Using Analytics To Address Supply Chain Challenges

Supply chain challenges stemming from the pandemic and other global events continue to plague manufacturing and distribution companies.  Logistics disruptions, production delays, an over reliance on 3rd parties, and labor market shortages are just some of these challenges.  Plus, changing consumer behaviors and demands are leading businesses to adopt newer technologies and innovations so they can adapt and evolve at a faster rate.  While beneficial, this also brings added complexity to the supply chain in a number of ways.

To address these issues, manufacturers and distributors are focusing more on the use of data analytics to help them make more strategic decisions related to supply chain performance. But how can organizations best use data analytics to enhance their supply chain management efforts? Below are three best practices that Silvon’s customers have adopted to keep potential supply chain issues at bay.

Drive actionable insights across multiple business processes

First, most companies are swimming in large volumes of data stored in diverse systems and databases. Supply chains also have the added complexity of numerous other data sources being generated from extended partners in outsourcing, logistics, and distribution. And for many, it’s a struggle to use this data to generate meaningful insights beyond top-level metrics.

A successful supply chain data analytics strategy should ensure that internal and external data are brought together in a structured format; that analytics tools can deliver deep, actionable and more accurate insights; that the results are simple to understand by business users and data scientists alike; and that the outcome of data projects is on the actions that should be taken to ensure the business moves forward.

A successful supply chain analytics project should also start from a ‘what does the data tell us’ standpoint, supported by an in-depth understanding of business processes. Partnerships between analytics teams and business users are absolutely critical to helping an organization explain what their data is showing and communicating those insights easily across the organization. Any report or dashboard being shared with cross-functional teams must tell a clear story that is easily understood.

Leverage real-time data for greater visibility

Second, even though data points change rapidly, analysis and decision-making is often based on outdated information and further impaired by the time needed to effectively analyze the data. To navigate this successfully, manufacturers and distributors require analytical platforms that optimize demand and supply by utilizing advanced analytics and real-time visibility across the supply chain. The use of advanced analytics on real-time data feeds allows supply chain managers to quickly model and assess the impacts of potential disruptions, so they can plan and execute on the fluctuations in demand, supply, and inventory.

These insights can also be used to understand the probable impacts of supply chain constraints on revenue forecasts. Near real-time visibility of data such as bookings, shipments, inventory levels, supplier commitments, discounting, and sales opportunities … and the real-time analysis of that information … allows supply chain teams to react instantly to changes, develop contingencies, and deliver more predictable revenue forecasts.

Make supply chain analytics broadly available

Since supply chain management involves multiple facets of the organization, data analytics and the insights they provide need to be shared liberally as well. Businesses need to make it easy for everyone in the supply chain to get the data and tools they need. This requires breaking down any ‘information silos’ and employing an end-to-end data management system (or data hub) from which all analytics can be applied.

It’s also important to remember that as a critical platform that touches multiple parts of the business, the supply chain needs to be managed from a holistic perspective. Let’s take product quality as an example.  The supply chain team should have access not only to configurations and metrics related to the final product, but also to product development data, supplier component data, and customer feedback data.

Collectively, such data can provide a multi-dimensional picture of the drivers behind quality outcomes. And in the end, such level of knowledge and knowledge sharing across an organization will reduce overall risk and enable both improved decision-making and performance.


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