Group items tagged

"LMI разработал решение для управления запасами товаров с редким спросом (изолированные всплески спроса), а также изделиями с частым, но весьма изменчивым спросом.Прогнозирование спроса на эти товары - независимо от того, насколько сложным был метод прогнозирования, - привело к годам плохих результатов, например, к избыточному запасу некоторых товаров. В конечном итоге, агентство достигло своих целей, связанных с инвентаризацией, улучшением обслуживания клиентов и снижения рабочей нагрузки покупателей, при этом не увеличивая запасы - все это благодаря покупке большего количества того, что продается и меньше того, что нет". "LMI developed the PNG inventory control solution to manage items with infrequent demand (isolated spikes in demand) as well as items with frequent, but highly variable demand. Such items account for the majority of stocked hardware at the Defense Logistics Agency (DLA). Forecasting demands for these items-no matter how sophisticated the forecasting method-had led to years of bad outcomes for DLA: excess inventory for some items, backorders for others, and excessive buyer workload. The implementation of PNG (a software package of two inventory solutions, Peak Policy and Next Gen) shifted DLA from trying to forecast individual items to a portfolio or risk management approach to inventory control. PNG presents a three-way tradeoff between customer service, inventory value, and replenishment workload. DLA can then make a single decision t

"Ingram Micro, the world's largest technology distributor operates in a high volume low margin environment. The company started its Business Intelligence & Analytics practice in North America (NA) about 6 years ago. Since then the group has built and deployed a scalable highly innovative price optimization engine, imprime™, for NA's $8B spot business, a set of analytics apps, imsmart™ for its internal sales organization and an integrated digital marketing platform, intelligence.ingrammicro.com, to run data-driven marketing campaigns to its customers and end customers. Since 2011, these products and analytical programs have yielded a cumulative benefit of $1.12 Billion of incremental product revenue and $28M of incremental gross profit. These solutions have been effective in driving profitable growth and are scalable. Our next steps are to continue to drive these best practices within the other regions outside NA and continue our sales enablement activities."

"We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these challenges and predict future demand. Regression trees - an intuitive, yet non-parametric regression model - prove to be effective predictors of demand."

Пример прогнозирования потребности в запчастях на основе системно-динамической модели

Расширение модели Басса для прогнозирования новых продуктов Intel Forecasting demand for new products is increasingly difficult as the technology treadmill drives product lifecycles shorter and shorter. We present a model that perpetually reduces forecast variance as new market information is acquired over time. Our model extends Bass's idea of product diffusion to a more comprehensive theoretical setting using the notion of demand-leading indicators in a Bayesian framework. Successful implementation at Intel demonstrates not only improvement in time/efforts but also reduction in forecast errors that leads to significant cost savings.

«SPRINT - это система поддержки принятия решений для оптимизации управления персоналом, предоставляющим услуги по связям с клиентами в HERA, одной из крупнейших итальянских многофункциональных компаний в области энергетики. SPRINT базируется на самых современных системах прогнозирования и оптимизации спроса, которые получили очень хорошие результаты по сравнению с другими существующими методами. После двух лет службы компания SPRINT представила значительное повышение уровня обслуживания клиентских сервисов, а также повышение производительности труда сотрудников ». "SPRINT is a decision support system for the optimization of staff management of desk customer relations services at HERA, one of the largest Italian multi-utility companies. SPRINT is based on state-of-the-art demand forecasting and optimization frameworks that obtained very good performance with respect to other existing methods. After two years of service, SPRINT introduced a large level-of-service improvement of desk customer services, together with a relevant increase of staff productivity."