Executive Briefings

Predictive Analytics Aim to Transform Supply Chain Risk Management

Analyst Insight: The key to supply-chain risk management is a predictive model that uses enhanced analytics to identify potential risk. This model would allow companies to move beyond historical metrics so they can determine risk for every part and supplier. It also would identify the appropriate response, promoting consistency within the organization. - Sven Dharmani, performance improvement principal and global automotive sector supply chain leader, Ernst & Young LLP

Predictive Analytics Aim to Transform Supply Chain Risk Management

Today's complex supply chains make it difficult for OEMs to understand risks beyond their Tier 1 suppliers. To gain visibility into Tier 2 and beyond, where 51 percent of supply-chain disruptions occur, companies need a holistic solution that can address potential risks before they happen. Such a solution needs to integrate data from disparate systems, identifying warning signs that could create disruption, such as excessive debt, pending lawsuits and M&A activity.

By 2020, greater visibility into the supply chain will become more common as companies adopt the following elements.

Operating model framework. Effective supply-chain risk management requires a common, integrated operating model across the enterprise. With a strategic direction guided by an executive sponsor, the framework defines roles and responsibilities; establishes activities to put the supplier risk strategies in motion; and creates systems to aggregate data, support analysis and report key risks.

Supply network visibility. This is probably the most acute challenge for OEMs. To gain greater visibility into the supply network, OEMs can use third-party solutions to help vector a risk, including its direction and magnitude. Proprietary datasets, such as financial transactions, can be used to track supplier relationships beyond Tier 1. While specific businesses cannot be identified, enough information is available to discover and react to risk.

Advanced decision support. OEMs currently have tremendous amounts of data related to logistics, quality, product development and capacity, but it needs to be properly analyzed to provide actionable insights. A next-generation predictive model, using leading indicators from internal and third-party data, can help assess the likelihood and impact of component and supplier risk events.

Risk management and response. Once risks have been identified, it’s important to have a consistent response. Such a process assesses the ground-level impact of a disruption and details potential supply-chain ramifications. It prioritizes needed parts from suppliers and identifies what actions need to be taken to resolve problems. The countermeasures used for mitigation should then be reviewed and revised based on results.

Data governance and integration. More effective management of data is critical to supply-chain risk management. Where possible, data collection should be automated and centralized so analytics can be applied easily across all data sources. Analyses then can be aggregated and linked in reports and application workflows to enable proactive and reactive risk-management processes.

The Outlook

It is only a matter of time before industry-leading companies take a leap of faith and adopt a holistic solution to address supply-chain risk. The resulting increased flexibility and agility, combined with improved capability to predict outcomes and make better decisions, will create cost savings and a competitive advantage.

Today's complex supply chains make it difficult for OEMs to understand risks beyond their Tier 1 suppliers. To gain visibility into Tier 2 and beyond, where 51 percent of supply-chain disruptions occur, companies need a holistic solution that can address potential risks before they happen. Such a solution needs to integrate data from disparate systems, identifying warning signs that could create disruption, such as excessive debt, pending lawsuits and M&A activity.

By 2020, greater visibility into the supply chain will become more common as companies adopt the following elements.

Operating model framework. Effective supply-chain risk management requires a common, integrated operating model across the enterprise. With a strategic direction guided by an executive sponsor, the framework defines roles and responsibilities; establishes activities to put the supplier risk strategies in motion; and creates systems to aggregate data, support analysis and report key risks.

Supply network visibility. This is probably the most acute challenge for OEMs. To gain greater visibility into the supply network, OEMs can use third-party solutions to help vector a risk, including its direction and magnitude. Proprietary datasets, such as financial transactions, can be used to track supplier relationships beyond Tier 1. While specific businesses cannot be identified, enough information is available to discover and react to risk.

Advanced decision support. OEMs currently have tremendous amounts of data related to logistics, quality, product development and capacity, but it needs to be properly analyzed to provide actionable insights. A next-generation predictive model, using leading indicators from internal and third-party data, can help assess the likelihood and impact of component and supplier risk events.

Risk management and response. Once risks have been identified, it’s important to have a consistent response. Such a process assesses the ground-level impact of a disruption and details potential supply-chain ramifications. It prioritizes needed parts from suppliers and identifies what actions need to be taken to resolve problems. The countermeasures used for mitigation should then be reviewed and revised based on results.

Data governance and integration. More effective management of data is critical to supply-chain risk management. Where possible, data collection should be automated and centralized so analytics can be applied easily across all data sources. Analyses then can be aggregated and linked in reports and application workflows to enable proactive and reactive risk-management processes.

The Outlook

It is only a matter of time before industry-leading companies take a leap of faith and adopt a holistic solution to address supply-chain risk. The resulting increased flexibility and agility, combined with improved capability to predict outcomes and make better decisions, will create cost savings and a competitive advantage.

Predictive Analytics Aim to Transform Supply Chain Risk Management