Zeichnen einer Silbersteinkurve mit Python
1. Silverstone-Kurvenvisualisierung
Dies ist der Code zur Visualisierung der Maxy Silverstone-Kurve mit Python. Es wurde bestätigt, mit JupyterNotebook zu arbeiten. Die derzeit enthaltenen Parameter sind sehr gut geeignet. Bitte ändern Sie sie und verwenden Sie sie selbst.
fix_cost_simulator
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
%matplotlib inline
# set investigation cost
PLANT_COST = 9_999_999_999 # set plant cost [!]
LINE_COST = 99_999_999 # set line cost [!]
TOOLING_COST = 69_999_999 # set tooling cost [!]
# set how much lines a plant will have [!]
PLANNED_NUM_OF_LINE = 8
# set each condition of the capacity. (pcs/yr = pcs/day * day/mo * mo/yr)
LINE_DAILY_CAPA = 999 # set daily line production capacity [!]
TOOLING_DAILY_CAPA = 599 # set daily tooling production capacity [!]
DAYS_PER_MONTH = 22 # set working days [!]
MONTHS_PER_YEAR = 12 # how many months in a year
LINE_CAPA = LINE_DAILY_CAPA * DAYS_PER_MONTH * MONTHS_PER_YEAR # calc line capacity per year
TOOLING_CAPA = TOOLING_DAILY_CAPA * DAYS_PER_MONTH * MONTHS_PER_YEAR # calc tooling capacity per year
# set the condition for this simulation
START_QTY = 100_000 # starting point(production number) for this simulation
PLOT_WIDTH = 1_000 # plotting width
MARGIN_RATIO = 0.9 # set safety margin (consider a risk of sales) [!]
PLANT_DEP_YEAR = 9 # set plant depreciation year [!]
LINE_DEP_YEAR = 9 # set line depreciation year [!]
TOOLING_DEP_YEAR = 2.2 # set tooling depreciation year [!]
END_QTY = PLANNED_NUM_OF_LINE * LINE_CAPA # calc the end of this simulation
# initialize number of line/tooling
line_num = math.ceil(START_QTY / LINE_CAPA) # initialize number of line
tooling_num = math.ceil(START_QTY / TOOLING_CAPA) # initialize number of tooling
plant_dep_qty = END_QTY * PLANT_DEP_YEAR * MARGIN_RATIO # calc plant depreciation quantity
# generate data for simulation
x_range = np.arange(START_QTY, END_QTY, PLOT_WIDTH)
Y = []
p_line_capa = line_num * LINE_CAPA # calc initial line production capacity
p_tooling_capa = tooling_num * TOOLING_CAPA # calc initial tooling production capacity
for x in x_range:
if x > p_line_capa: # if production qty is over line capa, add a new line
line_num += 1
p_line_capa = line_num * LINE_CAPA
if x > p_tooling_capa: # if production qty is over tooling capa, add a new tooling
tooling_num += 1
p_tooling_capa = tooling_num * TOOLING_CAPA
plant_alloc = PLANT_COST / plant_dep_qty # calc allocated cost of plant building cost
line_alloc = line_num * LINE_COST / x / LINE_DEP_YEAR # calc allocated cost of line cost
tooling_alloc = tooling_num * TOOLING_COST / x / TOOLING_DEP_YEAR # calc allocated cost of tooling cost
all_alloc = plant_alloc + line_alloc + tooling_alloc
if line_num > PLANNED_NUM_OF_LINE: # if line_num is over planned num of line, cause error.
print('Error: line_num is over PLANNED_NUM_OF_LINE.')
