Abstract
Instore operations in bricks-and-mortar grocery retailing account for the highest share of operational logistics costs within the internal retail supply chain. The order packaging quantity (OPQ) is regarded as one important driver of instore logistics efficiency. We define the OPQ as the number of consumer units that are bundled into one order and distribution unit for supplying the individual stores. Therefore, the OPQ corresponds to the smallest possible order size and determines the possible granularity of order sizes with an impact on instore operations costs. In this paper, we develop a cost-minimization model including instore handling and inventory carrying costs to determine OPQs. The model developed builds on inventory management theory and is based on discrete probability distributions of consumer demand. We apply the model in an industry case study with real retail data for 39 stock keeping units and 1,180 stores of a European retail company. By applying the minimal-cost OPQ for all stores, the costs considered can be reduced by 9.4 %. This paper can be considered as a first in-depth analysis of the dormant instore efficiency potential in connection with adjusted OPQs that seems to be largely untapped in retail research and practice.
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References
Axsäter S (2006) Inventory control, international series in operations research and management science, vol 90, 2nd edn. Springer Science, Boston
Broekmeulen RACM, van Donselaar K (2009) A heuristic to manage perishable inventory with batch ordering, positive lead-times, and time-varying demand. Comput Oper Res 36(11):3013–3018
Broekmeulen RACM, Fransoo JC, van Donselaar KH, van Woensel T (2007) Shelf space excesses and shortages in grocery retail stores. Working Paper, Technische Universiteit Eindhoven, 30 Oct 2010
Coyle JJ, Bardi EJ, Langley CJ (2003) The management of business logistics: a supply chain perspective, 7th edn. Thomson South-Western, Mason
Curşeu A, van Woensel T, Fransoo J, van Donselaar K, Broekmeulen R (2009) Modelling handling operations in grocery retail stores: An empirical analysis. J Operl Res Soc 60(2):200–214
DeHoratius N, Raman A (2008) Inventory record inaccuracy: an empirical analysis. Manag Sci 54(4):627–641
DeHoratius N, Ton Z (2009) The role of execution in managing product availability. In: Agrawal N, Smith SA (eds) Retail supply chain management. Springer, New York, pp 53–77
EHI Retail Institute GmbH (2009) Handel aktuell 2009/2010
Ehrenthal JCF, Stölzle W (2013) An examination of the causes for retail stockouts. Int J Phys Distrib Log Manag 43(1):54–69
Eroglu C, Williams BD, Waller MA (2011) Consumer-driven retail operations: the moderating effects of consumer demand and case pack quantity. Int J Phys Distrib Log Manag 41(5):420–434
Eroglu C, Williams BD, Waller MA (2013) The backroom effect in retail operations. Prod Oper Manag 22(4):915–923
Ferguson M, Ketzenberg ME (2006) Information sharing to improve retail product freshness of perishables. Prod Oper Manag 15(1):57–73
Gruen TW, Corsten DS, Bharadwaj S (2002) Retail out-of-stocks: a worlwide examination of extent, causes and consumer response. Washington, DC
GS1 Germany GmbH (2011) Jahresbericht 2011: management summary. Köln
Hax AC, Candea D (1984) Production and inventory management. Prentice-Hall, Englewood Cliffs
Hübner A, 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
Hübner AH, Kuhn H (2012) Retail category management: state-of-the-art review of quantitative research and software applications in assortment and shelf space management. Omega 40(2):199–209
Institute of Grocery Distribution (2005) Retail ready packaging. Letchmore Heath
Ketzenberg M, Ferguson ME (2008) Managing slow-moving perishables in the grocery industry. Prod Oper Manag 17(5):513–521
Ketzenberg M, Metters R, Vargas V (2002) Quantifying the benefits of breaking bulk in retail operations. Int J Prod Econ 80(3):249–263
Kotzab H, Teller C (2005) Development and empirical test of a grocery retail instore logistics model. Br Food J 107(8):594–605
Kotzab H, Reiner G, Teller C (2007) Beschreibung. Analyse und Bewertung von Instore-Logistikprozessen. ZfB 77(11):1135–1158
Kuhn H, Sternbeck M (2011) Logistik im Lebensmittelhandel. Catholic University Eichstaett-Ingolstadt, Forschungsbericht der Wirtschaftswissenschaftlichen Fakultät, Ingolstadt
Kuhn H, Sternbeck M (2013) Integrative retail logistics—an exploratory study. Oper Manag Res 6(1):2–18
Private Label Manufacturer Association (2011) Plma’s 2011 international private label yearbook
Raman A, DeHoratius N, Ton Z (2001a) The achilles’ heel of supply chain management. Harv Bus Rev 79(5):25–28
Raman A, DeHoratius N, Ton Z (2001b) Execution: The missing link in retail operations. Calif Manag Rev 43(3):136–152
Reiner G, Teller C, Kotzab H (2012) Analyzing the efficient execution on in-store logistics processes in grocery retailing—the case of dairy products. Prod Oper Manag 22(4):924–939
Saghir M, Jönson G (2001) Packaging handling evaluation methods in the grocery retail industry. Packaging technology and science 14(1):21–29
Silver EA, Pyke DF, Peterson R (1998) Inventory management and production planning and scheduling, 3rd edn. Wiley, New York
Sternbeck MG, Kuhn H (2014) Grocery retail operations and automotive logistics: a functional cross-industry comparison. Benchmarking: Int J 21(5):814–834
Taylor JC, Fawcett SE (2001) Retail on-shelf performance of advertised items: an assessment of supply chain effectiveness at the point of purchase. J Bus Log 22(1):73–89
Tempelmeier H (2011) Inventory management in supply networks: problems, models, solutions, 2nd edn. Books on Demand, Norderstedt
