supply chain with AI/ML Presenters: Paul McClure James Triggs - - PowerPoint PPT Presentation

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supply chain with AI/ML Presenters: Paul McClure James Triggs - - PowerPoint PPT Presentation

Oct 13, 2022 1.06k likes •1.53k views

Automate you companys supply chain with AI/ML Presenters: Paul McClure James Triggs Chief Revenue Officer AI Product & Delivery Lead Ultra Commerce Intellify pmcclure@ultracommerce.co james.triggs@intellify.com.au @paul_mcclure

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Automate you company’s supply chain with AI/ML

Paul McClure Chief Revenue Officer Ultra Commerce pmcclure@ultracommerce.co @paul_mcclure James Triggs AI Product & Delivery Lead Intellify james.triggs@intellify.com.au Presenters:

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Ultra Commerce & Intellify Benefits to Expect Journey to Automation AI/ML

Data Driven Commerce AI/ML Use Case Examples ML Optimised Order Processing Demand Forecasting Purchasing Recommendations

Summary Q&A

Agenda

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Ultra Commerce & Intellify

Data Driven Commerce Digital Commerce Data Science

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Benefits to Expect

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Deliver the desired customer/supplier experiences Improve productivity through digitisation Deliver cost efficiencies through automation

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Increased revenues

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Journey to Automation

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Journey to Automation

Offline Channels

1 Source: Accenture, Channel Shift: Measuring B2B Efforts to Shift Customers Online

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Journey to Automation

Offline Channels

While 30% of buyers would prefer to buy 90% or all of their products

  • nline, only 19% are doing so.

98% of buyers are forced to pursue

  • ffline help from a sales

representative at some point throughout the purchase journey.1

1 Source: Accenture, Channel Shift: Measuring B2B Efforts to Shift Customers Online

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Journey to Automation

Offline Channels Integrations

1 Source: Gartner, 2019 Strategic Roadmap for Digital Commerce

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Journey to Automation

Offline Channels Integrations

The three biggest challenges in launching a digital commerce site — including B2B implementations — are integrations to other systems, lack of internal resources and implementation costs.1

1 Source: Gartner, 2019 Strategic Roadmap for Digital Commerce

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Journey to Automation

Offline Channels Integrations

Much of the digital business technology platform is in essence an integration engine that adds some new digital capabilities to those the company already has.1

1 Source: Gartner, Survey Analysis: Building a Digital Business Technology Platform Requires New Technologies and Methods

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Journey to Automation

Offline Channels Integrations Data

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Journey to Automation

Offline Channels Integrations Data

“The world’s most valuable resource is no longer oil, but data”.1

1 Source: The Economist, May 6th 2017 edition

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Journey to Automation

Offline Channels Integrations Data Automation

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AI/ML

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By 2023, 80% of organizations using AI for digital commerce will achieve at least 25% improvement in customer satisfaction, revenue or cost reduction1

1 Source: Gartner Inc. - Use Personalization to Enrich Customer Experience and Drive Revenue

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Data Driven Commerce

DB App Streaming Data Sources Historical Data Time-series Forecasting Personalisation

Pre-defined ML Models Insights/BI ETL / Query Aggregate Data Repository

External Systems / Integrations Fraud Detection

CSV

File Upload

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AI/ML Use Case Examples

Search

Insights:

Customer Journey Analytics Voice of Customer Analysis Sentiment Analysis

Process Automation:

NLP & Visual Search

Decision-Making Augmentation:

Supply Chain (Shipping & Logistics) Product / Inventory Forecasting

AI/ML

Demand Forecasting Fraud Detection Customer Segmentation Dynamic/Optimised Pricing Trend Analysis Order Processing

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AI/ML Use Case Examples

Search

Insights:

Customer Journey Analytics Voice of Customer Analysis Sentiment Analysis

Process Automation:

NLP & Visual Search

Decision-Making Augmentation:

Supply Chain (Shipping & Logistics) Product / Inventory Forecasting

AI/ML

Demand Forecasting Fraud Detection Customer Segmentation Dynamic/Optimised Pricing Trend Analysis Order Processing

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Order Processing

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64% of B2B organizations claim that their long-term customers’ resistance to change is a barrier to driving more online sales.1

1 Source: Accenture, Channel Shift: Measuring B2B Efforts to Shift Customers Online

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ML Optimised Order Processing

Post JSON to Ultra Commerce API’s for Automated Order Processing & Fulfilment based on a workflow

By using machine learning to instantly “read” virtually any type of document to accurately extract text and data without the need for any manual effort or custom code.

Scan of a Purchase Order Automatically identify key information Instantly “read” virtually any type of document

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Order Processing

Ideal:

  • Efficiently and accurately process thousands of orders per month across all sales channels (phone, email and web/online)

including the ability to read order emails and their attachments for automated order processing and fulfillment Reality:

  • Manual order processing by 7-10 Customer Service Representatives
  • Limited visibility on stock availability / inventory levels due to disparate business systems and data silos
  • Limited ability for customers to track orders

Consequences:

  • Poor customer/supplier experience
  • Lost revenues due to order processing delays during high demand periods
  • Lost revenues due to human errors related to order processing
  • Increased costs due to higher staffing/resource requirements

Customer Service Representative @ B2B Manufacturing Company 15% of orders via Web, 85% of orders via Email (PDF) and Phone

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Email Orders Phone Orders Customer Service Representatives Web

