Authors:
Eren Esgin
1
;
2
;
Volkan Ozay
1
and
Gorkem Ozkan
1
Affiliations:
1
AI Research, MBIS R&D Center, Istanbul, Turkey
;
2
Informatics Institute, Middle East Technical University, Ankara, Turkey
Keyword(s):
Business Planning, Integer Programming, Network Optimization, Profit Maximization, SAP, Variance Analysis, What-if Scenario Evaluation.
Abstract:
What if we told you that “you already have 27% of net profit trapped in your misleading business”? As common de facto state in production planning, subjective human judgments play a significant role on demand point:plant assignments at product replenishment and this is mostly driven by myopic transportation minimization paradigm, disregarding production and profitability determinants. In this paper, we propose an integer programming characterized Network Optimization solution to find global optimal assignments that maximize the profitability in terms of contribution margin or net profit by taking sales, transportation and production planning perspectives into account and concerning potential capacity constraints. According to the experimental results obtained at a real-life implementation in cement industry, Network Optimization solution increases contribution margin by an average value of 6.33% and net profit by 26.3%. Moreover, proposed solution architecture promises a seamless net
work optimization experience over a large canvas that wholistically integrates SAP system, optimization logic and Microsoft Power BI tiers. As a result, our clients can concentrate on more value adding operations such as variance analysis and what-if scenario evaluation rather than manual, time consuming and error-prone data preparation.
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