Up until now demand driven inventory systems have proven barely adequate however, these models are all based on a push methodology and did not accurately respond to rapidly changing customer demand and individual demographics. For the most part it has been a mostly reactive process in inventory planning.
Surely anomalies like the Oprah factor can be planned for but that is a spike that may not reflect the demographics in where the spikes may take place. The Oprah factor refers to when she showcases a new book on her show demand for that title usually spikes and then curtails off. The upward shift in demand is needed to sell the books but after the anomaly ends demand usually continues towards their normal levels. For companies such as Indigo, Amazon, Borders, Barnes and Noble where actual books are sold through a brick a mortar storefront actual demand by store is actually calculated on usually a time-series analysis from past performance – not bad if that was all that was available.
Now a new type of inventory calculation has come to the forefront. Being in the industry for over 18 years I have always questioned why was demand calculated from a push model as opposed to reflecting actual consumer demand based from a pull model, to feed the inventory system. Then, it was more of a system limitation than it is now.
Flowcasting has been around for a while but only recently it has started to gain traction. This is also due to the changing technologies, easier networking connections, integration between systems now made easier, and widespread internet adoption
What is flowcasting you ask? Flowcasting is a “sensing of demand” from the store level upwards. The flow of data is fed up through the system which gives real-time inventory analysis to the DC and even suppliers of actual changing demand from individual stores. Traditionally the information rarely responded to the supply chain execution systems and barely accommodated demand from individual skus, locations, sizes and colors. With a system that can respond to actual changing customer demand is flowed through from the bottom up rather than top down. Flowcasting differs from traditional DRP(Distribution Resource Planning) in three ways: based on actual store level demand, scalable to handle the large flowback volume from the store level and is integrated into Red Prairie supply chain executions systems.
While Manhattan has the SCOPE platform that optimizes inventory and can be redeployed to reallocate from decision points and optimization through the chain for each point by combining business process and functions for inventory throughout the supply chain. Although the Manhattan supply chain execution suite can perform this function it takes a slightly different approach. The SCOPE equivalent to flowcasting is the software’s ability to break up the business processes into multi-echelons which allows a flowback of data and inventory through the system following the upward pull model, the actual demand sensing capability. This approach also readjusts each level of inventory backwards even the goods that are in transit are included in the new calculations.
The advantage to flowcasting is the breadth of information that is provided from the shelf level up and manufacturers have visibility to this through the collaboration chain and adjustments can therefore be made and then redistributed throughout the chain from raw materials down to manufacturer to DC to Store and all points recalculated from the store data that will also refresh the next day. The consequences of forecasting all levels is that information is flowed up through successive levels and is calculated as the sum of the levels below it. This information allows distribution plans and production schedules to be accordingly adjusted if changes occur and can also adjust to the DSD components if store allocation demand should shift and can influence cross dock consolidations or deconsolidations with the information available.
Overall a different way of doing things but about it is time that this technology now exists to make demand planning throughout the supply chain a little more predictable. The summary of these two approaches while capable of mostly the same things are very different especially in the marketing of the solutions. If your company is looking for a software in demand planning, retail or any other business enterprise software give us a call. Our Tru-Eval method will align the best software for your companies needs, lower project lifecycle times and increase ROI for your company’s technology investment. With the explosion of social networking influencing consumer demand it is now more important than ever for companies to get their inventory in order to prevent stock outs, maximize margins, reduce capital expenditure by optimizing the correct levels of inventory and drive customer value. Two approaches to demand sensing technology from two leading vendors, which one do you like?
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