Data-driven retail inventory management with backroom effect | OR Spectrum
Skip to main content
Springer Nature Link
Log in

Data-driven retail inventory management with backroom effect

  • Regular Article
  • Published:
OR Spectrum Aims and scope Submit manuscript

Abstract

The backroom effect (BRE) constitutes the handling effort of a replenishment that does not fit on the shelf of a retailer. This effect needs to be included in the decision making of inventory policy parameters as it influences the handling effort, which constitutes a major part of the retailer’s operational cost. We propose a mixed integer linear program to calculate the parameters of a periodic review (s, c, S, nq) policy while considering the BRE. The (s, c, S, nq) policy triggers an order when inventory drops below the reorder point s. Also, an order is triggered whenever the inventory drops below the can-order point c, provided at least one other product’s inventory level is below s and thus ordered. The order then comprises the smallest integer number n of case packs with size q that brings the inventory level to or above S. As retailers face stochastic non-stationary demand, a data-driven approach based on historical data is applied to this joint replenishment problem. The numerical study shows that including the BRE into the optimization can lead to cost savings with a median of 0.96% compared to neglecting its effects. Considering the stochasticity in the decision making, cost improvements with a median of 53.23% have been realized against an approach that only considers average daily demands and a safety stock. The advantage of an (s, c, S, nq) order policy over an (s, S, nq) policy is shown by median savings of 17.99%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Adan I, Van Eenige M, Resing J (1995) Fitting discrete distributions on the first two moments. Probab Eng Inf Sci 9(4):623–632

    Article  Google Scholar 

  • Aksoy Y, Erenguc SS (1988) Multi-item inventory models with co-ordinated replenishments: a survey. Int J Oper Prod Manag 8(1):63–73

    Article  Google Scholar 

  • Amorim P, Costa AM, Almada-Lobo B (2014) Influence of consumer purchasing behaviour on the production planning of perishable food. OR Spectr 36:669–692

    Article  Google Scholar 

  • Atan Z, Erkip N (2015) Note on “The Backroom Effect in retail operations”. Prod Oper Manag 24(11):1833–1834

    Article  Google Scholar 

  • Balintfy JL (1964) On a basic class of multi-item inventory problems. Manag Sci 10(2):287–297

    Article  Google Scholar 

  • Ban G-Y, Rudin C (2017) The big data newsvendor: practical insights from machine learning. https://doi.org/10.2139/ssrn.2559116

  • Bertsimas D, Doan XV (2010) Robust and data-driven approaches to call centers. Eur J Oper Res 207(2):1072–1085

    Article  Google Scholar 

  • Bertsimas D, Thiele A (2006) A robust optimization approach to inventory theory. Oper Res 54(1):150–168

    Article  Google Scholar 

  • Bertsimas D, Thiele A (2014) Robust and data-driven optimization: modern decision making under uncertainty. In: Johnson M, Norman B, Secomandi N (eds) Models, methods, and applications for innovative decision making. INFORMS Tutorials in Operations Research, Hanover, pp 95–122

    Google Scholar 

  • Beutel AL, Minner S (2012) Safety stock planning under causal demand forecasting. Int J Prod Econ 140(2):637–645

    Article  Google Scholar 

  • Bijvank M, Vis I (2011) Lost-sales inventory theory: a review. Eur J Oper Res 215(1):1–13

    Article  Google Scholar 

  • Bookbinder JH, Tan JY (1988) Strategies for the probabilistic lot-sizing problem with service-level constraints. Manag Sci 34(9):1096–1108

    Article  Google Scholar 

  • Brijs T, Swinnen G, Vanhoof K, Wets G (1999) Using association rules for product assortment decisions: a case study. In: Proceedings of the fifth international conference on knowledge discovery and data mining, San Diego (USA), pp 254–260

  • Broekmeulen RACM, Van Donselaar KH (2004) Excess shelf space in retail stores: an analytical model and empirical assessment. BETA publicatie: working papers, Technische Universiteit Eindhoven, Eindhoven, vol 109, pp 1–26

  • Broekmeulen RACM, Sternbeck MG, Van Donselaar KH, Kuhn H (2017) Decision support for selecting the optimal product unpacking location in a retail supply chain. Eur J Oper Res 259(1):84–99

    Article  Google Scholar 

  • Corsten D, Gruen T (2003) Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out-of-stocks. Int J Retail Distrib Manag 31(12):605–617

    Article  Google Scholar 

  • Curşeu A, Van Woensel T, Fransoo JC, Van Donselaar KH, Broekmeulen RACM (2009) Modelling handling operations in grocery retail stores: an empirical analysis. J Oper Res Soc 60(2):200–214

