Systems and methods for adaptive stock-keeping unit data structure updates

A computer-implemented method comprising:

• receiving, by an orchestration engine executed by one or more processors, procedure data from a plurality of heterogeneous source systems, the procedure data comprising a procedure identifier and a projected volume for a type of procedure;
• storing, in memory, a mapping data structure having a schema comprising:
• (i) a procedure-identifier mapping that maps procedure identifiers received from the plurality of heterogeneous source systems to a canonical procedure identifier;
• and
• (ii) a SKU-identifier mapping that maps SKU identifiers received from the plurality of heterogeneous source systems to canonical SKU identifiers;
• transforming, by the orchestration engine using the mapping data structure, the procedure data into a procedure record associated with the canonical procedure identifier;
• storing, in memory, for at least one adaptive stock-keeping unit (SKU) identifier, a combination SKU record having a schema comprising:
• (i) a membership field storing an ordered list of canonical SKU identifiers for a plurality of constituent SKUs for the type of procedure;
• (ii) a quantity-range field storing dynamic quantity ranges for at least a subset of the plurality of constituent SKUs;
• (iii) a rule field storing executable combination rules that define permitted membership modifications to the membership field, including at least one of combining, adding, removing, or substituting one constituent SKU with another constituent SKU;
• and
• (iv) a dependency field storing quantity relationship entries that each define a propagation rule mapping a quantity change of a first constituent SKU to a quantity change of a second constituent SKU;
• receiving factor-based data comprising operational parameter values correlated with anticipated consumption of at least one constituent SKU independent of the projected volume;
• receiving preference data and storing the preference data in a preference profile record associated with at least one site identifier;
• generating, by executing one or more trained machine learning models, a plurality of candidate combination SKU record identifiers and corresponding score values based on at least the canonical procedure record, the factor-based data, inventory data, and the preference profile record;
• selecting a target combination SKU record identifier based on the score values and at least one rule-based selection criterion;
• computing quantities for at least a subset of the plurality of constituent SKUs by applying at least one dynamic quantity range in the quantity-range field and applying at least one propagation rule in the dependency field;
• and
• performing an incremental recomputation operation that updates quantities for an affected subset of the plurality of constituent SKUs responsive to at least one of a change in inventory data, a change in preference data, or execution of at least one executable combination rule, and committing an updated quantity state to memory in association with the selected target combination SKU record identifier.

A non-transitory computer readable medium including one or more instructions stored thereon and executable by a processor to:

• receive, by an orchestration engine executed by one or more processors, procedure data from a plurality of heterogeneous source systems, the procedure data comprising a procedure identifier and a projected volume for a type of procedure;
• store, in memory, a mapping data structure having a schema comprising:
• (i) a procedure-identifier mapping that maps procedure identifiers received from the plurality of heterogeneous source systems to a canonical procedure identifier;
• and
• (ii) a SKU-identifier mapping that maps SKU identifiers received from the plurality of heterogeneous source systems to canonical SKU identifiers;
• transform, by the orchestration engine using the mapping data structure, the procedure data into a procedure record associated with the canonical procedure identifier;
• store, in memory, for at least one adaptive stock-keeping unit (SKU) identifier, a combination SKU record having a schema comprising:
• (i) a membership field storing an ordered list of canonical SKU identifiers for a plurality of constituent SKUs for the type of procedure;
• (ii) a quantity-range field storing dynamic quantity ranges for at least a subset of the plurality of constituent SKUs;
• (iii) a rule field storing executable combination rules that define permitted membership modifications to the membership field, including at least one of combining, adding, removing, or substituting one constituent SKU with another constituent SKU;
• and
• (iv) a dependency field storing quantity relationship entries that each define a propagation rule mapping a quantity change of a first constituent SKU to a quantity change of a second constituent SKU;
• receive factor-based data comprising operational parameter values correlated with anticipated consumption of at least one constituent SKU independent of the projected volume;
• receive preference data and storing the preference data in a preference profile record associated with at least one site identifier;
• generate, by executing one or more trained machine learning models, a plurality of candidate combination SKU record identifiers and corresponding score values based on at least the canonical procedure record, the factor-based data, inventory data, and the preference profile record;
• select a target combination SKU record identifier based on the score values and at least one rule-based selection criterion;
• compute quantities for at least a subset of the plurality of constituent SKUs by applying at least one dynamic quantity range in the quantity-range field and applying at least one propagation rule in the dependency field;
• and
• perform an incremental recomputation operation that updates quantities for an affected subset of the plurality of constituent SKUs responsive to at least one of a change in inventory data, a change in preference data, or execution of at least one executable combination rule, and committing an updated quantity state to memory in association with the selected target combination SKU record identifier.