CLIAMS:1. A computer implemented method for space planning and optimization of one or more departments corresponding to a plurality of stores, the method comprising:
obtaining key demographics and key parameters for each of the one or more departments, wherein the key demographics and the key parameters are parameters that are associated with performance of a department from the one or more departments;
clustering, by a processor (202), the plurality of stores into one or more department-level clusters based on the key demographics and the key parameters;
ranking, by the processor (202), the departments, for each department-level cluster, to obtain a set of optimal departments for the space planning and optimization, wherein the optimal departments are top predetermined number of departments in each department-level cluster;
ranking, by the processor (202), the stores, for each department-level cluster, to obtain a set of optimal stores for the space planning and optimization, wherein the optimal stores are top predetermined number of stores in each department-level cluster; and
generating, by the processor (202), space planning recommendations, for each of the set of optimal stores, by processing information associated with the set of optimal departments and the set of optimal stores using nonlinear space optimization mechanism, wherein the nonlinear space optimization mechanism utilizes one or more optimization parameters.
2. The method as claimed in claim 1, wherein the clustering further comprises computing, by the processor (202), space elasticity for each of the one or more departments, for each department-level cluster, based at least on the key demographics, the key parameters, current space allocated to the department and yield of the department.
3. The method as claimed in claim 1, wherein the obtaining further comprises:
receiving input data for each of the plurality of stores and the corresponding one or more departments;
processing, based on one or more processing rules, the input data, by the processor (202), for each of the plurality of stores, parameter data for the store, and performance data and demographics data for each of the one or more departments associated with the stores; and
identifying, by the processor (202), the key demographics and the key parameters, from the demographics data and the parameter data, respectively, based on correlation values and factor analysis results of the demographics data and the parameter data with respect to the performance data.
4. The method as claimed in claim 3, wherein the input data comprises pre-processed performance data, pre-processed demographics data, and pre-processed parameter data.
5. The method as claimed in claim 4, wherein the pre-processed performance data is indicative of performance of each store to be evaluated and each department within the stores, and wherein the pre-processed performance data includes values indicative of sales, volumes, margins, and footage, transactions at a per week per department per store level.
6. The method as claimed in claim 4, wherein the pre-processed demographics data, for each of the plurality of stores, is indicative of statistical data relating to population within a predetermined radius of distance around the store, and wherein the pre-processed demographics data includes store ID of the store, sales of the store, total population around the store, population type, age brackets, median age, total households around the store, average household size, annual household income, average household income, and a socioeconomic score.
7. The method as claimed in claim 4, wherein the pre-processed parameter data indicates characteristics and statistical data of each of the plurality of stores, and wherein the pre-processed parameter data includes a store size, store transactions, competitor stores, a store location, presence of educational institutions, and same retailer stores nearby.
8. The method as claimed in claim 3, wherein the key demographics include one or more of a store ID of the store in a particular zipcode, sales of the store, total population around the store, population type, age brackets, median age, total households around the store, average household size, annual household income, average household income, and a socioeconomic score.
9. The method as claimed in claim 3, wherein the key parameters include one or more of a store location type score, the nearby same store score, the nearby educational institutes score, and the competitor scores.
10. The method as claimed in claim 1, wherein the method further comprises determining, by the processor (202), a set of principal components for each of the one or more departments based on the key demographics and the key parameters.
11. The method as claimed in claim 1, wherein the clustering further comprises clustering, by the processor (202), the plurality of stores into one or more store-level clusters based on one or more store specific key demographics and key parameters.
12. The method as claimed in claim 1, wherein the optimization parameters include demographics, space elasticity, space constraints, i.e., maximum and minimum allowed footage, store and department yield, inventory, department interdependencies, competitors, labor costs, and consumer purchase behavior patterns.
13. The method as claimed in claim 1, wherein the departments in a store and the stores are ranked using a rapid linearization algorithm.
14. The method as claimed in claim 1, wherein the method further comprises generating, by the processor (202), a list of user-customization scenarios based on the key parameters.
