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Презентация компании Elpis Labs – для компании РусАгро
“Data is the new Oil” – German Greff, chairman & CEO of Sberbank
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НЕОБХОДИМОСТЬ ПОДНИМАТЬ ПРОИЗВОДИТЕЛЬНОСТЬ ТРУДА• Показатели качества управления
активами и инвестициями Русагро находятся выше других компаний сравнительной группы
• Однако, количество сотрудников Русагро, для достижения выручки в 1.3bn$, превышает показатели других компаний в 2-7 раз
• Можно сделать вывод о необходимости повышения производительности труда Русагро
• Это становится критично при объявленной стратегии об удвоении выручки
Источник: Financial Times, Bloomberg
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КАРТА РЕШЕНИЙ ДЛЯ ПОДНЯТИЯ ПРОИЗВОДИТЕЛЬНОСТИ ТРУДА • Farm management software – аналоги
ERP, Task & Workflow management• Next Gen Farms – выращивание
агрокультур в городской среде• Animal Data – с помощью сенсоров и
моделей машинного обучения предсказывают поведение животных
• Smart Irrigation – помогают перейти от полива по графику к поливу по потребности
• Sensors & Robotics and Drones – помогают повысить производительность за счет анализа данных с устройств
• Marketplaces – устанавливают прямой контакт между фермером и потребителем
• Precision agriculture and predictive analytics – помогают принимать оптимальные решения на основе данных, управлять рисками земледелия
Источник: https://www.cbinsights.com/blog/agriculture-tech-market-map-company-list/
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ПРИМЕНЕНИЕ ТЕХНОЛОГИИ ELPIS
LABSНА ПРИМЕРЕ САХАРНОГО БИЗНЕСА РУСАГРО
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ЦЕЛИ МЕНЕДЖМЕНТА ПРИ УПРАВЛЕНИИ УРОЖАЙНОСТЬЮ САХАРНОЙ СВЕКЛЫ
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ДОСТИЖЕНИЕ ЦЕЛЕЙ УПРАВЛЕНИЯ С ПОМОЩЬЮ КОНЦЕПЦИИ «ЗДОРОВЬЯ ПОЛЯ» ОТ ELPIS LABS
• Отслеживание большого количества факторов
• Агрегирование несвязанных факторов*
• Оповещение о предстоящей аномалии
• Сравнение состояний полей между собой
• Поиск взаимосвязей между полями
*заболел агроном, сломался трактор, обработка гербицидами произошла перед дождем => снизилось здоровье поля из-за несвоевременной борьбы с сорняками
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КОМАНДА ELPIS LABS• CEO – Duke MBA, 9 лет опыта в индустрии
(SAP, Microsoft)• CTO – 8 лет CTO в Paragon Software• CSO – PhD по информатике от University of
North Carolina• System Architect – основатель 4-х стартапов• Команда разработчиков – Обнинск/Москва• Команда ученых – Северная Каролина, США
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ELPIS LABS – ТЕХНОЛОГИЯ ОТСЛЕЖИВАНИЯ АНОМАЛИЙ• На основе обучающей выборки
устанавливается корреляция между распределением случайной величины на отрезке и целого дня
• Далее делаются попытки предсказать распределение на основе частичного наблюдения
• Подбираются параметры непрерывного равномерного распределения для эмпирических распределений
• Отслеживая частоту и значимость изменения модель позволяет выявлять аномалии
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ДОПОЛНИТЕЛЬНЫЕ СЛАЙДЫ ПРО ТЕХНОЛОГИЮ
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Events, outcomes, and conditions are defined as many-dimensional shapes in the data space.
Data comes in through any number of data sources to be analyzed and modeled
EMRFINANCI
AL
FITNESS
LAB TEST
S
MEDIA
PASSIVE
ACTIVE
SOCIAL
Anomalies in data are “raw materials” for analysis, allowing for automated modeling based on event definitions.
