Talking Supply Chain Design, AI, and Resiliency After COVID-19 With Canadian Tire
A chat with Canadian Tire about design, AI-powered analytics, and building a culture of resiliency.
Our VP of Industry Strategy, Matt Tichon, sat down virtually with Shannon Macey, AVP – Supply Chain Capabilities at Canadian Tire Corporation during the Supply Chain Canada National Conference October 8, 2020 to explore the topic “Post-Pandemic Supply Chain Design Imperatives.”
In the hour-long session, Matt and Shannon shared perspectives from their vast experience by exploring four key topics, each punctuated by a polling question that provided insights into the scenario planning and decision-making techniques of the participants’ organizations across Canada.
We offer our key takeaways and a summary below. The recorded session will give you a deeper context around Canadian Tires’ decision-making experience leading up to and during the recent series of supply chain disruptions.
- Be data driven. Data has never been more usable than right now. Data combined with today’s tools and artificial intelligence can boost an organization’s performance to deliver on its set goals.
- Don’t do AI for AI’s sake — AI isn’t a magical silver bullet.
- Cultivate citizen data scientists in your organization. These are people who are good with data, can draw insights from data, and know how to communicate and sell those ideas internally.
- Democratize decisioning with AI by deploying applications designed for your citizen data scientists. Taking this approach allows you to increase your organization’s decision-making capacity and speed.
- Baby steps. Leverage an AI win to gain organizational traction with an AI mindset and apply to areas you know well, yet need further improvement.
- The human spirit is a pioneer for innovation. This current disruption has spurred people to look for new areas to apply and leverage AI to transform and improve processes. There will be a shift in the skill sets needed, but it’s doubtful that AI will take over jobs.
Summary & Polling Questions
Visibility & Data
“We are really at a time of transformation, where data leveraged by new analytical tools and models can amp up performance, reduce costs, and improve service, resiliency, or any other goal you’re trying to achieve.” —Shannon Macey
The answers to polling question No. 1 (“Which of these statements describes your current decision-making process?”) appear to reflect evolutionary stages in decision-making approaches. Also, the poll responses unsurprisingly show that many organizations are at the same stage, with 47% following “some combination of above.”
Capacity / Decision-Making Speed
We were surprised that using a Centre of Excellence was not as widely adopted as we expected from our polled audience. When we discuss capacity, it typically relates to Distribution Center (DC) capacity. For this conversation, we shifted to explore capacity in terms of decision-making capabilities related to the people and structure of decision making itself.
You can boost decision making through citizen data scientists. Matt and Shannon used examples to highlight the merits of creating an environment that allows any analyst to contribute to increasing capacity for decision making. In such an environment, a data analyst who has some data science skills but who may not be advanced or specialized as a data scientist can readily make day-to-day decisions within the business. That frees up data scientists for more complex analyses.
Canadian Tire has a well-established central decision-making Centre of Excellence (COE) function. When COVID-19 hit, the Canadian Tire team was aligned and could make better and faster decisions when required because the tactics and strategies executed within the COE had already been agreed to by the senior team.
People are holding on tight to their Excel analysis, with 70% of polling respondents indicating they still use Excel.
“You can’t buy AI and then put it on the shelf and expect it to move the needle.” —Matt Tichon
“I think we’re getting to a point where AI is actually starting to hit the mark rather than just be kind of this promise out there.” —Shannon Macey
Canadian Tire continues to build out its in-house algorithms and models to enhance their decisioning and improve supply chain resiliency. One example is a machine learning model they’ve built to predict daily and weekly demand at an aggregate level for their DCs. Because their model is “pull,” they have very little visibility into what the stores will order. Using these new algorithms, combined with weather data and historical stock-ordering patterns based on stock levels, they developed a much better sense of the forecast. They are currently exploring broader capabilities to achieve end-to-end supply chain visibility and deliver predictions and alerts to drive the right decisions across their supply chain.
Building a Culture of Resiliency
Resiliency is tested during times of disruption and downturns. The Canadian Tire team had dealt with previous disruptions as a result of port and rail strikes, so they were well versed in how to manage them. They’ve also completed practice exercises and plans to help prepare for future disruptions. The team has grown more resilient over the years as a result.
“You can build all kinds of models and use all kinds of data, but your team needs to be resilient.” —Shannon Macey
Both our speakers were surprised that 31% of polling respondents said their business hasn’t changed! Perhaps that is more reflective of the fact that a sense of complacency makes people comfortable in their roles. Sometimes it takes an outside event to drive change.
From Canadian Tire’s perspective, while the COE was a big help, COVID-19 brought on the need for a variety of new decisions. Using better supply chain analytics, they were able to take down barriers and make decisions in a matter of weeks that pre-pandemic would have taken years to accomplish. Innovation is driving businesses to reevaluate their practices.
These pioneers are looking at ways to harness AI and technology’s power — not to remove humans from the decision-making process but instead to allow humans space and time to innovate and find new ways to solve problems.