doctor of science - operations research by weoptit · doctor of science - operations research ......
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Optimization & ML by Weoptit
Tuomas LahtinenDep. CEO, Head of Development
Weoptit / Visma Consulting Finland
Doctor of Science - Operations Research
Expert in mathematical modeling, optimization and operational strategy
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Weoptit in a nutshell
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Optimization & Machine learning in Resource Management
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Planning and allocation to ensure that…
the right amount of
the right kinds of resources, are in the right place,
at the right time
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the right amount of
the right kinds of resources, are in the right place,
at the right time
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The rostering puzzle - combinatorial optimization
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Benefits of optimization in rostering
● Quality – Ergonomic rosters increase staff well being and productivity
● Efficiency – Reduced overtime compensations & use of flexible resources
● Automation – Less time spent, easier to react to changes
● Scenario analysis – “How many more we should hire to satisfy future demand”
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Prediction problems in resource management
Demand for products and services● Sales success
● Orders per customer
● …
Supply of resources● Resignations
● Sickleaves
● …
Benefits of using ML & statistical techniques● Higher accuracy – more firm basis to prepare for the future
● Automation – less time spen
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Examples
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Rostering for care homes
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Challenge
● Care business is very labor intensive -> thousands of rosters produced annually
for thousands of staff
● Difficult to create a roster that satisfies all the legal requirements, soft
constraints and is ergonomic and cost-effective
● Lot of time spent on planning by health care professionals
● The rosters could be better
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Efficient high-quality rosters produced in less than an hour
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Scheduling of tasks in car manufacturing
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Challenge
● Planning of electrical tests is time consuming and difficult
● ~500 tests with complicated interdependencies
● Very complex optimization problem: Minimum time to complete all tests not
known
● Weoptit hired to challenge an internal team
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Illustrative example
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Product Manual Internal WeoptitProduct 1 800,2s 347,4s 241,8
Product 2 858,8s 259,5 241,8s
Product 3 854,3s 259,5 239,8s
Product 4 58,3s 52,5s 32,1s
Product 5 815,8s 310,9s 194,8s
Product 6 815,8s 310,9s 149,3s
Product 7 57,3s 51,9s 32,1s
Product 8 835,5s 278,6s 162,3s
Product 9 835,5s 278,6s 145,5s
Product 10 807s 278,5s 145,5s
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Project win/loss prediction for a project based business
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Challenge
● Business participates in tender competitions to win projects
● Projects rather homogenous, lots of data in CRM system
● Predicting win/loss to improve resource planning & purchasing
● Estimating the relationship between price and winrate supports pricing
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75% accuracy in data with 50/50 wins/losses
Predictability to resourcing, purchasing of materialsOptimal pricing
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Conclusion
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Modern optimization & ML technologies create significant value in business development
Explore the full potential of your data with ML & optimization pilots
Together with Visma we can offer very strong full-scale solutions when a business case is found