Disruptive Innovation Through
Digital Transformation
- A Supply Chain Focus
Dr. Robert de Souza
TRANSFORMATIONAL
STRATEGIES FOR SUPPLY
CHAIN AND LOGISTICS
MANAGEMENT
Review
Plan
THE COMPETITION
PARADIGM
1
Basis of Competition
THE CHANGING
WORLD
2
Demographic Trends
Industry 4.0
The Marketplace
Sociological Trends
external factors
speed
costs
throughput
organization realignment
process realignment
integrating systems
logistics infrastructure
Increase of lifespan
ageing population
increasing urbanization
more multi-cultural
more middle class
brands are less important
omni-channel buying
well-informed & social buying
sharing economy
mergers & acquisitions
outsourcing strategies
disintermediation/ governance
growth ecommerce
automated vehicles
lights-out warehouse
3D printing
big data analysis
quick development of apps
IMPACT ON
COMPANY & SUPPLY
CHAIN (CASES)
3
TRANSFORMATION
CHALLENGES
4
Pressure on Supply Chain
Distribution
Manufacturing
Multi-dimensional
more postponement
changing batch sizes
manage customer ship-to locations
later cut-off times in warehouse
multiple shipping locations
distributed order management
pricing differentiation in delivery options
supply chain/ costs & time
capacity
new technologies
global focus on core business
broad alignment
guidance & direction
resource allocation
high dependency on IT data
current state analysis
what-if analysis
Digital Twinning
Executive Support
Organization Structuring
M
Transformation
Digital Twinning
Industry 3.0
Automated Mass
Production
Transition Phase
Industry 4.0
Atomization and
Digitalization
Traditional
Supply Chain
Digital
Supply Chain
E-Commerce Logistics versus
Conventional Logistics
Future Supply Chain: Digital Supply Chain
Digital supply chain is
customer-centric
Digital supply chain is
shifted to a more
connected network
Digital supply chain is
fast-changing, requiring
automation & flexibility
Cha
ly Chai
n
Macro
Challenges in the Digital Supply Chain Journey
10
Import duties vary widely across
countries
Warehouse utilization is quite low
Source: Duty Calculator; A.T. Kearney Analysis
Source: Singapore Department of Statistics,
2016
Efficient road networks are required in
order to ensure efficient last-mile delivery
Source: Jones Lang LaSalle; A.T. Kearney Analysis
1
2
3
Cross-border
Domestic
1. Heterogeneous and
time-consuming
custom processes
4. Inefficient last mile
delivery
2. Poor-transport infrastructure
3. Warehouse readiness for e-commerce
Strategic Process & Solution Change
More Collaborative
More Transparent
More Flexible
Digital Twinning
Supply Chain Self-Orchestration
“A Supply
-
Chain Specific Integrated Platform”
15
Key Strategies in
Digital Supply
Chain
Data
If handled and managed properly, data can help generate smarter supply chain and
logistics solutions and improved decision making processes.
(1)
Visualization
(1)
It would reveal insights and
provide suitable forecasting
mechanism to maximize
revenue of business and reduce
costs/losses/risks over the
chain.
(2)
Optimization
It selects the “best” solutions
from a set of alternative
solutions (usually using
mathematical model) by
considering several factors.
(3) Network
Optimization
It is used to find the best
configuration of a supply chain
network structure as well as the
flows based upon an objective
function, which typically
maximizes profits.
(4)
Simulation
It generates a set of “what
scenarios for determining best
-
if”
strategies in a supply chain
network optimization.
(2)
(3)
Supply Chain Self-Orchestration
“A Supply
-
Chain Specific Integrated Platform”
1. As-Is
2. To-Be Ideal
Supply Chain Network
Design Tool
Dynamic Resource
Allocation Tool
and Scheduling and
Routing Tool
Problem Statements
18
Product availability is extremely
important. Increasing on-shelf-availability
increases sales and consumers' loyalty.
A complex distribution network with limited cost-time-risk
Opportunities
Utilization in DCs
New strategies (i.e. (re)scheduling, delivery tracking and
postponement) improves manpower utilization in the DCs as
well as optimized truckload.
Supply Chain Visibility
The Last Mile!
?
