Generative AI - Driving Resilient Supply Chains
Summary
TLDR本次网络研讨会由C3 AI公司主办,介绍了其供应链套件和创新应用——C3生成式AI。C3 AI的供应链套件是一系列针对供应链规划和执行的企业AI应用,涵盖从采购优化到需求预测、生产调度优化、库存优化和供应链风险管理等用例。特别引人注目的是C3供应链数字孪生技术,它整合了来自不同ERP系统和其他企业数据源的供应链数据,提供全面的供应链视图。C3生成式AI作为一个AI驱动的知识助手,允许用户通过自然语言查询访问企业信息,提高了决策效率并减少了数据隐私风险。演示部分展示了C3 AI如何帮助用户通过生成式AI快速获取供应链信息,并采取行动以缓解潜在问题。
Takeaways
- 🚂 数字化转型是企业生存的根本问题,关乎是否能够适应时代发展。
- 🛠️ C3 AI 提供了一整套端到端的企业AI应用,涵盖供应链规划和执行的各个方面。
- 🌐 C3 AI 的供应链数字孪生技术整合了ERP系统、企业数据源和外部数据,为供应链管理提供统一的数字视图。
- 🔮 C3 AI 利用生成式AI技术,帮助企业快速访问和分析大量企业信息,提高决策效率。
- 🛡️ C3 AI 的生成式AI架构通过知识嵌入和检索模型保护原始数据,避免了标准大型语言模型(LLM)的数据安全风险。
- 📊 C3 AI 应用提供可预测的软件套件,帮助企业在全球范围内实现细粒度的可见性,并应用不同类型的AI模型。
- 👥 C3 AI 为供应链中不同角色的用户提供完整的工作流程,包括需求规划师、库存经理、供应商关系经理和采购经理。
- 📈 C3 AI 供应链应用的客户包括航空航天、国防、石油和天然气、零售、消费品、制造业和医疗保健等行业的大型组织。
- 🔄 C3 AI 的生成式AI产品允许用户通过自然语言查询访问跨各种数据库和信息源的数据,提高用户效率。
- 📚 C3 AI 通过案例研究展示了其供应链解决方案如何帮助农业生产商优化供应链,提高准时交货率和服务水平。
- 🔑 C3 AI 通过提供访问控制,确保数据隐私,并防止敏感信息泄露给未经授权的用户或竞争对手。
Q & A
什么是C3 AI供应链套件的核心组成部分?
-C3 AI供应链套件的核心组成部分是C3供应链数字孪生(Digital Twin),它提供了对所有供应链操作的细粒度和时间历史记录,整合了来自不同ERP系统、企业数据源和外部数据源的数据。
C3 AI如何利用人工智能和自动化来改变世界?
-C3 AI通过使用包括AI在内的技术,使人们能够创造价值并为客户提供结果,从而利用科学和自动化的潜力来改变整个世界。
C3 AI供应链套件如何帮助企业提高效率?
-C3 AI供应链套件通过提供端到端的企业AI应用程序,涵盖从采购优化到需求预测、生产计划优化、库存优化以及供应链风险识别等多个方面,帮助企业提高效率。
C3 AI的供应链数字孪生如何处理不同ERP系统的数据?
-C3 AI的供应链数字孪生能够整合不同的ERP系统,无论是Oracle、SAP还是其他系统,将其统一到一个共同的数字视图中,为企业提供完整的供应链数据视图。
C3 AI如何确保数据的安全性和访问控制?
-C3 AI通过将大型语言模型(LLM)与数据检索模型分离,确保LLM无法直接访问原始数据,从而保护数据安全。此外,C3 AI还实施了企业级访问控制,确保用户只能访问其被授权的数据。
C3 AI的生成性AI与传统的LLM有何不同?
-C3 AI的生成性AI通过知识嵌入和检索模型来提供答案,而不是直接让LLM访问数据。这种方法提供了可追溯性、确定性的答案,并且没有信息泄露的风险。
C3 AI生成性AI如何帮助用户更有效地获取信息?
-C3 AI生成性AI作为一个AI驱动的知识助手,允许用户通过聊天或搜索形式提问,系统会将问题转换为查询,并从检索模型中获取数据,以人类可读的格式提供答案。
C3 AI生成性AI如何处理数据缺失的情况?
-如果查询的数据缺失,C3 AI的检索模型将返回没有数据的查询结果,并通过LLM告知用户没有数据或无法访问相关信息。
C3 AI生成性AI如何与现有的供应链解决方案集成?
-C3 AI生成性AI可以集成到现有的供应链解决方案中,通过查询和分析现有系统中的数据,为用户提供更深入的洞察和决策支持。
C3 AI的实施周期通常需要多长时间?
