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
Weoptit in a nutshell
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Optimization & Machine learning in Resource Management
Planning and allocation to ensure that…
the right amount of
the right kinds of resources, are in the right place,
at the right time
the right amount of
the right kinds of resources, are in the right place,
at the right time
The rostering puzzle - combinatorial optimization
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”
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
Examples
Rostering for care homes
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
Efficient high-quality rosters produced in less than an hour
Scheduling of tasks in car manufacturing
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
Illustrative example
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
Project win/loss prediction for a project based business
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
75% accuracy in data with 50/50 wins/losses
Predictability to resourcing, purchasing of materialsOptimal pricing
Conclusion
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