Policy of Decrease Customer’s No-show at Restaurants

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    No-show reduction at restaurants can not only mitigate losses incurred when customers fail to honor a booking but also affect consumers’ reservation behaviors. This study analyzed the ability of restaurant booking policies to reduce no-shows as well as the negative impacts. We herewith to summarize the results of the interview and suggest four reduce No-Show policies for restaurants, they are Re-offering seats, Overbooking, Partial reservations, and No-show penalties. These four methods are also the most common reduce No-Show policies for restaurants. A survey was conducted to understand the booking policies of the Taiwanese restaurant industry. The findings indicated that each sector of the restaurant industry possesses unique characteristics. This study shows that restaurant differentiation is important in setting booking policies. Reservation no-shows cause problems to wasted capacity and result in restaurants’ losses. Our goal is to solve the wasted capacity and mitigation no-show loss, and to offer recommendations on what restaurants should do. Study was found that the restaurant operators generally believed that it was effective to have no-show penalty. Only each restaurant still has its own operating factors that affect its execution ability; the study also found that restaurants of different sizes may have variant reservation policies.  So, The Restaurants can decrease No-show by 1.Re-offering seats, 2.No-show penalties, or 3.Overbooking and Partial reservations are interactions impact degree of customer’s no-show.

     

     


  • Keywords


    No-show; reoffering seats; overbooking; partial reservations; no-show penalties.

  • References


      [1] Alexandrov, Alexei., Lariviere, Martin A., (2012). Are Reservations Recommended? Manufacturing & Service Operations Management, Vol. 14, No. 2, Spring 2012, pp. 218–230.

      [2] Bertsimas, Dimitras and Romy Shioda., (2003). Restaurant Revenue Management. Operations Research, 51 (3), pp.472- 486.

      [3] Chen, C.C., Xie, K.L., (2013). Differentiation of cancellation policies in the U.S. hotel industry. International Journal of Hospitality Management, Vol. 34, p.66-72.

      [4] Dana, J.D.J., (1998). Advanced purchase discounts and price discrimination in competitive markets. Journal of Political Economy, 106 (2), pp.395–422.

      [5] Garrow, Laurie A., Koppelman, Frank S., (2004). Predicting air travelers’ no-show and standby behavior using passenger and directional itinerary information. Journal of Air Transport Management, 10(6), pp.401-411.

      [6] Garrow, Laurie A., Koppelman, Frank S., (2004). Multinomial and nested logit models of airline passengers' no-show and standby behaviour. Journal of Revenue and Pricing Management, Vol. 3, No. 3, pp. 237–253.

      [7] Gosavii, A., Bandla, N., & Das, T. K. (2002). A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking. IIE Transactions, 34, pp.729–742. doi:10.1080/07408170208928908

      [8] Gary M. Thompson (2002). Optimizing a Restaurant’s Seating Capacity: Use Dedicated or Combinable Tables? Cornell Hotel and Restaurant Administration Quarterly, 43 (August), pp.48-57.

      [9] Hwang, J., & Wen, L. (2009). The effect of perceived fairness toward hotel overbooking and compensation practices on customer loyalty. International Journal of Contemporary Hospitality Management, 21(6), pp.659–675. doi:10.1108/09596110910975945

      [10] Kimes, Sheryl E., Jochen, Wirtz., Breffni M. Noone (2002). How Long Should Dinner Take? Measuring Expected Meal Duration for Restaurant Revenue Management. Journal of Revenue and Pricing Management, 1, pp.220-33.

      [11] Kimes, Sheryl E., Gary M. Thompson (2004). Restaurant Revenue Management at Chevys: Determining the Best Table Mix. Decision Sciences, 35, pp.371-92.

      [12] Kimes, Sheryl E., Gary M. Thompson (2005). An Evaluation of Heuristic Methods for Determining the Best Table Mix in Full-Service Restaurants. Journal of Operations Management, 23, pp.599-617.

      [13] Lambert, C., Lambert, J., & Cullen, T. (1989). The overbooking question: A simulation. Cornell Hotel and Restaurant Administration Quarterly, 30(2), pp.14–20. doi:10.1177/ 001088048903000206

      [14] Martin, M. (2001). Side dish. Riverfront Times (May 2), http://www.riverfronttimes.com/2011-05-02/restaurants/side-dish/.

      [15] Moe, W., Fader, P.S., (2002). Using advance purchase orders to forecast new product sales. Marketing Science, 21 (3), pp.347–364.

      [16] Noone, B. M. and Lee, C. H. (2011). Hotel overbooking: The effect of overcompensation on customers’ reactions denied service. Journal of Hospitality & Tourism Research, 35(3), pp.334–357.

      [17] Shugan, S.M., Xie, J., (2005). Advance-selling as a competitive marketing tool. International Journal of Research in Marketing, 22 (3), pp.351–373.

      [18] Toh, R. S. (1985). An inventory depletion overbooking model for the hotel industry. Journal of Travel Research, 23(4), pp.24–30.

      [19] Toh, R. S. (1986). Coping with no-shows, late cancellations and oversales: American hotels out-do the airlines. International Journal of Hospitality Management, 5(3), pp.121–125. doi:10.1016/0278-4319(86)90004-6

      [20] Toh, R. S. and DeKay, F. (2002). Hotel room-inventory management: An overbooking model. Cornell Hotel and Restaurant Administration Quarterly, 43(4), pp.79–90.

      [21] Thompson, Gary M., Kwortnik, Jr.,Robert J., (2008). Pooling Restaurant Reservations to Increase Service Efficiency. Journal of Service Research, Volume 10, No. 4, pp.335-346

      [22] Tony S. M. Tse & Yiu-Tung Poon (2017). Modeling no-shows, cancellations, overbooking, and walk-ins in restaurant revenue management. Journal of Foodservice Business Research, 20:2, 127-145, DOI: 10.1080/15378020.2016.1198626

      [23] Weatherford, L., & Bodily, S. (1992). A taxonomy and research overview of perishable-asset revenue management: Yield management, overbooking, and pricing. Operations Research, 40(5), pp.831–844. doi:10.1287/opre.40.5.831

      [24] Webb Pressler, M. (2003). Wonder why you’re simmering? Turnover, atmosphere shape policies on making diners wait. Washington Post (July 27) F5.

      [25] Wilson, Robert H. (2007). Internet Hotel Reservations: The ‘Terms and Conditions’ Trap. Cornell Hotel and Restaurant Administration Quarterly, 48, pp.361-69.

      [26] C. Giolli, A. Scrivani, G. Rizzi, F. Borgioli, G. Bolelli, and L. Lusvarghi, “Failure mechanism for thermal fatigue of thermal barrier coating systems”, Journal of Thermal Spray Technology,vol.18,pp.223–230,2009.

      [27] C. Zhou, Q. Zhang, and Y. Li, “Thermal shock behavior of nanostructured and microstructured thermal barrier coatings on a Fe-based alloy”, Surface & Coatings Technology,vol.217,pp. 70–75,2013.



 

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Article ID: 28308
 
DOI: 10.14419/ijet.v8i1.10.28308




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