Why do manufacturing companies need cost modeling and cost forecasting?
The challenge: Small and medium sized businesses need to be able to forecast a wide range of quantities: SKUs, commodity costs, product costs, sales volumes and revenues and so on. Forecasting is inherently a difficult process. There are challenges involved in developing forecasts from raw data and then when it comes to integrating multiple forecasts into a meaningful takeaway to help with business decisions, it may require additional statistical modeling skills. Typically small and medium sized manufacturing businesses neither have the IT infrastructure nor the expertise to be able to perform these complex but value adding business tasks.
A specific problem: Many SMBs purchase commodities which are then used to produce more sophisticated finished goods. These finished manufactured products have a life span of several years. When underlying commodity prices fluctuate, the overall produced cost must remain fixed because it is being supplied to customers under a long term contract. Monitoring commodity prices and determining how these price fluctuations would impact the aggregate costs, such as the finished product cost, and hence profits to the business then becomes an important activity. (The aggregate can also be related to shipping the goods to a final destination in which case, the problem becomes a transportation cost modeling issue).
The App: It would help for companies to have a dashboard that would allow them to continually track key commodities and measures. If the same app would enable companies to forecast select commodities and integrate these forecasts into an aggregate cost, that would be even more valuable. So any cloud based app should perform three essentially productivity related tasks:
1. Track commodity: The app must enable the business to build a database of commodities that need to regularly tracked. These could be specific to a given business or to a given set of products. These measures may be propreitary and may need to be purchased from third-party sources or they could be publicly available such as producer price index or consumer price index.
2. Forecast commodity: Periodically the SMB may need to generate forecasts for specific commodities. Forecasting may be regular or one time. The app could show confidence bands around the forecast as seen below.
3. Analyze aggregate costs: The ability to combine forecasted commodity costs into an aggregated cost or manufactured product cost will allow businesses to run what-if scenarios. What if one of the key commodity prices was forecasted to be 20% higher? Would this seriously cut into the profits? Can we recover this loss over the lifespan of the product? A company can request via the app a custom built model which would combine the forecasts into an aggregate cost as seen below.
The advantage of this sort of app is the on-demand nature of analytics. Companies can only request new information as needed and do not need expensive investments in software or personnel. Once a commodity database is built for the company and a small initial effort is spent on creating the first model, future analyses and forecasts come at a fraction of that price.
Get in touch with us today if you are small or medium business that is looking for affordable solutions for your analytics!