-C3 AI的实施通常从3到6个月的试点项目开始,在这段时间内,将完成数据集成、模型训练和用户工作流程配置,确保在项目结束时拥有一个生产就绪的应用。
Outlines
🌐 数字化转型与企业生存
本段讨论了数字化转型对于企业生存的重要性。强调了利用新技术,包括人工智能,来创造和提供价值的必要性。提到了数据作为一种核心产品的价值,并强调了理解如何利用数据的重要性。同时指出这是一个大规模且极具挑战性的努力,涉及与世界上一些最大的公司合作,目标是使世界变得更好。
🤖 C3 AI 供应链套件与创新
介绍了C3 AI供应链套件,这是一系列针对供应链规划和执行的企业AI应用。套件涵盖了从采购优化到需求预测、生产计划优化、库存优化以及供应链风险识别的多种用例。强调了C3供应链数字孪生的重要性,它整合了来自不同ERP系统和其他企业数据源以及外部数据源的数据。此外,还提到了C3 AI的差异化特点,包括其预测性、全球可见性、AI模型的灵活性、用户工作流程的完整性以及可扩展性。
🛠️ C3 生成式AI与企业风险管理
讨论了C3生成式AI如何为供应链提供创新解决方案。与标准的语言模型不同,C3 AI通过知识嵌入和检索模型提供安全的数据访问,确保了答案的确定性、可追溯性,并符合企业数据隐私控制。解释了C3 AI如何通过整合非结构化和结构化数据,提供比传统语言模型更安全、更可控的AI能力。
📈 提高供应链效率与案例研究
本段强调了C3生成式AI如何帮助提高供应链效率,通过提供一个统一的真相来源,使供应链团队能够快速访问所有相关信息。讨论了C3 AI如何集成到现有的ERP和规划软件中,并强调了访问控制的重要性。通过一个农业生产商的案例研究,展示了C3 AI如何在六个月的试点项目中帮助统一数据源,使分析师能够提出基于文本的问题,并快速获得测试结果和产品数据。
🔍 C3 AI 供应链网络风险的实时演示
Justin Kendig进行了C3 AI供应链网络风险的实时演示,展示了如何通过提问和应用程序获取详细信息,预测即将出现的问题,并提供解决方案以避免这些问题成为供应链中的实际问题。演示包括了全球供应商性能的查询、特定供应商的详细信息、预测延迟的采购订单行、机器学习模型的解释性分析以及采取行动以减轻问题的建议。
📊 C3 AI与PowerBI的区别及ROI问题
讨论了C3 AI与PowerBI的不同之处,强调C3 AI是为特定业务角色量身定制的全工作流程应用程序,而不仅仅是增强版的PowerBI。还讨论了C3 AI生成式AI如何通过提供自然语言接口,使用户更容易访问所需信息。此外,还回答了关于C3 AI投资回报率(ROI)的问题,指出C3 AI可以显著提高预测准确性、降低库存成本、减少采购成本,并提高订单履行率。
🗓️ C3 AI的实施时间和数据外泄防护
解释了C3 AI的实施通常从3到6个月的试点项目开始,在这期间将整合所需的数据,并配置搜索界面。还讨论了C3 AI如何防止数据外泄,即通过防止大型语言模型(LLM)访问企业数据来实现。最后,讨论了如何处理供应链数据中的空白,包括处理不确定性和使用相似性技术来填补数据缺口。
Mindmap
Keywords
💡数字转型
💡人工智能(AI)
💡供应链优化
💡数字孪生
💡生成式AI
💡预测分析
💡数据集成
💡访问控制
💡机器学习模型
💡自然语言处理
Highlights
数字化转型是企业生存的基本问题,关乎是否能够跟上时代的步伐。
C3 AI提供端到端的企业AI应用家族,涵盖供应链规划和执行的多种用例。
C3 AI供应链数字孪生提供了所有供应链操作的细粒度和时间历史记录。
C3 AI供应链数字孪生整合了不同的ERP系统,提供了统一的数字视图。
C3 AI供应链应用的关键是其预测性,能够预测风险和提供最优决策参数。
C3 AI的供应链应用提供全球可见性,支持多种AI模型,具有高度灵活性。
C3 AI的供应链应用通过工作流程为不同用户提供定制化的体验。
C3 AI的供应链应用支持大规模AI,与全球大型组织合作,每日改善业务成果。
C3 AI的生成性AI产品允许用户通过聊天界面快速访问企业信息。
C3 AI的生成性AI与标准语言模型不同,提供了数据保护和访问控制。
C3 AI的生成性AI架构通过知识嵌入和检索模型提供安全性。
C3 AI的生成性AI确保了回答的确定性、可追溯性,并防止了数据泄露。
C3 AI的生成性AI通过自然语言界面使用户能够更有效地提出问题并获取信息。
C3 AI的生成性AI可以集成到现有的供应链解决方案中,提供额外的价值。
C3 AI提供3到6个月的试点项目,快速启动并集成数据。
C3 AI的生成性AI能够处理数据缺失问题,提供不确定性处理和相似性方法。
C3 AI的供应链应用通过机器学习模型提供预测和解释能力,帮助用户理解并采取行动。
C3 AI的供应链应用提供双向集成和APIs,实现应用程序之间的实时数据同步。
Transcripts
[Music]
what this digital transformation mandate
is about is a fundamental issue of
corporate survival whether you want to
be on the train or whether you want to
be on the
tracks we're trying to think about New
Visions new models new methods and how
we use technology including AI to enable
our people to to create value and to
deliver results for our customers and
for the company the potential for the
ways that that sort of science and
automation can change the entire world
is really mind-bending the options are
Limitless you need to understand that
the data you have available is a core
product that you have it is value and in
order to realize that value you need to
understand how to leverage that this is
a very large scale effort it's um
extraordinarily challenging we're
working with some of the largest
companies in the world and we're making
the world a better
[Music]
place hello everyone welcome to the C3
AI webinar series my name is Kon I'm a
senior associate outbound product
manager at C3 aai and I'll be your
moderator today I'm excited to be joined
by Layla Fridley and Justin kendick lla
Fridley will introduce the C3 AI supply
chain suite and C3 generative AI for
supply chain and Justin Kendig will help
demonstrate C3 generative AI as part of
C3 AI supply network risk before we get
started I do want to point out that we
have a Q&A window if you have any
questions during the presentation please
feel free to drop those in our team will
either help answer them through chat or
at the live Q&A at the end thank you
again for joining us with that I will
hand it over to
lla thank you Kon I'm thrilled today to
get to discuss with you um our C3 AI
supply chain Suite as well as some
exciting new innovation of applying
generative AI for supply chain
software so I'll start by introducing
our supply chain Suite which is an
endtoend family of Enterprise AI
applications for supply chain planning
and execution our applications cover use
cases from sourcing optimization to
improve procurement decisions demand
forecasting with granular and accurate
demand forecasts production schedule
optimization which drives uh efficiency
in manufacturing production C3 AI
inventory optimization to improve
inventory service levels and reduce
inventory holding costs and C3 AI supply
network risk which identifies um risks
in advance of ordered delays from
suppliers and going out to customers and
I'm really excited that we have Justin
joining us later today to show a demo of
c3i supply chain supply network risk
with C3 generative
AI one thing that is really important
and found foundational to all of our
supply chain applications is the C3
supply chain digital twin the supply
chain digital twin provides a granular
and time history of all of the supply
chain operations um and all of the data
flowing in from various erps from other
Enterprise data sources and from
external data feeds the C3 supply chain
digital twin integrates disparate Erp
systems whether that's Oracle sap or
other systems and unifies them into into
a common digital view of all of your
data across your supply chain this means
that if you're using different erps
across the world across different
business units or if you've invested in
different systems over time our supply
chain digital twin is unifying all of
that information all of that
transactional data and making it a Time
series time history of all of the
different movements across your supply
chain that includes the history of
demand forecasts of order movements
sales histories inventory
um supplier relationships all of the
traditional transactional data is
represented in the supply chain digital
twin on top of that we unify other
Enterprise data sources so that could be
coming from things like your CRM from
your Marketing Systems so you're
building out the context of what's going
on in your business not just looking at
the transactional records and finally as
any supply chain professional knows you
don't operate your supply chain in a
vacuum the world outside is really
affecting the business decisions that
you're making and so our supply chain
digital twin comes pre-piped with
external data feeds that track things
like weather um as well as macroeconomic
data like market indices supplier
Financial Risk ratings vessel movements
and Port traffic so all of this
contextual information is providing the
complete visibility you need to know in
order to manage your supply chain and
make predictive uh predictive decisions
not just reactive
decisions some of the key
differentiators of our supply chain
digital twin and our supply chain suite
