MC.9 与 LIS

用户反映 MC.9 的值与期望不符,经调试,发现居然采用了 S032 和 S031 两个表,也就是说数据源自 LIS:Logistics Information System。

The information systems used to plan, control, and monitor business events at different stages in the decision-making process.

The Logistics Information System is made up of the following information systems which all have a modular structure:

  • Sales Information System
  • Purchasing Information System
  • Inventory Controlling
  • Shop Floor Information System
  • Plant Maintenance Information System
  • Quality Management Information System

Tools are available in Customizing to allow a self-defined information system to be created and tailored to specific requirements.

简要来说,S032记录了库存物料的现值,S031记录了库存物料的发生合计数(根据日/周/月,因设置而异),据此倒推可得物料特定时间的库存状况。

LIS 聚集可能出现错误(我不知道原因),错误的修正也不简单。总而言之,用户这个问题,有待深入研究LIS后,同时他们能提供准确的参考数值后,放好解决。

SAP Note: 64636、512416、456106

 来源:http://www.mexp.net/wordpress/?p=62

import numpy as np import matplotlib.pyplot as plt import math def count(lis): lis = np.array(lis) key = np.unique(lis) x = [] y = [] for k in key: mask = (lis == k) list_new = lis[mask] v = list_new.size x.append(k) y.append(v) return x, y mu = [14, 23, 22] sigma = [2, 3, 4] tips = ['design', 'build', 'test'] figureIndex = 0 fig = plt.figure(figureIndex, figsize=(10, 8)) color = ['r', 'g', 'b'] ax = fig.add_subplot(111) for i in range(3): x = np.linspace(mu[i] - 3*sigma[i], mu[i] + 3*sigma[i], 100) y_sig = np.exp(-(x - mu[i])**2/(2*sigma[i]**2))/(math.sqrt(2*math.pi)) ax.plot = (x, y_sig, color[i] + '-') ax.legend(loc='best', frameon=False) ax.set_xlabel('# of days') ax.set_ylabel('probability') plt.show() plt.grid(True) size = 100000 samples = [np.random.normal(mu[i], sigma[i], size) for i in range(3)] data = np.zeros(len(samples[1])) for i in range(len(samples[1])): for j in range(3): data[i] += samples[j][i] data[i] = int(data[i]) a, b = count(data) pdf = [x/size for x in b] cdf = np.zeros(len(a)) for i in range(len(a)): if i > 0: cdf[i] += cdf[i - 1] cdf = cdf/size figureIndex += 1 fig = plt.figure(figureIndex, figsize=(10, 8)) ax = fig.add_subplot(211) ax.bar(a, height=pdf, color='blue', edgecolor='white', label='MC PDF') ax.plot(a, pdf) ax.legend(loc='best', frameon=False) ax.set_xlabel('# of days for project') ax.set_ylabel('probability') ax.set_title('Monte Carlo Simulation') ax = fig.add_subplot(212) ax.plot(a, cdf) ax.legend(loc='best', frameon=False) ax.set_xlabel('# of days for project') ax.set_ylabel('probability') ax.grid(True) plt.show()修改一下代码
05-18
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