This document summarizes a presentation on incorporating consumer attitudes to minimize waste and out-of-stock situations in food retail. It discusses the background, motivation, methodology and preliminary results of a simulation study on perishable inventory management. The study examines the impact of factors like demand uncertainty, batch size, customer picking behavior and expiry date information on waste percentage and out-of-stock percentage. The preliminary findings show that out-of-stock is highly sensitive to demand uncertainty and lead time for highly perishable products. Tracking expiry dates can help reduce waste and out-of-stock. Future work includes experimenting with longer shelf lives and price discounts.

1. www.cranfield.ac.uk/som
Incorporating Consumer
Attitudes To Minimise
Waste And Out-of-stock
Situations In Food Retail
Emel Aktas
Danqian Wang, Soroosh Saghiri, Zeynep Topaloglu,
Tamara van ‘t Wout, Akunna Oledinma, Zahir Irani,
Amir Sharif, A. K. Samsul Huda
13 November 2016
This presentation was made possible by NPRP grant # [NPRP 7-1103-5-156] from the Qatar National Research Fund (a
member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

2. 2https://blogs.commons.georgetown.edu/safeq/
• Background
• Motivation
• Literature
• Methodology
• Results
• Future work
Outline

3. 3
Background
• Food Loss: decrease in quantity or quality of food.
• Food waste: removal from the Food Supply Chain of food which is fit for
consumption, or which has spoiled or expired, mainly due to
• economic behaviour,
• poor stock management or
• neglect.
Source: FAO, 2014. Definitional Framework of Food Loss.
http://www.fao.org/3/a-at144e.pdf

4. 4
Country Info and Food Profile
• Area: 4468 sq mi (11,586 km2)
• Compare: Orlando: 4,012 sq mi (10,390 km2)
• Climate: arid with mild, pleasant winters; very hot, humid
summers
• Population (2016 est.): 2,545,603
• Doha (capital) 956,460
• GDP (2014 est.): $298.4 billion (49th)
• GDP per Capita (2014 est.): $145,894 (1st)
• Human Development Index (2014): 0.850 (very high, 32nd)
Source: AOAD 2013, Arab Agricultural Statistics Year Book, Alpen Capital, CIRS Georgetown University, Qatar.
Source: Google Maps
Source: https://en.wikipedia.org/wiki/Qatar, date retrieved: 10 Apr 16
90% imported

5. 5
http://www.qatarday.com/news/local/warning-check-the-expiry-date-when-shopping/11335

6. 6https://blogs.commons.georgetown.edu/safeq/
• Perishable products deteriorate and lose value in a short period of time
• Consumers more conscious of the “freshness”
• Retailers face the dilemma: food waste versus out-of-stock
• Perishable inventory management
§ Shelf life
§ Demand uncertainty
§ Order batch size
§ Delivery lead time
§ Customer picking preference
§ Retailer’s actions to change customer picking
§ Order-up-to policy
§ Without expiry date
§ With expiry date
Motivation
http://www.davinfoods.co.uk

7. 7https://blogs.commons.georgetown.edu/safeq/
Shelf
Life
Demand
Uncertainty
Order
Batch Size
Lead
Time
Customer
Picking
Behavior
Change in
Picking
Behavior
Hu et al. (2016) √ √
Haijema, (2014) √ √
Piramuthu and Zhou (2013) √ √
Broekmeulen and Donselaar, (2009) √ √ √ √ √
Williams and Patuwo (2004) √ √ √
van Donselaar et al. (2006) √ √ √
Tekin, Gürler and Berk (2001) √ √
√
Mena, Adenso-Diaz and Yurt (2011) √ √ √ √
Stanger et al., (2012)
√
√ √
√
Olsson and Tydesjö (2010) √
√
Duan and Liao (2014) √ √ √
Qin, Wang and Wei (2014) √ √ √
Chew et al. (2014) √ √ √
Minner and Transchel (2010) √ √ √ √
Zhou, Leung and Pierskalla (2011) √ √
Literature

8. 8https://blogs.commons.georgetown.edu/safeq/
• Reduce costs of shortages and lost sales
• Reduce costs of inventory
• Reduce wastage
• Improve customer service and loyalty
Multiple objectives in perishable inventory
management
Reduce
waste
Decrease shortage/
backorder
Reduce average
stock costs
Increase
profit
Improve service
level / others
Duan and Liao, (2013) √ √
Sharda and Akiya, (2012) √ √
Haijema, (2014) √ √ √
Broekmeulen and Donselaar, 2009) √ √
Tekin, Gürler and Berk, (2001) √ √
Gómez et al. (2004) √ √
Ferguson and Ketzenberg, (2006) √ √
√
Minner and Transchel, (2010) √
√
Gunpinar and Centeno, (2015) √ √
√

9. 9https://blogs.commons.georgetown.edu/safeq/
1. A single-product, a single-retailer, replenished by a single supplier.
2. The product is a time dependent perishable product.
3. The order-up-to inventory policy: At the end of each day, an order is
placed if the stock position drops below the target order-up-to level.
The target level is 1000.
4. Unsatisfied demand is lost.
5. The product is a kind of grocery products with printed expiration date.
6. The units received by the retailer are assumed to be of the same age
upon receipt and received products begin to lose their life time once
the order is received.
7. The retail staff issue products following FIFO policy.
8. Simulation duration is 365 days.
Methodology

