We are entering into a period of rapid change for planning and the redefinition of supply chain planning architectures...Concurrent optimization, unstructured text mining, streaming data and sensing, and cognitive learning are converging. I believe that these forms of innovation will redefine supply chain planning into learning systems within five years. Using cognitive learning, our supply chain planning solutions will think and resolve exceptions as we sleep. Sensing through pattern recognition and sensors will shorten cycles for response. This will transform planning.
— Lora Cecere of Supply Chain Insights

Demand Sensing

Demand Forecasting is still stuck in 90s for many enterprise customers due to inherent limitations in prevalent SCM solutions. Basic time series based trend/seasonality smoothing models do not cut it any more in the world of fickle end-consumer demand and pressures to maintain service levels while reducing inventory and relieving working capital.  

Enterprises across several industries (Electronics, Consumer Products, Retail, Healthcare, Automotive, and others) are realizing the need for a low latency and high accuracy demand signal as they are challenged to manage exploding SKU portfolios with minimal inventory buffers and lean supply chain planning teams.

These teams are looking to migrate from backward looking time series based statistical forecasting inside-out focused processes to outside-in processes driven by the need to sense demand signal at downstream locations and shape it to produce forecastable demand patterns that meet revenue and profitability targets. After couple false starts, organizations quickly realize they cannot scale the challenge of generating a consistently accurate and low latency demand signal based on current human intensive data analytics processes and fragmented data.

Several factors conspire against the demand management team:

  • rampant SKU proliferation due to innovation centric business strategies
  • exploding data most of which gets generated outside the enterprise systems
  • inherent scalability challenges with in-house data scientist teams

GitaCloud orchestrates insights and accurate demand signals as a service with our:

  • cognitive learning platform that automatically generates demand models from the data
  • turnkey data / integration services to source / clean / blend data from downstream customer channels and other structured / unstructured sources outside the enterprise
  • seasoned demand model builders, and industry domain experts that partner with your business people to build / maintain prescriptive demand models as a service with a low payback period.
  • demand sensing service does not require a rip and replace of existing solution landscape and can nicely augment your current planning systems (such as SAP IBP, APO).
  • end to end Integrated Business Planning IBP cloud based solutions are also a great fit for growing mid-tier companies who are outgrowing their existing systems or excel based planning processes.