are again as I mentioned it is innately
a predictive software suite we no longer
have to manage Supply chains based on
what happened last week or what are
emerging issues today but instead our
applications are providing predictive
and granular insights about what risks
are coming and what are like what is
likely to change what are the optimal
decision parameters in order to account
for that
uncertainty we provide Global visibility
at a very granular granular level with
part level tracking across the entire
supply chain it's very flexible to be
able to apply different types of AI
models I'll talk in a second about how
we apply generative Ai and the
generative models but we also apply
various forms of forecasting models
unsupervised learning supervised
learning very flexible to any kind of
AI every single application provides a
complete workflow for that for the
different types of users across the
supply chain that could be uh demand
planners inventory managers in uh
supplier relationship managers
procurement managers all of the
different people that are involved in
supply chain decisions have uh very
specialized and tailored workflows
within our supply chain applications and
finally we have scalable AI we work with
some of the largest organizations across
the globe whether that's in Aerospace
and defense oil and gas retail and cpg
Manufacturing and Healthcare um our
customers are deploying the C3 AI supply
chain Suite in order to improve their
business outcomes on a daily
basis now I mentioned I'm really excited
to share some exciting new innovation
around how we apply generative AI for
the supply chain our generative AI
product was launched last year um based
on customer feedback and the request to
say how do I have Google for my
Enterprise how can I quickly access all
of that Enterprise information that you
are unifying for my applications that in
that supply chain digital twin across
all of the various data bases and
information sources that we integrate
into C3 make the users more efficient
and give them access to the information
they need in order to make Better
Business decisions so this is the AI
powered knowledge assistant that we are
applying across all of our different
customer deployments and we are
embedding into our uh various
applications
so I'm going to talk a little bit about
what makes the C3 generative AI
capability different than standard
llms as everyone on the planet is aware
of today llms and generative AI are
taking Enterprises by storm and like
many of you I too use generative models
uh to make myself more efficient but
these generative models have some
downsides and some risks the standard
architecture is that a user either um a
a business user or someone who is using
one of these models for personal use
interacts with the llm in a chat-like
interface they could posst a question um
they could ask for you know a travel
itinerary when that llm is coming up
with the response it's actually
searching and querying and is trained on
all of the available data to that llm in
most cases the llm actually has access
to text documents uh to HTML which is
websites and in some cases to code we've
seen examples of that where um employees
are starting to use these llms to help
them write boiler plate code but when
they do that it exposes these companies
to a fair amount of risk first is that
the responses that the llm is generating
are random um the if I ask the same
question twice I'm going to get two
different answers and many of you have
probably experienced this in your
personal use second is there's no
traceability the llm is trained on the
data but it doesn't have a way to
identify what data is actually being
used to generate its response and so
that means that as a user I have no idea
where this answer is coming from do I
trust the data source do I know what the
ground truth is and in the standard
architecture it does
not third is there's no Enterprise
access controls I may be limited in the
type of data I have access to as a
business uner business user I may sit
right now I'm sitting in California I
really shouldn't have access to data
based out of um Europe for example but
the standard llm architecture has no way
to protect the data privacy to make sure
that the right users have access to the
right information and no information
leakage is happening speaking of
information leakage this is a major
concern for our Enterprise customers
where uh they do not want to have their
private data exposed to competitors to
the broader internet again this is
something we're seeing clinging out in
the news right now now where some users
have already um copied and pasted
private IP into these llms in order to
make themselves more efficient but now
it's exposing their organizations to
massive IP risk because their uh I you
know their Source IP their code um their
privacy is being exposed to the entire
internet and finally these models are
prone to hallucination if I ask a
question that the llm doesn't know the
answer to the standard AR architecture
does not provide any controls on that
model to prevent it from making up an
answer what it you know it thinks is the
the next best word in a sentence is
going to predict that is the right
answer to my question there's no way for
it to know whether or not it actually
has the right answer because it doesn't
have the ability to trace down to ground
truth the C3 generative AI architecture
is really different what we do is we
protect the llm from the raw data so I'm
going to walk through this architecture
from the top we incorporate the
unstructured data files like you would
with a standard llm so text HTML and
code but we also incorporate structured
data now this is unique because standard
llms are only looking at text files
we're incorporating the structured data
that we integrate into for example the
supply chain digital twin into the
application object model like tables
application data sensor data log files
transaction data all of that is
represented in the supply chain digital
twin but the llm does not have access to
that data itself instead we apply um
knowledge embedding and the llm only has
access to the retrieval model so when a
user asks a question either in chat form
or in search form say how much inventory
do I have at my Distribution Center the
llm is interpreting that question and
converting it into a query that it
passes through to the retrieval model
and the retrieval model is what has
access to the data itself in that way we
are providing a layer of security to
protect against the risks of the
standard
architecture so what that means is our
answers are deterministic because they
