Use Case Library

Author:

Philippe Thys - Tangent Works

Tags:

Retail

Healthcare

Transportation

Pharmacauticals & Life Sciences

Manufacturing & Natural Resources

Operational Excellence

Control Towers

Companies who monitor (in real time) tactical and strategic change to identify gaps and discover market opportunities will maintain or increase their competitive edge. Operations managers and senior executives use control towers to get visibility in supply chain operations. By collecting and combining data from a growing number of new information sources like IoT, GPS, and electronic logging devices companies get an additional layer of intelligence across their operations, and across enterprises. Information on production processes, stock levels, shipment and orders can now be tracked to a new level of detail, enabling supply chains to optimize contingency plans by monitoring disruptions, evaluating the impact towards the plan, and acting in real time. Incorporating this type of new data streams into traditional track and trace, S&OP, or supply chain monitoring activities is not straightforward; organizations are looking into data science to open up new options to produce meaningful outputs and to use that output for improved risk mitigating strategies and operational processes to boost their performance when reacting to unexpected disruptions or market opportunities. 99% of the data used in control towers to monitor supply chain information consists of time series data streams. This information can be used in AI/ML to evaluate future behavior, calculate the impact on performance, and act accordingly. Evaluating the potential performance improvement from the introduction of AI/ML with the goal to adapt your operational scripts is not an easy task. Defining the right AI/ML strategy, what ML approach to use, how to train and configure your models and then deciding how to deploy them tends to be a timely and costly project. And then there is also the enormous number of potential uses, scenarios, and configurations of supply chain networks to take in account. Once you choose, configure and deploy your models, you will need to continuously monitor your setup for performance (accuracy) deterioration due to changes in the data sources, changes in supply chain and logistics networks, changes of business models, and last, but not least, the dynamics of your business and industry. Typically, this is covered by having a department of specialists who maintain and optimize these configurations and deployments.

TIM’s Value

With TIM’s Real Time Instant Machine Learning (RTInstantML) forecasting organizations can skip the configuration process and immediately deploy and execute ML models that adapt to the input data streams without the need of human intervention – and this in near real time. This allows companies to embed AI/ML into their control towers with the benefit of better insights into future events and their impact and hence react faster and smarter. And all this at a fraction of the cost and the time required using traditional ML approaches.

The Data

Typical inputs of this use case include data from the supply chain operations (IoT, schedules, planning, throughput, etc.), logistics (ELD, GPS, IoT, stock levels, order status, etc.), sales and marketing (campaigns, new orders, etc.) and environmental data (infrastructure, weather, etc.) Typical outputs of this use case consist of time series forecasts on various reporting aspects in the control towers (performance, ETA’s…). This data can be compared to the to-be situation to calculate predicted performance, potential diversions to the plan, etc. as input to your contingency actions.

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Author:

Henk De Meetsenaere - Tangent Works

Tags:

Healthcare

Transportation

Government

Operational Excellence

Risk Management

Forecasting

Strategic Supplies

Consumption of medical supplies and the need for certain raw materials or other potentially scarce resources need to be forecasted by governments and medical institutions. Normal fluctuations in consumption patterns can be complemented by sudden structural changes due to extreme events, climate change influences and epidemiological changes. This requires adaptive forecasting models that capture new dynamics fast and give insights into the underlying demand influencers.

TIM’s Value

TIM RTInstantML models allow end users and operational experts to automatically generate predictive models. The Augmented Machine Learning capabilities of TIM elucidate insights in the dynamics that underpin the forecasted values. TIM allows for fast recalibration or recalculation in minutes so models remain accurate and update as new data flows in.

The Data

Typical data sources constitute weather information, calendar information, demographics, major event planning and epidemiological indicators.

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Author:

Henk De Metsenaere - Tangent Works

Tags:

Healthcare

Operational Excellence

Risk Management

Forecasting

Hospital Admission Rate

Admission rates of patients in hospitals affect both the Supply Chain and Human Resources planning of a hospital. Admission rates fluctuate based on human factors linked to weather, calendar and time of day information. Disease spread and epidemiological evolutions introduce potential structural changes. The differences between day and night, correlations with weather, public holidays, events and medical parameters further define admission rates. Hospitals need to organise and optimise their supply chains and staffing accordingly.

TIM’s Value

TIM’s RTInstantML technology gives business users the capability to generate predictive models in an automated and fast way. This allows for fast results and what-if analysis. TIM’s Augmented Machine Learning capabilities give users insights in the underlying influencing parameters, enabling them to understand and analyse forecasted results. The adaptability TIM brings, allows fast adjusting of forecasts to structural changes in the data, so that forecasting models can adapt to new situations and events, such as pandemic information.

The Data

Typical data for admission rate forecasting includes weather information, calendar information, time of day insights, epidemiological data, etc.

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Industries

Areas

Type

Additional Tags

Control Towers

TIM’s Value

The Data

Strategic Supplies

TIM’s Value

The Data

Hospital Admission Rate

TIM’s Value

The Data

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