As illustrated across our website, time series are everywhere. Each industry vertical, each domain, each company comes into contact with time series in one way or another. In the use case library below, you can explore the extensive applications of time series, or look for applications in your specific field of interest.
Philippe Thys - Tangent Works
Pharmacauticals & Life Sciences
Manufacturing & Natural Resources
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.
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.
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.
Philippe Thys - Tangent Works
Manufacturing & Natural Resources
Supply chains are under continuous pressure to maintain or improve their market position. The digital revolution lead to a surge of digital transformation initiatives as well as the emergence of new players who leverage new technological innovations to create new business models that trigger a tidal wave of disruptive contenders in an already highly competitive world. The speed of innovation leads to unprecedented dynamics; only the most agile supply chains are able to (re-)act and adapt. As a result of these new dynamics, traditional mid- to long-term strategies must be reviewed and adapted at a higher frequency. Evaluating the impact of market disruption, both from the demand and the supply side, requires advanced intelligence and analytics that can be set up and reconfigured rapidly to evaluate risk and discover opportunity. Traditional AI, and even automated machine learning approaches are expensive, slow and difficult to adapt to support the agility and velocity required to keep your business on track on the short and the long term. By combining business data and market prognosis scenarios with real time instant machine learning, organisations can create new, improve existing, and evaluate more what-if scenarios and simulations for strategic planning and business transformation. Some example of business strategy planning processes that benefit from InstantML forecasting are strategic budgeting exercises, business transformation and design initiatives, strategic product lifecycle planning and optimisation, and the product and product maintenance design process.
TIM (Real Time) Instant Machine Learning can be used to complement what-if and simulation scenarios for budget exercises, adapting maintenance for product support strategies, run forecasting and anomaly detection on digital twins in product design, do risk assessments in your business transformation process, etc. With TIM users can shorten the time to run and compare scenarios, include different future market projections as predictor candidates and easily interface with simulation tools.
Typical inputs for this use case include historical demand, supply, production, prices, costs and strategic performance data, complemented with external data concerning weather, sales periods, global and regional disruptive events, sales campaigns, product introduction information, etc. In return, TIM’s output consists of middle to long term time series forecasts on budget, sales, etc.