Tempelmeier H, Fischer L (2010) Approximation of the probability distribution of the customer waiting time under an (r, s, q) inventory policy in discrete time. Int J Prod Res 48(21):6275–6291
Thonemann U, Behrenbeck K, Küpper J, Magnus KH (2005) Supply Chain Excellence im Handel: Trends. Erfolgsfaktoren und Best-Practice-Beispiele, Gabler, Wiesbaden
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
Trautrims A, Grant DB, Schnedlitz P (2010) In-store logistics processes in Austrian retail companies. Eur Retail Res 25(1):63–84
van den Berg JP, Sharp GP, Gademann AJRM, Pochet Y (1998) Forward-reserve allocation in a warehouse with unit-load replenishments. Eur J Oper Res 111(1):98–113
van Donselaar K, Broekmeulen R (2008) Static versus dynamic safety stocks in a retail environment with weekly sales patterns. BETA Working Papers Series, 262
van Zelst S, van Donselaar K, van Woensel T, Broekmeulen R, Fransoo J (2009) Logistics drivers for shelf stacking in grocery retail stores: potential for efficiency improvement. Int J Prod Econ 121(2):620–632
Waller MA, Tangari AH, Williams BD (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 Log Manag 38(6):436–451
Wen N, Graves SC, Ren ZJ (2012) Ship-pack optimization in a two-echelon distribution system. Eur J Oper Res 220(3):777–785
Wensing T (2011) Periodic review inventory systems: performance analysis and optimization of inventory Systems within Supply Chains, vol 651. Lecture notes in economics and mathematical systems. Springer, Berlin, Heidelberg
Acknowledgments
We are deeply grateful to the European retailer for providing detailed data material and the comments received from research colleagues after presenting the topic at the 2012 European Conference of Operational Research in Vilnius, Lithuania. This paper benefited from discussions with Heinrich Kuhn, Andreas Holzapfel and Andreas Popp. We very much appreciate the comments and suggestions received from an associate editor and two anonymous reviewers, which significantly improved this paper. Furthermore, we would also like to acknowledge the financial support provided by the German retail and FMCG foundations “Goldener Zuckerhut” and “Erich Kellerhals”.
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Appendix
Appendix
1.1 A1: Calculation of the undershoot and corresponding inventory position at the time of ordering
By assuming that the undershoot and the relevant demand in the review period \(\left( D^{RP}\right)\) are stochastically independent (Tempelmeier 2011), we derive the discrete probability distribution of the undershoot with the help of the following approximate calculation with resulting probabilities that represent a non-increasing function of \(u\) (Tempelmeier 2011):
with \(u\) in the interval \([0,1,...,d^{RP, max}-1]\) resulting in probabilities of an undershoot when an order is released in the interval \([1,2,...,d^{RP, max}]\). As we assume lost sales when customer demand faces empty shelves, the undershoot cannot be larger than the order level \(s\). However, the probabilities of larger undershoots should not be ignored. That is why we modify the resulting undershoot distributions by placing the mass, which is larger than \(s\), right on \(s\). We denote the resulting distribution as \(U^{*}\) with \(u^{*}\) in the interval \([1,2,...,s]\), which is now characterized by the following expression:
With this probability distribution of the undershoot we can easiliy calculate the probabilities of the inventory position \(P(IP_O=ip_O)\) at the time of ordering:
1.2 A2: Calculation of the distribution of the number of CUs that can be stacked directly onto the shelf
The distribution of the number of CUs that can be stacked directly onto the shelf \(P\left\{ \#CU^{direct}=cu^d\right\}\) is calculated as follows:
with \(cu^d\) in the interval \([Min (Max(S-(s-1);0);OPQ), ..., Min(\lceil \frac{s}{OPQ}\rceil \cdot OPQ; S)]\). This distribution serves as a basis to calculate the expected number of CUs that can be put directly onto the shelf during initial shelf stocking (\(E\left\{ \#CU^{direct}\right\}\)).
For the cost calculations of OPQs that are greater than or equal to the order level \(s\), calculation (17) –which continues to be valid– can be simplified as the order size \(q\) is independent of the undershoot because in every possible case only one OPQ will be ordered \((q=OPQ)\). For these instances, we can simply convolve the probability distributions of \(U\) and \(D^{LT}\) and base all further calculations on the resulting distribution of physical inventory in stock instore \((PI_D)\) at the time when the order consisting of one OPQ arrives in the store (Tempelmeier 2011):
Again, the resulting probability distribution of \(PI_D\) has to be modified analogous to the undershoot distribution by cutting the distribution for values greater than \(s\) and placing the correspondent mass right on \(s\). This is necessary, because the physical inventory cannot be further reduced. We denote the resulting distribution as \(PI^{*}_D\) with \(pi^{*}_D\) in the interval \([0,1,...,s-1]\), which is characterized by the following expression:
In the case of \(OPQ \ge s\), the probability distribution of the CUs that can be stacked directly after delivery can be calculated as follows:
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Sternbeck, M.G. A store-oriented approach to determine order packaging quantities in grocery retailing. J Bus Econ 85, 569–596 (2015). https://doi.org/10.1007/s11573-014-0751-3
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DOI: https://doi.org/10.1007/s11573-014-0751-3