name: 节拍时间计算器 description: 用于生产线平衡和产能规划的节拍时间和周期时间分析技能。 allowed-tools: Bash(*) Read Write Edit Glob Grep WebFetch metadata: author: babysitter-sdk version: “1.0.0” category: 精益生产 backlog-id: SK-IE-010
节拍时间计算器
您是节拍时间计算器 - 专门用于计算节拍时间、周期时间及相关指标，以支持生产计划和产线平衡的专业技能。
概述
本技能支持AI驱动的节拍时间分析，包括：
- 根据需求和可用时间计算节拍时间
- 周期时间测量与分析
- 操作员周期时间跟踪
- 节拍时间达成率监控
- 节奏化取料的节拍计算
- 考虑效率因素的计划周期时间
- 加班和班次调整计算
前提条件
- 生产需求数据
- 可用工作时间信息
- 周期时间观测数据
功能
1. 节拍时间计算
def calculate_takt_time(customer_demand, available_time, time_unit='seconds'):
"""
节拍时间 = 可用生产时间 / 客户需求
参数：
customer_demand: 每期所需单位数
available_time: 期间内可用的生产时间
time_unit: 输出单位 ('seconds', 'minutes', 'hours')
返回：
节拍时间及相关指标
"""
if customer_demand <= 0:
raise ValueError("客户需求必须为正数")
takt_seconds = available_time / customer_demand
conversions = {
'seconds': takt_seconds,
'minutes': takt_seconds / 60,
'hours': takt_seconds / 3600
}
return {
"takt_time": conversions[time_unit],
"time_unit": time_unit,
"customer_demand": customer_demand,
"available_time_seconds": available_time,
"interpretation": f"必须每 {takt_seconds:.1f} 秒完成1个单位"
}
def calculate_available_time(shift_length_hours, breaks_minutes,
planned_downtime_minutes, shifts_per_day):
"""
计算净可用生产时间
"""
shift_seconds = shift_length_hours * 3600
breaks_seconds = breaks_minutes * 60
downtime_seconds = planned_downtime_minutes * 60
net_per_shift = shift_seconds - breaks_seconds - downtime_seconds
total_daily = net_per_shift * shifts_per_day
return {
"gross_time_per_shift": shift_seconds,
"breaks_deduction": breaks_seconds,
"planned_downtime": downtime_seconds,
"net_available_per_shift": net_per_shift,
"shifts_per_day": shifts_per_day,
"total_daily_available": total_daily
}
2. 周期时间分析
import numpy as np
from scipy import stats
def analyze_cycle_times(observations):
"""
观测周期时间的统计分析
"""
data = np.array(observations)
analysis = {
"count": len(data),
"mean": np.mean(data),
"median": np.median(data),
"std": np.std(data, ddof=1),
"min": np.min(data),
"max": np.max(data),
"range": np.max(data) - np.min(data),
"cv": np.std(data, ddof=1) / np.mean(data) * 100 # 变异系数
}
# 百分位数
analysis["p5"] = np.percentile(data, 5)
analysis["p95"] = np.percentile(data, 95)
# 均值置信区间
ci = stats.t.interval(0.95, len(data)-1,
loc=np.mean(data),
scale=stats.sem(data))
analysis["ci_95"] = {"lower": ci[0], "upper": ci[1]}
# 异常值检测（IQR方法）
q1, q3 = np.percentile(data, [25, 75])
iqr = q3 - q1
outliers = data[(data < q1 - 1.5*iqr) | (data > q3 + 1.5*iqr)]
analysis["outliers"] = outliers.tolist()
return analysis
def compare_to_takt(cycle_time_stats, takt_time):
"""
比较观测周期时间与节拍时间
"""
mean_ct = cycle_time_stats['mean']
p95_ct = cycle_time_stats['p95']
comparison = {
"takt_time": takt_time,
"mean_cycle_time": mean_ct,
"takt_attainment": takt_time / mean_ct * 100 if mean_ct > 0 else 0,
"at_risk": mean_ct > takt_time * 0.9,
"exceeds_takt": mean_ct > takt_time,
"p95_vs_takt": p95_ct / takt_time * 100,
"buffer_available": takt_time - mean_ct
}
if comparison["exceeds_takt"]:
comparison["recommendation"] = "周期时间超过节拍时间 - 需要立即改进"
elif comparison["at_risk"]:
comparison["recommendation"] = "周期时间接近节拍时间 - 应对变异的缓冲有限"
else:
comparison["recommendation"] = "存在健康的缓冲时间"
return comparison
3. 操作员周期时间跟踪
class OperatorCycleTimeTracker:
"""
跟踪和分析操作员周期时间
"""
def __init__(self):
self.observations = {} # {操作员ID: [观测数据]}
def record_observation(self, operator_id, cycle_time, timestamp=None):
if operator_id not in self.observations:
self.observations[operator_id] = []
self.observations[operator_id].append({
"cycle_time": cycle_time,
"timestamp": timestamp or datetime.now()
})
def analyze_operator(self, operator_id):
obs = [o['cycle_time'] for o in self.observations.get(operator_id, [])]
return analyze_cycle_times(obs) if obs else None
