Download Multi-echelon inventory management at Sligro Food Group N.V.

Multi-echelon inventory
management
at Sligro Food Group N.V.
Tim van Pelt
Who I am
• 23 years old
• Live in Eindhoven, the Netherlands
• Master study Operations Management & Logistics,
Eindhoven University of Technology
• Graduation project at Sligro Food Group
• Supervised by dr. ir. R. Broekmeulen and prof. dr. T. de Kok
• Master study Strategic Management,
Tilburg University
Key facts Sligro Food Group (2013)
• Established in 1935
• Food retail activities: 130 EMTÉ supermarkets
• Foodservice activities: 9 delivery centers (BSs) and
46 cash-and-carry wholesalers
• 5.829 full-time equivalent employees
• Net sales of almost 2.5 billion euros
• Operating profit (EBIT) of 89 million euros
Problem introduction
• Literature: importance of improving inventory
management in retail and wholesale cannot be
overemphasized
• Focus on supply chain efficiency – offering a certain
desired service level at least costs
• Integrated inventory management: key role
information sharing
“What is the cost saving potential of
the application of an installation stock policy with
central demand pooling?”
Problem scope
Multi-item two-echelon distribution system
ZB (46)
CDC
Focus only on inventory holding costs
Supply chain breakdown
Supplier
Supplier
BS (8)
CDC
CDC
EMTE
RDC 1
CDC
(67)
RDC 2
CDC
(63)
EMTE
Current situation (benchmark)
Regular installation stock policies
CDC
BS 1
BS 2
BS 8
Scenarios
1
Benchmark scenario
• Application regular installation stock policy
• Benchmark for determination cost saving potentials
2
Demand pooling scenario
• Application installation stock policy with central demand pooling
• Expectation: lower demand uncertainty at CDC
3
Optimal scenario
• Near-optimal situation: application of multi-echelon inventory
control policy
• Expectation: Most inventory positioned downstream and lower
internal fill rates
Demand pooling scenario (2)
Installation stock policies
with central demand pooling
CDC
BS 1
BS 2
BS 8
Optimal scenario (3)
Multi-echelon inventory control policy
CDC
BS 1
BS 2
BS 8
Modelling
• Customer fill rates
• Demand pattern: mean and variation
• Review periods
• Lead times: mean and variation
• MOQs and Qs
• DoBr tool: exact evaluation scenario 1 & 2
• ChainScope: accurate approximation scenario 3
Inventory value index numbers
BS supply chain part
RDC supply chain part
120%
120%
-27%
100%
80%
100%
-22%
60%
60%
40%
40%
20%
20%
0%
0%
Benchmark
scenario
Demand pooling
scenario
-58%
80%
Optimal
scenario
CDC Demand variability: -49%
-11%
Benchmark
scenario
Demand pooling
scenario
Optimal
scenario
CDC Demand variability: -62%
Inventory weeks
BS supply chain part
7
6
Pipeline
0,4
BS
4
12
0,4
10
Pipeline
0,4
0,7
RDC
CDC
- 6,3
8
-1,3
6
3
2
CDC
-1,6
5
2,0
RDC supply chain part
2,0
3,4
1
0,4
4
2,1
2
2,0
0,5
0
Benchmark
scenario
9,2
2,9
18% : 82%
Fill rate: ±45%
- 1,1
0,4
1,6
0,9
0
Demand pooling Optimal scenario
scenario
0,4
0,7
Benchmark
scenario
Demand pooling Optimal scenario
scenario
36% : 64%
Fill rate: ±65 %
Conclusion & recommendation
• Substantial potential inventory holding cost
reductions – relatively and absolutely
• Should be practically validated
• Impact on inventory planning activities and other
logistical operations should be determined
• Therefore, it is recommended to Sligro to set up a
pilot project with an adapted or acquired inventory
management system