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Data Management, Part 4

By Tom Weber

November 11, 2021

The following is my final recap of our four-part data management series, this one covering materials. The entire series was hosted by your AICC education team, which I moderated. I thought you might find this recap once again both relevant and compelling toward finding a better way in 2021 and beyond. I encourage you to obtain all four of the recorded versions. The recordings are available through your outstanding AICC education contacts, Chelsea May and Taryn Pyle.

The goal of this fourth and final session was to identify uses of manufacturing data to precisely forecast, sense demand, and ultimately balance “inventories to demand” in near-real time.

The following are the top five key webinar takeaways:

  1. Demand Planning – How to drive this critical aspect.
    • Use your software to sense demand signals that indicate marketplace changes faster.
    • Translate demand signals, such as seasonality, costs, promotions, events, and merchandising, into a more effective market-driven response.
    • Take a visual approach to analyzing demand planning to unearth patterns and insights regarding sales, shipments, pricing, promotions, and operational performance.
  2. Generate accurate forecasts at every level, down to specific product line SKUs.
    • Foster collaboration among sales, marketing, finance, and operations.
    • Utilize an interactive dashboard that can monitor, track, and report on forecasting performance.
    • Use statistically driven consensus forecasting to ensure maximum accuracy.
  3. Analyze huge quantities of data using algorithms to compare and adjust forecasting demands.
    • Get near real-time insight into supply-and-demand dynamics.
    • Calculate optimal inventory policies to ensure continual optimization.
    • Manage overall inventory costs while still driving product(s) where it is need most.
  4. Use time-weighted forecasting to build models that reflect your business realities.
    • Use time series and machine data to build models that consider intermittent demand, new product needs, and expiring SKUs.
    • Utilize data inputs from both inside and outside the organization to round out accuracy.
    • Predictive modeling and what-if analysis can reveal how different variables may impact the supply chain material flow.
  5. Use your data to create multichannel inventory optimization.
    • Generate unbiased consensus forecasts that work in conjunction with all data acquisition processes.
    • Avoid under- and overstocking at all costs to improve planning outcomes.
    • Lastly, collaborate and share customer and supply chain intelligence freely.

Don’t expect your materials planner(s) to deliver top performance without the aid of advanced analytics. For instance, with artificial intelligence and analytics doing the heavy data lifting, a company might analyze as many as 1,000 variables and 10,000 constraints to help manufacturers figure out what to buy, what to make, and how they should make it to yield the most profit in each period. Also worth noting: The digital universe is doubling in size every two years. By the end of this year the data we create and copy annually will reach 44 trillion gigabytes, according to International Data Corp.

This session four materials recap was intended to create the thought that perhaps there is a better, faster, and smarter way to do tomorrow, through the utilization of software data, what we have been wrestling with for quite some time. If I have somehow piqued your interest, please request the complete final session recording from your AICC education team or me. It might well trigger one novel useful thought for you and your team to utilize yet in 2021!

PortraitTom Weber is president of WeberSource LLC and is AICC’s folding carton and rigid box technical advisor. Contact Tom directly at