By Beau Blessing (a working paper) November 4, 2015
From the front of the house to the back, U.S. table-service restaurants are busy with people working for a less-than-otherwise minimum wage and so too, dependent on customers’ tips. From the hostess to the server to the expediter to the bartender, the post-service payment that the customer eventually makes typically includes a 15-20% gratuity, from which the server will also make a distribution to the other aforementioned service roles at the end of his own shift. An expediter delivers ready food from the kitchen on the busiest nights to the indicated table as soon as ready, reducing the risk that remain under heat lamps and perhaps come back to that even on the busiest nights that it does not sit and so there is a reduced change a customers will need to send it back. the kitchen along with a complaint. A hostess greets arriving parties and rotates the seating of guests among servers’ sections so that the workload of the server is spaced out, increasing utilization; just after dropping off ready food at table 3 and returning change from a payment made by table 4, food orders may be taken from table 2 just after table 1 is greeted and places their order for drinks. A bartender makes adult drinks from their own floor space, adding expertise while alleviating the server from a decidedly different task than his own. (Bartender too are cross-functional and often have table guests of their own.)
In 2015 criticism of the tipping system is percolating, and it is argued that customers are in fact serving as restaurant servers and facilitators’ paymaster while restaurateurs dodge a higher cost of capital by keeping less than a fifth of the cash of the cash on hand for meeting payroll than would otherwise be required. Indeed, evidence supports that tipping does not increase the servers performance, and too may lead to subtle discrimination. But that the sentiment against tipping tracks closely those who also oppose GMO’s and hail from Colorado, Oregon or California, I am curious what lies upstream.
The performance of a service delivery system relies on many things, not least the human resources required. The explosive casual-fast dinning sector, which includes the likes of Subway, Chipotle, Evos and Moe’s among others, has opted to omit drive-throughs and table service in favor of quickly filled orders taken away or consumed within the finite, on-site seating capacity. Chiptole and Moe’s with high sales prices than Subway are particularly robust queuing systems posing as restaurants. From a single queue multi-phase system with poison arrivals and mean distribution of service times, the number of servers required to facilitate their mass customized production of burritos is effectively a science.
They also benefit from the law of large numbers and asymmetries of information. A chain restaurant by definition has more instances from which to collect data, the collection and processing benefits from a zero-cost of replication; and greater data integrity control processes than say, a small town Mexican restaurant with only a few investors and the labor contributions of owner-operators.
In reality, demand in the restaurant business is less than ideal to forecast as it deals principally in people and food; the former are socially complex creatures with wide-ranging variability in their arrival patterns; the latter, a perishable that must be ordered ahead of time based on assumptions about the foretasted demands of the former. The exact arrival pattern and sales figures for a given night for the smallest restaurants may never be accurate enough to be actionable so that increasing the wage rate of servers and facilitators means the cost of over scheduling capacity increases, yet without the economies of sales of co-sharing of inventories to stymie overage food order loses.
And so the ability to increase or decrease capacity quickly and efficiently is important. Like in the classic “overage, underage” news-vendor problem, there is a cost to unsold, perished food. There is also a cost to foregone profits had you sold an extra plate. We want to find a balance.
That servers “tip out” as a function of sales, we are partly down the right track using “sales dollars” as a metric. So it falls to management of even the smallest restaurants to forecast sales and still find a mix of servers, expediters and hostesses to facilitate those sales, independent of the rate at which those sales occur. Thus is may be forecasted that Wednesday’s dinner result in $1,800 of food and beverage sales. And so based on the inter-arrival period, this may be telling us that 9 servers will each sell $200 worth of food facilitated by two hours of hostess and two hours of expediter over a period a say, 5:478-9:20pm.
It may also be telling us that 9 servers sold $200 worth of food without any facilitators over a slightly longer period with less variability in the inter-arrival periods; business was steady and uniform with no “rush”.
Restaurants using the gratuity system have the unique position from which they can increase capacity through human resource decisions quickly and for little cost. The servers themselves are the ones who in exchange for an average hourly pay in excess of similarly available wage jobs, are sometimes sent home early. Thus their “over-payment” relative to other wage jobs is in fact compensation for unrewarded set-up costs.
So then, it seems servers who are employees for a normalized set wage might expect to be scheduled and indeed perform a full shift without being sent home early and so forgo their previous compensation for set-up costs. The integer is now not server hours but nightly servers, forcing on the firm the decision to which they can only lead, lag or straddle forecasted demand.