Selecting advanced analytics in manufacturing: a decision support model

Figures & data

Figure 1. Illustrative efficiency frontier and technology space T under CRS (solid line) and VRS (dashed line) with s = 1 and m = 1. DMUs are shown as points.

Figure 2. Proposed DEA framework in order to assess the efficiency of AATs.

Figure 3. Costs and effectiveness intensities across different AATs.

Figure 4. Exemplary selection of chosen cost drivers and application areas.

Table 1. DEA results for Geberit.

AAT	Efficiency	Confidence interval*	Selection	Input slacks		Output slacks
				DPC	MC	PP	QDI	PC
Diagnostic
1. Descriptive	0.909	[0.825 0.908]		1.749	0	0	2.604	0.004
2. Visual	1	[0.880 0.998]	X	0	0	0	0	0
3. Clustering	0.919	[0.855 0.917]		0	0	1.534	1.418	0.153
Predictive
4. Fully-supervised	1	[0.877 0.999]	X	0	0	0	0	0
5. Weakly-supervised	0.898	[0.833 0.896]		0	0.097	0	2.628	0.544
6. Semi-supervised	0.888	[0.827 0.887]		0	0	0.401	2.141	0.450
7. Supervised deep learning	1	[0.876 0.998]	X	0	0	0	0	0
8. Inexact-supervised	0.963	[0.879 0.959]		0	0	0	1.472	0.783
9. Distant-supervised	0.867	[0.818 0.866]		0	0	2.976	1.469	0.328
10. Generative	0.908	[0.851 0.906]		0	1.088	0.500	3.100	0.500
11. Instance-based	0.868	[0.809 0.866]		0.434	0	0	0.472	0.072
12. Rule-based	0.950	[0.883 0.949]		0.041	0	0	0.616	0.066
13. Knowledge-based	0.953	[0.892 0.953]		0	0	0	0.700	0
14. Image-based models	0.894	[0.859 0.893]		0	1.056	5	8.900	0.500
15. Audio-based models	0.922	[0.886 0.922]		0	1.397	5	9.600	0.500
16. Time-series models	1	[0.870 0.999]	X	0	0	0	0	0
17. Unsupervised	1	[0.957 0.999]		0	0	5	9.400	0.500
Prescriptive
18. Reinforcement learning	0.988	[0.926 0.987]		0	0	0.508	1.140	0
19. Inverse reinforcement learning	0.975	[0.896 0.973]		0	0.304	0.400	2.189	0
20. Apprenticeship learning	0.990	[0.910 0.989]		0	0.351	0.400	2.289	0
21. Mathematical optimisation	1	[0.877 0.997]	X	0	0	0	0	0
22. Evolutionary	0.945	[0.849 0.942]		0	0	0.182	0.757	0
23. Markov decision process	1	[0.875 0.999]	X	0	0	0	0	0
24. Hidden Markov models	1	[0.876 0.998]	X	0	0	0	0	0
25. Open loop simulation	0.931	[0.856 0.930]		0	0	0	4.038	0
26. Closed loop simulation	0.900	[0.870 0.899]		0	0	2.161	5.292	0
27. Dynamic programming	0.997	[0.944 0.994]		0.883	0	0	1.324	0
28. Inductive logic programming	0.925	[0.879 0.922]		0	0	0.653	7.107	0
29. Fuzzy modelling	0.873	[0.815 0.872]		0	0	0.433	0	0
30. Stochastic processes	0.935	[0.865 0.932]		0	0.471	0	0.288	0
31. Queuing models	0.862	[0.809 0.861]		0	0	1.046	5.271	0.105

AATs with an x are efficient and part of the selection.

DPC: data preparation costs; MC: modelling costs; PP: production planning; QDI: quality driver identification; PC: process control.

*The confidence intervals are for the bias corrected efficiencies at the 95% level.

Table 2. DEA results for ABB.

AAT	Efficiency	Confidence interval*	Selection	Input slacks		Output slacks
				DPC	MC	QI	MP
Diagnostic
1. Descriptive	0.900	[0.794 0.899]		1.700	0	3.400	1.200
2. Visual	1	[0.856 0.998]	X	0	0	0	0
3. Clustering	0.919	[0.862 0.916]		0	0	1.496	3.077
Predictive
4. Fully-supervised	0.881	[0.813 0.880]		0	1.906	1.100	4.400
5. Weakly-supervised	0.894	[0.823 0.894]		0	0.073	4.700	2
6. Semi-supervised	0.888	[0.819 0.886]		0	0	5.611	1.661
7. Supervised deep learning	1	[0.835 0.999]	X	0	0	0	0
8. Inexact-supervised	0.985	[0.869 0.980]		0	0	0	2.896
9. Distant-supervised	0.993	[0.906 0.988]		0	0	0	2.800
10. Generative	0.908	[0.836 0.907]		0	1.088	2	2.400
11. Instance-based	0.935	[0.845 0.929]		0.727	0	0	1.696
12. Rule-based	0.908	[0.807 0.904]		0	0	0	0.055
13. Knowledge-based	0.862	[0.796 0.859]		0	0	0.712	3.056
14. Image-based models	1	[0.819 0.994]	X	0	0	0	0
15. Audio-based models	0.922	[0.850 0.921]		0	1.397	5.400	1.400
16. Time-series models	1	[0.815 0.995]	X	0	0	0	0
17. Unsupervised	1	[0.951 0.999]		0	0	7.400	10
Prescriptive
18. Reinforcement learning	0.853	[0.809 0.851]		0	0	3.258	7.833
19. Inverse reinforcement learning	0.945	[0.854 0.942]		0	1.224	0	4.300
20. Apprenticeship learning	0.959	[0.868 0.957]		0	1.269	0	4.200
21. Mathematical optimisation	0.812	[0.775 0.810]		0	0	5.291	5.457
22. Evolutionary	0.770	[0.740 0.769]		0	0	5.952	6.880
23. Markov decision process	1	[0.856 0.997]	X	0	0	0	0
24. Hidden Markov models	0.920	[0.827 0.918]		0	0	1.960	0
25. Open loop simulation	0.919	[0.830 0.916]		0	0	2.454	0.178
26. Closed loop simulation	0.880	[0.846 0.878]		0	0	4.655	5.403
27. Dynamic programming	0.815	[0.739 0.812]		0	0	2.664	1.585
28. Inductive logic programming	0.925	[0.871 0.923]		0	0	1.506	6.368
29. Fuzzy modelling	0.790	[0.718 0.788]		0	0	0.349	1.475
30. Stochastic processes	0.881	[0.786 0.880]		0	0.497	5.400	0.500
31. Queuing models	0.862	[0.808 0.860]		0	0	4.316	3.357

AATs with an x are efficient and part of the selection.

DPC: data preparation costs; MC: modelling costs; QI: quality inspection; MP: maintenance planning.

*The confidence intervals are for the bias corrected efficiencies at the 95% level.