are based on the data the llm is only
interpreting the answer and returning
the result in a human readable format in
a chat-like interface U but the actual
response is coming from that Vector
store coming from the retrieval model
and so it is determinist if you asked
how much inventory do I have at my
Distribution Center if you phrase that
question differently um or if two
different users ask it on two different
days as long as they have access to that
information through the access controls
and the data protection then they'll get
the same
response we have full traceability
because we know exactly where the
response is coming from through the
retrieval model so that means that you
know exactly what the ground truth is
and as a user I can go follow up and
make sure that I agree that this is the
right answer answer um in as you'll see
later in the demo from Justin we have a
Google like interface so the user can
interface with the chat model but then
we surface the ground truth results and
they can click into the underlying
either uh Enterprise data source the
planning software or the C3 AI
application where that information is
coming
from we apply the full Enterprise access
controls I'll walk through this at a
high level in a second but basically
this means that given our 10 years plus
in Enterprise AI applications and
handling unified aggregated data at
scale we have a lot of experience with
Enterprises that need to protect their
data even within their organization so
if one user sitting in California has
different data access than a different
user sitting in London we respect those
data privacy
controls um we do not suffer from llm
caused leakage of proprietary
information because again the llm does
not have access to the data itself
furthermore we Host this either in a C3
hosted Cloud environment or a customer
hosted cloud cloud environment so it's
not exposed to the rest of the internet
but even still the llm itself is not
accessing the data and therefore it's
not learning it's not um being able to
take those insights and then surface
them to a different
user and finally there's no
hallucination again because the llm is
simply playing the role of understanding
the user's question and providing the
response in a human readable format it's
not actually coming up with the answer
itself the retrieval model is finding
the answer and in that way if there's no
response if a user asks a question you
know who's the uh King of England well
your Enterprise data doesn't have the
answer to that question and so in that
way there's there's no response and so
the uh retrieval model will not be able
to surface any data to that question and
the llm will simply respond there's no
response to this
question
so how is this making people more
efficient you know that's really the
name of the game with generative Ai and
the use cases that people are applying
it to in the current supply chain
process a supply chain analyst is faced
a question um you know why is my
supplier contract is the supplier
contract different why why is this um
order coming from a different port than
I expected it to you know all all kinds
of questions they might be asking
they're going to have to go and look
across various disperate data sources
they might look up that transaction in
Erp they're going to go find the
supplier contract they might compare to
other supplier contracts they might have
to look up the bill of materials to
understand what this impacts Downstream
and then ultimately they also are going
to consult with people who are
experienced seasoned professionals that
they work with if they don't have as
much experience with these types of
issues as a result it takes them hours
days weeks to come up with the answer to
various types of questions and to to
keep things on track with C3 generative
AI for supply chain however supply chain
teams can interact with the C3
generative AI application which is
providing them access to all of their
supply chain data all of their
information in one place and they no
longer have to pull together that
information for themselves but rather
have a single source of truth that they
can then go and start their
workflows Justin will show a demo of
this in action in a second uh but again
this helps supply chain teams ask the
most relevant questions that allows them
to provide very seamless access across
all of their Enterprise applications
that are integrated this does not just
apply to the C3 supply chain Suite
applications like C3 AI inventory
optimization which it absolutely is
integrated with but it also pulls that
information from again that whole supply
chain digital twin which is integrating
data from existing erps from existing
planning software regardless of who the
provider is so if you're using sap and
Oracle and blue yonder and canais and
all of the different planning tools this
application the C3 generative AI
application can pull and query data from
across those Source
systems I mentioned that access controls
are really important for our users so
I'm just going to walk through this
quick example of how this works in
action
as a user so let's say I'm a supply
chain analyst I submit a query again how
much inventory do I have at my
distribution center now the way I phrase
that maybe I you know I'm using
different language I'm not going to ask
the same query every time people just
don't right humans interact in in very
colloquial text so the large language
model converts that into a query that
gets pass to the retrieval model but if
I ask that question and I only have
access to some of the data sources the
response I get will be different so in
this example as a supply chain analyst I
might only have access to document one
the application data two application
data three uh but I don't have access to
document two maybe this is a private
legal contract that or a commercial
contract that as a supply chain analyst
I'm not on the commercial team I don't
have access to that and I don't have
application data Maybe I don't have
access to CRM data and so I actually
don't know what were the different
transactions that we had with Downstream
customers so as a result the retrieval
model will return an answer but it's
only based on the data that I have
access to and furthermore the evidence
it provides the ground truth it provides
will tell me exactly where that data is
coming
from so in that way we are making sure
that data privacy and access control
limits are respected across not only C3
generative AI but also all C3 AI
applications where we apply Access
Control
limits so finally I'll end on a case
study where we have a C3 generative AI