10. 10https://blogs.commons.georgetown.edu/safeq/
Variable Explanation Low High
SD Standard deviation of demand 100 250
BS (units)
Batch Size: Ordering quantity is a multiple of the discrete
delivery batch size.
50 100
PB (percentage)
Picking Behavior: Percentage of customers searching
for the freshest items.
0.5 0.8
Simulation Inputs and Outputs
DtE (days) = 1
Days to Expire: the shelf life of the product, which begin
to decline when order is received. For example, DtE
being equal to 3 means the perishable goods has 3 days
for sale once they are received.
Out-Of-Stock: Quantity of stock out per day / Daily demand
Waste: Quantity of stock expired per day / Opening StockOutputs
LT (days) = 1
Lead Time: time elapsed from when an order is until the
ordered items are received.
Target
Service
Level = .9

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Preliminary Findings
0.0
0.5
1.0
1.5
2.0
2.5
Waste Percentage OOS Percentage
SD=100, BS=50
PB=.5 PB=.8
0
0.5
1
1.5
2
2.5
Waste Percentage OOS Percentage
SD=100, BS=100
PB=.5 PB=.8
0
1
2
3
4
5
6
7
8
9
Waste Percentage OOS Percentage
SD = 250, BS = 50
PB=.5 PB=.8
0
1
2
3
4
5
6
7
8
9
Waste Percentage OOS Percentage
SD = 250, BS = 100
PB=.5 PB=.8

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The impact of Days to Expire
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
1 3 5 7
Days to Expire
Average Waste Average out-of Stock

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The impact of Lead Time
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
1 2 3
Lead Time
Average waste Average out of stock

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Item vs Case Level Waste
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
SD=100 SD=200 SD=300
Item Level Waste
PB=.3 PB=.5 PB=.7
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
SD=100 SD=200 SD=300
Case Level Waste
PB=.3 PB=.5 PB=.7

15. 15https://blogs.commons.georgetown.edu/safeq/
• Out of stock is significantly affected by demand uncertainty
• Shortage is highly sensitive to the lead time for highly perishable
products;
• Order batch size has subtle effect on the outcomes
• Unsatisfied demand is more sensitive to customer picking behavior
when expiry time and lead time of the product are shorter.
• Tracking expiry date helps reduce food waste and out-of-stock
Results

16. 16https://blogs.commons.georgetown.edu/safeq/
• Studied the impact of
• Uncertainty
• Lead time
• Batch Size
• Picking behaviour
• Item level expiry information in ordering decisions
• Limitations
• Single product
• No substitution effects
• Price discounts not included
• Outlook
• Experiment with longer shelf lives and retailer’s activities to change
the picking behaviour incorporating price discounts
Conclusions and Future Research

17. 17
Thank you
Questions / Comments?
Emel.Aktas@cranfield.ac.uk
@emelaktas
http://uk.linkedin.com/in/emelaktas/
https://blogs.commons.georgetown.edu/safeq/

18. 18https://blogs.commons.georgetown.edu/safeq/
• Broekmeulen, R.A.C.M. and Donselaar, K.H. (2009) ‘A heuristic to
manage perishable inventory with batch ordering, positive lead-
times, and time-varying demand’, Computers and Operations
Research, 36(11), pp. 3013–3018.
• Chew, E.P. et al. (2014) ‘Optimal dynamic pricing and ordering
decisions for perishable products’, International Journal of
Production Economics, 157(1) Elsevier, pp. 39–48.
• Duan, Q. and Liao, T.W. (2013) ‘A new age-based replenishment
policy for supply chain inventory optimization of highly perishable
products’, International Journal of Production Economics, 145(2)
Elsevier, pp. 658–671.
• Ferguson, M., & Ketzenberg, M. E. (2006). Information sharing to
improve retail product freshness of perishables. Production and
Operations Management, 15(1), 57.
• Gomez, M. I., McLaughlin, E. W., & Wittink, D. R. (2004). Customer
satisfaction and retail sales performance: an empirical
investigation. Journal of Retailing, 80(4), 265-278.
• Gunpinar, S. and Centeno, G. (2015) ‘Stochastic integer
programming models for reducing wastages and shortages of
blood products at hospitals’, Computers and Operations Research,
54 Elsevier, pp. 129–141.
• Haijema, R. (2014) ‘Optimal ordering, issuance and disposal
policies for inventory management of perishable products’,
International Journal of Production Economics, 157(1) Elsevier, pp.
158–169.
• Hu, P., Shum, S., Yu, M. (2016) Joint Inventory and Markdown
Management for Perishable Goods with Strategic Consumer
Behavior. Operations Research 64(1):118-134.
• Mena, C. et al. (2011) ‘The causes of food waste in the supplier-
retailer interface: Evidences from the UK and Spain’, Resources,
Conservation and Recycling, 55(6) Elsevier B.V., pp. 648–658.
• Minner, S. and Transchel, S. (2010) ‘Periodic review inventory-
control for perishable products under service-level constraints’, OR
Spectrum, 32(4), pp. 979–996.
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• Tekin, E., Gürler, Ü., & Berk, E. (2001). Age-based vs. stock level
control policies for a perishable inventory system. European
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• van Donselaar, K. et al. (2006) ‘Inventory control of perishables in
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• Williams, C.L. and Patuwo, B.E. (2004) ‘Analysis of the effect of
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