    Article  Google Scholar 

  • DeHoratius N, Ton Z (2009) The role of execution in managing product availability. In: Agrawal NM, Smith SA (eds) Retail supply chain management. Springer, New York, pp 53–77

    Google Scholar 

  • East R, Lomax W, Willson G, Harris P (1994) Decision making and habit in shopping times. Eur J Mark 28(4):56–71

    Article  Google Scholar 

  • Ehrenthal JCF, Honhon D, Van Woensel T (2014) Demand seasonality in retail inventory management. Eur J Oper Res 238(2):527–539

    Article  Google Scholar 

  • Eroglu C, Williams BD, Waller MA (2011) Consumer-driven retail operations: the moderating effects of consumer demand an case pack quantity. Int J Phys Distrib Logist Manag 41(5):420–434

    Article  Google Scholar 

  • Eroglu C, Williams BD, Waller MA (2013) The Backroom Effect in retail operations. Prod Oper Manag 22(4):915–923

    Article  Google Scholar 

  • Feng H, Wu Q, Muthuraman K, Deshpande V (2015) Replenishment policies for multi-product stochastic inventory systems with correlated demand and joint-replenishment costs. Prod Oper Manag 24(4):647–664

    Article  Google Scholar 

  • Gaur V, Fisher ML (2004) A periodic inventory routing problem at a supermarket chain. Oper Res 52(6):813–822

    Article  Google Scholar 

  • Goyal SK, Satir AT (1989) Joint replenishment inventory control: deterministic and stochastic models. Eur J Oper Res 38:2–13

    Article  Google Scholar 

  • Holzapfel A, Hübner AH, Kuhn H, Sternbeck MG (2016) Delivery pattern and transportation planning in grocery retailing. Eur J Oper Res 252:54–68

    Article  Google Scholar 

  • Hua Z, Yang J, Huang F, Xu X (2009) A static-dynamic strategy for spare part inventory systems with nonstationary stochastic demand. J Oper Res Soc 60(9):1254–1263

    Article  Google Scholar 

  • Hübner AH, Kuhn H, Sternbeck MG (2013) Demand and supply chain planning in grocery retail: an operations planning framework. Int J Retail Distrib Manag 41(7):512–530

    Article  Google Scholar 

  • Huh WT, Levi R, Rusmevichientong P, Orlin JB (2011) Adaptive data-driven inventory control with censored demand based on Kaplan-Meier estimator. Oper Res 59(4):929–941

    Article  Google Scholar 

  • Iyer AV, Schrage LE (1992) Analysis of the deterministic \((s, S)\) inventory problem. Manag Sci 38(9):1299–1313

    Article  Google Scholar 

  • Johansen SG, Melchiors P (2003) Can-order policy for the periodic-review joint replenishment problem. J Oper Res Soc 54(3):283–290

    Article  Google Scholar 

  • Kahn BE, Schmittlein DC (1989) Shopping trip behavior: an empirical investigation. Mark Lett 1(1):55–69

    Article  Google Scholar 

  • Khouja M, Goyal SK (2008) A review of the joint replenishment problem literature: 1989–2005. Eur J Oper Res 186(1):1–16

    Article  Google Scholar 

  • Kouki C, Babai MZ, Jemai Z, Minner S (2016) A coordinated multi-item inventory system for perishables with random lifetime. Int J Prod Econ 181:226–237

    Article  Google Scholar 

  • Kuhn H, Wensing T, Sternbeck MG (2015) Evaluation of case-pack sizes in grocery retailing using a Markov Chain approach. In: Liberopoulos G (ed) 10th Conference on stochastic models of manufacturing and service operations (SMMSO 2015). University of Thessaly Press, Volos, pp 103–110

    Google Scholar 

  • Law AM (2015) Simulation modeling and analysis, 5th edn. Mc Graw Hill, New York

    Google Scholar 

  • Martel A, Diaby M, Boctor FF (1995) Multiple items procurement under stochastic nonstationary demands. Eur J Oper Res 87(1):74–92

    Article  Google Scholar 

  • Nagasawa K, Irohara T, Matoba Y, Liu S (2015) Applying genetic algorithm for can-order policies in the joint replenishment problem. Ind Eng Manag Syst 14(1):1–10

    Google Scholar 

  • Ohno K, Tomonori I, Toshiaki Y (1994) A new algorithm for a multi-item periodic review inventory system. Math Method Oper Res 39:349–364

