This document presents a model for optimizing the strategy for resolving gaps between standard ERP features and specific customer requirements. The model takes into account customization options, their costs and benefits, and the vendor's planned feature releases. The objective is to maximize benefit achieved from satisfying requirements over time. Experimental studies show the model can generate a gap resolution strategy and highlight the impact of factors like customization preferences. A sample application illustrates identifying gaps in a lead management process and potential resolutions.

1. 1
Jānis Grabis
Optimization of Gaps
Resolution Strategy in
Implementation of ERP
Systems
Institute of Information Technology, Riga Technical University, Latvia

2. 22
 ERP are packaged applications delivering
standard features
 Customers have specific requirements
 Misfits between the standard features and
the specific requirements are called gaps
 Various customization options can be used
to resolve the gaps
 Vendors incrementally deliver new
features
Background

3. 3
 Fit-gap analysis
– Gulledge (2006)
– Parthasarathy and Daneva
(2016)
 Customization benefits and
drawbacks
– Kholeif et al. (2007)
 Customization typologies
– Aslam et al. (2012)
– Hustad et al. (2016)
Existing Work
Fits

4. 44
Customization options
Roadmap
Illustration of customization
options and roadmap for
Microsoft Dynamics Sales

5. 55
Objective
Strategy defines selection of customization options and
their timing to reduce gaps between the required
functionality and the standard features.
It takes into account vendor’s ERP development roadmap
and aims to optimize business value achieved by satisfying
requirements in the best possible manner.
To develop a model for optimization of
the gaps resolution strategy

6. 66
 Conceptual modeling
 Mathematical model development
 Experimentation
 Sample application
Research Method

7. 77
Conceptual Modelclass Customization
OptionGap
Requirement
Strategy
CustomizationChoice
Roadmap
Effort
Utility
Customization
preference
1
*
1
Used
0..*
1
Guide
1
1
1
1 1
1
Resolve
0..11..*
0..*
1
Drive
1

8. 8
Identify gaps
Estimate input parameters
• Vendor roadmap, utility, effort
Strategy optimization
Strategy implemenetation
Decision-making Process
 Business analysis is
performed to identify gaps
 Required input parameters
– Benefits gained by
resolving the gaps or
utility
– Customization effort
– Information provided by
vendor on forthcoming
features or roadmap
 Mathematical model to
optimize strategy
 Adoption of the strategy

9. 99
 Decision variables
– Selected implementation option Xijt
 Binary, i – gap, j – implementation option, t – time period
 Objective
– Maximize (Gap Resolution Utility –
Implementation Effort)
 Constraints
– No more than one implementation option is used
– Capacity restrictions
– Cannot adopt standard feature if not released
– Gap resolution utility adjustment
Optimization Model: Textual
Description

10. 1010
 U – utility, E-
effort, R –
resources, t –
new feature
release timing
 Utility depends on
implementation
timing
 Utility adjustment
is introduced for
experimentation
purposes
Optimization Model:
Mathematical Representation
 *
1 1 1
1 1 1
( )
I J TT
ij ij ijti j t
I J TT
ij ijti j t
Z TT t U U X
E X
  
  
  

  
  
1 1
1,
J TT
ijtj t
X i 
  
1 1
, 1,...,
I J
ij ijt ti j
E X R t TT 
  
1 , ,i i tt X i tt 
* *
, , ,ij ij ij ijU V U V i j   

11. 11
 To highlight impact of
customization preferences
 Setup
– 20 gaps
– 5 customization options
– Utility is on average by 20%
larger than the effort
– 12 periods
 4 new releases by vendor
 12 gaps could be resolved by
newly released standard features
 Customization preference is
varied from 0.25 to 2
 10 randomly generated values
utility and effort
Experimental Studies

12. 1212
Sample Gap Resolution Strategy
Gap
14
Gap
13
Gap
12
Gap
11
Gap
10
Gap
9
Gap
8
Gap
7
Gap
6
Gap
5
Gap
4
Gap
3
Gap
2
Gap
1
1 2 3 4 5 6 7 8 9 10 11 12
5
None
2
Std.
feature
5
Std.
feature
5
Std.
feature
Std.
feature
4
3
Std.
feature
Std.
feature
Std.
feature
Gap 4 is resolved in
the first month using
Resolution approach 3
Gap 3 is resolved in
the fourth month by
adopting the newly
release standard
feature
Gap 13 is never
resolved

13. 1313
Sample Application: Lead
Qualification process
Create Lead
Find
Duplicates
Merge Lead
Records
Make
Qualification
Call
Convert Lead
Create Folow
UpTask
Update Lead
Data
Close Lead
Dublicates
found
No dublicates
Create
opportunity
Not ready
Further action
required
No further
action

14. 14
Process tasks Gap Customization options
Create Lead G1: The data entry is too time-
consuming due to extra
navigations steps
Std. feature; Data view; UI;
Basic process; Workflow
Find
Duplicates
G2: Provided data are not
appropriately tailored and a lot of
manual work
Data view; UI; Report; Basic
process; Workflow; Add-on
Make
Qualification
Call
G3: The conversation is not
scripted
Std. feature; Basic process;
Workflow
Update Lead
data
G4: The update is manual and
involves extra navigation steps
UI; Basic process; Workflow
Create Follow
up Tasks
G5: Not all information to decide
on follow up tasks is available
Data view; UI; Report
Close Lead G6: Closing is manual UI; Basic process; Workflow
Convert Lead G7: Conversion is manual UI; Report; Basic process;
Workflow
Gaps and Customization Options

15. 15
Gap Customization Effort Utility
G1 Std. feature
Data view
UI
Basic process
Workflow
0
1
3
3
8
10
2
3,5
3,5
10
G2 Std. feature
Data view
UI
Report
Basic process
Workflow
Add-on
0
1
3
5
3
8
13
10
1,5
4
12
4
10
25
G3 Std. feature
Basic process
Workflow
0
3
13
3
3,2
20
G4 UI
Basic process
Workflow
5
3
5
3,3
3,2
6
Effort and Utility

16. 1616
Gap Customization option Time period
G1 Std. feature 3
G2 Add-on 1
G3 Std. feature 3
G6 Basic process 1
G7 Basic Process 1
Gap Resolution Strategy
• Gaps are resolved as quickly as possible to realize gains
• Results are strongly affected by available development
resources
• Comparison
• Without knowing the roadmap – objective function
value decreases by 58%
• Greedy approach – objective function value
decreases by 87%

17. 1717
 The model helps to evaluate different
views on ERP customization
 Analysis of relationships among vendors
and customers
 Extensions
– Dynamic programming
– Capacity treatment
– Dependencies among gaps
– Roadmap uncertainty
Conclusion

18. 18
grabis@rtu.lv
http://iti.rtu.lv/vitk/lv/katedra/darbinieki/janis-grabis
Thank you!