act / recommend

• Learn (from results or from correction by people)


Procurement / supply chain on auto-pilot?


Expectations of using AI in the context of Procurement…

Based on the simulation and all what we said so far, what expectations do you/your organization has/could have from AI?


Effectiveness (better outcomes)

Big Data • Data cleansing

• Data collection

• Data analysis / crunching

Insights / Actionable intelligence • From descriptive to prescriptive

• Recommendations (opportunities, risks,…)

• Actions

AI in Procurement = from tech. as an “admin.” to tech. as a colleague or consultant.

Efficiency (less energy)

• More and smarter automation

• Query mgmt. / Digital assistant

• Context awareness / proactive

• Customization to each user


AI in procurement, how?


Challenges to AI (in Procurement)?

• What are the challenges?

• Ranking / importance of each challenge?

• What to do to address them?


Some of the challenges for adoption of AI (in Procurement)

• Fears: future of work & jobs, machines taking over humans, black box…

• Strategic approach: buy-in / vision / roadmap / budget

• Maturity/unproven technology

• Regulations

• Data: • Availability, completeness

• Bias

• Skills & talent


● Data quality remains poor (supplier master, spend, contract, invoice etc)

● Data silos across source-to-pay processes

● Data flows between procurement and ERP systems, often “out-of-sync”

The Potential is Enormous, but…

There are big roadblocks:


Data hygiene isn’t a “nice to have”—it must be mandatory for every company taking a serious look at AI

• Perform extensive data cleansing activities: In many cases, the root of bias in AI systems sits in the data that feeds it. Enterprises must make sure their data is clean and fit for purpose.

• Train algorithms with varied data sets: Fueling algorithms with restricted data sets will focus the model too much on particular characteristics and will inevitably build a bias that misrepresents the environment.

• Investigate de-biasing tools: The range of tools and expertise is growing rapidly; enterprise leaders must bring in the brains and brawn they need to mitigate bias in their platforms using the raft of solutions in development and already available in the market.

• Create a culture of data ownership: Ensure data governance is in place, connect data creator and data consumer.

Data hygiene…


The future of Procurement requires new skills


Ivalua’s approach to AI


Ivalua enables its customers to build strong data foundations to get ready for AI…


Data foundations…

Suppliers + BOM/Parts/Services + Org. (users/taxonomy) + Documents (Contracts, PR, PO, Inv.) + Internal data/KPIs + External data/KPIs

MASTERDATA TRANSACTIONS CONTEXT

360° view of supplier information and activity

(>> ERP, strategic and business not just tactical)

Agility & operational risk assessment & action

(Actionable intelligence)

Single, controlled source of truth

(You cannot manage what you cannot measure)


Data foundations…










Data foundations…


+

Adoption by internal users (Procurement + Stakeholders)

+

Adoption by external partners

=

Compliance, visiblity, agility…



Data foundations… for better decisions!

Analytics Everywhere - The Power of A Connected Suite!

Real-time360° view on all “objects” (suppliers, parts, contracts, POs,…)


Then, build AI capabilities on top of your strong data foundation…


Our Strategy & Vision For AI (and our pragmatism…) Embed AI into core Ivalua Platform for Multiple Use Cases Across S2P

Unified Data Foundation

Supplier risk,

performance,

master data

Sourcing projects,

BOM & outcomes

Contract Data,

Clauses, Terms

Catalog, forecasts,

orders, receiving

data

Invoice, payment,

discount data Spend Data

Data Layer

Intelligence Layer Deep Learning NLP Machine Learning

Engagement Layer

Improve decision making for

all users by providing easy

access to relevant insights

Automate more tasks / work

in order to free capacity for

more strategic efforts

Enable a new and different

way to engage and interact

with solutions


AI Roadmap


Example of Robotic Process Automation (RPA) + AI: invoice data capture

• PO-Flip • Receipt-Flip • Web form • Integration

via EDI/cXML

• Scan/OCR

• PO-Flip • Receipt-Flip • Web form • Integration

via EDI/cXML

• Scan/OCR

• Automated, rules driven workflow Invoices matched appropriately are processed straight-through or else routed based on workflow configuration.

• Validate invoice data prior to processing Have suppliers or AP validate extracted invoice data with supplier information, PO or receipts and trigger onboarding for any new suppliers.

• Digitize paper and PDF instantly Capture and digitize data from PDF or scanned invoices instantly using OCR and machine learning.

• Machine learning for Invoice Data Capture Self-learning algorithm to accelerate the conversion and mapping of scanned invoices, and improve accuracy


AI in our solution (today and plans for 2019)

Natural User Interface • Natural Language Understanding • Multi-intents • Adapted Answers • Multilingual, Multimodality (text, speech, ...)

Free Up Capacity with more automation • IDC Industrialization • Spend Classification • Supplier Deduplication

Contract Intelligence • Contract Analysis • Contract Data Capture


Integrated Virtual

Assistant

An entirely new way to interact with your Procurement

application(s). IVA helps users make better, faster decisions.


IVA – Your Integrated Virtual Assistant

This multi-faceted virtual assistant lives on the Ivalua Platform and interacts

with users in natural language. Iva will be able to…

Retrieve information

on spend, suppliers, transactions +

Instantly create/edit shopping cart,

requisitions, expense reports +

Generate quick insight charts to display data

Search web for relevant information,

e.g., pricing

Image recognition

Provide “How-To” guidance for users

Understand voice commands and actions via Amazon

Alexa (e.g., approve/reject)

*Above is still in development and all the use cases above may not be available in version 1 of the virtual assistant


Conclusion…


Summary (Source = McKinsey)

Humans vs. Machines… A zero-sum game…


Humans + Machines… A win-win…


Procurement people, live long and prosper

Technology to augment, not replace people.

Robots will make jobs more human.

Cooperation between humans and machines will get us to where we have never been before!


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