Cart / Checkout Storefront/Marketplace

Order Processing

85% of order volume Business Systems Supply Chain Management

Warehouse Manager

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Amazon Textract Email Orders Phone Orders Customer Service Representatives Web

Business Systems

Assisted Shopping Customer Account/Profile Payment Preferences Cart / Checkout

Supply Chain Management

Approvals Workflow Content Management Order Management Product Management Quotes Shipping Preferences Storefront/Marketplace

ML Optimised Order Processing

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Outcomes Delivered

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Increased order status visibility for customer Reduced operational headcount for CSR teams Increased revenues through increased order processing capacity and reduction in human errors

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Demand Forecasting

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Time Series Forecasting Examples/Scenarios

Product Demand Utilities Usage (Electricity, Gas and Water) Call Center Staffing Flight Ticket Prices Broadband Usage (Telco) Real Estate Prices

Time Series Forecasting

Sensor Network Monitoring Cash Flow Cash in an ATM Selling Price of Crops Transportation Needs (Geography based) Ad Impressions Deliveries per Post Code Inventory Planning PAYG service usage Patient Volume

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Demand Forecasting

Ideal:

  • Easy mechanism to produce accurate demand forecasts.

Reality:

  • Software based on traditional statistical techniques.
  • Good intuition on what drives demand, but not enough time to apply this to the forecast for each SKU.

Consequences:

  • Forecasts that vary greatly in quality.
  • Forecast errors flow through the rest of the supply chain planning.
  • Demand forecasts are not trusted by the purchasing planners.

Demand Planner @ Trade Distributor Forecasting demand across 200,000 SKU’s

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Product demand

Actual Demand vs Forecast Demand Actual Demand Forecast Demand

Over-forecasting leads to wasted resources Under-forecasting leads to lost opportunity

The True Costs of Inaccurate Forecasting

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  • Amazon.com achieved 15x forecast accuracy improvement from 2007 to 2017.
  • Shift from univariate (each forecast is done in isolation just looking at volume) to

multivariate (pulling in other data that drives demand)

  • Categorical data about products (e.g. Colour, size, product category, brand)
  • Related time series (e.g. How prices, promotions and the weather changed over time)
  • Seasonality hints (e.g. Holidays, weekends, closedown periods)
  • Algorithm improvements from ARIMA of the 1970’s to today’s deep learning

models.

ML Optimised Demand Forecasting

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Outcomes Delivered

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Improved forecast accuracy. More granular forecasts by SKU, by location, by warehouse. Scenario modelling such as how pricing could impact demand.

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The enemy of accuracy-based planning is uncertainty. Uncertainty makes the plan inaccurate. What ML really helps with is its ability to convert unknown uncertainty to known variability.1

1 Source: Gartner, Mastering Uncertainty: The Rise of Resilient Supply Chain Planning

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Purchasing Recommendations

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Purchasing Recommendations

Ideal:

  • Optimised inventory purchases for each SKU, maintain the right stock in the right locations at the right time.

Reality:

  • Pressure from finance to reduce working capital
  • Pressure from sales to improve service levels
  • Notified late of promotional items (too late to source via lower cost transport)

Consequences:

  • Working capital tied up in slow moving stock
  • Can’t invest in the inventory required to improve service levels on critical products

Purchasing Planner @ Distributor Responsible for ordering 5,000 SKU’s

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How ML can make Finance, Sales and Inventory happy at the same time

  • Drivers of safety stock:
  • Unexpected variability in demand
  • Unexpected variability in lead time
  • ML generates recommended inventory purchases to the purchasing planner – “Buy

300 widgets on 1st July.”

  • Reduces over and understocks. Sanity check for the purchasing planner.
  • Run simulations of the recommended purchases to demonstrate the value.

ML can forecast most of this variability, reducing the amount of safety stock required.

Purchasing Recommendations

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15 x

Demand forecast Improved accuracy Inventory Forecast Purchasing Decisions Demand Forecast Safety stock Working capital tied up in inventory Inventory turns +26% Service levels +16%

AI Optimised Supply Chain

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Outcomes Delivered

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Reduce working capital tied up in inventory Reduce out of stocks Reduced express shipping

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Summary

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Digital Commerce is central to digital business strategy By 2023, artificial intelligence will be used by at least 90% of digital commerce organizations.1

1 Source: Gartner, Digital Commerce Vendor Guide, 2020

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Ultra Commerce & Intellify

Data Driven Commerce Digital Commerce Data Science

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Summary

1 2 3

Deliver the desired customer/supplier experiences Improve productivity through digitisation Deliver cost efficiencies through automation

4

Increased revenues

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Summary

1 2 3

Deliver the desired customer/supplier experiences Improve productivity through digitisation Deliver cost efficiencies through automation

4

Increased revenues

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Summary

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Improve productivity through digitisation Deliver cost efficiencies through automation

4

Increased revenues Easier to do business with

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✓ Free expert consultation with Ultra Commerce & Intellify ✓ Discuss your current business challenges and pain points ✓ Advice on how to digitise your business using digital commerce and AI/ML ✓ Recommended roadmap

Book a Discovery Workshop www.ultracommerce.co/contact

To book please visit

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Q&A

Paul McClure Chief Revenue Officer Ultra Commerce pmcclure@ultracommerce.co @paul_mcclure James Triggs AI Product & Delivery Lead Intellify james.triggs@intellify.com.au Presenters: