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How BT is Building a Connected Supply Chain Amidst Ongoing Transformation

AIMMS

We spoke with David Mackenzie , Transformation & Inventory Director at BT, to learn more about this shift and the role AIMMS has played in enabling it. AIMMS was on that list, as it could help us with some of the decision-making to support this change. How is your team currently using AIMMS? – Tweet this.

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7 Pitfalls of Stalled IBP Initiatives and How to Overcome Them

AIMMS

Other reviews (prepared for decision-making versus attending for receiving information) are shared by the owners of each strategy element/pillar. For instance, COOs own the supply strategy, marketing the portfolio strategy, and so forth. How can you account for new products that don’t exist in master data?

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Funding Your Way to a Decarbonized Supply Chain

AIMMS

For that, long-term supply chain planning and budgeting needs data to back every decision. See how a manufacturing giant revamped their demand planning with AIMMS. See how Tata iQ built an idle freight optimization model with AIMMS and integrated it with a 3D simulation model for loading.

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The next frontier: supply chain data architecture for your needs

AIMMS

Data overload and quality issues are common problems faced by all organizations. This makes it really difficult to start getting value out of data with analytics. That’s why 60% of the time spent in analytical supply chain projects is spent collecting data. Data architecture to fit a multitude of your needs.

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Easily Build an Optimization App and Empower Your Data

Speaker: Gertjan de Lange

If the last few years have illustrated one thing, it’s that modeling techniques, forecasting strategies, and data optimization are imperative for solving complex business problems and weathering uncertainty. Experience how efficient you can be when you fit your model with actionable data. Watch this exclusive demo today!

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Prescriptive Analytics is Going Mainstream. Is your Organization Ready?

AIMMS

AIMMS has been in the market of prescriptive analytics (otherwise known as mathematical optimization) since 1989. At AIMMS, we recognized this trend several years ago. This ultimately led us to launch AIMMS PRO and AIMMS SC Navigator later on. . More investment in data science . We turned 30 this year.

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The next frontier: supply chain data architecture for your needs, not to feed the needs of numerous supply chain tech vendors

AIMMS

Data overload and quality issues are common problems faced by all organizations. This makes it really difficult to start getting value out of data with analytics. That’s why 60% of the time spent in analytical supply chain projects is spent collecting data. Data architecture to fit a multitude of your needs.