for supply chain deployment with one of
our supply chain customers so this is a
large agricultural producer who's using
the C3 supply chain site and our
applications to make their supply chain
more efficient in order to optimize and
predict what's coming and reduce the
variability in order to improve their
ontime info rates and service levels to
their customers
so the problem they faced is that they
had a variety of data sources that again
supply chain analysts were having a hard
time um cating and understanding
synthesizing to make good decisions so
over the course of a six-month pilot
which is how we typically get started
with a new customer in a new use case we
unified data from what what's called
here unclassified data sources so this
is data that the entire Enterprise had
access to um any one could access
publicly like news articles public
statements from the company fact sheets
about their products and their
services and then um we also unified the
their quotequote classified data sources
so this is internal privately protected
IP and data where um the access controls
are really important so that's things
like test result documents test
descriptions and the Ron process test
data so as this agricultural producer is
testing their final product making sure
that it is meaning the quality um and
you know the yield expectations pulling
in that unified data into the C3
generative AI for supply chain
application and as a result these users
are able to ask text based questions ask
for references query things like you
know how many of my batches met my
expected Target performance um am I able
to ship on time uh for this order
ID um and the application returns charts
and graphs to summarize the test results
and provides ranked list of additional
search results so they can again go into
for example the lab information system
Limbs and kind of investigate in more
detail or go straight to the fact sheets
and product data sheets to understand
what are the requirements that matter to
this uh particular product and
test so with that I'm really excited to
turn over to Justin Kendig my colleague
to provide a demo of C3 generative Ai
and C3 supply chain products in
action awesome thank you lla all right
let's switch over to the live demo and
see some software based on what lla just
described all right so as you heard lla
describe this there's a lot of questions
and we as humans naturally just ask a
lot of questions and what I have found
in the supply chain profession is you
fall into two camps you're either the
leader the manager the VP who is asking
questions about your supply chain why
did this happen what should we do next
you know what's going on over here
what's our best practice where am I
failing the most or you're in the other
Camp of you're the one that's being
asked the question and you have to
figure out how am I going to answer this
and there's frustration on both sides
the leaders are saying how come I can't
get this question answered faster and on
the other side it's why are you asking
so many questions these are very
detailed so the ability to be able to
have that Google interface as I'm about
to show you and be able to say hey I
don't know what my questions are going
to be I just want to be able to start my
day figure out here's a situation I get
an email whatever it is I see something
I want to be able to ask a question and
then it's not just the first question
that you want to be able to ask it's
then those follow-up questions and the
drill downs and the next question and so
that's what I'm about to show you so
when we look at our generative Ai and
our Enterprise search applied to our
applications you start to see so let me
navigate you you've got a Google like
interface and so start my day and I want
to say you know what is my current
Global supplier
performance and so very quickly it comes
back and it says hey your Global on time
INF full performance over the last two
months is 82% now let's also see what it
is I can see there's documents there's
news articles there's images videos I
can put context into this I can also see
other search results along with the
initial one about you know supplier
details performance evaluations again
this is tapping into your Enterprise
data to be able to pull back what's the
relevant information for that along with
them what people also ask so what are
other their variations as lla was
describing of that same question well so
great now I know what my Global
performance is let's say I'm the North
American manager I want to know what my
performance is of my suppliers coming in
to North America so I can say which
suppliers are worst performing in North
America so I type that up answer it and
then I quickly get a list of here here
are the top 10 worst performing
suppliers so what's their name what's
their on time and full of the suppliers
delivering into my network where are
they located what's the supplier City
but then I also in the search results
see all right the top supplier is
Bearing Distributors this one has the
lowest on time in full I can also see
that there was a supplier performance
evaluation and so that evaluation may be
in PDF I can then click on it the search
results came back they found and I can
go through and see okay here was this
company it was EV when it was evaluated
what their quality was who the
evaluators were I can get that
information very quickly to now see all
right what do I know about this
particular supplier or I can drill in
and go to information Pages or other
information that's available but now I
really want to know okay what do I need
to worry about today with Bearing
Distributors so which purchase orders
are at risk for Bearing Distributors so
I open this up and it tells me there are
three active purchase order lines where
is their destination that requested
delivery date the predicted delivery
date again this is just going in to the
data that's pulled into our supply chain
digital twin and saying all right these
three are now predicted to be delayed so
we have a model a machine learning model
within our supply network RIS that is
predicting the day that a purchase order
line is placed until when it's actually
going to arrive and every single day
it's updating and so two of these are
predicted to be delayed and one of
them's already delayed but now I can
also go see all right what are those
purchase orders so I can open this up
maybe this is opening up your Oracle
instance your sap maybe you have these
in PDF forms you can now start to see
okay this is a YW motor here's you know
here's who I am that's ordering it where
I'm ordering this from so again just
pulling all that information along with
