TUM Data Innovation Lab · 2018-04-10 · Team Celonis TUM Data Innovation Lab 2018 10 UNDESIRED ACTIVITIES Analyze where undesired activities happen –and why. EFFICIENCY ANALYSIS

Team CelonisTUM Data Innovation Lab 2018

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Master in Data

Engineering and Analytics

Margarita Ageeva

Master in Management

Keesiu Wong

Master in Mathematical

Finance and Actuarial Science

Olha Tupko

Master in Computational

Science and Engineering

Sebastian Roßner

TEAM CELONISTUM Data Innovation Lab

Master in Electrical and

Computer Engineering

Master in Management

Ahmed Ayadi


2

AGENDA

STRATEGIC GOAL

PHASE I: DATA ANALYSIS

PART 1: EXISTING USE CASES

PART 2: NEW USE CASES

PHASE II: MACHINE LEARNING

STRATEGIC GOAL


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STRATEGIC GOALStrategic positioning of the project within the Ansoff Matrix

MARKET DEVELOPMENT

MARKET PENETRATION

DIVERSIFICATION

PRODUCT DEVELOPMENT

Existing Products

Exi

stin

g M

ark

ets

New

Mark

ets

New Products

Phase I Part 1: Existing Use Cases

Phase I Part 2: New Use Cases


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AGENDA

STRATEGIC GOAL




PHASE II: MACHINE LEARNING



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PART II: NEW USE CASESPART I: EXISTING USE CASES


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PART I: EXISTING USE CASES


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EXISTING USE CASES11 new Analyses in the Content Store

2016-12-01 CREATE PURCHASE ORDER #12342016-06-23 START PRODUCTION #56782016-07-14 RECEIVE PAYMENT #12342016-07-14 SEND EMAIL #9012

EVENT LOG

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AI-POWERED ROOT

CAUSE ANALYSIS &

IMPROVEMENT

VISUALIZATION OF THE

ACTUAL PROCESSES


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PART II: NEW USE CASESPART I: EXISTING USE CASES


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PART II: NEW USE CASES


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UNDESIRED ACTIVITIES

Analyze where

undesired activities

happen – and why.

EFFICIENCY ANALYSIS

See exactly which

process steps can be

improved – for sure.

COMPLEXITY ANALYSIS

Get a feeling for the

complexity for your

processes – quantified.

THREE NEW USE CASESfor any process, for any ERP platform


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COMPLEXITY ANALYSISGet a feeling for the complexity of your processes – quantified.


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Perfect world



13

Perfect world



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Perfect world Expectation



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Perfect world Expectation



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Perfect world Expectation Reality



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Perfect world Expectation Reality



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AGENDA

STRATEGIC GOAL




PHASE II: MACHINE LEARNINGPHASE II: MACHINE LEARNING


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ROBLEMwhere machine learning empowers process mining


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ARIMA ARD Regression LSTM

Classical Statistics Advanced Machine LearningBayesian Trade-off


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ARIMAAutoRegressive Integrated Moving Average

• Time series specific

• Removes polynomial trend

• Easy to implement

• Computationally fast

Advantages

• Works with stationary

time series only

• Cannot deal with trend

other then polynomial

Limitations


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ARDAutomatic Relevance Determination regression

• Automatic feature selection

• Confidence intervals

• Easy to implement

• Computationally fast

Advantages

• Not times series specific

• Not robust to

distributional changes

Limitations


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LSTMLong Short-Term Memory recurrent neural network

http://prog3.com/article/2015-11-25/2826323

• Sequential data specific

• Captures long and short-

term dependencies

• Can handle complex

data structures

Advantages

• Gives point estimates

• Requires large datasets

• Computationally expensive

Limitations


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RESULTSin terms of Mean Absolute Error

Before Truncation

ARD LSTM LSTMARIMA ARIMA ARD

105.4

120.5

53.1

105.4

57.858.2

After Truncation


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MACHINE LEARNING


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2

Logistic

Regression

3

K-Nearest

Neighbors

4

Decision

Trees

5

Random

Forests

1

Naïve

Bayes

6

Feedforward

Neural

Networks


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𝑝

𝑥𝑆

𝑥𝐷

512 256 128 64 1

ReLu

Sigmoid

PCA


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Random Forests

63

74

79

85

89

86

84

86

81

85

78

Neural Networks

85

88

89

85

92

93

90

89

84

83

88

1

2

3

4

5

6

7

8

9

10

Overall

Logistic Regression

14

37

41

50

63

60

68

72

66

74

45

KNN

59

64

69

75

83

77

81

80

78

78

65

Decision Trees

60

70

75

81

85

79

79

80

79

81

74

Naïve Bayes

38

40

42

46

46

47

55

52

50

59

43


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Random Forests

63

74

79

85

89

86

84

86

81

85

78

Neural Networks

85

88

89

85

92

93

90

89

84

83

88

1

2

3

4

5

6

7

8

9

10

Overall

Logistic Regression

14

37

41

50

63

60

68

72

66

74

45

KNN

59

64

69

75

83

77

81

80

78

78

65

Decision Trees

60

70

75

81

85

79

79

80

79

81

74

Naïve Bayes

38

40

42

46

46

47

55

52

50

59

43


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CONCLUSION

MARKET DEVELOPMENT

MARKET PENETRATION

DIVERSIFICATION

PRODUCT DEVELOPMENT

Existing Products

Exi

stin

g M

ark

ets

New

Mark

ets

New Products

Phase I Part 1: Existing Use Cases

Phase I Part 2: New Use Cases


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Documents

TUM Data Innovation Lab · 2018-04-10 · Team Celonis TUM Data Innovation Lab 2018 10 UNDESIRED ACTIVITIES Analyze where undesired activities happen –and why. EFFICIENCY ANALYSIS