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.
Michal Bezak - Tangent Works
Contact centers typically operate with a pool of resources. For contact centers, predicting volume of incoming requests at specific times is critical to make proper resource scheduling. In such case, forecasts are expected for very short, and short-term, horizon (e.g. a week ahead).
High-quality short-term forecast brings confidence that FTEs (full time equivalent) planned for the next week are just right for delivering on SLAs, not to mention other benefits, such as higher confidence when planning absence, or improving morale of employees who would not face overload from “sudden” volume peaks.
Predicting volume of requests for mid-term horizons, e.g. 3-months ahead for weekly data, is important input to resource management. It takes time (weeks if not longer) moving people around, hiring, upskilling, or down-sizing pool of resources. Because of this, forecasts for longer horizon are needed, starting from one to more months.
The picture below depicts how contact centers are typically linked to WFM (work force management), internal departments and other factors. This provides intuition which factors should be included in data used for building models and forecasting.
Big contact centers would support not one, but multiple regions, cultures, and languages. Very likely forecast by language, or country would be beneficial.
Having forecast for just one perspective, one or two prediction horizons is not sufficient, also dynamics of ever-changing business means that using model built one month ago is suboptimal. This means that having capability to build models and make new predictions instantly is necessary for successful management of resources.
TIM can forecast for any prediction horizon, from intra-day to days or weeks ahead, and thus can be used for short term, midterm as well as long term forecasting. It can build new model in faction of time, building models from the latest data which helps to achieve better accuracy.
It’s automated, thus your analysts and data scientists have free capacity to focus on other agenda. You’d gain new capability, more frequent forecasting or forecasting per various perspectives with minimal additional effort is possible.
Effort and time required to set up forecasting is reduced to fraction of what would be typically required. TIM, by design, automates most of the steps required for set up and operations, and offers robust ML solution capable to quickly adapt to structural changes.
High-quality forecasts would deliver following benefits:
Explanatory variables should include historical actual volume values, meteo predictors, holiday information, marketing activity (campaign) information, factors describing customer base, planned outages, and/or other relevant data with as low latency as possible.
TIM’s output consists of forecasted volume of requests per each hour/day/week in selected prediction horizon.
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.