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Supply Chain Performance Declined In the Last Decade. The Question is Why?

Supply Chain Shaman

Yes, companies held more inventory (measured in days of inventory) in 2019 than at the start of the 2007 recession. Commercial and operating teams in manufacturing organizations greater than 5B$ in annual revenue were more aligned in 2007, at the beginning of the recession, than in 2020, the start of the pandemic. Alignment Barriers.

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How We Stubbed Our Toe in The Evolution of S&OP

Supply Chain Shaman

Forty interviews and two quantitative studies helped me build the model in my mind. Tight coupling of the supply chain forecast to the financial forecast will improve value. Industries carried on average 32 days more inventory in 2020 than in 2007. (I Why do we have 32 days more inventory by company in 2020 than in 2007?

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2020 Requires Big Wings and Feet

Supply Chain Shaman

The budget is not sufficient and is often a detrimental input for supply chain forecasting. Why Is the Financial Forecast Not a Good Proxy for a Supply Chain Forecast? There are many reasons why the budget cannot be used as a supply chain forecast. The supply chain forecast is a rolling forecast.

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Kimberly-Clark Makes Sense of Demand

E2open

So when store supplies for the company’s retail partners are out of synch with production forecasts it can have a very real impact on the $20.8 For years, Kimberly-Clark relied on historical data to guide forecasts, but that changed in 2007 when the company began a complete end-to-end overhaul of its supply chain and invested.

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Is A Customer-Centric Strategy the Same as Demand-Driven? Outside-In?

Supply Chain Shaman

A Demand-Driven Value Network as defined by AMR Research in 2007: A network that senses demand with minimal latency to drive a near real-time response to shape and translate demand. It is about much, much more than Vendor Managed Inventory (VMI ) or Collaborative Forecasting and Replenishment. Market-Driven Processes. Demand Sensing.

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Building Outside-In Processes

Supply Chain Shaman

2) Market-Driven Forecasting. Inside-out, traditional processes forecasting processes use statistical methods to predict the future based on order and shipment patterns. So, what are the market signals to model to forecast demand at the cadence of the market? How can you build market-driven forecasting processes?

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Segmenting Supply Chain using Portfolio Matrix (2x2 Matrix)

Supply Chain Opz

Case Studies. But the quick and easy way is to sort product by volume and use the level of forecast error (MAPE or MAD) to determine the level of variability then put everything into a quadrant. One of the most popular supply chain risk case study is " Ericssons serious sub-supplier accident ". A Case Study.