you know other item details that you
want to order or you can jump into that
particular order line and get more
information so now I'm navigating into
our supply network risk application
where it starts to show this particular
order line for an item a yaw motor it's
coming from this supplier going to my
facility in Joliet okay but before I
jump into here that was me asking
questions about my performance and
ultimately getting to an individual
perch sorder line now I could also jump
back and go in the more traditional
route from our C3 aai applications we
start off with overview Pages where you
can start to see configurable key
performance indicators such as how many
open purchase order lines do I have what
percentage of my order lines are delayed
predicted delayed um predicted to be on
time what's my supplier My overall
supplier performance then you can start
to see what's my open purchase order
lines have I been improving am I getting
worse um but ultimately you can then
start to prioritize you know if I want
to move the needle on my supplier
performance which supplier should I work
on or maybe there are certain items that
I should be focusing atten on that are
causing my performance issues or maybe
there's a specific facility again
prioritize your work to figure out how
am I going to improve my supplier on
time and full performance the other
available option is that say you're a
planner a a material planner and inbound
planner maybe you're in charge of a
specific facility or a group of products
maybe it's a set of suppliers you can
filter down and then see an alert driven
workflow allows you to see what are the
purchases store lines that are predicted
to be delayed so now you can come in
each day and start to see these things
um pop up on your list start to go work
on them prioritize by value how many
days they're predicted to be late along
with an other contextual information and
then you can drill into those purchase
order lines so now I've taken it from
asking questions and getting into a
specific purchase order line detail or I
can go in from a prioritize standpoint
and those two different workflows merge
though into the same location of what do
I need to do about this particular
purchase order line so now that I'm
drilled into this order line I can see
it's predicted to be delayed by 10 days
I can see general information um I can
also see activity feed so this is all
the information that was fed into the
machine learning model from various
Milestones so the day it was order was
created when it was accepted when the
carrier picked it up from the supplier
when it arrived at the Port of origin
when and then now it's on on on a vessel
and so it's been departed but then
sometime after the vessel Departed the
AI model predicted that it was going to
start to be delayed so now I want to
dive in and understand what is the
machine learning model telling me so I
go into my AI explainability to go look
at the evidence package I'm going to
turn off some of this to start to see
and explain what's going on so I'm in
this purchase order line and over time
because every single day our machine
learning models are running for all of
your open purchase order lines I can see
the expected lead time was going to be
45 days and when originally it was
created the machine learning models were
predicting that this particular order
was going to arrive early and then
eventually it got a little bit more
delayed a little bit more delayed and
then about a week ago it started to
predict oh you're going to start being
late and so now we're at the point where
we're 10 days late what I want to
understand though is in the hundreds of
features that are going into this model
how do I categorize them and that's what
these color components start to bring in
is we have grouped features together
into these different areas called
quantity Network time allocation demand
and those help you to start to
understand where are the problems within
your network at least for this
particular order that are causing the
delay another feature I can look at as I
scroll down is I can go pull up any past
model so I said the machine learning
model is running every single day well I
can go back in time to understand what
were the features saying what were the
contributions and so as I look at the
feature contribution I can see that this
network category is causing the biggest
amount of contribution to my delayed
what does Network mean so as I read
through this through our Insight cards
you can start to see that the lead times
between milestones for similar orders
has increased by 22% over the last six
months so over this time maybe you
didn't see it in like a single event but
it started to happen over time you
started to see these increase well what
are all the features that make that up
so now these are the individual features
that make up the machine learning model
port-to-port lead time terminal delivery
supplier promise delivery changes I can
see what a description is of all those
features what the value is and then what
the contribution and so the whole goal
of this explainability or our what we
call our evidence package is to open up
that black box that is a machine
learning model or an AI model and start
to explain this in layman's term
so that everyone can start to understand
and you can start to learn all right
Port toport lead times something's
happening with my vessels that's
probably going to impact more of your
network than just this one purchase
order line so now I understand a lot
more about what's going on with this
model why it's predicting what it is so
now let's go dive in and see what can I
do about it are there actions so I've
predicted that it's going to be delayed
ahead of time now I want to start to
take action and see um what some of my
options are so I'm going to switch over
to a more global view to understand you
know physical premise where is this um
where is this container where is this
particular order line um at today so I
can quickly see here was the supplier
over in China the port of origin the
container because we're tracking as lla
said all of the AIS um data so we can
track where vessels are around the world
so I can see where that container is at
I can see the port of destination and
then I can see the Final Destination um
over here in Joliet if I look at this
particular container I can start to get
information about here's the port here's
the destination but I see that this
container so my Transportation team has
this container as a low priority what I
can do though is we can work with all of
our customers and we can configure
