How to Analyze and Use Data to Keep Your Supply Chain Resilient

A recent Celestica study led by IDC reports that, “at no prior time in the history of the supply chain has there been more change — or the potential for change — than there is today.” ^[1] Keeping your supply chain resilient in the face of constant change is an enormous challenge. However, you can turn this challenge into opportunity by using the ever-growing volumes of data your organization collects to become more proactive in identifying and mitigating supply chain constraints before they arise. Data is the tie that binds the combination of people, processes and tools required to architect a more flexible global supply chain that keeps pace with the accelerated speed of business today.

Using Data to Unlock Potential

Three powerful forces are driving what we define as the Acceleration Economy: higher-than-ever customer expectations, constant technological innovation, and globalized competition. Your company must become more nimble, adaptive and innovative in delivering products, services and experiences on a globalized scale, while simultaneously optimizing for your customers’ needs. Leveraging data with analytics will supply the insight you need to adapt, innovate and deliver in the Acceleration Economy.

“The big data hype cycle is running at full speed, and that has companies rushing to collect ever-growing volumes of data,” says Osgood Vogler, Celestica’s Director of Analytic Services. “Often, people then find themselves asking ‘Why’ and ‘What now?’ They’re learning that collecting data is just half the battle. Having a reason to collect it and actually doing something with it is the other half.”

Data is at the center of any digital transformation effort, including building a more flexible global supply chain. More so than ever before, processes are driven by information. However, traditional processes, such as manually pulling from multiple sources and trying to aggregate what’s relevant into spreadsheets and BI tools, can trap human capital in the mechanics of working with data. In the Acceleration Economy, companies are examining quality and supply chain trends over multiple years, and asking more challenging questions than ever before.

Meeting these higher-than-ever expectations is not a solo undertaking. Look to your trusted partners who have the resources and expertise you don’t have in-house to help you overcome common data analysis constraints.

You want to ensure the person who’s about to make a decision has accurate, relevant and timely information at their fingertips. Take people out of the day-to-day grind of scrubbing and preparing data so they can focus on driving better outcomes like on-time deliveries, better quality, or creating a different strategic sourcing setup for a particular bill of material or an entire product line.

For example, one opportunity to create more flexibility lies with conducting value engineering and assessment processes prior to engaging with customers in order to provide a sourcing setup. This enables them to react to dynamic shortages in the market because you understand what alternate parts exist, and where to find them. It comes down to strategic sourcing: when you're suddenly in a bind, you have a plan in place with all options on the table so you can identify parts that exist both inside and outside the organization.

People at the Centre of the Artificial Intelligence (AI) Opportunity

Fight the temptation to try to implement AI technologies to remove humans from the equation entirely. AI can be tremendously helpful for specific applications, such as helping the information security team protect data from the infinite number of malware attacks that bombard organizations every day. However, there's no substitute for human intelligence when it comes to optimizing global supply chain management.

You can run the simplest of analytics on a demand plan and tell a customer that there’s room for improvement with its forecasting. But that doesn't do anything to solve the problem. It’s still up to people to develop a hypothesis and use data analytics to challenge that hypothesis. Effective self-modeling AI doesn’t exist (at least, not yet), so people provide AI the context it needs to succeed.  People still have to frame the problem to put all the hardware and software to work analyzing how to address the issue.

A machine can’t replicate the passion Celestica’s people have for serving customers and modernizing the global supply chain. That is core to our company culture, and our people are just as critical as our leading-edge tools and capabilities are to enabling our customers to cope with unexpected supply chain disruptions. We help you manage a global supply chain that is resilient to changes because you already know your Plan B before an issue arises.

^[1] IDC Study, “Surviving Supply Chain Disruption – Digitally Transforming from Innovation to Execution”