When it comes to demand forecasting, most companies have way too many forecasting mechanisms in play across their organization. Each segment of the business ends up siloed from the others, relying on its own data and analysis, which impacts both efficiency and effectiveness.
Most organizations do a poor job forecasting, with just one in five coming within 5% of forecasts. This statistic is staggering, and it implies that although many organizations understand the value of forecasting, the majority of them are doing it inaccurately.
George Santayana, the Spanish-American philosopher, famously mused, "Those who cannot remember the past are condemned to repeat it”, but Mr. Santayana didn’t foresee the Era of Big Data.
There are six powerful factors driving the COO’s rising interest in unified demand forecasting. This is partially driven by companies shift from siloed, stand-alone approaches to unified, enterprise-wide operations. Market leaders are paying attention to these six additional drivers:
Leveraging Artificial Intelligence to Upgrade Demand Forecasting
on July 5, 2018
| By Sivakumar Lakshmanan & Arash Sangari
|
Whitepaper,
Demand Forecasting,
Demand Analytics
For consumer packaged goods companies serving retailers, forecasting the true demand for perishable products is the key to maximizing revenue while reducing the costs associated with salvaged goods. This can translate into millions of dollars of savings and additional revenue annually for some companies.
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