break
Y.append(all_alloc)
# show the precondition of this simulation
precondition_dict = {}
precondition_dict['plant_building_cost [Fabrikbaukosten]'] = f'{PLANT_COST:,}'
precondition_dict['line_building_cost [Kosten für das Verlegen von Leitungen]'] = f'{LINE_COST:,}'
precondition_dict['tooling_cost [Formkosten]'] = f'{TOOLING_COST:,}'
precondition_dict['max_line_num_per_a_plant [Maximale Anzahl von Strichen für das Linienlayout pro Werk(pcs/day)]'] = PLANNED_NUM_OF_LINE
precondition_dict['max_production_daily_capa [Produktionskapazität pro Tag(pcs/day)]'] = LINE_DAILY_CAPA
precondition_dict['max_tooling_daily_capa [Formproduktionskapazität pro Tag]'] = TOOLING_DAILY_CAPA
precondition_dict['plant_depreciation_year [Anzahl der Jahre für die Abschreibung des Fabrikgebäudes]'] = PLANT_DEP_YEAR
precondition_dict['line_depreciation_year [Linienamortisation festgelegte Jahre]'] = LINE_DEP_YEAR
precondition_dict['tooling_depreciation_year [Form Amortisationseinstellungsjahre]'] = TOOLING_DEP_YEAR
precondition_dict['max_line_num [Maximale Anzahl verlegter Linien]'] = line_num
precondition_dict['max_tooling_num [Maximale Anzahl Formen]'] = tooling_num
precondition_dict['plant_production_capa [Produktionskapazität in der Fabrik(pcs/yr)]'] = f'{END_QTY:,}'
df = pd.DataFrame(precondition_dict.values(), index=precondition_dict.keys(), columns=['precondition [Voraussetzungen]'])
display(df)
# set your plan
yourX = 1_000_000 # set your sales plan [!]
yourY = 999_999_999
for x, y in zip(x_range, Y):
if x > yourX:
yourY = y
break
# show simulation result
plt.rcParams["font.size"] = 15
fig, ax = plt.subplots(1, 1, facecolor='#F5FBFF', figsize=(15,10))
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, loc: f'{int(x):,}'))
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, loc: f'{int(x):,}'))
ax.plot(x_range, Y, c='green')
ax.vlines(x=yourX, ymin=min(Y), ymax=max(Y), color='red')
ax.text(x=yourX*1.03, y=max(Y)*0.995, s=f'{yourY:,.2f} @ ( line_num:{np.ceil(yourX/LINE_CAPA):.0f}, tooling_num:{np.ceil(yourX/TOOLING_CAPA):.0f} )', color='red', fontsize=20)
fig.savefig('./data/img/FixedCostSim.png')
plt.show()
- Wenn Sie mit "Maxcy et al. [1965] Silver Stone" googeln, Finanzstrategie eines Automobilherstellers, der Toyota als Material verwendet - Ich wurde erwischt. Interessant. »1965 gab es natürlich kein EXCEL. Mr. Maxy hat es wahrscheinlich von Hand geplant. Ich bin wirklich dankbar, dass Python so einfach zeichnen kann.
2. Banana Smashing Visualisierung
banana_sim
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def make_and_plot_data(p1, p2, b):
X = np.arange(p1, p2, 1)
Y = []
for x in X:
Y.append(b)
ax.plot(X,Y,c='green')
X0 = 0
X1 = 50 # [!]
X2 = 100 # [!]
X3 = 200 # [!]
C0 = 2000 #Kaufbetrag X0 oder mehr, Verkaufspreis unter X1[!]
C1 = 1000 #Kaufbetrag X1 oder mehr, Verkaufspreis unter X2[!]
C2 = 500 #Kaufbetrag X2 oder mehr, Verkaufspreis unter X3[!]
yourX = 120 # [!]
yourY = 500 # [!]
plt.rcParams["font.size"] = 15
fig = plt.figure(facecolor='#F5FBFF', figsize=(15,10))
ax = fig.subplots(1,1)
make_and_plot_data(X0, X1, C0)
make_and_plot_data(X1, X2, C1)
make_and_plot_data(X2, X3, C2)
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, loc: f'{int(x):,}'))
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, loc: f'{int(x):,}'))
ax.vlines(x=yourX, ymin=0, ymax=C0, color='red')
ax.text(x=yourX*1.03, y=yourY*1.1, s=f'{yourY} @ {yourX:,}', color='red', fontsize=20)
fig.savefig('./data/img/bananaCostSim.png')
plt.show()
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