As product lifecycles shrink and new generations of products enter the market more quickly, achieving bottom-line results requires the estimation of overall demand and product mix as accurately as possible. Products with manufacturing lead times of weeks, months or even quarters are all the more critical to forecast correctly to ensure optimum levels of inventory holdings and eliminating stock outs.
Market Equations developed a Demand Forecasting and Inventory Management predictive model for an E-Commerce fashion retailer in the United Kingdom.
Market Equations developed a Demand Forecasting and Inventory Management predictive model for a large Textile Manufacturer in the United Kingdom.
The above case studies helps you understand how our Predictive Analytics and Modeling solutions helps organizations manage demand through accurate forecasting, placing optimum order quantities based on forecasted demand and efficiently manage logistics by accurately estimating product lead times.
Addressing Key Challenges:
- Frequent Stock Outs - Demand-Supply gap
- Large Inventory and Stock levels - Unsold, Out fashioned etc
- Shipment delays - Unmanageable Lead times
Four fundamental sources of noise cause difficulty in determining true market demand: current data, such as orders and inventory; market assessment, such as intelligence and consensus on how appealing products and promotions (and competing products) might be to the market; market objectives, the goals the company has for its products, such as unit sales, average price, market segment share, and product leadership; and, strategic plans, such as the decisions about which products and stock keeping units (SKUs) to sell, how to price them, and how to take advantage of manufacturing efficiencies. The question is how do we account for these factors in advance to systematically do the best possible job of forecasting and planning.
Market Equations can help you balance your demand and supply. Hold Optimum levels and never refuse your customers.
Two problems with managing forecasts: the data being passed down from group to group lacks the required credibility. Adjusting numbers to future judgment has its own challenges.
Data sets in isolation are meaningless and unless they are compared to past expectations. Instead of transmitting more data from group to group, companies must focus on transmitting knowledge from and intelligence from sources that have it to sources ho need it to make those critical go to market decisions. This is being done quite rigorously through the application of technologies in most well managed companies but it has its own failures. Key employee turnover, limited bandwidth of individual and identifying the key resources.
The source of unreliable forecasting starts with biased signals from the consumers themselves sometimes, excitement leading to over supply or even deflated to stock out.
One the Geo forecasts are published the company the planning group publishes the official forecasts that guide the supply network. Sales Growth, Segment market share, product mix by number of attributes, various inventory data etc are used to produce the official forecasts. The inclusion of these factors does reduce the volatility of the geo data and improves accuracy. However, research shows that overshooting or undershooting is not a very rare phenomenon even in this case.
There are other issues as well such as estimating the demand for not just the entire product family but also including the SKU's within those products. However, some companies have gone a long way in forecasting the family of products and some key products within the family that have the most impact on the financial performance. Another major issue is, realistically markets are better suited for the short term. Sometimes a year ahead could seem to far stretched.
Use Demand Forecasting and increase bottom line. Talk to Us