# data[].attributes.body
Group2

# data[].attributes.body
8

# data[].attributes.body
Two mental models:

Initial Model: The "Rational Maximizer"

This model assumes bees only care about their personal score. If a single metric is used, they will find ways to exploit the system. For instance, if only honey volume is measured, they will resort to stealing. If only flower visits are counted, they will perform tasks inefficiently. This approach would ultimately lead to a society of unproductive, dishonest workers and the hive's collapse. This model highlights the need for checks and balances.

Intermediate Model: The "Community Contributor"

This model assumes that bees, while pursuing individual goals, also value the health of their community. A performance system designed for this model would include both individual and collective rewards to encourage collaboration over competition. This approach could lead to a healthy, cooperative hive culture where bees share information and innovate together, ensuring long-term prosperity.

Based on these two models, below is a comprehensive performance system that aims to encourage efficiency, collaboration, and ethical behavior.

Outcome Metric (30%): Verified Honey Weight. At the beehive station, a smart scale would weigh the honey. An ID scanner would confirm the bee's identity to prevent fraud.

Process Metric (50%): Effective Flower Visits. Instead of just counting visits, a tiny sensor at the flower would only register a visit as "effective" if a certain amount of honey is collected. This ensures bees are working efficiently, not just touching flowers.

Behavioral Metric (20%): Community Contribution.

Incentive Structure

Combined Score: Each bee receives a single score based on the weighted average of all three metrics.

Collective Reward: If the entire hive reaches a production goal, all bees receive a special reward. This encourages teamwork and makes individual cheating less appealing.

Tiered Consequences: Minor negative behavior results in a warning, while repeated offenses lead to more severe penalties.

# data[].attributes.body
group3

# data[].attributes.body
group3

# data[].attributes.body
What happens if we only count flower visits?

•Bees might just fly around a lot without bringing back much honey.

•They look busy, but they’re not really productive.

What happens if we only count honey weight?

•Bees might try to get more honey by stealing from others or draining one flower patch completely.

•That gives short-term gains, but in the long run the flowers die out and the whole hive suffers.

Combine the two is Efficiency.

A better way is to measure how much honey a bee brings back per flower visit.

This is called Honey per Visit (HPV).

Don’t just count how many times a bee flies out, and don’t just count the honey weight. Look at how much honey each trip produces. This way, bees are rewarded not just for being busy or greedy, but for being both productive and efficient.

# data[].attributes.body
Performance=amount of flower*30%+region diversity*10%+efficiency*60%

efficiency=(amount of honey*time)/distance)

Now: bees are just dividing labor and working together, relying on dance or simple learning mechanisms to improve efficiency.

In a few years: Bees may evolve a stronger distributed "intelligence" that automatically maintains ecological balance and can even predict the rate of flower recovery.

# data[].attributes.body
BeeMentalModels andEnvisioned Future P ossibilities,

' Hardworking and· honest· bees: Bees· that regularly visit flowers· and· collect honey' honestly·will-receivehighperformance·scoresand·correspondingrewards.

Lazy· bees:· Bees· that visit· fewer flowers: will- score lower· and· receive fewer· rewards, . which· will'encourage them·to·forage·more.,

Thieving· bees: The' performance· scores· of· bees· that· steal·honey·will-becapped·at·expected· levels,; making theft·unprofitable.Over-time,they·may-shift·to·legitimate·honey·collection. Adaptivebees: Bees may·learn·to· target·high-yield·flowers to·increase· their· expected·honey· collection·(E),therebymaximizing theirperformance·scoreswithin·legitimatemeans..

Cooperative bees: Bees might shareinformation· about· high-yield flower sources,; improving the·overall-efficiency·of· the·hive.«

Competitive· bees: Bees may· compete· to· visit more flowers· or find· better nectar sources,* fostering healthy·competition·within·thesystem'srules.

Short-Term Possibilities:

Initially,* some· bees· may* attempt· to· test· the* system· by* stealing, but· due* to· the* cap on* performance scores, they'will-quicklyfind that theft·is not·worthwhile.

Bees may· focus" on· visiting more flowers· to· increase" their expected· honey' yield,; leading· to· an· increase inforaging·activity.

Long-TermPossibilities (Several-Years Later):

As·bees adapt·to·seeking out·high-yield·flowers,' the·hive'sforaging efficencywill-improve.

Overall- honey· production· will- increase due· to· reduced· theft· and· a· rise* in· legitimate· honey·collection.-

The· system· may* evolve with· more advanced· technology· (e.g.; real-time honey· tracking at flowers),' further·refining performancemeasurement.

If+ not· properly maintained, bees· may find· new* ways· to cheat,' necessitating· regular· system· updates and· audits.

# data[].attributes.body
Reasonable Assumptions

· Limited but renewable flower resources

Flowers contain a limited amount of nectar that replenishes over time. Over-harvesting in one area may reduce future nectar availability.

· Energy costs of flying

The farther a bee flies, the more energy it consumes, which reduces the net amount of honey it can bring back.