15. A space planning and optimization system (102) comprising:
a processor (202);
an analysis module (212) coupled to the processor (202) to obtain key demographics and key parameters for each of one or more departments corresponding to a plurality of stores, wherein the key demographics and the key parameters are parameters that are associated with performance of a department from among the one or more departments;
a clustering module (214) coupled to the processor (202) to cluster the plurality of stores into one or more department-level clusters based on the key demographics and the key parameters; and
a space optimization module (108) coupled to the processor (202) to,
rank the departments, for each department-level cluster, to obtain a set of optimal departments for space planning and optimization, wherein the optimal departments are top predetermined number of departments in each department-level cluster;
rank the stores, for each department-level cluster, to obtain a set of optimal stores for space planning and optimization, wherein the optimal stores are top predetermined number of stores in each department-level cluster; and
generate space planning recommendations, for each of the set of optimal stores, by processing information associated with the set of optimal departments and the set of optimal stores using a nonlinear space optimization mechanism, wherein the nonlinear space optimization mechanism utilizes one or more optimization parameters.
16. The space planning and optimization system (102) as claimed in claim 15, wherein the clustering module (214) further computes space elasticity for each of the one or more departments, for each department-level cluster, based at least on the key demographics, the key parameters, current space allocated to the department and yield of the department.
17. The space planning and optimization system (102) as claimed in claim 15, wherein the analysis module (212) further,
receives input data for each of the plurality of stores and the corresponding one or more departments;
processes, based on one or more processing rules, the input data to obtain, for each of the plurality of stores, parameter data for the store, and performance data and demographics data for each of the one or more departments associated with the store; and
identifies the key demographics and the key parameters, from the demographics data and the parameter data, respectively, based on correlation values and factor analysis of the demographics data and the parameter data with respect to the performance data.
18. The space planning and optimization system (102) as claimed in claim 17, wherein the input data comprises pre-processed performance data, pre-processed demographics data, and pre-processed parameter data.
19. The space planning and optimization system (102) as claimed in claim 18, wherein the pre-processed performance data is indicative of performance of each store to be evaluated and each department within the stores, and wherein the pre-processed performance data includes values indicative of sales, volumes, margins, footage, transactions at a per week per department per store level.
20. The space planning and optimization system (102) as claimed in claim 18, wherein the pre-processed demographics data, for each of the plurality of stores, is indicative of statistical data relating to population within a predetermined radius of distance around the store, and wherein the pre-processed demographics data includes store ID of the store, sales of the store, total population around the store, population type, age brackets, median age, total households around the store, average household size, annual household income, average household income, and a socioeconomic score.
21. The space planning and optimization system (102) as claimed in claim 18, wherein the pre-processed parameter data indicates characteristics and statistical data of each of the plurality of stores, and wherein the pre-processed parameter data includes a store size, store transactions, competitor stores, a store location, presence of educational institutions, and same retailer stores nearby.
22. The space planning and optimization system (102) as claimed in claim 15, wherein the analysis module (212) further determines a set of principal components for each of the one or more departments based on the key demographics and the key parameters.
23. The space planning and optimization system (102) as claimed in claim 15, wherein the clustering module (214) further clusters the plurality of stores into one or more store-level clusters based on one or more store specific key demographics and key parameters.
24. The space planning and optimization system (102) as claimed in claim 15, wherein the optimization parameters includes demographics, space elasticity, space constraints, i.e., maximum and minimum allowed footage, store and department yield, inventory, department interdependencies, competitors, labor costs, and consumer purchase behavior patterns.
25. The space planning and optimization system (102) as claimed in claim 15, wherein the space optimization module (108) ranks the departments using a rapid linearization algorithm.
26. The space planning and optimization system (102) as claimed in claim 15, wherein the space optimization module (108) further generates a list of user-customization scenarios based on the key parameters.
27. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method of space planning and optimization of one or more departments corresponding to a plurality of stores, the method comprising:
obtaining key demographics and key parameters for each of the one or more departments, wherein the key demographics and the key parameters are parameters that are associated with performance of the department;
clustering the plurality of stores into one or more department-level clusters based on the key demographics and the key parameters;
ranking the departments, for each department-level cluster, to obtain a set of optimal departments for the space planning and optimization, wherein the set of optimal departments are top predetermined number of departments in each department-level cluster;
ranking the stores, for each department-level cluster, to obtain a set of optimal stores for the space planning and optimization, wherein set of the optimal stores are top predetermined number of stores in each department-level cluster; and
generating space planning recommendations, for each of the set of optimal stores, by processing information associated with the set of optimal departments and the set of optimal stores using nonlinear space optimization mechanism, wherein the nonlinear space optimization mechanism utilizes one or more optimization parameters.
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