Optimal Multi-Temperature Deliveries to Small-format Stores Mayurpankhi Barooah, Seung Hwan Shin

Agenda 1 1. Context 2. Approach 3. Analysis and Results 4. Recommendations

This project is aimed at optimizing multi-temperature deliveries to small-format stores Large format stores Small format stores A new DC to store delivery strategy is required for small format stores 1 Context > Average 180,000 sqft > High volume, frequent deliveries > Average 40,000 sqft > Higher proportion of grocery > Multi-temperature products > Smaller volume but require frequent delivery Source: Source: Source: 2

Multi-Temperature Trailers (MTT) could potentially help consolidate delivery volumes to stores Key challenges in small format deliveries > Large number of stops > Limitations on allowable driving time 3 A cost benefit analysis was used to estimate advantages of using MTT 1 Context Single Temp Trailer Multi-TempTrailer AmbientFrozen AmbRefrigFrozen Refrigerated Advantages of Multi-Temp Trailers (MTT) > Consolidate demand onto one trailer > Fewer stops Store 1Store 2Store 3 Store 1

We considered a sample of small format stores to identify the optimal delivery policy 4 > Sample small format stores: > 9 stores delivered by same DC > Data collected: > Daily demand volume by product category > Distances > Current delivery frequencies 2 Approach

We organized policy options by number of stops and single vs multi temperature trailers Cost model helped identify optimal trailer configuration, product mix and delivery frequency MSSTMSMT SSSTSSMT Single Multi Single Multi Temperature Stops Policies Minimize total costs = Transportation costs + Stoppage costs + Holding costs Optimization 5 2 Approach

Cost, trailer utilization and delivery frequency were the key factors considered to evaluate delivery policies 6 1. Cost per pallet: Average per pallet cost (Transportation + Stop + Holding) 2. Trailer utilization rate: No. of pallets delivered per week / Total trailer capacity 3. Delivery frequency: No. of deliveries to each store every week Three Key Factors 3 Analysis and Results Key assumptions 1. Demand: Daily average in pallets for 3 months in Transportation and stop cost > Cost per mile for ambient and temp. controlled > Stoppage cost 3. Inventory holding cost > Holding cost rate > Product value per pallet by category 4. Minimum 4 deliveries a week

For the ‘Base’ case, MSMT policy showed the lowest cost per pallet, the highest truck utilization rate and delivery frequency 7 Cost per Pallet: $37 Utilization Rate: 37% Delivery Frequency (Wk): A 4.0 / F 4.0 / R 4.0 Cost per Pallet: $17 Utilization Rate: 85% Delivery Frequency (Wk): A 5.2 / F 5.2 / R 5.2 Cost per Pallet: $19 Utilization Rate: 86% Delivery Frequency (Wk): A 4.1 / F 4.0 / R 4.1 Cost per Pallet: $16 Utilization Rate: 94% Delivery Frequency (Wk): A 5.3 / F 5.2 / R 5.2 Stops Temperature SingleMulti Single Multi * A: Ambient / F: Frozen / R: Refrigerated 3 Analysis and Results

We tested 4 scenarios to better understand which factors influence the policy selection 8 Base Case Doubled demand 1 Doubled distance between DC and Stores Doubled distance between DC and Stores 2 7-day deliveries per Week 3 Half demand 4 3 Analysis and Results

> The optimal policy changes from MSMT to MSST. > Higher demand drives the single-temp trailer more attractive. For ‘Doubled demand’ scenario, the MSST becomes the most economical policy. 9 Doubled demand $ % A 4.6 / F 4.0 / R 4.3 $ % A 9.9 / F 9.9 / R 9.9 $ % A 4.0 / F 4.0 / R 4.0 $ % A 10 / F 10 / R 10 S M SM Stops Temperature MSSTMSMT SSSTSSMT 3 Analysis and Results 1 S M SM Stops Temperature MSSTMSMT SSSTSSMT

For ‘Doubled distance’ scenario, the MSMT is the most economical policy. 10 Doubled distance between DC and stores $ % A 4.3 / F 4.0 / R 4.4 $ % A 5.9 / F 5.9 / R 5.9 $ % A 4.0 / F 4.0 / R 4.0 $ % A 5.2 / F 5.2 / R 5.2 S M SM Stops Temperature MSSTMSMT SSSTSSMT > No optimal policy changes from the base case. > Longer distance from DC drives the multi-temp trailer preferred as it increases utilization rates and minimizes the linehaul travel. 3 Analysis and Results 2 S M SM Stops Temperature MSSTMSMT SSSTSSMT

