This document discusses digital transformation in transport and logistics through the use of big data. It first provides an introduction and background about the speaker. It then discusses using improved inventory control and forecasting of promotions in retail settings. Specifically, it examines using a management coefficients model to better balance workloads and reduce excess inventory. It also evaluates various machine learning techniques for more accurately forecasting demand during promotion periods versus non-promotion periods. Finally, it outlines opportunities for applying big data methodologies like predictive analytics and prescriptive analytics to challenges in logistics planning and connecting data to optimization of operations.

1. Digital Transformation in Transport
and Logistics
prof.dr. Tom Van Woensel
http://www.tomvanwoensel.com
@tomvanwoensel

2. About me
• BSc and MSc Applied Economics from the University of Antwerp (Belgium)
• PhD January 2003 from the University of Antwerp (queueing models for traffic
networks)
• Full Professor at the Eindhoven University of Technology
– Head of the TU/e Smart Logistics Lab (www.smartlogisticslab.nl)
– Research on Freight Transport and Logistics
• Associate Editor Transportation Science, OR Spectrum, Logistics Research, Urban Science
– Teaching at BSc, MSc, PostMSc, PhD and Executive (Tias Business School) level
– Board member of the European Supply Chain Forum
– Director of the Data2Move community
• Antwerp Management School:
– Academic Director Global Supply Chain Management program
– Academic Director Expertise centrum Smart Mobility

3. Contents of today
• Retail setting
• Improved inventory control
• Improved forecasting of promotions
• Outlook

4. Retail Setting
Problem features
• Case pack size
– Optimal inventory policy under lost sales is not known
• Handling workload
– Varies across days of week due to demand seasonality
– Handling cost p.u. varies across days of week and characteristics of items
– Handling capacity constraints  Joint replenishment problem
• Shelf space constraints
• Backroom constraints
• Recommended policies in the literature and off-the-shelf software
– Independent for each item
– Myopic and/or ignorant of parameters

5. Store managers do not follow orders recommended by automated replenishment
systems
0
10
20
30
40
50
60
70
Monday Tuesday Wednesday Thursday Friday Saturday
Sales ASO Orders Actual Orders
Case pack size (Q) = 6 cu,Average weekly sales () = 2.78 cu.
Total # of
consumer
units (cu)
Example: Weekly sales and ordering pattern for an SKU (body lotion)

6. Weekly seasonality patterns across all
items in a store show similar
differences
0%
5%
10%
15%
20%
25%
30%
35%
Mon Tue Wed Thu Fri Sat
(%)
Sales Pattern
Automated System Ordering Pattern
Actual Ordering Pattern
Range of variation in
Sales = 16.2%
ASO orders = 24.3%
Actual orders = 13.1%

7. Data description
• Five stores selected from a supermarket chain with 50 stores
• Types of data
– Daily POS sales data for 54 weeks.
– Orders and deliveries (invoice)data from the warehouse database
• Orders and deliveries data also contain potential errors and mismatches
– Classification of items by absolute and relative margin
– Local and central planograms
• Selection of SKUs
– Ordered fromthe central warehouse using theASO system; At least 50 selling weeks
– Ratio of inflow to outflow [0.9, 1.11]
– Effects not considered: promotions, phasing-in and phasing-out of skus

8. Application to inventory replenishment
• Bowman (1963): management coefficients
model of decision-making performs better than
the optimization system or the manager
• Our method
– Obtain ‘order advancement’target for each item
from regression model
– Modify order up to levels to achieve target order
advancement

9. Effect of management coefficients model on the range of the
weekly order pattern
S1
S3
S5
S2 S4
0
5
10
15
20
25
30
35
40
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Multiplier for the linear interpolation parameter, theta
Rangeofweeklyorderpattern(%)
Store 1
Store 2
Store 3
Store 4
Store 5
Extent of order advancement
Measure of
workload
imbalance
during the
week
Management coefficients model results in greater reduction in workload imbalance
for similar or lower amounts of excess inventory.
Inventory levels determined by simulation do not differ from order advancement
index.