various recommendations one of them
might be you may have a different option
for Port processing priority so maybe I
want to increase the priority of this
container and then I can see because the
predictive model has learned that if you
change this from low to high I can shave
two days off of my processing time so
I'm going to go ahead and do that so I'm
going to change this up to high I'm
going to accept that and what that did
is not only did it change it in our
application because of the bidirectional
integration and the ability for all of
our apis to write we now push this out
to your transportation team they now
have awareness and so you're making this
more efficien for your supply chain
teams to work together I want to look at
some other options for what can I do
about this so now I'm going to zoom in
on my um actual facility where it's
going I can see you know where's the
location it's over in Juliet I can see
there's a stockout risk on this yaw
motor that's part of this purchase order
line and I can see that there are a
couple options there are containers that
have been sitting around for a while um
in one in Houston and then one in um
LaGuardia and so I can start to look at
those I can see one would reduce it by
six days my stock out time one by 10
days so what I'm going to do is I'm
going to pull this one from LaGuardia
I'm going to basically accept it here in
the application that will write out to
your system and what it'll do is it'll
start to reroute that container and say
all right send this one to Juliet
because I need to avoid a stock out risk
and so whether it's containers with
inventory whether it's other facilities
with inventory we're pulling in those
Network views and we can configure these
recommendations so you can start to take
action so let's quick go back and
summarize what we saw was we started
asking questions and then through the
applications I started to get more and
more detailed information about what are
my problems and I started to see
problems that were about to happen and
then the C3 AI Suite of applications
then provided you with recommendations
for how to mitigate those action
mitigate those problems before they
become an actual problem within your
supply chain so I will turn this now
over to Kon and we will start our
Q&A great thank thank you Justin and lla
for the great presentation and demo for
the audience please continue to use the
Q&A window for questions that you want
to ask we'll start off with a few
questions U the first one is for Layla
what's the difference between C3 Ai and
powerbi is C3 aai just an enhanced
version of
powerbi yeah great question um as I
think became evident in Justin's demo
the C3 AI supply chain applications are
not um just an enhanced version of
powerbi these are fully workflow enabled
applications that address primary
business needs um and are specifically
tailored to specific business personas
so the demo that Justin just shared is
really focused on an order manager to
keep orders on track and make sure that
there are not um ins suing delays for
things like manufacturing or for
customer delivery we have similar
application workflows and I urge all
attendees to watch our explainer videos
or follow up with us for additional
demos for our inventory um optimization
application demand forecasting sourcing
optimization and production schedule
optimization what generative AI does is
it provides users with a new interface
to ask questions and get easy access to
the information that they need so if
there is um information that is already
integrated to the C3 supply chain
digital twin or to their um into their
application um instance then they can
start to ask more natural language
questions that traditionally would
require additional reporting or pulling
data in order to to run a query so
instead it's providing a more natural
language interface to allow you to ask
more free form questions but ultimately
neither are in my mind at all similar to
powerbi um or business reporting
dashboards really they are number one
providing endtoend user driven workflows
and number two providing a more natural
language interface to access
information great thank you lla Justin
we have a question for you uh the
question asks I've already made
significant investments in advanced
planning and scheduling Supply Chain
Solutions can C3 aai generate enough Roi
to justify investing in another
tool yeah that's a great question so you
know I actually think the need for
investing into more process driven
Solutions in advanced planning is
absolutely critical but where those
applications start to fall short is the
ability to make decision operational
decisions using Advanced AI ML and
optimization and what we have seen with
many of our customers is when they go in
they do those Investments and then we
come in and sit in parallel and help to
enhance those Solutions is we can see
demand forecast increase of 10 to 20% in
accuracy Improvement we can see
reductions of inventory around 35 to 50%
we can see reductions in sourcing costs
of upwards of 10% and we can ultimately
as you're focused on your customers see
on time and full order fulfillment
increases of around 20% so that's on top
of the Investments that you've already
made those are coming in because the
artific intelligence because the machine
learning models have been specifically
applied along with then some of the um
alert K based um workflows that we sit
on top of your Advanced planning
solutions they can start to enhance and
provide definitely provide that Roi
needed um to make it worth it great
thanks Justin lla we have a question for
you we just saw Justin provide a demo on
J with SNR or Supply C3 AI supply
network risk lla will other C3 AI supply
chain applications have C3 generative AI
capabilities yeah um absolutely we are
incorporating C3 generative AI into all
of our C3 AI applications um we also
sell C3 generative AI as a standalone
product um we will configure the search
interface and ability to chat with the
llm and query the underlying data the C3
generative AI product be deployed on its
own um or it can be um codedeploy and is
part of all of our other C3 AI
applications so if you choose to pursue
C3 AI inventory optimization or C3 AI
supply network risk those applications
come out of the box with C3 generative
AI um the difference is really what are
the data that are Incorporated and are
you applying the predictive models that
are inherent to those other applications
as well as those end user workflows so
it really depends on the business
problem that you need to
solve thanks lla Justin we have a
question for you can C3 generative AI
populate results with different graphs