    Article  Google Scholar 

  • Pires M, Amorim P, Liz J, Pratas J (2015) Design of retail backroom storage: a research opportunity? In: Póvoa A, de Miranda J (eds) Operations research and big data, Studies in Big Data, vol 15. Springer, Cham, pp 167–174

    Chapter  Google Scholar 

  • Raman A, DeHoratius N, Ton Z (2001) Execution: the missing link in retail operations. Calif Manag Rev 43(3):136–152

    Article  Google Scholar 

  • Reiner G, Teller C, Kotzab H (2013) Analyzing the efficient execution of in-store logistics processes in grocery retailing-the case of dairy products. Prod Oper Manag 22(4):924–939

    Article  Google Scholar 

  • Sachs AL, Minner S (2014) The data-driven newsvendor with censored demand observations. Int J Prod Econ 149:28–36

    Article  Google Scholar 

  • Saliby E (1990) Descriptive sampling: a better approach to Monte Carlo simulation. J Oper Res Soc 41(12):1133–1142

    Article  Google Scholar 

  • Silver EA (1974) A control system for coordinated inventory replenishment. Int J Prod Res 12(6):647–671

    Article  Google Scholar 

  • Silver EA, Pyke DF, Peterson R (1998) Inventory management and production planning and scheduling, 3rd edn. Wiley, New York

    Google Scholar 

  • Sternbeck MG (2015) A store-oriented approach to determine order packaging quantities in grocery retailing. J Bus Econ 85:569–596

    Article  Google Scholar 

  • Sternbeck MG, Kuhn H (2014) An integrative approach to determine store delivery patterns in grocery retailing. Transp Res Part E 70:205–224

    Article  Google Scholar 

  • Taube F, Minner S (2017) Data-driven assignment of delivery patterns in retail with handling effort considerations. Comput Oper Res. https://doi.org/10.1016/j.cor.2017.08.004 (forthcoming)

    Article  Google Scholar 

  • Tempelmeier H, Hilger T (2015) Linear programming models for a stochastic dynamic capacitated lot sizing problem. Comput Oper Res 59:119–125

    Article  Google Scholar 

  • Ton Z, Raman A (2010) The effect of product variety and inventory levels on retail store sales: a longitudinal study. Prod Oper Manag 19(5):546–560

    Article  Google Scholar 

  • Tsai CY, Tsai CY, Huang PW (2009) An association clustering algorithm for can-order policies in the joint replenishment problem. Int J Prod Econ 117:30–41

    Article  Google Scholar 

  • Van Donselaar KH, Broekmeulen RACM (2013) Determination of safety stocks in a lost sales inventory system with periodic review, positive lead-time, lot-sizing and a target fill rate. Int J Prod Econ 143(2):440–448

    Article  Google Scholar 

  • Van Donselaar KH, Van Woensel T, Broekmeulen RACM, Fransoo JC (2006) Inventory control of perishables in supermarkets. Int J Prod Econ 104(2):462–472

    Article  Google Scholar 

  • Van Donselaar KH, Gaur V, Van Woensel T, Broekmeulen RACM, Fransoo JC (2010) Ordering behavior in retail stores and implications for automated replenishment. Manag Sci 56(5):766–784

    Article  Google Scholar 

  • Van Woensel T, Erkip N, Curşeu A, Fransoo JC (2013) Lost sales inventory models with batch ordering and handling costs. BETA publicatie: working papers, Technische Universiteit Eindhoven, Eindhoven, vol 421, pp 1–34

  • Van Zelst S, Van Donselaar KH, Van Woensel T, Broekmeulen RACM, Fransoo JC (2009) Logistics drivers for shelf stacking in grocery retail stores: potential for efficiency improvement. Int J Prod Econ 121(2):620–632

    Article  Google Scholar 

  • Waller MA, Heintz A, Brent T (2008) Case pack quantity’s effect on retail market share: an examination of the backroom logistics effect and the store-level fill rate effect. Int J Phys Distrib Logist Manag 38(6):436–451

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the three anonymous reviewers for their constructive comments.

Author information

Authors and Affiliations

  1. Logistics & SCM, Technische Universität München, School of Management, Arcisstraße 21, 80333, Munich, Germany

    Özgün Turgut, Florian Taube & Stefan Minner

Authors
  1. Özgün Turgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Florian Taube
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Minner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Florian Taube.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turgut, Ö., Taube, F. & Minner, S. Data-driven retail inventory management with backroom effect. OR Spectrum 40, 945–968 (2018). https://doi.org/10.1007/s00291-018-0511-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00291-018-0511-9

Keywords

Search

Navigation