Case Study
Supply Chain Decision Challenge
Supply Chain Decision Challenge
Western Tractor's Supply Chain Crisis and the AI Opportunity
A Decision Case Study
Monday, 8:47 AM — Sarah Chen's phone buzzed urgently. It was her primary parts supplier in Illinois. "Sarah, I'm sorry, but we're suspending shipments to Canada effective immediately. The new tariffs make it unprofitable, and we're redirecting inventory to domestic customers."
Sarah hung up, her mind racing. As Supply Chain Manager for Western Tractor, she knew this wasn't just one supplier—it was a pattern. Three suppliers had already reduced Canadian allocations in the past month. Her inventory dashboard showed critical parts dropping below reorder points. The spring planting season was six weeks away, and farmers across Alberta were counting on Western Tractor to have their equipment ready.
She glanced at her calendar: a board meeting in 48 hours where she'd need to present a solution. The traditional approach—calling suppliers, negotiating, finding alternatives—wasn't working fast enough. But there was another option: Microsoft 365 Copilot, the AI tool the company had just started piloting. Could AI help her navigate this crisis faster than human analysis alone?
Western Tractor had grown from a single location in Lethbridge, Alberta, to four locations across southern Alberta (Lethbridge, Taber, Burdett, and Medicine Hat). Founded in 1985, the company had built its reputation on reliability, customer service, and deep relationships with the agricultural community. The company served over 2,000 customers, from large commercial farms to small family operations.
The business model was straightforward: sell new and used agricultural equipment (primarily John Deere tractors, combines, and implements), provide parts and service, and maintain long-term customer relationships. Parts inventory was critical—when a farmer's combine broke down during harvest, every hour of downtime cost thousands in lost productivity.
In early 2024, new trade policies introduced tariffs averaging 15-25% on agricultural equipment parts imported from the United States to Canada. For Western Tractor, this meant:
The agricultural equipment industry operated on tight margins. Western Tractor couldn't simply pass all costs to customers—farmers were already facing their own economic pressures from commodity price volatility and rising input costs.
When critical parts weren't available, service appointments were delayed. Delayed service meant frustrated customers. Frustrated customers considered competitors. In a relationship-driven business, one bad experience could cost a customer for life—and their referrals.
Sarah Chen had joined Western Tractor three years ago after completing her MBA at the University of Lethbridge. At 34, she was the youngest member of the senior management team. Her background was in operations and logistics, but she'd quickly learned the agricultural equipment business wasn't just about moving parts—it was about understanding farming cycles, weather patterns, and the urgent needs of customers whose livelihoods depended on their equipment running.
Sarah was known for being data-driven and innovative. She'd successfully implemented a new inventory management system in her first year, reducing stockouts by 30%. But this crisis was different. It wasn't about optimizing existing processes—it was about fundamentally rethinking how Western Tractor managed supply chain risk.
She'd been skeptical about AI tools initially. "How can a computer understand the nuances of agricultural equipment parts?" she'd asked when the company first discussed Microsoft 365 Copilot. But after attending the training session last month, she'd seen how Copilot could analyze complex data, identify patterns, and generate insights faster than traditional methods.
Now, facing this crisis, Sarah wondered: Could Copilot help her analyze supplier alternatives, predict parts demand, optimize inventory levels, and identify cost-saving opportunities faster than her team could do manually? The training had shown her the potential, but this was real—with real consequences if she made the wrong call.
Sarah's team had compiled data on the current situation. The numbers were sobering:
| Metric | Before Tariffs (Q4 2023) | Current (Q1 2024) | Projected (Q2 2024) |
|---|---|---|---|
| Average Parts Cost | $125 CAD | $145 CAD | $155 CAD (est.) |
| Stockout Rate | 2.1% | 8.3% | 12-15% (est.) |
| Average Lead Time | 6 days | 18 days | 22-25 days (est.) |
| Customer Satisfaction (Parts) | 4.6/5.0 | 3.9/5.0 | 3.5/5.0 (est.) |
| Parts Margin | 32% | 24% | 18-20% (est.) |
The board wanted answers: How would Western Tractor maintain service levels? What would it cost? How long would this last? And most importantly: What was the strategic plan?
During the recent Copilot training, Sarah had learned about data analysis capabilities. The trainer had demonstrated how Copilot could:
Sarah had access to several datasets that could be analyzed:
The question was: Could Copilot help Sarah analyze this data quickly enough to develop a comprehensive strategy for the board meeting? And even if it could, should she rely on AI insights for such a critical decision?
As Sarah prepared for the board meeting, she considered several approaches:
Her team could manually analyze the data, call suppliers, and develop recommendations. This was the "safe" approach—what they'd always done. But it would take 2-3 weeks to complete a thorough analysis, and the board needed answers in 48 hours. Plus, manual analysis might miss patterns or opportunities that AI could identify.
Sarah could use Microsoft 365 Copilot to analyze the datasets, identify patterns, compare supplier alternatives, and generate strategic recommendations. This could be done in hours rather than weeks. But she'd need to verify the insights, and she wasn't entirely confident in AI-generated recommendations for such a critical business decision.
Use Copilot to generate initial insights and identify key areas to investigate, then have her team verify and refine the analysis. This balanced speed with human judgment, but required careful coordination and might not be fast enough for the board meeting.
Request more time from the board to conduct a thorough analysis. But every day of delay meant more stockouts, more frustrated customers, and potentially more suppliers reducing Canadian allocations.
As Sarah weighed her options, several factors complicated the decision:
Sarah looked at her watch: 2:15 PM. She had 42 hours until the board meeting. Her team was waiting for direction. Suppliers were waiting for decisions. Customers were waiting for parts. And she had to decide: trust the AI tool she'd just learned about, or stick with what she knew?
As Sarah Chen prepares for the board meeting, she must make critical decisions about how to address Western Tractor's supply chain crisis. Consider the following questions:
Case Study Information:
Case Type: Decision Case
Industry: Agricultural Equipment Distribution
Focus: Supply Chain Management, AI Implementation, Strategic Decision-Making
Protagonist: Sarah Chen, Supply Chain Manager
Setting: Western Tractor, Southern Alberta, Canada, 2024
This case study is designed for discussion and analysis. There is no single "correct" answer. The goal is to explore decision-making processes, evaluate options, and consider the implications of using AI tools in critical business situations.