def compare_operators(self):
"""跨操作员性能比较"""
comparison = {}
for op_id in self.observations:
stats = self.analyze_operator(op_id)
if stats:
comparison[op_id] = {
"mean": stats['mean'],
"cv": stats['cv'],
"count": stats['count']
}
# 按平均周期时间排序
ranked = sorted(comparison.items(), key=lambda x: x[1]['mean'])
return {
"operator_stats": comparison,
"ranked_by_speed": [op_id for op_id, _ in ranked],
"best_performer": ranked[0][0] if ranked else None,
"spread": ranked[-1][1]['mean'] - ranked[0][1]['mean'] if len(ranked) > 1 else 0
}
4. 节拍计算
def calculate_pitch(takt_time, pack_quantity):
"""
节拍 = 节拍时间 × 包装数量
节拍是节奏化取料的时间间隔
（多久移动一次容器）
"""
pitch_seconds = takt_time * pack_quantity
return {
"pitch_seconds": pitch_seconds,
"pitch_minutes": pitch_seconds / 60,
"takt_time": takt_time,
"pack_quantity": pack_quantity,
"withdrawals_per_hour": 3600 / pitch_seconds,
"interpretation": f"每 {pitch_seconds/60:.1f} 分钟移动 {pack_quantity} 个单位"
}
5. 计划周期时间
def calculate_planned_cycle_time(takt_time, efficiency_factors):
"""
计划周期时间考虑了预期的低效率因素
efficiency_factors: 包含以下组件的字典：
- oee: 整体设备效率 (0-1)
- quality_rate: 首次通过率 (0-1)
- availability: 机器可用性 (0-1)
"""
total_efficiency = 1.0
for factor, value in efficiency_factors.items():
total_efficiency *= value
planned_ct = takt_time * total_efficiency
return {
"takt_time": takt_time,
"efficiency_factors": efficiency_factors,
"combined_efficiency": total_efficiency,
"planned_cycle_time": planned_ct,
"buffer_percentage": (1 - total_efficiency) * 100,
"interpretation": f"目标 {planned_ct:.1f}秒以达到 {takt_time:.1f}秒的节拍"
}
6. 加班和班次调整
def adjust_for_demand_changes(base_takt, base_demand, new_demand,
base_available_time, options):
"""
计算需求变化所需的调整
options: 包含可用杠杆的字典：
- overtime_available: 每班最大加班分钟数
- additional_shifts: 布尔值，是否可以增加班次
- weekends: 布尔值，是否可以周末工作
"""
demand_ratio = new_demand / base_demand
if demand_ratio <= 1:
new_takt = base_takt / demand_ratio
return {
"action": "none_needed",
"new_takt": new_takt,
"demand_decrease": (1 - demand_ratio) * 100
}
# 需要更多产能
additional_time_needed = base_available_time * (demand_ratio - 1)
solutions = []
# 加班选项
if options.get('overtime_available'):
overtime_per_shift = options['overtime_available'] * 60 # 转换为秒
shifts_needed = additional_time_needed / overtime_per_shift
if shifts_needed <= 5: # 5天工作周
solutions.append({
"method": "overtime",
"overtime_minutes_per_day": additional_time_needed / 60,
"feasible": True
})
# 增加班次
if options.get('additional_shifts'):
solutions.append({
"method": "additional_shift",
"coverage_percentage": min(100, (base_available_time / additional_time_needed) * 100),
"feasible": additional_time_needed <= base_available_time
})
# 周末工作
if options.get('weekends'):
solutions.append({
"method": "weekend_work",
"days_needed": additional_time_needed / base_available_time,
"feasible": True
})
return {
"demand_increase_percent": (demand_ratio - 1) * 100,
"additional_time_needed_hours": additional_time_needed / 3600,
"solutions": solutions,
"new_takt_with_increase": base_available_time / new_demand
}
流程集成
本技能与以下流程集成：
standard-work-development.jsline-balancing-analysis.jsvalue-stream-mapping-analysis.js
输出格式
{
"takt_analysis": {
"customer_demand": 460,
"available_time_hours": 7.5,
"takt_time_seconds": 58.7,
"takt_time_formatted": "58.7 秒/单位"
},
"cycle_time_comparison": {
"observed_mean": 52.3,
"observed_std": 4.2,
"takt_attainment_percent": 112.2,
"buffer_seconds": 6.4
},
"status": "healthy",
"recommendations": [
"当前周期时间提供足够的缓冲",
"监控变异性以维持性能"
]
}
最佳实践
- 使用净可用时间 - 减去休息、会议、维护时间
- 包含变异性 - 不要按精确节拍设计
- 定期更新 - 需求变化时重新计算
- 可视化显示 - 在工作站张贴节拍时间
- 多次观测 - 获取统计有效的周期时间
- 考虑所有产品 - 可能需要产品特定的节拍
约束
- 节拍时间是目标，不是固定规则
- 适用时考虑产品组合
- 记录所有时间假设
- 需求模式变化时重新评估