How can I get off this spreadsheet merry-go-round?
  • Is it getting harder and harder to find empty Excel spreadsheets cells, as you run out of columns and rows?
  • Do your spreadsheet cell labels have more letters than the license plate on your car?
  • Do you find yourself waking up in the middle of the night in cold sweats because you can't scale to live streaming data and digital information using spreadsheets?
  • Are you feeling confused during business meetings because your spreadsheet results are not the same as others, even though you used the same data sources?
  • Feeling dizzy from trying to figure out why your spreadsheet keeps crashing, requiring a blueprint to find the bad cell calculations?

If you have any of the symptoms described above, you may be suffering from demand planner spreadsheet overload syndrome.

A healthy well-rounded diet of SAS data mining, event stream processing, predictive and prescriptive analytics, visual analytics with interactive reports and graphics, all aided by artificial intelligence and machine learning, is the best cure to help wean you off total reliance on Excel spreadsheets.

Why change now?   

Digitization is making the supply chain faster, more intelligent, connected and autonomous. Forward-thinking C-level decision makers are presenting opportunities for companies to embrace the digital ecosystem, becoming a strategic partner with their customers and consumers. This is aided by the convergence of three factors:

  1. Powerful, more affordable computing power.
  2. Abundant data.
  3. The availability of analytics and algorithms — especially with cloud-based open source analytics.

All of this is giving rise to an awareness and willingness to apply analytics to everything. Not just to strategic initiatives, but to day-to-day tasks. There are more and more business analysts embracing and using advanced analytics and machine learning, interpreting and applying the results, effectively becoming citizen data scientists.

Companies are now looking to invest in new analytics-driven forecasting and planning technology supported by artificial intelligence and machine learning that allows them to measure sales promotions and marketing events to mathematically calculate promotion lifts and determine if they generate revenue and profit - without complicated spreadsheets. Scenarios can be run in real time and the impact automatically reconciled up/down the business hierarchy using a web interface instead of spreadsheets.

As data collection and analytic tools, applications and solutions have become more affordable and powerful, they’ve become easier for companies to justify. For many companies, data management technology has advanced so quickly that the challenge now is not about getting the budget, but how to make practical use of all the data collected via live streaming of data from IoT devices. Spreadsheets are just not scalable enough to handle live streaming information and data.

Furthermore, data storage costs have declined significantly over the past decade making it more affordable to store the transactional data collected at increasingly granular levels across markets, channels, brands, products and key account configurations. Easy access web-based applications can be used to access the data, without requiring downloads or pivot tables to support spreadsheets. Faster in-memory cloud processing is making it possible to run “what if” simulations in seconds that previously had to run overnight.

SAS Intelligent Planning

SAS Intelligent Planning, powered by advanced analytics, simplifies data management, streamlines common planning processes, and supports sophisticated workflow creation and deployment.     

The SAS Intelligent Planning solution suite allows the user to:

  • Improve sell-through rates by accessing and modeling downstream data (POS/syndicated scanner data) to better anticipate and predict consumer demand. It uses consumption-based forecasting to shape shipments (transactions), also known as sell-in demand based on point of sale or sell-out demand. This results in more accurate shipment plans.
  • Improve planning process efficiency using artificial intelligence and machine learning capabilities to provide demand planners with a digital assistant to improve their forecast value add (FVA). The digital assistant guides demand planners up/down the business hierarchy to manage overrides by exception, thereby reducing errors and planning efforts.
  • Scalable planning capabilities provide an environment for data scientists and business users to collaborate more effectively through the integration of web-based dashboards, reports and planning workbooks in an integrated navigation environment. All with the look and feel of spreadsheets, with unlimited rows and columns, supported with robust data integration capabilities combined with advanced analytics and machine learning.
  • Ease of implementation, expansion and orchestration of analytic workflows. Cloud-ready, out-of-the-box modeling strategies with predefined models solve complex demand forecasting problems faster. Open API’s provide an extension of modeling capabilities with open source tools like Python and R. This allows companies to put Python and R into production, as well as provide scalability.

The goal is to provide an environment that encourages adoption and the ability to introduce an automated low touch demand planning process that helps manage demand planning by exceptions. Artificial intelligence and machine learning is key to this kind of low touch forecasting. No pivot tables and spreadsheet cell calculations required!

Learn more about SAS Intelligent Planning capabilities, and how this solution is helping companies improve demand forecasting and planning accuracy. 


About Author

Charlie Chase

Executive Industry Consultant/Trusted Advisor, SAS Retail/CPG Global Practice

Charles Chase is the executive industry consultant and trusted advisor for the SAS Retail/CPG global practice. He is the author of Next Generation Demand Management: People, Process, Analytics and Technology, author of Demand-Driven Forecasting: A Structured Approach to Forecasting, and co-author of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation, as well as over 50 articles in several business journals on demand forecasting and planning, supply chain management, and market response modeling. His latest book is Consumption-Based Forecasting and Planning: Predicting Changing Demand Patterns in the New Digital Economy. To learn more, please see his Author page.

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