Data Department helps restaurant chains and food service operators use machine learning (ML) and AI (artificial intelligence) for the decisions that drive daily cost and service: how much food to prepare, how many staff to schedule, how much stock to hold and when to deliver it. Our restaurant projects have lowered labour cost by 5% to 20% at the same service level and cut order waiting times by about 30% on average.
Where does AI pay off in restaurants?
A restaurant chain repeats the same decisions in every store, every day, often every hour. Small errors in preparation, staffing or ordering add up to waste, overtime and slow service. Forecasts tied to those decisions fix the problem at the source.
| Decision | What ML, AI or optimisation does | Typical data |
|---|---|---|
| How much of each product to prepare, and when | Very short-term sales forecasting, so food is ready ahead of demand | POS (point of sale) transactions by store, product and time |
| How many staff to schedule per shift | Load forecasting plus shift optimisation at a set service level | Sales by hour, weather, holidays, local events, staffing rules |
| How much stock each restaurant needs | Store- and product-level demand forecasting | Sales, stock counts, menu changes, promotions |
| When to deliver supplies to each restaurant | Forecasting combined with delivery-schedule optimisation | Restaurant demand, warehouse stock, delivery costs and capacity |
| How to cut waste without running out | Forecast ranges instead of single numbers, so preparation matches risk | Sales history, waste records, shelf life |
| How staff find procedures and recipes | A RAG (retrieval-augmented generation) assistant over your own manuals | Operations manuals, recipes, food safety procedures |
Relevant case studies
- Order waiting time forecasting: a quick-service restaurant chain. Very short-term sales forecasting, so food is prepared ahead of demand, gave about 30% shorter order waiting times on average.
- Restaurant staffing and shift optimisation: premium restaurant groups. Load forecasting and shift optimisation, using weather, holiday and event features, lowered labour cost by 5% to 20% at the same service level. The range reflects differences across locations.
- Restaurant supply and delivery planning: a quick-service restaurant chain. Forecasting and delivery-schedule optimisation gave about 10% lower inventory and logistics cost.
- Restaurant demand forecasting: for two national quick-service restaurant chains, we tested our approach on their own data. Forecasts per store, per product and per 15-minute slot had an average error of about 1.1 items per product per store per slot. A second, store-level model for 10 restaurants and 574 products was ~19% better than the existing baseline.
What are typical first projects for a restaurant chain?
- Intraday sales forecasting for food preparation, starting with a few stores and best-selling products.
- Staffing and shift planning driven by forecast load, with your labour rules as constraints.
- Store-level stock forecasting for ordering and waste.
- Supply and delivery planning between a central warehouse and restaurants.
Each starts with a two-week Discovery: we rank use cases, check the data and agree the business metric, such as labour cost or waiting time, before any model is built. The free 30-minute AI opportunity review is a quick first step. Our demand forecasting and optimisation services describe the methods in more detail.
Data and integration notes
- POS data is the foundation. Transactions with timestamps by store and product allow forecasts down to 15-minute slots.
- External signals such as weather, public holidays and local events often matter most on unusual days.
- Workforce data such as shift rules, skills and contracts is needed for shift optimisation.
- ERP (enterprise resource planning) and warehouse data supply stock, purchasing and delivery information for supply planning.
- Infrastructure: we deliver on AWS (Amazon Web Services), Google Cloud, Azure or on-premises, and write results back to the tools your managers already use.
Frequently asked questions
Can sales forecasts really run every 15 minutes?
Yes, if your POS records timestamped transactions. In our quick-service restaurant work, forecasts per store, per product and per 15-minute slot had an average error of about 1.1 items per product per store per slot.
Does shift optimisation mean fewer staff at peak times?
No. The service level is a constraint, and shifts are planned against forecast load. In our staffing work, labour cost was 5% to 20% lower at the same service level.
Which project should a restaurant chain start with?
It depends on where the cost is: labour, waste, stock or slow service. A two-week Discovery compares these with your own data.
Do you work with single restaurants?
Our methods pay off most for chains and groups with several locations, where the same decision repeats across stores.