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Automatic Pattern Detection Digs through Big Data to Identify Optimal Product Offerings
Technology Category
- Analytics & Modeling - Data Mining
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Agriculture
Applicable Functions
- Product Research & Development
- Sales & Marketing
Use Cases
- Inventory Management
- Predictive Maintenance
- Supply Chain Visibility
Services
- Data Science Services
- System Integration
The Challenge
Thousands of possible product configurations created complex data that the organization couldn’t untangle, and hence no one had visibility into what customers were buying or how that aligned with the supply and delivery chain. The result was additional complexity and expensive inventory and production challenges, including extended planning cycles, longer lead times, and higher product costs. AGCO was manufacturing high horsepower tractors at the cost of $225,000 to $275,000 per unit, a high number by industry standards. The strong cyclical demand for the units overtaxed production capacity half the year, and the company lacked a reliable method for accurately forecasting that demand. Thirty percent of orders were for retail, customer-specific products and these were accurate, but 70% were dealer projections, based on what sold last year and dealers’ estimates on how demand may change in the coming months. Compounding the problem was the wide range of product variety. The range of option combinations and product adjustments for each market forced a very low ‘repeat rate’ of 1.5 tractors per year, meaning only three of the exact same tractor were manufactured every two years. The product variety created excessive design and coordination costs and the company’s ability to meet market demand was uneven. Finding they had the wrong option combinations in stock, AGCO and their dealers were too often saddled with vehicles customers didn’t want. These had to be discounted to sell. Meanwhile, an order that defied the forecast meant building a single product to satisfy customer demand. What’s more, excess inventory created an expensive problem for dealers who had to store and maintain extra product.
About The Customer
AGCO, a worldwide manufacturer and distributor of agricultural equipment, offers a broad range of tractors, combines, sprayers, forage and tillage equipment, implements and hay tools. The Duluth, Georgia-based company relies on 2,600 independent dealers and distributors in more than 140 countries worldwide to distribute its products. AGCO is known for its extensive product range and its commitment to innovation and quality in the agricultural sector. The company has a significant global presence and a strong reputation for delivering high-performance agricultural machinery that meets the diverse needs of farmers and agricultural businesses around the world. AGCO's products are designed to enhance productivity and efficiency in farming operations, making it a key player in the agricultural equipment industry.
The Solution
Emcien’s Pattern-based analytics led to the identification of six base units that satisfied 90% to 95% of customer demand. These units are manufactured in advance and additional components can be added as needed to create a more agile production process that’s better equipped to meet market needs. Emcien enables AGCO to quickly and regularly identify buying patterns in real time. Collecting and processing the company’s retail history data enabled analysis of hundreds of options and thousands of configurations to create product models optimized to demand. After an initial phase of three months, AGCO revamped their entire production process over the following nine months; the solution was fully operational within a year. Emcien’s pattern-based analytics solutions allowed AGCO to reduce product variety by 61% and slash days of inventory by 81%, all while maintaining service levels. Resulting benefits have accrued in the form of significant savings in terms of cash flow and the ability to meet customer needs more quickly. Cutting AGCO’s configuration complexity has also increased the manufacturing plant’s capacity by 25%. Supplying all that variability had consumed capacity, but by finding a way to offer the same product variety more manageably, AGCO is able to reap the benefits of expanded capacity without construction costs—and without the typical 18-month wait for those benefits.
Operational Impact
Quantitative Benefit
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