AUGMENTO

augmento

Predictive analytics

Machine Learning

Artificial Intelligence

While doing planning automation, it became clear that the time had come for advanced AI-enabled forecasting solutions to replace widely used statistical methods.

This led to the creation of the Augmento business unit which employs data scientists, data engineers and business process consultants.

We have a very specific work domain. We develop forecasting models using the most progressive machine learning algorithms. These include demand forecasting, replenishment, innovations planning, promotions planning, as well as assistance in adjusting your current planning and S&OP processes to accommodate a ML-enabled forecast.

Machine Learning has opened up a new era of forecasting. It gears businesses up for management by exception when time and resources are focused on what makes a real difference. 

OUR SERVICE

Solutions
  • Machine Learning-enabled forecasting
  • Front-end visualization of model outcomes (forecast) for analysts and business users
Deliverables
  • Forecast accuracy growth
  • Forecast bias decrease
  • Less resource intensive forecasting
Technology
  • Multivariate Machine Learning algorithms:
– Gradient Boosting– Neural networks– Random Forest– ArimaX– Ensemble
  • Innovation
    sales forecast
  • Promo investments
    forecast
  • Baseline sales
    forecast
  • ML
    Forecast
  • Optimal pricing
    forecast
  • Off shelf
    forecast
  • Promo-sales
    forecast

A NEW ERA OF FORECASTING

Univariate

Statistical models


Highly dependent on expertise and availability of responsible people

angle
angle
angle

Multivariate

Machine Learning algorithms


Process any available internal and external data sources to find the best correlations and build an optimal forecast

Managing by exception when it comes to forecasting

FORECAST BASED ON BIG DATA

Actual sales

Trade terms

Promotions

Assortment movements

Customer stocks

Weather

Store openings

Market data

Pricing 

Media spend

Other data to feed

Competitive moves
Macroeconomic indicators
Holidays

CORE STAGES OF LAUNCHING AN ML FORECAST

  • 1

    Data
    discovery & preparation

    ~ 4-7 weeks
  • 2

    Model
    and process
    building

    ~ 8-15 weeks
  • 3

    Integration
    and go live

    ~ 3-5 weeks
  • 4

    Technical
    support

    Indefinite

ACHIEVABLE RESULTS AFTER LAUNCH

  • ~20%
    Increased Forecast accuracy
  • 50%
    By up to 50% time Efficiency Improvement for planners
  • S&OP
    Simplified Process
  • CORE STAGES OF LAUNCHING THE MODEL

    1

    Data & Process discovery

    Investigation into all input data available to build the model as well into the current Forecasting and S&OP processes to fit in. Alignment on Project goals and KPIs before the start.
    2

    Causals engineering

    Selection of relevant data inputs (causals) to start building algorithms. Data pre-processing to feed into the model.
    3

    Algorithms development

    Writing scripts for chosen multivariate ML algorithms like Gradient boosting, Random Forest, Neural network etc. Training and testing for stability, bug fixing.
    4

    Algorithms tournament

    “Tournament” – the model picks up the most suitable algorithm for each forecast item so in the end there is an ensemble of various algorithms which make up an integral model.
    5

    Product segmentation

    ML-generated forecast is split into 3 core segments: No touch, Light touch и Heavy touch depending on the need to manually adjust the forecast.
    6

    Frontend visualization

    The model output can be displayed in Domo or any other BI platform via a special dashboard for planners and S&OP managers as well as for business users.

    Архитектура решения

    Архитектура решения

    Нажмите на иконку

    ACHIEVABLE ROI OF THE PROJECT

    If Forecast accuracy grows by + 10%

    Productivity growth

    Less manual work:

    • Less time to collect data
    • Less errors and time spent to correct them
    • Less IT involvement
    • Less time for forecasting

    Higher forecast accuracy => more reliable P&L => higher quality management decisions 

    More accurate long term forecast => more sustainable production facilities utilization => higher quality CapEx decisions

    Higher customer satisfaction: better contract terms, less penalties, stronger negotiation power

    Costs optimization

    Less out of stock:

    • Higher service level + 3%’
    • Higher revenue +1%’ (a conservative estimation for highly efficient business)

    Less overstock

    • Optimized stocks – 25%’
    • Less write-offs- 17%’

    Leaner production

    • Higher OEE (by up to 11% due to a more forecastable production schedule’)
    • More sustainable and efficient labor management

    ‘Based on an internal assessment by Advanced

    FREQUENTLY ASKED QUESTIONS

    1

    How do you prove that a ML-based forecast is better than the existing one?

    During the Test stage, followed by Train (model building), we create a forecast and compare it with historical data for a selected period, as well as with an existing statistical forecast that was available at that time. Then after launch, we track accuracy by comparing actual data (e.g. registered inventory) with ML forecast data, created several weeks back (usually 4-6 weeks back).
    2

    Which are the key requirements for data to build and run the model?

    No less than 2 years of historical data, several input causals to feed the model (e.g. sales, promotional spend, inventory, distribution etc.), a reliable future forecast for each causal so the model can generate a quality forecast.
    3

    Does the model create a total forecast or a baseline forecast?

    A total forecast containing all relevant forecast drivers which come out of causal data. The model can also be tuned to generate a cleansed forecast called Baseline.
    4

    What is the forecast horizon which comes out of the model?

    The horizon is directly dependent on a future forecast for each causal. For instance, if a forecast for promo investments is available for the next 3 months, the model will generate a reliable 3-month future forecast.
    5

    Can the model be based only on a single causal?

    Potentially yes, but the potential of multivariate algorithms would be lost. There should at least be historical data for a target forecasting metric (e.g. sales) and data for that single causal, for example, promotional expenses to develop and make the model work.
    6

    What do you mean by “manage by exception?”

    The forecast generated by the model is split into several segments: 1) No touch – this is a forecast (e.g. a sales forecast), which does not require review by forecast specialists; 2) Light touch – a forecast which is recommended to be double-checked by experts; 3) Heavy touch – a forecast which has to be created manually. For example, when it comes to a sales forecast, these are the products which have no historical sales data.
    7

    Does the model have any limitations?

    Historical data (no less than 2 years), as well as a future forecast for all chosen causals, are instrumental in making the model run well. In the case of data insufficiency for certain forecasting items, a “no touch” approach will have to be used for manual correction.
    8

    How long does it take to launch the model in full?

    It depends on the complexity of the brief (e.g. a model for the whole business or a part of it), data accessibility etc. On average, the project takes 4-6 month.
    9

    What is specific in your approach to one of the big consulting firms like BCG, SAAS have?

    First, we deliver an open model, not a black box. This means that after launch, a client can develop and support the model themselves without our involvement. Second, our team of data scientists and project managers have extensive experience in working in planning departments of big multinational companies. Third, a relatively low cost of model development and support. Thanks to a frontend visualization solution like Domo, each business user in a few clicks can understand what the forecast is made of and what the reasons are for all outliers.
    10

    What involvement from the business you need?

    Mainly, a client needs to participate in helping define objectives for the project, reviewing all data available, providing needed access to databases as well as during the go-live stage when change management to existing ways of working and processes needs to be made.