With the development of the times, the wave of digitalization has swept across all major industries, driving enterprises to carry out new changes in different industry fields, and the HR SaaS industry is no exception.
Ohqu Technology, as a SaaS service provider deeply involved in human resources, combines the special management needs of various industries and uses technology + service to solve the problems encountered in the labor organization during the development process of China's service industry. helps enterprises reduce costs and improve efficiency in the process of digital construction.
html On June 22, at the "Practice of Artificial Intelligence in Enterprise Digitalization" AI afternoon tea event, the product manager of X-Lab of Ohqu Technology shared online the in-depth application of intelligent scheduling integrating AI technology in the chain retail industry. Scenario, the following is the essence of the speech:
Workforce management in the post-epidemic era
In the third year of the outbreak, all walks of life have been greatly impacted, especially the retail and catering industries.
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Usually, we use the total retail sales of consumer goods in society as a representation of consumption popularity. Judging from the data released by the National Bureau of Statistics in May 2022, the retail sales of goods were 3,053.5 billion yuan, a year-on-year decrease of 5.0%; the catering revenue was 301.2 billion yuan, a decrease of 21.1%. People’s consumption enthusiasm is showing signs of fatigue.
At the same time, the overall business confidence of enterprises has also been severely affected.
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Rent, raw material costs and labor costs are the three big mountains that weigh on catering business operators. Among them, rising labor costs have become one of the three fastest-rising factors in cost changes in catering enterprises. .
Enterprises have also experienced grassroots losses, an increase in the proportion of flexible employment in chain enterprises, and changes in employment scenarios. Employees have increased requirements for risk resistance and self-security, working environment, and control over their own time and arrangements. is constantly improving.
How to meet the needs of customers, store managers, regional managers, group operations and other parties, how to solve matching labor resources with business needs, and building a scheduling plan that meets business needs and other challenges are issues that most companies are currently concerned about. For example:
Unexpected situation, population flow surges, store is short of manpower?
Shift scheduling requirements are complex and changeable. How to quickly realize flexible adjustment of manpower?
...
Intelligent scheduling solution
It is time to delve deeply into reconstruction and empower the physical industry.
In order to help enterprises truly implement human capital value management, Ohqu Technology has currently launched intelligent attendance, organizational personnel, intelligent scheduling, salary performance, BI data analysis and other modules, covering enterprise labor forecasting, labor management, labor incentives, Full-scenario applications such as workforce analysis.
Ohqu Technology’s intelligent scheduling system relies on intelligent algorithms and historical business data to accurately estimate future business volume. The
system supports docking with city data, and realizes business district data prediction and store passenger flow prediction based on people flow trends, weather trends, and promotional events, thereby achieving accurate working hours prediction and labor number prediction, and finally automatically generating shift scheduling results.
shares a true story about a customer of a large Chinese health and beauty products retail chain.
This company has 4,000+ stores and 20,000+ employees. Before using intelligent scheduling, it always encountered the following problems:
① The number of stores is large and the area is wide, and the headquarters cannot understand the store situation in a timely manner;
② Stores have staff shortages and redundancies. Due to the situation, the deployment of offline applicants often lags behind;
③ The scheduling efficiency is low, the labor cost of sales staff is high but the average performance output is not high.
The intelligent scheduling solution provided by Ohqu Technology for this company uses the system to accurately predict the hourly passenger flow, number of laborers, and labor hours of each store in advance, and combines employee parameters to achieve a scientific and reasonable scheduling plan. Ultimately, the company Achieved:
. Optimize manpower allocation and reduce redundancy of working hours
Ohqu Technology derived the optimal plan to reduce redundancy through historical data and labor standard rationality analysis.
2. Reduce operational risks and improve employee satisfaction
Satisfy the enterprise's compliance and rational employment, strengthen cost control, and meet the personalized needs of stores in different scenarios, such as task allocation and employee priority...
Refinement The new shift scheduling method gives employees more humane management and achieves "work-rest balance".
3. Improve the level of standardized operations
By standardizing enterprise-level employment standards and scheduling rules, the personal experience of outstanding store managers is transformed into standardized operational actions, which also promotes the development of standardized operations of enterprises.
I have traveled thousands of miles and been brave and perseverant. The road to research and development of artificial intelligence has a long way to go. Ohqu Technology will deeply explore the real needs of customers, accumulate innovation energy, integrate artificial intelligence technology in the HR SaaS industry, and make more new attempts.
Q: The retail and catering industries often open new stores. Faced with this lack of historical data, how does Ouqu make predictions?
A: The amount of historical data, or the size of the entire data set, is closely related to the capabilities of some algorithms. Making predictions without data support is a challenge for most companies.
This problem, in the early stages of product development, we found an effective solution. By collecting and accumulating a large amount of store data, we gradually developed a store data model for industries, business districts, etc., and created a proprietary database for Ohqu.
Therefore, when a new store appears, we can benchmark it against the Ohqu database, generate a store portrait, and make a scientific and reasonable prediction.
