Systems and methods for risk processing of supply chain management system data

Abstract

Apparatus, system and method for supply chain management (SCM) system processing. A SCM operating platform is operatively coupled to SCM modules for collecting, storing, distributing and processing SCM data to determine statistical opportunities and risk in a SCM hierarchy. SCM risk processing may be utilized to determine risk values that are dependent upon SCM attributes. Multiple SCM risk processing results may be produced for further drill-down by a user. SCM network nodes, their relation and status may further be produced for fast and efficient status determination.

Description

RELATED APPLICATIONS[0001]The present application claims the benefit of priority to International Application No. PCT / US2018 / 033804, filed May 22, 2018, entitled “Systems and Methods for Risk Processing of Supply Chain Management System Data” which claims priority to, is related to, and incorporates by reference, U.S. provisional application No. 62 / 509,660, filed May 22, 2017, entitled “Systems and Methods for Risk Processing of Supply Chain Management System Data”; U.S. provisional application No. 62 / 509,669, filed May 22, 2017, entitled “Systems and Methods Optimized Design of a Supply Chain”; U.S. provisional No. 62 / 509,665, filed May 22, 2017, entitled Systems and Methods for Interfaces to a Supply Chain Management System”; U.S. provisional application No. 62 / 509,675, filed May 22, 2017, entitled Systems and Methods for Assessment and Visualization of Supply Chain Management System Data; U.S. provisional application No. 62 / 509,653, filed May 22, 2017, entitled Systems and Method...

AI Technical Summary

Benefits of technology

The patent describes an apparatus, system, and method for managing a supply chain. It uses a platform to collect, store, distribute, and process data about the supply chain to identify opportunities and risks. The system can produce risk values based on different attributes of the supply chain. Users can drill down into the data to gather more details. The system also shows the connections and status of different nodes in the supply chain. Overall, this patent provides a way to efficiently manage the supply chain and mitigate risks.

Problems solved by technology

Supply chains have become increasingly complex, and product companies are faced with numerous challenges such as globalization, shortening product lifecycles, high mix product offerings and countless supply chain procurement models.

In addition, challenging economic conditions have placed additional pressure on companies to reduce cost to maximize margin or profit.

Supply chain risk, or the likelihood of supply chain disruptions, is emerging as a key challenge to SCM.

Ideally, such risk management and assessment would be performed during the design of a supply chain for a product or line of products, but design tools and data analysis to allow for such design capabilities are not available in the known art.

Generally speaking, this approach is based on the belief that supplier problems account for the large majority of shutdowns and supply chain failures.

Such conventional systems are needlessly complicated and somewhat disorganized in that multiple layers of classification risks are utilized and, too often, the systems focus mainly on proactively endeavoring to predict disruptive events instead of analyzing and processing underlying root causes and large-scale accumulated data to assess potential disruptions.

Further, these conventional systems fail to provide tools to aid in the design of a supply chain at the outset to address potential breakdown and disruption, and they also give little insight or visibility into the actual supply chain over its entirety.

Moreover, conventional supply chain management has historically been based on various assumptions that may prove incorrect.

By way of example, it has generally been understood that the highest risk in the supply chain resides with suppliers with whom the highest spend occurs—however, the most significant risk in a supply chain may actually reside with small suppliers, particularly if language barriers reside between the supplier and the supply chain manager, or with sole source suppliers, or in relation to suppliers highly likely to be subject to catastrophic events, such as earthquakes, for example.

Further, it has typically been the case that increased inventory results in improved delivery performance—however, this, too, may prove to be an incorrect assumption upon analysis of large-scale data over time and across multiple suppliers, at least in that this assumption is true only if an inventory buffer is placed on the correct part or parts, and at the correct service level.

Needless to say, such information would be difficult to glean absent automated review of large-scale data over time, and without visibility across an entire supply chain.

Yet further, present supply chain management fails to account for much of the available large-scale data information.

By way of further example, conventional systems often deem certain events, such as significant geopolitical events, to pose a very high risk to the supply chain.

However, large scale data analysis, such as from the inception of the design of many supply chains in a given vertical and from end-to-end of such supply chains throughout their respective life cycles, may reveal that this supposition has generally not been the case—rather, the supply chain risk may instead be revealed as far more dependent on sole source items and the size and language spoken by certain suppliers than on geopolitical events, by way of non-limiting example.

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