The encyclopedia · Software & IT · Technical decision · 2016–2024
Bianlifeng let one algorithm run its stores and scaled to 3,000 outlets
Bianlifeng put ordering, pricing, display and scheduling in software; staff just execute, so it trained managers in a year and opened 1,500 stores in four.
Bianlifeng (便利蜂)
the move
Convenience stores were considered a slow, people-dependent business, but Bianlifeng founder Zhuang Chenchao believed a store could be run by software. After starting with a simple digital notebook, he made a radical decision in late 2018: let the system fully control people. A 1,000-person technology team built 200-plus modules covering ordering, pricing, display, scheduling, internal control and fresh-food processes.
Sales forecasting drives everything: the system calculates demand per store tier, generates all order quantities, matches delivery routes, schedules shifts and issues task reminders that staff must complete and photograph. Store managers are not responsible for waste because the system decides how much to order. The payoff was speed — inexperienced staff could work within 10 days, and store managers were considered ready in one year versus three at traditional chains.
The results validated the model early: Beijing's 500-plus stores reached overall profitability within three years, and by 2020 the chain operated 1,500 stores in 20 cities. In 2018 the ordering algorithm beat 60% of qualified human managers and added about 500 yuan per store per day. That pace outstripped Japanese convenience brands, which took 8-14 years to turn profitable in Japan.
why it works
- Codified decisions remove dependence on scarce, expensive store-manager talent.
- One forecast system coordinates ordering, logistics and staffing simultaneously.
- Standardized execution lets the chain expand faster than people-based rivals.
- A 1,000-person tech team front-loads cost but compounds across thousands of stores.
what transfers
In labor-heavy retail, codifying decisions into software converts experience into scale — but the system must keep learning from reality, or it becomes a brittle single point of failure.
what came after
Expansion stalled after 2021: a 'hibernation plan' closed about 700 stores, then 2022 saw 1,300 closures; store count fell from 3,000-plus to about 1,000 and cities from 38 to 9. Franchisees later complained that the algorithm could not adapt to local demand, and the company moved toward franchising and human override.
references
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