Waste and Out-Of-Stock in Food Retailpublic.cranfield.ac.uk/e800578/Presentations/Aktas... · 2016. 11. 21. · Incorporating Consumer Attitudes To Minimise Waste And Out -of-stock

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Incorporating Consumer Attitudes To Minimise Waste And Out-of-stock Situations In Food Retail

Emel AktasDanqian Wang, Soroosh Saghiri, Zeynep Topaloglu, Tamara van ‘t Wout, Akunna Oledinma, Zahir Irani, Amir Sharif, A. K. Samsul Huda

13 November 2016

This publication 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.

2https://blogs.commons.georgetown.edu/safeq/

• Background• Motivation• Literature• Methodology• Results• Future work

Outline

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

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

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


• 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


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


• 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) √ √ √


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


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, DtEbeing 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) = 1Lead Time: time elapsed from when an order is until the ordered items are received.

Target

Service

Level = .9


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


SD=100, BS=100

PB=.5 PB=.8

0123456789


SD = 250, BS = 50

PB=.5 PB=.8

0123456789


SD = 250, BS = 100

PB=.5 PB=.8


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


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


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


• 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


• 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

Thank you

Questions / Comments?

[email protected]

@emelaktas

http://uk.linkedin.com/in/emelaktas/

https://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.

References

• Olsson, F. and Tydesjö, P. (2010) ‘Inventory problems with perishable items: Fixed lifetimes and backlogging’, European Journal of Operational Research, 202(1) Elsevier B.V., pp. 131–137.

• Piramuthu, S. and Zhou, W. (2013) ‘RFID and perishable inventory management with shelf-space and freshness dependent demand’, International Journal of Production Economics, 144(2) Elsevier, pp. 635–640.

• Qin, Y. et al. (2014) ‘Joint pricing and inventory control for fresh produce and foods with quality and physical quantity deteriorating simultaneously’, International Journal of Production Economics, 152 Elsevier, pp. 42–48.

• Sharda, B. and Akiya, N. (2012) ‘Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation’, International Journal of Production Economics, 136(1) Elsevier, pp. 161–171.

• Stanger, S.H.W. et al. (2012a) ‘What drives perishable inventory management performance? Lessons learnt from the UK blood supply chain’, Supply Chain Management: An International Journal, 17(2), pp. 107–123.

• Tekin, E., Gürler, Ü., & Berk, E. (2001). Age-based vs. stock level control policies for a perishable inventory system. European Journal of Operational Research, 134(2), 309-329.

• van Donselaar, K. et al. (2006) ‘Inventory control of perishables in supermarkets’, International Journal of Production Economics, 104(2), pp. 462– 472.

• Williams, C.L. and Patuwo, B.E. (2004) ‘Analysis of the effect of various unit costs on the optimal incoming quantity in a perishable inventory model’, European Journal of Operational Research, 156(1), pp. 140–147.

• Zhou, D., Leung, L. C., & Pierskalla, W. P. (2011). Inventory management of platelets in hospitals: Optimal inventory policy for perishable products with regular and optional expedited replenishments. Manufacturing & Service Operations Management, 13(4), 420-438.

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Waste and Out-Of-Stock in Food Retailpublic.cranfield.ac.uk/e800578/Presentations/Aktas... · 2016. 11. 21. · Incorporating Consumer Attitudes To Minimise Waste And Out -of-stock