RECORDS
1 3
2
Data Sources & Event Definition
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Events or Outcomes are created when data is profiled automatically and a many-dimensional model is built automatically.
Machine Learning & Data Integrity
Greater data availability increases resolution
Analysis degrades gracefully under loss conditions.
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Models evolve over time as behaviors change, or as more data sources become available.Predictions are created by modeling precursory indicators of key events.
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Low Anomal
y
Increasing
Anomaly
Increasing
Anomaly
Trigger Data
Return to Stable State
InterventionStable (non-anomalous) data flows can begin to exhibit anomalous data.
1 Trigger Data is data shaped like the precondition to an event of interest and indicate in imminent undesireable event.
2 Interventions are designed to help return behaviors to healthy, stable, low-anomaly states.
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Interventions
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Escalating Anomaly Event
Intervention
Return to
Normal
Anomaly Diagnosi
s
Anomaly Detectio
nUpdated Models
1 The data anomaly is diagnosed, and its “fingerprint” is used to identify the impending event.
2 An intervention is executed. The models are automatically updated based on results.
3Here, an escalating anomaly is detected and an intervention takes place (shown below).
Return to NormalEmergent EventNormal Behavior
Screenshot
Demo: Anomaly Detection & Automated Modeling
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1Predictive thresholds are created to show anticipated future data values (in this case, predictions are 24 hours in advance).
2When measurements are profiled, the underlying data and analytics are displayed to understand causes of threshold violations, anomalies, etc.
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Data is streamed from data sources. As it comes in, the stream is profiled for the desired metrics & thresholds.
Screenshot
Demo: Predictive Thresholding
Anomalous Context Data
Underlying
Data
Analysis
Predicted
Thresholds
1
2
3
Users can fine-tune the algorithm to make adjustments for real-world knowledge and concerns.4
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In addition, rather than creating models first, then performing model-based analysis, we analyze the raw data to create the models. These models are true representations of real-world events and phenomena.
Model-less Analysis
Unlike other analyses, ours does not make any assumptions about the data (such as normal distribution). Because of this, we can analyze any kind of data.
No Assumptions
Unlike other approaches, our analysis creates commensurable measurements. This means we can make meaningful relationships between unrelated data, yielding comprehensive models from available data.
Data Bridging
As a result, models can evolve in response to changing data and sources. This means self-adjusting analysis that adapts to changing conditions, while staying focused on desired objectives.
Evolving Models
Predictions are made created in the same way, with the system modeling preconditions of specified (or described) events and outcomes.
Predictive Analytics
All this means effective, integrated, real-world interventions can be executed to avoid undesirable outcomes. Interventions are measured, and can be adjusted for increasing efficacy.
Effective Intervention
Tech Features: How the Technology is Different
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While the system can work in a hands-off fashion, it is often desirable to let experts make real-time adjustments to targets, outcomes and more based on real-world business interests. People can make adjustments at any stage and the system will adapt seamlessly to the new instructions and directions.
Human Oversight
Security is paramount. Elpis Labs focuses on security as a first-class concern. The analysis can be run as a “black box” solution keeping PHI or other sensitive data completely private at all times.
Security-First
Sometimes analysis can be drastically improved by increasing the breadth of available data sources. Clients may optionally participate in the Elpis Labs data ecosystem to share data for more effective analytics.
Data Ecosystem
In many cases, interventions can be integrated directly into the system for complete automation. Integrated Intervention Solutions sets Elpis Labs apart from other analytics solutions by offering a complete, closed solution loop—while maintaining flexible human oversight.
Integrated Interventions
When interventions take place, metadata such as results of the intervention are fed back into the system. This allows for the system to learn from experience and create more effective analyses, predictions, and interventions.
Learning from Experience
Solutions
Rather than working in terms of esoteric data objectives, Elpis Labs solutions work towards real-world objectives, such as cutting specific costs, improving operations, and increasing specific revenue.
Real-World Objectives
Solution Features: Solving Real Problems