Challenge: Maintaining Economies of Consolidation from First
to Last Mile
Opportunity:
Container within
Challenge: Coordinating Assets
Urban Area
Retailers
Shopping
Centers
Factories
Businesses
End-Customers
Picking
points
Urban Area
Retailers
Shopping
Centers
Points
Urban Freight
Consolidation Center
(UCC)
Warehouses
VC ratio
Opportunity:
Multiple Use
Facilities
Opportunity: New
Transporters
Opportunity:
Asset & Capacity
Network Optimization and
Simulation Modelling:
24
Network Design Framework
Humanitarian Operations
Performance Measures
Pre-filtering of
candidate
locations
Data Visualization
MCDM
Optimization
Dynamic Simulation
Optimum network
configuration
Facility and Asset
Optimization
Implementable
Facility and Asset
Management
Value-added GIS
Visualization
Inventory &
transportation
policies
Identification of site
selection criteria and
candidate locations
Structuring and optimizing
the supply network
Supply chain operational
performances
Qualitative Inputs
Network Visualization
Comparison of alternative network
performances
Fleet and Inventory Policies
Coverage Index
Access to Affected Zones Index
Risk Index Infrastructure Index
Corridor Accessibility
Index Airport Congestion
Index Trasportation
Cost Index NDP Index -
Relative Pekanbaru 1.000 1.000 1.000 0.707 1.000 0.252 0.717 1.000 0.919 Medan 0.769 0.294 0.899 1.000 1.000 0.095 0.996 1.000 0.756 Bengkulu 0.648 0.478 0.260 0.613 1.000 1.000 0.415 1.000 0.591 Palembang 0.216 0.319 1.000 0.427 1.000 0.244 0.360 0.719 0.550 Surabaya 0.809 1.000 1.000 0.920 1.000 0.220 0.872 1.000 0.927 Semarang 1.000 0.321 0.925 0.760 1.000 1.000 0.896 1.000 0.809 Denpasar 0.313 0.545 0.285 1.000 1.000 0.204 0.591 1.000 0.586 Jakarta 0.302 0.148 0.618 1.000 1.000 0.125 1.000 1.000 0.583 Banjarmasin 1.000 1.000 0.230 0.930 1.000 0.041 1.000 0.719 0.787 Balikpapan 0.715 0.938 0.155 1.000 1.000 0.020 0.298 0.579 0.677 Samarinda 0.712 0.635 0.165 0.448 1.000 1.000 0.246 0.719 0.570 Pontianak 0.017 0.226 1.000 0.793 1.000 0.075 0.092 1.000 0.546 Ambon 0.495 0.833 1.000 1.001 1.000 0.504 1.000 1.000 0.868 Ternate 1.000 1.000 0.788 0.730 0.000 1.000 0.001 0.842 0.773 Timika 1.000 1.000 1.000 0.457 1.000 0.572 0.177 0.719 0.854 Jayapura 0.502 0.431 0.767 1.001 1.000 0.236 1.000 0.842 0.712 Sorong 0.738 0.381 0.673 0.892 1.000 0.566 0.408 1.000 0.698 Manokwari 0.612 0.688 0.664 0.566 1.000 0.629 0.636 0.719 0.689 Palau Biak 0.641 1.089 0.623 0.566 0.000 1.000 0.050 0.298 0.646 Manado 1.000 1.000 0.639 0.680 1.000 1.000 0.530 1.000 0.866 Makassar 0.442 0.357 1.000 1.000 1.000 0.617 1.000 1.000 0.759
Criteria
Weightage 0.1813 0.2155 0.2081 0.1666 0.0972 0.0476 0.0461 0.0376 Kalimantan
Maluku
Papua
Sulawesi
Potential
Locations
(nodes)
Geographic
Area
Location Criteria
Score
Sumatra
Delivery Fulfilment Framework
Templated data set
Locations (Lat/Long)
Sourcing/Flow
Customer Characteristics
Vehicle Characteristics
Delivery Fulfilment
Performance Measures
Dynamic
Scheduling and
Routing
Data Visualization
Data Analytics
Optimization
Multimethod Modelling
Delivery Consolidation
Facility and Asset
Optimization
Implementable
Facility and Asset
Management
Value-added GIS
Visualization
Fleet Optimization
Dynamic Vehicle
Routing Problem
(VRP)
Delivery
Postponement
Delivery
Self-collection
Other Data Sensor and Telematics