· Possibility of opportunistic behavior

Some bees may steal honey from other bees instead of collecting it themselves, which creates unfair performance measurement outcomes.

· Information sharing and cooperation

Bees can communicate (e.g., through a “dance”) to share locations of nectar-rich flowers, which improves the collective performance of the colony.

multiple models

1. Outcome

Amount of honey brought back by each bee

Total honey production of the colony

2. Process

Number of flower visits

Diversity of foraging paths (to prevent over-exploitation of the same flower patch)

3. Behavioral / Sustainability Indicators

Presence of aggressive or honey-stealing behavior

Participation in teamwork (e.g., sharing flower location information)

Short term mechanism

· Reward bees that bring back a balanced amount of honey and visit flowers regularly.

· Punish cheating (stealing, false reporting).

· Set a daily reward cap to prevent excessive plundering.

Long-term mechanism (Several Years)

Introducing an "Ecological Performance" metric: Ensure the sustainable recovery of flower resources over a certain period of time.

Reward bees that explore new flower sources and improve overall colony resilience.

Establishing "Collective Performance": Rewards are based not only on individual performance but also on overall prosperity.

Upgrades in the coming years

In the short term (within half a year) : Use simple weighing and light prompts to collect data and establish the first statistical system.

Mid-term (1-2 years) : Install cameras to identify the direction in which bees return and estimate the yield of each flower field. Weather sensors optimize attendance plans.

Long-term (2-5 years) : Build a "digital twin" model of the bee colony and use a computer to simulate the best collection strategy for the next few days. Coordination among multiple beehives to avoid excessive competition.

# data[].attributes.body
1. Expanding Performance Metrics (not just sales)

Right now, the company focuses almost only on sales revenue. We think this creates a bias in the performance system, because service quality and customer satisfaction are ignored.

Adding more indicators:

Customer satisfaction: short surveys after service and tracking the number of complaints.

Service quality: measuring repeat customers and rework rate (how often customers return because they are unhappy).

Employee-related metrics: turnover rate and engagement.

This is similar to the idea of a Balanced Scorecard, where performance should be looked at from multiple angles, not only financial outcomes.

2. Redesigning the Incentive System

The current pay system is demotivating. Many managers cannot even reach the basic target (Level 1), and Level 2/3 goals are seen as unrealistic. This leads to frustration.

We suggest raising the base wage slightly so that employees and managers feel more secure, and then combining commissions with team-based rewards that include both sales and service quality.

Apart from money, non-monetary incentives such as recognition, employee-of-the-month awards, or offering training and promotion paths can also motivate employees.

For targets, we think tiered goals work better:

Level 1 should be achievable by most.

Level 2 should be more challenging but not impossible.

Level 3 should be aspirational, to encourage top performers.

Using relative benchmarks (comparing stores in similar locations/size) could also make evaluations fairer.

# data[].attributes.body
1. Add customer service satisfaction evaluation in the salary system, and use a relative ranking method to measure employee performance, avoid only looking at sales, and make service quality also a key indicator.

2. According to the sale and location, the stores are managed in different level, and the base wage of the store manager is adjusted on this basis to make the salary more in line with the actual operating conditions.

3. close the stores that are not operating well, focus resources on stores with better performance, give priority to quality, and gradually improve the overall operation level, like provide special service and products in specific stores.

# data[].attributes.body
1.Total Compensation = Base Salary (raised to industry average) + Sales Commission (weighted at 40%) + Service Revenue Commission (weighted at 40%) + Customer Satisfaction Bonus (weighted at 20%) - Negative Customer Reviews

Managers' bonuses should be linked to the overall performance of their teams, including staff turnover rates and customer satisfaction, rather than based solely on sales results. This encourages managers to support their employees instead of pushing them for sales.

Provide non-monetary incentives:

Recognition and Rewards： Select a "Service Star" or "Customer Satisfaction Award" winner each month based on customer feedback, offering public recognition or additional time off.

Career Development: Provide training or promotion opportunities for high-performing employees to enhance their sense of belonging.

2.Restructure the store network: Close underperforming and remote locations. Establish premium flagship stores based on user profiles, foot traffic, and store locations.

3.Shift to "service-oriented sales," training staff to replace hard selling with professional care advice.

# data[].attributes.body
1.Shut down shops with relatively lower sales, use the capital cost to invest in other shop’s expansion

2.Stores: Instead of small-scale and locating in the suburbs, the brand should set up some larger and more professional branches in city center.

3.Setting a inspector --- sending inspectors randomly from the head offices every month and do performance appraisal, to raise vigilance.

4.cancel level 2/3 sales goals because they could seldom achieve these two levels.increase the reward level: meet level 1→ 5%of the monthly sales reward for the whole store

5.Introduce a "Service Quality/Retention Bonus" that makes up about 10–15% of their total income. This is to shift our focus to long-term value instead of just quick sales.

6.Also, KPIs for this bonus must be included the Customer Return Rate/Repeat Purchase Rate.