Increasing labor cost and falling margins
Industry best practices demand that labor costs be kept within about 30% of sales; however, wages are increasing all over the world year on year. National Living Wages in the UK increased by 4.9% in 2019 and 6.2% in 20201. Raise the Wage Act in the US intends to increase the wages to $15 per hour by 2024 from the current minimum federal rate of $102 per hour. It is not always possible to stay within the optimal limits. Additionally, the reluctance in passing on these increased costs to customers to maintain competitive edge hits the bottom-line further. The consequence being very low or even negative Front Line Contribution in many cases.
Labor cost includes more than just wages. Aspects that factor into labor cost calculation include:
Additionally, reducing labor costs involves challenges at multiple levels:
A diagnostic approach to reducing labor cost
Breakdown of the problem into smaller units is a great way to start. Segregation into locations (region/country), business units (education, healthcare, entertainment etc.), segments (new business, existing business), contract types (cost plus, P&L) is one way of narrowing the problem. For e.g.: higher costs in P&L accounts are a bigger worry as the risk associated with any cost increase or labor issue lies with the contractor. Similarly, new businesses carry a higher cost ratio initially, as the revenue stream is not yet stabilized.
Though labor cost is a number in itself and performance is a qualitative measure, various matrices can help understand the extent of the success or failure of the cost order.
Several KPIs can be used to assess this:
The variances here, when benchmarked against internal/external indicators can indicate:
Combination of technology and human intervention
Demand forecasting
Since wages account for nearly 70% of the total labor cost, the first step towards reducing labor cost is to get the forecasting right. Demand forecast in a catering business needs to show the shape of the day and the anticipated demand. Demand is forecast typically in blocks of hours (9am-12pm, 12pm-3pm, 3pm-6pm etc). That way, managers can see exactly how many employees they need in each area to meet that demand. It also takes into account exactly how much time employees need to deliver each activity. This even includes non-revenue-generating (but necessary) activities, like preparation and clean up.
This type of forecasting is a typical use case for supervised machine learning regression, which can make accurate predictions utilizing many factors like seasonality, changing trends, local events, holiday periods, time of the day etc. It can create schedules that cater to the unique demand of each location, helping to avoid over or under-staffing across the organization, both of which have significant cost implications.
Right employee mix
Finding the appropriate mix of skill sets (Manager, chef, cashier etc) and employee type (full time/part time) can be a challenge. The consistency provided by the permanent staff heavily outweighs the expense due to additional benefits like health care, pension etc. Artificial Intelligence (AI) solutions that work toward optimization3 can be explored. Closed loop intelligence optimization4 is an iterative method where results are compared and fed back to the system to find the optimum mix. The right employee mix is the optimum combination of workers at the lowest possible cost with maximum productive efficiency.
Attrition
The attrition rate in the contract catering industry is high since they hire the highest percentage of students to keep costs low. This employee base is transient and easily switch to other opportunities for a marginal increment in wages. The key to retention here is flexibility and training opportunities to upskill.
Using AI solution built on preference learning can recommend potential shifts to employees based on their previous scheduling preferences. This self-scheduling can provide fair, equitable, balanced schedule that meets their requirements as well as the company's business requirements.
While AI/ML can definitely contribute towards employee retention, some additional humane measures can go a long way. These include:
Continuous monitoring
Triggers can be set up based on continuous learning that can push out notifications where:
Timely information as well as using AI/ML learning can pave the way for intervention to remediate the issues that can have significant bearings on the cost element.
To Conclude
Effective use of AI in key areas can be a significant differentiating factor in the catering industry. Its use can range from forecasting demand, planning promotional activities, reducing employee attrition to getting the optimum labor mix. These in turn can reduce wastages in both labor and food, thereby increasing productivity and margins. However, AI can only be successful when used in conjunction with humane initiatives to improve employee morale and satisfaction. This winning combination can be the definite success mantra for businesses.
Reference
1 https://www.gov.uk/national-minimum-wage-rates
2 https://en.wikipedia.org/wiki/Minimum_wage_in_the_United_States
3 https://www.researchgate.net/publication/326274817_Optimization_of_Team_
Mix_to_Reduce_Cost_and_Increase_Profitability_in_Projects_and_Bids
Industry :
Priya Kanniah
Managing Consultant, Data Analytics & AI, Wipro
Priya is a techno functional consultant with over 15 years of analytics experience across industries such as banking, asset management, insurance consumer electronics etc.
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