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Aligning Supply Chain Metrics to Improve Value

Supply Chain Shaman

In follow-up qualitative interviews, one of the largest issues with organizational alignment was metric definition and a clear definition of supply chain excellence. To manage continuous improvement, companies need a clear definition of excellence and organizational alignment to that goal. What Drives Value?

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Probabilistic Forecasting: The Future of Supply Chain Planning

ToolsGroup

The Science of Demand Planning: Improving Forecast Accuracy Consider this example: A traditional forecasting system analyzing tire sales might observe 12 units sold annually and determine an average monthly demand of one tire. However, this approach ignores real purchasing behavior, such as customers buying complete sets of four tires.

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What Today’s Modern BI Systems Do That Legacy Reporting Tools Can’t

Silvon Software

Analytics and business intelligence (BI) are no longer optionaltheyre essential. Thats why modern BI systems are quickly becoming the go-to solution for data-driven enterprises. They integrate, align, and activate data across the business to drive better, faster decisions unlike legacy reporting tools that can’t.

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Uh-Oh! Insights On How P&G Failed And What This Means For You

Supply Chain Shaman

Supply chain excellence is easier to say than to explain. The supply chain is a complex non-linear system. At each company, there is a relationship between the metrics of growth, margin, inventory, customer service, and asset strategy. Understanding this relationship requires modeling. The reason? Was this by design?

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Organizational Alignment: Overlooked, but So Important.

Supply Chain Shaman

” Followed by “How are you organized, and what defines functional excellence? And, how do you tie functional excellence to corporate value?” Companies became less clear on the definition of supply chain excellence and how to implement decision support technologies. Functional Metrics.

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Pandemic Lessons For Supply Chain Leaders

Supply Chain Shaman

The systems–based on shipment and order data–were out of step with the market. With fixed models and hard-wired data feeds, teams could not adjust the planning systems to use consumption data or market indicators. Next Steps: Start to model demand based on market data to align the organization on baseline demand.

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Machine Learning in Demand Planning: How to Boost Forecasting

ToolsGroup

Modern ML-powered demand planning systems simplify this by analyzing multiple factors simultaneously – product trends, competitive pricing, market conditions, and social media activity – to uncover patterns in promotional events and quantify their precise effect on sales.