For ‘7-day Deliveries’ scenario, the MSMT is the most economical policy. 11 Min. 7-day deliveries per week $ % A 7.0 / F 7.0 / R 7.0 $ % A 7.1 / F 7.1 / R 7.1 $ % A 7.0 / F 7.0 / R 7.0 $ % A 7.0 / F 7.0 / R 7.0 S M SM Stops Temperature MSSTMSMT SSSTSSMT > No optimal policy changes from the base case. > The higher delivery frequency target (higher freshness) makes the multi-temp trailer policy more attractive. 3 Analysis and Results 3 S M SM Stops Temperature MSSTMSMT SSSTSSMT

For ‘Half Demand’ scenario, the MSMT is still the most economical policy. 12 Half demand $ % A 4.0 / F 4.0 / R 4.0 $ % A 4.7 / F 4.7 / R 4.7 $ % A 4.0 / F 4.0 / R 4.0 $ % A 4.0 / F 4.0 / R 4.0 S M SM Stops Temperature MSSTMSMT SSSTSSMT > No optimal policy changes from the base case. > Smaller demand drives the multi-temp trailer more attractive. > It needs to consider using smaller size trailers. 3 Analysis and Results 4 S M SM Stops Temperature MSSTMSMT SSSTSSMT

Summary - Cost per Pallet Comparison 13 3 Analysis and Results > In almost all scenarios, MSMT emerged as the lowest cost policy. > The cost gap between single-temp trailer policies with multi-temp trailer policies was narrowed when demand increased. ($)

Demand, distance to stores and delivery frequency emerged as key determinants of delivery policy > Small demand: Multi-temp trailer > Large demand: Single-temp trailer 14 Consideration of demand Consideration of distance between DC and stores > Longer distance Multi-stops: Minimizing the linehaul trips Multi-temp trailer: Increasing trailer utilization rate Delivery frequency (Freshness) > Higher delivery frequency: Multi-temp trailer 4 Recommendations

Future research can refine the existing model and address current limitations 15 Limitations > Variability of daily demand > Different holding cost rate > Inventory space constraints > Loading/unloading cost change Future research > Incorporating limitations > Combinations of scenarios (e.g. higher demand with longer distance between DC and stores) > Other characteristics: Intra- zone distance, dramatically different volumes by store, different labor costs, etc. > ‘Flex-temp’ 4 Recommendations

16 Questions?

Back up 17  Assumptions  Summary of results  Optimization model

Assumptions 18 ParameterDefinitionValueUnit Trailer cost per mile Cost incurred by a trailer depending on trailer type Ambient: 2.35 Temperature controller trailer (Frozen or refrigerated): 2.72 $ per mile Stop cost Cost paid to the carrier for every stop 50$ per stop Product value Average product value assumed for inventory cost calculation Ambient: 560 Frozen: 720 Refrigerated: 640 $ per pallet Holding costAnnual holding cost rate25% Trailer TypeMaximum Weight Maximum Cubic Feet Maximum No. of Pallets Ambient temperature trailer47,0002,10028 Refrigerated and Frozen temperature trailer 41,0001,80028 Multiple temperature trailer41,0001,80026

Summary of results (1/3) 19 ScenarioSSSTSSMTMSSTMSMT Base Doubled demand Doubled distance day delivery Half demand Cost per Pallet by Policy ($) ScenarioSSSTSSMTMSSTMSMT Base231%106%119%100% Doubled demand 127%114%100%107% Doubled distance 238%107% 100% 7 day delivery382%129%159%100% Half demand383%139%172%100% Cost per Pallet Comparison to Optimal Policy

Summary of results (2/3) 20 Utilization by policy (%) ScenarioSSSTSSMTMSSTMSMT Base Doubled demand Doubled distance day delivery Half demand ScenarioSSSTSSMTMSSTMSMT Base 40%91%86%100% Doubled demand 67%94%96%100% Doubled distance 37%86%81%100% 7 day delivery 21%68%64%100% Half demand20%61%57%100% Utilization Comparison to Optimal Policy

Summary of results (3/3) 21 ScenarioProductSSSTSSMTMSSTMSMT Base Ambient Frozen45.24 Refrigerated Doubled demand Ambient Frozen Refrigerated Doubled distance Ambient Frozen Refrigerated day delivery Ambient Frozen Refrigerated Half demand Ambient Frozen Refrigerated Delivery Frequency (Number of Deliveries per Week) by Policy

Model: Objective 22 Optimization Model Developed by Unahalekhaka (2015)

Model: Constraints (1/4) 23 Optimization Model Developed by Unahalekhaka (2015)

Model: Constraints (2/4) 24 Optimization Model Developed by Unahalekhaka (2015)

Model: Constraints (3/4) 25 Optimization Model Developed by Unahalekhaka (2015)

Model: Constraints (4/4) 26 Optimization Model Developed by Unahalekhaka (2015)

Model Summary (1/3) 27

Model Summary (2/3) 28

Model Summary (3/3) 29