Automation, security, efficiency, and visibility are key factors for Banks and Managed Service provider alike when striving towards an optimized Cash Processing operation. FOUR (4) recommendations how you can reach this!
Cash processing is about counting, sorting, packing and storing cash (banknotes and coins), and other valuables in secured vaults. Automation is key to drive a more secure, efficient and traceable operation where security breaches, such as loss of cash, reduced operator cost and capacity planning are on the top of the agenda. We give you four (4) recommendations to consider when optimizing your cash processing operation.
True Artificial Intelligence to drive cost reductions in Cash up to 25% and to minimize human intervention during and post-COVID era
Artificial Intelligence has long established itself as a pivotal technology for the future, driving efficiency, cost-savings, and minimizing human intervention. However, truly 100% Artificial Intelligence automation, supporting complete self-learning algorithms and bare minimum human intervention, are hard to find in the Cash Supply Chain. In the COVID era where human contact should be downscaled to a bare minimum this becomes even more important.
International publications of the planfocus® teams
Chosen international publications:
- Springer-Book on mathematical Optimization. Integer Optimization by Local Search – A Domain-Independent Approach. J.P. Walser. Lecture Notes in Artificial Intelligence, LNAI-1637, Springer Verlag, August 1999.
- Capacity planning. An Integer Local Search Method with Application to Capacitated Production Planning, J.P. Walser, R. Iyer and N. Venkatasubramanyan. In Proceedings of the 15th National Conference on Artificial Intelligence, AAAI-98, Madison, WI, USA 1998.
- Integer Optimization. Solving Linear Pseudo-Boolean Constraint Problems with Local Search. In Proceedings of the 14th National Conference on Artificial Intelligence, AAAI97, Providence, RI, USA 1997.
- Planning. Integer optimization models of AI planning problems. H Kautz, J.P. Walser, The Knowledge Engineering Review, 15:101–117, 2000. Cambridge University Press.
- Scheduling. Solving hierarchical constraints over finite domains. Annals of Mathematics and Artificial Intelligence, M.Henz, L.Y. Fong, L.S. Chong, S.X. Ping, J.P. Walser, and R.H.C. Yap. March 2004, vol. 40, no. 3-4,pp. 283-301(19).
- Probabilistic estimation. Tuning local search for satisfiability testing.A.J. Parkes and J.P. Walser. In Proceedings Thirteenth National Conference on Artificial Intelligence (AAAI-96), pages 356–362, 1996.
Patents on Methods of mathematical Optimization
- Method and apparatus for Optimizing Constraint Models, J.P. Walser, US Pat. 6,031,984. Integer Local Search Basis-Solver für verschiedenartige kombinatorische Optimierungsprobleme.
- Method for Optimizing Price Schedules, J.P. Walser, V. Kalyan, J. Crawford, S. Palamarthy, M. Dalal, U.S. Patent Application, filed, April 2001. Assigned, i2 Technologies. Signifikante Ertragssteigerung durch optimierte Preissetzung im Produktabverkauf.
- System and Method for Collaborative Batch Aggregation and Scheduling, J.P.Walser, D. Joslin, C. Schmidt. U.S. Patent No. 6,560,501. Granted May 2003. Assigned, i2 Technologies. Optimierte Ablaufplanung in der Prozess- Industrie.
- Computer Implemented Scheduling System and Process using Abstract Local Search Technique, J. Crawford, M. Dalal, J.P. Walser. U.S. Patent No. 6,456,996. Granted September 2002. Assigned, i2 Technologies. Optimierte Ablaufplanung in der diskreten Fertigungsindustrie.
- Method for Generating an Optimized Pricing Plan, J.P. Walser, L. Oren, U.S. Patent Application, filed, November 2002. Assigned, i2 Technologies. Signifikante Ertragsteigerung durch konsistente Preis-Planung von Produkt-Kategorien.