Pareto Chart
Deskripsi:
Pareto Diagram digunakan untuk pengambilan keputusan berdasarkan prioritas. Sehingga keputusan lebih didasarkan pada faktor yang memberikan pengaruh signifikan terhdap hasil. Pengaruh masing-masing faktor digambarkan dalam diagram batang. Absis dari grafik adalah kriteria atau kategori atau jenis faktor yang mempengaruhi hasil. Ordinat dari grafik adalah prosentase pengaruh masing-masing faktor. Disamping diagram batang, grafik kumulatif pengaruh semua faktor juga ditampilkan.
Tujuan:
Prinsip Pareto menyatakan bahwa umumnya 20% dari faktor yang mempengaruhi 80% dari hasil. Diagram pareto digunakan untuk memvisualisasikan hasil pengamatan pengaruh faktor terhadap hasil. Tujuan dari penggunaan Pareto Chart adalah untuk membantu dalam mengambil keputusan berkaitan dengan prioritas faktor yang berpengaruh 80% ke hasil.
Berikut adalah pengaruh Suhu (C), Waktu (menit), kecepatan pengadukan (rpm), ukuran partikel (mikrometer), dan Konsentrasi asam (M) pada pelindihan tembaga dari batuan. Data prosentase dihitung dari slope kurva regresi perubahan masing-masing faktor terhadap konsentrasi tembaga yang terlindi ketika kedua faktor yang lain dibuat tetap di nilai minimal.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({
'Faktor': ['Suhu', 'Waktu', 'RPM', 'Partikel', 'Asam'],
'Orders': [0.315, 0.1670,0.0095, 0.450, 0.75]
})
def pareto_plot(df, x='Faktor', y='Orders', title=None, show_pct_y=False, pct_format='{0:.0%}'):
xlabel = x
ylabel = y
tmp = df.sort_values(y, ascending=False)
x = tmp[x].values
y = tmp[y].values
weights = y / y.sum()
cumsum = weights.cumsum()
fig, ax1 = plt.subplots()
ax1.bar(x, y)
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax2 = ax1.twinx()
ax2.plot(x, cumsum, '-ro', alpha=0.5)
ax2.set_ylabel('', color='r')
ax2.tick_params('y', colors='r')
vals = ax2.get_yticks()
ax2.set_yticklabels(['{:,.2%}'.format(x) for x in vals])
# hide y-labels on right side
if not show_pct_y:
ax2.set_yticks([])
formatted_weights = [pct_format.format(x) for x in cumsum]
for i, txt in enumerate(formatted_weights):
ax2.annotate(txt, (x[i], cumsum[i]), fontweight='heavy')
if title:
plt.title(title)
plt.tight_layout()
plt.show()
pareto_plot(df)
Mengutip Buku Richard Koch: The 80/20 Principle:
What is the 80/20 Principle?
"The 80/20 Principle asserts that a minority of causes, inputs or effort usually lead to a majority of the results, outputs or rewards. Taken literally, this means that, for example, 80 per cent of what you achieve in your job comes from 20 per cent of the time spent. Thus for all practical purposes, fourfifths of the effort—a dominant part of it—is largely irrelevant. This is contrary to what people normally expect. So the 80/20 Principle states that there is an inbuilt imbalance between causes and results, inputs and outputs, and effort and reward. A good benchmark for this imbalance is provided by the 80/20 relationship: a typical pattern will show that 80 per cent of outputs result from 20 per cent of inputs; that 80 per cent of consequences flow from 20 per cent of causes; or that 80 per cent of results come from 20 per cent of effort.
Why the 80/20 Principle is so important
The reason that the 80/20 Principle is so valuable is that it is counterintuitive. We tend to expect that all causes will have roughly the same significance. That all customers are equally valuable. That every bit of business, every product and every dollar of sales revenue is as good as another. That all employees in a particular category have roughly equivalent value. That each day or week or year we spend has the same significance. That all our friends have roughly equal value to us. That all enquiries or phone calls should be treated in the same way. That one university is as good as another. That all problems have a large number of causes, so that it is not worth isolating a few key causes. That all opportunities are of roughly equal value, so that we treat them all equally. We tend to assume that 50 per cent of causes or inputs will account for 50 per cent of results or outputs. There seems to be a natural, almost democratic, expectation that causes and results are generally equally balanced. And, of course, sometimes they are. But this ‘50/50 fallacy’ is one of the most inaccurate and harmful, as well as the most deeply rooted, of our mental maps. The 80/20 Principle asserts that when two sets of data, relating to causes and results, can be examined and analysed, the most likely result is that there will be a pattern of imbalance. The imbalance may be 65/35, 70/30, 75/25, 80/20, 95/5, or 99.9/0.1, or any set of numbers in between. However, the two numbers in the comparison don’t have to add up to 100 "