Shades of Blue

Managing Demand Shocks: Evidence from Quick Service Restaurants

FOCUS AREA

Improving Work Environment

LOCATION

Colombia

REACH

3500 workers

PARTNERS

Good Business Lab, Latin America

STAGE

DESIGN

EVALUATE

ANALYZE

DISSEMINATE

SCALE-UP

External shocks shape firm performance, growth and survival in profound ways. How does organizational design and, more specifically, managerial decision making change and adapt to these shocks?

Challenge

Adapting to the impact of external shocks is an integral part of managing an organization– whether these are as drastic as the impact of the Covid-19 pandemic, or the less dramatic but still disruptive impact of technological advancements. While there is budding literature focused on the responsive adaptation of organizational hierarchy, this project focuses on peeking inside the “black box” of managerial adaptation by studying how the allocation decisions of managers change in the face of a large demand shock.

Design

We partnered with a leading quick-service restaurant (QSR) firm that operates 76 restaurants located in 9 cities of Colombia. The firm averages a monthly sale of nearly 3 million units (items). Our partner implemented online ordering via a third-party delivery platform that brokered a deal to be the sole delivery service for its restaurant locations in Colombia. In lieu of the deal, the delivery company scaled up their service (by hiring and training more drivers) in each city before making the app available to the QSR firm. The phased roll out of the service gave little control to store managers as to when the delivery channel was implemented, and hence little opportunity to prepare for the shock caused by increased demand. Since each store implemented the app delivery service at different times, we can measure the impact of the app on different store performance indicators before and after the implementation. Empirically testing our hypotheses was made possible by the fact that almost all production processes, employee and manager functions, and capital are, by design, identical across the restaurants in our sample. We were also given access to records of how managers allocated workers to or changed their shifts, and what trainings were provided by managers to employees. On a personnel level, data included demographic characteristics (age, store, position, and gender) and the date of hiring and termination for each worker across all the restaurants.

Findings

  1. There was an overall increase in demand due to the implementation of the delivery app, which was concentrated during peak hours (lunchtime and dinnertime). The increase was nearly immediate and persisted throughout an 18-week sample of post-implementation performance data.
  2. Managers met this increase in demand, and achieved an increase in performance without adding additional workers to their teams. This was accomplished with through

    a. Changes in shift allocation: Managers rescheduled employees’ modal shifts to better meet times with peak demand, increased the number of shifts per employee, while also increasing the number of short notice reschedules. While it has been theorized that short notice schedules can cause an increase in turnover and absenteeism in the workforce, we surprisingly observed no significant changes to either at store level. b. Increased training: Managers invested in training of their existing workforce, conducting more refresher trainings per store. Since workers must be trained before they can work different stations in a store, the additional trainings make it easier for managers to optimally allocate workers to meet demand.

  3. Gender balanced stores did better in driving sales and matching productivity, in comparison to stores that had a gender mismatch between the management and workers.

    a. The impact of women managers on performance increases with the share of women workers in the stores. The same holds true for men dominated teams led by managers who are men. However, productivity is considerably lower when the gender of the managers differs from that of the workers. b. Stores with a gender mismatch – with either men-heavy management (for eg. a store with 20% men managers and 70% women crew) or women-heavy management (for eg. a store with 70% women management and 20% men crew) carried out fewer reschedules, and saw increased turnover and absenteeism in comparison to gender balanced stores.