We recently co-hosted a webinar with the Manufacturing Leadership Council and Coupa on the topic of evolving a digital supply chain with advanced analytics. The panelists included West Monroe supply chain director Brian Pacula, David McGraw, a senior manager in our Consumer & Industrial Products practice at West Monroe, and Matt Tichon, vice president of industry strategy at Coupa.
The webinar touched on several different topics including leveraging AI and data to improve visibility to issues, optimize digital supply chains, and react with increased flexibility to disruptive conditions. While organizations may be on different phases of their digital supply chain journey, there were key takeaways for all in how to test, assess, implement, and sustain their advanced analytics journey.
Our panelists agreed that most companies have some type of reporting in place, so they understand what’s happening. The basics are accounted for. But where advanced analytics come into play is in predicting events or using data to inform the steps needed to optimize and achieve your end goal. It’s something organizations must mature into, meaning there are stages in the process.
Attendees were asked to identify underlying supply chain strategy driving their respective organizations to prioritize digital efforts. Of those, 73% responded that their digital supply chain journey was to improve overall agility and responsiveness, followed by 52% seeking cost reduction. An additional 45% added that enhancing customer experience was key to their decision-making.
This didn’t surprise our panelists. COVID-19 brought on disruptive and unprecedented change to the industry that no one could have predicted. Supply chains were flipped on their heads overnight, while organizations were left with a helpless reality. The results of the last year have changed plenty, including the reasons for why going digital makes so much sense.
“If you asked this question pre-COVID, reducing costs would have jumped to the top. Now we all understand that anything can happen tomorrow,” Tichon said. “There’s a lot of vulnerability in these supply chains. We’re seeing our customers and prospects saying, ‘I need to understand my supply chain. What areas are fragile? How can I make it more robust? I’m going to plan for the best future when something happens in the supply chain, so I can respond quickly.’”
Still, the panelists admitted that cost is still important and will come into play.
“Most companies still have to be competitive and manage costs,” Tichon added. “Advanced analytics that can do that in the digital supply chain and be responsive but (also) manage costs, that really is the sweet spot.”
Half of our attendees said their organizations are predominantly using Excel to harness AA/AI in their decision-making process. Another 32% are currently assessing the potential of AI capabilities, 30% have a proof of concept/value project underway, and 23% are using data scientists.
“Not too surprised here,” Pacula said. “As we’re out in the market, we’re seeing a lot of foundational areas where systems might not even be in place to manage core areas of the business. It’s good to see there’s a decent number of businesses that do have data scientists or AI. Companies are making more and more investments in that and seeing value.”
The maturity of the company comes into play as well. For larger organizations, Excel simply can’t scale and it may be time for an upgrade in a rapidly changing environment. Organizations likely need a better system that can keep up and help manage more variables and dependencies and provide greater transparency across the organization and with key partners. Centers of Excellence are becoming more prevalent, and so are Chief Data Analytics Officers.
“Data scientists are not cheap or easy to retain,” Tichon said. “A lot of companies are adopting as they get this maturity, a center of excellence around data analytics where it can be leveraged around multiple business units.”
We gauged the attendees on where the data analytics function currently resided within their company. The top answer (42%) was a combination of both dedicated centralized analytics team and within the business units. That hybrid approach resonated with our panelists, who agreed that one isn’t necessarily better than another and that both can work.
“I wouldn’t say one’s better than the other,” Tichon said. “It really depends on the organization. A lot of business leaders don’t want to have a bottleneck in data analytics support infrastructure…And then you see the centralized model working really well and maturity evolving and having support teams for different business structures. It’s more of a culture and a preference.”
Pacula added: “What works well in practice is a hybrid with a data scientist living within the business in a centralized function.”
The benefits to digital supply chains are obvious. But that doesn’t make the deployment or sustainability of them easy. Challenges exist, which our panelists admitted, but they also provided solutions to best work through those potential pitfalls.
Pacula: “Make sure all stakeholders are involved where relevant and that they're willing to underwrite whatever that value definition is.”
Tichon: “Don’t invest in a problem you can’t solve. First look at a problem where you’ve been able to move the needle. If you’re going to jump into AI, take something that you know really well, build a cross-functional team, and then see how much better you can do with predictive and prescriptive by applying AI and advanced analytics.”
So where to begin? What are the steps to take on your digital journey to avoid pitfalls and maximize the benefits of a move to digital supply chains? Here’s what our panelists suggested:
McGraw: “Transformation is a journey. Start small and focus intently, ensuring you understand the business case and the value you are trying to obtain and how you are going to operationalize it. We often see results from advanced analytics fall short because the overarching business case was never fully developed or vetted.”
Pacula: “Cross-functional teams with diverse backgrounds from both technical and operational experience. Don’t do this in a silo within the business or within IT. Also plan time for data discovery, cleansing, refreshing data.”
Tichon: Develop a culture and mindset of AI and have your organization establish a baseline for the technology and demystify it. Then, it’s up to executive leadership to help their organizations take this learning journey to understand the basics and value of AI and ML so it’s not intimidating. That educational component is critical for an organization.”