10. Motivation
• Forecasting SKU demand accurately increases the efficiency of grocery
retailers operations
– Less inventory
– Less stockout
• But demand prediction is challenging in real world
– Marketing efforts by products and competitors
• Discounts
• Advertising and Promotions
– Dynamic categories
• New SKUs introduced to the category or store
• SKUs discontinued

11. Contents of today
• Retail setting
• Improved inventory control
• Improved forecasting of promotions
• Outlook

12. Experimental Setup
• Focus on 4 related categories with 48 SKUs and 4 stores
• Train models on 51 weeks and test on 25 weeks
• Measure of evaluation
– mean absolute error (MAE) computed over the test period
• There were 7766 total observations in training set over 51 weeks, and 4074
observations in the test set over 25 weeks

13. The cost – performance tradeoff
Traditional
statistical
methods
Machine
Learning
Increasing
Technique
Complexity
Increasing
Data Prep
Cost Features
Complex Model
Raw Data
Simple Model
Raw Data
Complex Model
Features
Simple Model
Benchmark

14. The Data
– Multiple store (multiple category) SKU-level data
from a Dutch grocery chain
– Weekly quantities sold, prices, marketing mix
values (TV, radio, windowsheet) ...
– Spans a period of 76 Weeks
– Does not contain information that can be used to
define SKUs (item name, brand, variety, packaging
information, size, flavor,etc)

15. Last lift benchmark is quite good, RBF SVR with raw data
beats it, regression tree with features is best
Data
preperation
costs
Technique complexityTraditional Machine Learning
Feature stepwise
4.86 (+10.44%)
RT w features
3.35 (-23.77%)
Benchmark
4.40
Regression +Sm
4.50 (+0.19%)
Regression
5.03 (+14.34%)
SVR Poly1+Sm
3.91 (-11.15%)
SVR Poly2+Sm
5.65 (+28.39%)
SVR RBF+Sm
3.85 (-12.50%)
SVR Poly1
4.38 (-0.47%)
SVR Poly2
5.45 (+23.84%)
SVR RBF
4.18 (-4.92%)

16. Forecast accuracy(MAE) for promotions versus no promotions weeks
Bench-
mark
SVR Poly1 SVR RBF SVR
Poly1+Sm
SVR
RBF+Sm
RT Feat
Promotions 22.19 17.50
(-21.13%)
15.43
(-30.48%)
16.94
(-23.66%)
14.98
(-32.50%)
7.73
(-65.17%)
No promotions 2.60 3.05
(+17.46%)
3.04
(+17.23%)
2.58
(-0.64%)
2.72
(+4.83%)
2.91
(+12.12%)

17. Contents of today
• Retail setting
• Improved inventory control
• Improved forecasting of promotions
• Outlook

18. Big Data methodologies
• Descriptive analytics
– Understand what happened
– Correlations
– Statistics and data mining
• Predictive analytics
– Predict what is going to happen
– Statistics and data mining
– Correlations yield early warning signals
• Prescriptive analytics
– Prescribe what to do when something happens
– Make something happen
– Mathematical modelling, statistics, and data mining
– Data-driven optimization

19. Source:DHL

20. Logistics and big data
• Combining logistics and IT
– New business concepts: e-commerce, cooperation and coordination, control
towers
– Information technologies: internet of things, block chain, cloud computing, big
data platforms
• Challenges: logistics data is of poor quality and poor availability
– Lack of common standards, a unified data architecture, and unclear
arrangements concerning data ownership and regulation
– Lack of analytical tools for Big Data analysis tailored to the logistics sector

21. Logistics and big data
• Connection between data and logistics planning
– Technology
– Behavior aspects
• Research areas of interest include (but are not limited to):
– Formulation of convincing business cases to attract the interest of the logistics industry;
– Creation of awareness among key logistics companies of the possibilities offered by big data on
the efficiency of the sector;
– Making the connection between the data and logistics planning;
– Collection and analysis of real-time data for real-time data for logistics planning, and the
possibility to predict patterns in this type of data (predictive analytics);
– Development of data-driven methods for smart, real-time planning.

22. Big Data and Logistics: wrap-up