based on the prompt that you
ask yeah no that's a great question um I
will I go back to a little bit of the
first time I was introduced to
generative AI as we our teams were
starting to show it to us and we learned
that if you ask a question about where
something happens it pulls up a map
because the models are smart enough to
know that if you want to ask where it's
going to bring you a map and so I've
seen in you know we're going to get more
mature in this everyone's getting more
mature I've seen you know different bar
charts line charts I've seen Maps I've
seen sanky diagrams can be generated I
obviously the maps I talked about so
yeah it's it's really cool the power
that can be generated and the way that
information can be shared and what's
really cool is that the application will
start to present you with that data in
the best way that it should be presented
and so and it'll continuously learn over
time and get better at how should it
present the results to specific
questions that are answered and so
really excited to see where this is
going to go um as we continue to enhance
it and make it even better awesome
thanks Justin uh lla question for you
how long does implementation
take yeah we usually get started with
customers with three to sixth month
pilot projects over the course of that 3
to six month Pilot We Will integrate the
data required for the use case now again
if it's just a a generative AI use case
then we are integrating unstructured
data some structured data and
configuring that search interface if we
are deploying a C3 aai supply chain
application that also involves training
the predictive models the optimization
models configuring the end user workflow
such that the end of the 3 to six month
period you have a live production
application with users that have access
to start get getting started you know
immediately um so so it just depends on
which use case is selected to decide if
it's a three or six month
project great Justin a question for you
can C3 generative AI be applied to my
existing Supply Chain
Solutions yeah absolutely and by tapping
it into those databases as L is showing
you can start to pull those results in
and start to query whether it's your
planning systems whether it's your um
powerbi systems your Tableau click you
know whatever your um reporting systems
are and you can take whatever you've
invested in today start to bring that
data in and start to query questions
against it and it'll present it back to
you the way you want to so absolutely
you can get started and then it gets
enhanced even more as you start to bring
in some of the C3 AI supply chain Suite
of applications as
well great thanks Justin lla the next
question is for you uh it relates to
data so how does C3 generative AI
prevent data exfiltration can you speak
a little bit about what happens when
there are gaps in supply chain
data yeah so uh two questions one on
datax filtration which is where I will
start um so I as I presented um in the
slides we actually prevent the llm from
accessing our customer data so the llm
is a trained deep learning model that
knows how to interpret and synthesize
you know human language um but it
doesn't know anything about your data
and so what that llm is doing is it's
simply understanding what is the intent
of the question what information are you
looking for and passes that to the
retrieval model because the llm doesn't
ever have access to the Enterprise data
it never learns from it it doesn't
become um expert about your you know
your operations your supply chain it
doesn't actually know anything about
your suppliers your inventory your
distribution your manufacturing
facilities so there's no information it
has that it could be exposed to
providing out into the world um so that
is a really important part of our our
architecture that prevents all of those
downsides of standard llm architecture
um the second question you asked is how
does it overcome data gaps um I'll
answer in two ways so first is you know
if there's a gap in the data and we're
just talking about the search based
interface you know I ask a question I'm
looking for a response if that data
doesn't exist again the retrieval model
will return no data in the query and
then it presents an basically an empty
array and the llm will say okay my
response is I don't have data I don't
know how to answer that question and
we've you know we've tested this
countless times um if you ask a question
the that C3 generative AI doesn't have
an answer to the L will respond you know
I don't know we don't have access to
that information so that's one part of
the answer the second is well what if I
really need that data to make better
decisions you know I need to know if um
how much inventory I have at my
different different distribution centers
what if that data is missing um and all
of our applications come with pre-built
pipelines that address different types
of problems and data gaps is a really
common problem we run into all the time
um we address that by handling
uncertainties so we know that and if we
see an outlier where maybe there is a
missing data entry point where it looks
like inventory is zero but all of the
previous data um was you know inventory
of 10,000 units we might treat that as
an outlier and then smooth over it um
there's other types of missing data
handling Techni techniques that we use
um we also apply similarity for things
like demand forecasting so you know if
you have no data it's new product
introduction if you're missing data data
from sales we can handle that with
similarity to other products there's a
number of different approaches that we
take depending on the type of missing
data that we're talking about when
applied to specific predictive problem
or specific type of insight that we're
generating um so again it depends on
whether or not you're asking how do you
handle missing data whether it's you
know search-based I'm just trying to
query and get access to that data and
it's missing we'll say we don't have
access to the data but if it's about a
predictive model and a supply chain
workflow then yes we do have ways to um
overcome those data gaps so that you can
still get really good Insight from your
data that you do
have great well thank you Laya and
Justin that's all the time that we have
today as a quick reminder this webinar
will be available on demand and you will
receive an email with the link later
today if you enjoyed today's content
please follow c3ai on LinkedIn Twitter
and YouTube and thank you again for
joining us and we hope you have a great
rest of your
day
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