Founded in 1953, KapphAl is a major Swedish retail company. It designs and sells fashion apparel for men. women and children.
成功案例
Kappahl
Allocation
Kappahl 利用 Blue Yonder 强大的人工智能和机器学习方案，管理欧洲 260 家门店 95% 的分配
公司案例
业务挑战
Performing roughly 3,800 allocations per night on two home-grown systems was no longer meeting the demand for leading Nordic retailer KappAhl. “Our former systems were automated but didn’t let us work at the lower level of detail required to achieve our goals,” said Director of Merchandise Management, KappAhl. With 12,000 unique style/color combinations per season across 400 stores in Sweden, Norway, Finland and Poland, the retailer needed greater flexibility and expanded capabilities to improve sales while maintaining margins.
解决方案
The entire implementation took about 12 months, including two months of testing. “It was a perfect rollout!” said Director of Merchandise Planning, KappAhl. “Coming in on time and under budget, the project has been the best and most painless implementation that KappAhl has ever experienced. We extend our thanks to the consultants who helped us throughout.”
“Since implementing, we have optimized our allocations with 20 percent better precision and improved size compliance with local demands.”
–Director of Merchandise Management
Expanding capabilities
Using the most powerful artificial intelligence (AI) and machine learning (ML) technology, Blue Yonder Retail now manages 95 percent of all KappAhl allocations. This represents 80 percent of crossdock flows and 100 percent of replenishment.
KappAhl 的商品管理总监指出："通过使用该软件以较低的详细程度处理分配和补货，我们实现了投资回报和显著收益。”
“Everything is running smoothly, and we have not had a single style/color combination go to the wrong store since the system has been live,” said Director of Merchandise Management, KappAhl.
Blue Yonder 的成果
“Now our customers are finding that just the right sizes are available for them whenever they shop in our stores,” added Director of Merchandise Planning, KappAhl. “We’ve achieved our goal for constant improvement by increasing profits and customer satisfaction by understanding our business needs and translating them into a data-driven model for recommending pricing changes.”
Related success stories
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