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Showing posts with label Supply Chain Management. Show all posts
Showing posts with label Supply Chain Management. Show all posts

May 05, 2021

How Amazon Delivers On One-Day Shipping

 

What they may do behind the scenes?

  • The key to success is Inventory management
  • Quarterly / Early / Weekly Demands updates is the key
  • Understanding buying patterns/trends and updating the numbers mastering the accuracy
  • Demand Planning, Forecast run ahead, Stocked up earlier
  • Overstock / Before every quarter keep the items stocked up in all DCs for the anticipated frequent products in each category
  • Not all products/categories may make money but the experience will make customers come again / buy again
  • Buy in Bulk and Stock it
  • Robotics arms, Robotics package movers
  • Order early and keep good margins
  • Ship between / Leverage different countries / In a way optimize unsold inventory
  • They have their own Aircraft / Careers / Own Transportation network
  • Building own logistics network
  • Marketplace / Third party sales 
    • Path1 - Seller - Warehouse - Customer
    • Path2 - Seller - Fulfilment - Customer
  • Pickers are measured by rates / only 30 mins break / Demanding pickers
  • Amazon is good at customer service but not sure how they treat their own Employees :)
Amazon product distribution 

I ordered product X from Amazon

Flow #1
  • The product was shipped from Hyderabad
  • The product was shipped to Chennai
The product had some repair, I placed a replacement

Flow #2
  • Product shipped from Coimbatore
  • Product in transit near salem
Insights
  • They have a warehouse in Salem
  • For every order, they find the nearest distribution centres to ship (Hyderabad, Coimbatore)
  • Their demand system might stock up the product from Manufacturers to different distribution centres and further leverage for customer demands
Keep Exploring!!!

Digital Transformation of Last-Mile Delivery

Key Notes
  • Grouping Deliveries
  • Everyday some set of deliveries to a subset
  • Delivery constraints - Size of truck, time sla
  • 200 Customers, 10 Trucks - different combinations
  • Academic point of view not possible
  • Never ever be able to go through all possibilities
  • Route Optimizer - Combine computer possibilities with ideas

  • Hard problem in delivery
  • Changing traffic patterns
  • Decreased parking availability
  • Smaller deliverables
  • Increasing cost per delivery
  • Challenges meeting same day delivery

Challenges
  • Too much emphasis on pieces
  • Extensive manual effort to generate route plans
  • Map on wall vs Map on computer vs real-time conditions
  • Assuming no variability and uncertainty
  • Continuous improvement is needed
  • Mobile Device = Data Capture Device
  • Data Science - Be smart dealing with data
  • Not easy thing to figure out data is right
  • With cloud store large data of customers
  • Spot problems/inefficiencies
  • Datahub is easy with tech
  • Lat / Long in the ballpark
  • Different Delivery problems
  • Ecommerce has created a headache for the delivery business
  • Handling off-cycle orders
  • Planning for truck drivers
  • Spread out
  • Keep track of master routes
  • Returns are expensive
  • Master Routes
  • Day Balance
  • Deliver similar items on same truck
  • Assign delivery day for efficient routes
  • Data requirements
  • Product data
  • Volume estimations
  • Truck data
  • Stop time = fixed time + lineitems*variabletime
  • Every customer has a distribution time
  • Drivetime
  • Google, bing, open street
  • With lots of customers route optimization difficult



  • Easier to build mobile platforms





Collect drop point samples



Keep Learning!!!

May 04, 2021

Supply Chain - Session #4 - Digital Supply Chain

Key Notes
  • Visibility during covid times
  • Measure value potential
  • Enterprise context order status
  • Mapped knowledge of Enterprise
  • NLP based search 

  • Detailed information flow of order
  • Google like queries on Enterprise data
  • Physical representation of supply chain


  • Plant, resources Trends
  • Digital brain of Enterprise
  • Planning, Supply chain tracking
  • Commercial, Revenue, Supply Chain Planning
  • Configurable platform / Packaged Solutions
Starbucks use case



  • Replenishment automation


  • Improve forecasts at starbucks
  • Include external factors, local events
  • Leading indicators for planning
  • Forecast + Drivers - Seasonality
  • Automated correlation for them



  • Special store ordering + Update + Comment
  • Planner to Store Manager collaboration

  • Interactive Portal
  • Simple and collaborative solutions
  • Legacy of i2 technologies
  • Native cloud based solutions
  • DBT

  • Pandemic has accelerated SCM
  • Pandemic Learning's

  • Demand variability
  • Trouble towards product visibility / demand
  • Channel shift - Phygital
  • Lockdown vs Opening up 
  • Supplier factor shutdown
  • Working on decreased capacity
  • Enterprise Collaboration goes digital

  • Capabilities in Platform to address challenges
  • Piece meal software 
  • Software DNA platforms are required
  • Digital Operating models
  • Amazon Model

  • Customer Engagement
  • Using it for Supplier Engagement
  • Knowledge base of Suppliers
  • Visibility across products / suppliers / customer demands
  • Current state of business

  • No Systems / 10 Different systems for RCA
  • Silo Systems
  • Decision making ends in excel and ppt
  • Slow adoption of Systems

  • Bring Consumer experience to enterprise

  • Missing similar experiences in Enterprise
  • Capabilities and Use cases
  • B2C Model
  • Opportunities with B2B Customers



  • Convert Knowledge to data is key
  • Best restaurants I have been to
  • Notion of continuous learning
  • Spot Reliable Suppliers by Automated recommendations
  • Customers for Upsell
  • Capabilities
  • Real time market knowledge
  • Leading indicator based forecast
  • Synchronize supply and demand plans - what if scenarios - sumulations
  • Cost impact for new demands/ financial implications


  • Data sources that power data driven decisions
  • Add nodes in graph dynamically

  • Network Graph relationships
  • Tags creation / product mapping

  • Considering competitor product knowledge
  • Weather impact
  • Initiatives / new product launch visibility
  • Sales and Operations planning reporting based
  • Demand planning opportunity

  • Collect external drivers
  • Competitor / Sentiment 
  • Public information
  • Google Trends
  • Demographic trends
  • Elders
  • Holiday Events
  • Sporting Events
  • Data collection for market knowledge
  • Overlay temperature for region
  • Promotion days
  • ISLE Promotion
  • Overlay and evaluate
  • Holidays



  • Digital Meetings






Gem of Session!!!