presentation for Rusagro

1. Презентация компании Elpis Labs – для компании
РусАгро
“Data is the new Oil” – German Greff, chairman & CEO of Sberbank

2. 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 2

3. КАРТА РЕШЕНИЙ
ДЛЯ ПОДНЯТИЯ
ПРОИЗВОДИТЕЛЬН
ОСТИ 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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4. ПРИМЕНЕНИЕ
ТЕХНОЛОГИИ ELPIS LABS
НА ПРИМЕРЕ САХАРНОГО БИЗНЕСА РУСАГРО
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5. ЦЕЛИ МЕНЕДЖМЕНТА ПРИ УПРАВЛЕНИИ
УРОЖАЙНОСТЬЮ САХАРНОЙ СВЕКЛЫ
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6. 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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7. 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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8. 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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9. СВЯЖИТЕСЬ С НАМИ ДЛЯ
ОБСУЖДЕНИЯ ПИЛОТНОГО ПРОЕКТА
sergey@elpis.global,
oleg.fateev@elpis.global
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10. ДОПОЛНИТЕЛЬНЫЕ СЛАЙДЫ
ПРО ТЕХНОЛОГИЮ
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11. 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
EMR
FINANCIA
L
FITNES
S
LAB
TEST
S
MEDI
A
PASSIV
E
ACTIVE
SOCIA
L
Anomalies in data are “raw materials”
for analysis, allowing for automated
modeling based on event definitions.
RECORD
S
1 3
2
Data Sources & Event Definition
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12. 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.
1
2
3
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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13. Low
Anomaly
Increasing
Anomaly
Increasing
Anomaly
Trigger Data Return to
Stable
State
Interventio
n
Stable (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.
3
Interventions
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14. Anomaly
Diagnosis
Anomaly
Detection
Updated
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.3
Here, 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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15. 1
Predictive thresholds are created to show
anticipated future data values (in this case,
predictions are 24 hours in advance).2
When measurements are profiled, the
underlying data and analytics are displayed
to understand causes of threshold
violations, anomalies, etc.
3
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
1
2
3
Users can fine-tune the algorithm to make
adjustments for real-world knowledge and
concerns.4
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16. 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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17. 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
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