Methods and apparatus to forecast new product launch sourcing
Methods and apparatus are disclosed to forecast new product launch sourcing. An example method includes identifying shared attributes between the new product and a plurality of existing products in the target market, calculating theoretical co-penetration values between the attributes shared between the new product and at least one of the plurality of existing products, calculating actual co-penetration values between the attributes shared between the new product and at least one of the plurality of existing products, calculating an attribute distance value between corresponding ones of the theoretical and actual co-penetration values, and calculating a percent volume of the new product expected to be sourced from one of the plurality of existing products based on the attribute distance value.
1. An apparatus to reduce volume calculation error, the apparatus comprising:
at least one processor;
a product category comparator, implemented by the at least one processor to execute instructions thereon, to:
access, via a network, first attributes associated with a new product in a new product attribute database;
access, via the network, second attributes from existing products in a product reference library database, the existing products identified based on attribute levels associated with the first attributes; and
identify attributes shared between the new product and the existing products in a target market based on a degree of similarly between the first attributes and the second attributes;
a Dirichlet modeling engine, implemented by the at least one processor to execute instructions thereon, to generate a theoretical co-penetration matrix, the theoretical co-penetration matrix including theoretical co-penetration values between the attributes shared between the new product and at least one of the existing products;
an empirical co-penetration engine, implemented by the at least one processor to execute instructions thereon, to:
access, via the network, product market activity data from a panelist database including purchase data from merchants; and
generate an empirical co-penetration matrix based on the product market activity data, the empirical co-penetration matrix including actual co-penetration values between the attributes shared between the new product and at least one of the existing products;
a distance calculator, implemented by the at least one processor to execute instructions thereon, to reduce a model-based fair share sourcing error of the Dirichlet modeling engine by calculating attribute distance values based on a ratio of corresponding pairs of the theoretical and actual co-penetration values of the theoretical and empirical co-matrices, respectively, to generate a distance matrix; and
a volume sourcing calculator, implemented by the at least one processor to execute instructions thereon, to:
calculate a percent volume of the new product expected to be sourced from one of the existing products based on the attribute distance values of the distance matrix,
generate a forecast corresponding to sourcing the new product based on the percent volume, and
cause sourcing of the new product based on the forecast.
2. The apparatus as defined in claim 1 , further including a substitutability engine, implemented by the at least one processor to execute instructions thereon, to calculate a substitutability index between the new product and the one of the existing products based on the attribute distance values.
3. The apparatus as defined in claim 2 , wherein the substitutability engine is to calculate the substitutability index based on a degree of polarization associated with at least one of the attributes shared between the new product and existing products in the target market.
4. The apparatus as defined in claim 3 , wherein the substitutability engine is to weight the degree of polarization by the attribute distance values.
5. The apparatus as defined in claim 3 , wherein the degree of polarization includes an inverse Dirichlet parameter associated with the theoretical co-penetration values.
6. The apparatus as defined in claim 1 , wherein the Dirichlet modeling engine is to estimate a Dirichlet model associated with attributes of the existing products in the target market.
7. The apparatus as defined in claim 1 , wherein the attributes shared between the new product and existing products in the target market are associated with a product category.
8. The apparatus as defined in claim 1 , wherein the attributes shared between the new product and existing products in the target market include at least one of a brand, a product type, a size, a feature or a flavor.
9. The apparatus as defined in claim 1 , wherein the volume sourcing calculator is to output data pertaining to cannibalization effects between the new product and the at least one of the existing products.
10. The apparatus as defined in claim 9 , wherein the volume sourcing calculator is to output data pertaining to alternative markets based on the cannibalization effects.
11. The apparatus as defined in claim 2 , wherein the volume sourcing calculator is to cause substitution of at least one of the new product or the one of the existing products based on the substitutability index.
12. A tangible machine-readable storage device or storage disk comprising instructions that, when executed, cause a processor to, at least:
access, via a network, first attributes associated with a new product in a new product attribute database;
access, via the network, second attributes from existing products in a product reference library database, the existing products identified based on attribute levels associated with the first attributes;
identify attributes shared between the new product and the existing products in a target market based on a degree of similarity between the first attributes and the second attributes;
generate a theoretical co-penetration matrix including theoretical co-penetration values between the attributes shared between the new product and at least one of the existing products;
access, via the network, product market activity data from a panelist database including purchase data from merchants;
generate an empirical co-penetration matrix based on the product market activity data, the empirical co-penetration matrix including actual co-penetration values between the attributes shared between the new product and at least one of the existing products;
reduce a model-based fair share sourcing error by calculating attribute distance values based on a ratio of corresponding pairs of the theoretical and actual co-penetration values of the theoretical and empirical co-matrices, respectively, to generate a distance matrix;
calculate a percent volume of the new product expected to be sourced from one of the existing products based on the attribute distance values of the distance matrix;
generate a forecast corresponding to sourcing the new product based on the percent volume; and
cause sourcing of the new product based on the forecast.
13. The machine-readable storage device or storage device as defined in claim 12 , wherein the instructions, when executed, cause the processor to calculate a substitutability index between the new product and the one of the existing products based on the attribute distance values.
14. The machine-readable storage device or storage device as defined in claim 13 , wherein the instructions, when executed, cause the processor to base the substitutability index on a degree of polarization associated with at least one of the attributes shared between the new product and existing products in the target market.
15. The machine-readable storage device or storage device as defined in claim 14 , wherein the instructions, when executed, cause the processor to weight the degree of polarization by the attribute distance values.