The three elements of an agile and efficient supply chain are 1) connection across multiple supplier tiers, where information about demand and supply changes can rapidly flow downstream or upstream, so alterations in plan can be made rapidly based on any changes in demand/supply and execution can be streamlined; 2) a collaborative supply chain where suppliers and OEMs leverage connectivity to work together on the entire lifecycle of a product from concept to design to ramp-up and production to sunset; and 3) visibility into key performance metrics/analytics and comparisons with industry benchmarks across the entire supply base, so OEMs and suppliers can work together to continually improve the performance and reduce risk in the supply chain.
Business Network Analytics vs. Traditional Supplier Analytics
In today's fast-changing world, speed and quality of analytics matters more than ever. Significant currency swings due to geo-political crisis; demand forecasts affected by the world economy; and supplier-specific challenges due to currency fluctuations, rising local wages, sudden labor strikes, floods, earthquakes and bankruptcies have affected nearly every organization. We should put memories of normal demand and supply cycles in the rear-view mirror for some time to come - continued volatility is forecasted by nearly every industry and financial research organization. These issues are being compounded by a number of other factors, such as the addition of some three billion consumers to the global middle class over the coming two decades, and the strains they will place on global resource supplies.
At the same time, changing competitive dynamics are pressuring companies to introduce more and more product variations to chase new customers in new markets. This only increases business complexity for any organization. Externally induced supply-demand volatility factors combined with increase in business complexity creates a significant supply chain risk, which can only be mitigated by better predictive analytics - i.e., the ability to holistically look at the supply chain and be able to identify where hot spots will/could occur and proactively developing a plan to address it. Traditional analytics that focus on the operational aspects of a tier-1 supplier (i.e., trend chart for metrics such as on-time delivery rates, supply quality or even number of corrective action requests in a given period) don't provide the 360-degree view needed to identify potential issues. In addition, most organizations don't have any connectivity with lower-tier suppliers. As a result they have almost no visibility into potential risk from those suppliers. The following statistic illustrates unknown supply risk around the corner due to lack of holistic visibility into the supply base: according to the Original Equipment Suppliers Association, 12 percent of the auto industry suppliers do not have sufficient working capital to support a 10-percent to 25-percent expansion in production, and need their suppliers to step up. As automakers ramp up, their suppliers pose a risk to their expansion.
What is Business Network Analytics?
Business network, at its core, addresses collaboration and connectivity with every partner in the network. This ensures that organizations can easily share information with companies in the lower tier of their network. Such connectivity provides the foundation for implementing inter-company processes such as supply and demand planning/execution, customer order management, direct procurement, vendor-managed inventory, demand forecasting, shipment notifications, invoicing and quality management. These inter-company transactions are also critical to gaining visibility and control over an outsourced manufacturing and logistics environment. Business network connectivity permits not only inter-company transactions, but also collaboration with partners on a wide range of activities such as product design, marketing launch and issue resolution. Such activities involve exchange of unstructured data, including documents, charts, graphics etc. As participants transact and collaborate using the network, they accumulate performance data which provides insights into supplier's past operational performance.
Advanced supply chain analytics collect key performance information about your suppliers from such inter-company transactions and collaboration, as well as data provided by suppliers in their dashboards. It then collects similar data from the systems of a number of your peers in the industry and aggregates it - since a business network technology hosts a number of OEMs and their suppliers (under a Software as a Service model), it can get access to such information. It then combines this aggregated information with publicly available financial, credit, demographic and other disclosure information and latest industry news about this supplier, to give you a 360-degree view into your supplier - operational, financial and business. Analytics on top of this data allows a user to:
"¢ Benchmark supplier performance against relevant KPIs and how that relates to the performance of that same supplier for other customers in the network. Imagine knowing that your quality metrics from a specific supplier is on par with your peers and their metrics for on-time delivery to you are two points below your peers, while your landed cost is on par with your peers. Such insight can enable you to prioritize where to focus your continuous improvement efforts with that supplier. They can also be early indicators of a supplier experiencing operational or financial issues like capacity constraints.
"¢ Predict supplier performance based on the trends in KPI scores in the aggregated information. For example, what if by looking at the trends both internally and at other manufacturers, as well as external news sources about labor problems at one of their plants, the analytics showed that that the delivery time from a specific supplier will increase by at least two days in the next quarter? Such valuable insights give you advanced notice to either identify another component source and/or to proactively reset expectations with customers or proactively work with current suppliers.
"¢ Maximize margins by aggregating different internal sources with external third-party data sets to create a better landed cost model that incorporates predictive component costs and supplier risk and then use this data to optimize order volumes that maximizes margins. It can then link this volume forecast into supply chain modeling tools to create a better plan.
"¢ Assess future risk within your supply base and proactively take corrective actions. The risk may come from issues at your supplier such as plant closings or from one of your supplier's suppliers such as financial issues with one of their key suppliers. Such information can be derived by combining external data with internal performance data. As organizations seek to get a better handle on their overall risk as a result of pressure from activist shareholders, board members and other stakeholders, an ability to assess risk and proactively make course corrections is an important operational capability.
Such advanced analytics that are often cloud-based, go beyond the traditional supplier scorecards and give you a 360-degree view of your suppliers and their suppliers. Armed with such information, you are now positioned to incorporate external and operational factors to not only assess /identify potential supply chain risk and create plans to mitigate it but you are also set up to benchmark their current performance and create very specific shared plans to continuously improve it. The result is an effective, efficient and agile supply chain in this constantly changing and volatile world.
Keywords supply chain management IT, supply chain solutions, supply chain systems, sourcing solutions, supply chain risk management, analyzing supply chain performance
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