Optimity is a Swedish-Australian developer of supply chain planning and optimization software. The company develops specialist tools to maximize supply chain performance and profitability. Since 2020, Optimity has added significant machine learning capabilities to its software – an initiative that has quickly won approval from customers.
“We use machine learning to feed our supply chain optimization models with more relevant and detailed information. Better input means higher quality plans and decision support for our customers. The feedback is that forecasts have become more precise and that the support for downstream planning is getting better”, says Mattias Ahlström, Product Manager at Optimity and a specialist in optimization of complex supply networks.
Machine learning – a natural fit
Since the outset, advanced mathematical modelling has been at the core of Optimity’s supply chain products, so incorporating machine learning into the software was a natural progression.
“Our proprietary machine learning solution has been a real boost for our customers as we can better adapt to their needs. Companies can now incorporate more external variables into their forecasts, e.g. raw material spot prices, weather information and marketing campaigns”, explains Pontus Stefansson, Product Manager at Optimity and responsible for machine learning and forecasting.
Until recently, Optimity used embedded third-party machine learning technology in their products. However, the company decided to develop its own machine learning solution to meet its customers’ requirements better.
“Customers have come to expect this type of functionality. Demand has generally become more volatile and campaign-driven, with large fluctuations as a result. The algorithms and models are in place. The challenge now is to identify, consolidate and harmonize relevant data of sufficient quality”, says Pontus and emphasizes that there’s also a need for more education about machine learning.
Better planning, better decisions
“We want to help our customers make better planning decisions. That’s why the ability to identify correlations with external factors is so important for our offer. Once we understand this, machine learning becomes a natural part of the planning process”, says Mattias.
Mattias and Pontus agree that accurate, quality-assured data in the correct format is fundamental to a good outcome and acknowledge that this can be a challenge.
Published in Supply Chain Effect, January 20, 2022 (original article in Swedish)
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