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.
How to prepare your retail and online production, inventory and distribution for changing COVID-19 measures?
External factors can have a huge impact on your demand forecast for specific products in specific locations and channels. The constantly changing government guidance during the current COVID pandemic can also cause huge swings in demand, especially when wide reaching regulation – such as decisions to close restaurants or limit movement – can come and go within hours for entire towns, cities or countries. To help businesses dynamically allocate their scarce resources of staff and inventory, the use of TIM and real-time instant ML can create forecasts that are as dynamic as the events that influence them. This can enable fast business decisions to take advantage of opportunities and limit the costs of reacting to current events.
Talk to our specialist about adaptive retail sales forecasting
The TIM Engine is perfectly suited for this application, because of the speed, resilience and ease of deployment. Other forecasting methods, such as statistical forecasting, are far too slow to react to the modern business climate. Single variate models will miss on the complex interaction between seasonal changes, externally driven changes to demand and externally driven changes to mobility. The speed of forecasting is also incredibly important in determining what factors are causing permanent changes to demand and customer behavior and which changes will be temporary.
Using the TIM Engine, an analyst can quickly iterate on models using dozens or even hundreds of features to get predicted impacts on demand in near real-time. This is a must-have tool for anyone in an organization who is responsible for planning of inventory and/or staffing levels.
As an example, using the TIM product we can immediately predict the impact a new restriction in a specific town would have on online ordering in specific postcodes. This can be used to ensure the correct allocation of capital equipment (trucks), staffing (drivers) and product (warehouses) even in advance of the restriction being implemented. With the TIM Engine, this forecast can be available in minutes to respond to significant events that may be happening within only a few days. We can see that a new announcement of a restriction to restaurants causes an immediate surge in online grocery demand that lasts for several days before subsiding to pre-restriction levels. This analysis can be extended to review the impact on specific products at a SKU level – including the ability to run independent demand forecasts for individual SKUs at individual stores in seconds.
Demand Data has real-time access to external data of significance, such as weather data (forecast and history) for any geographic point, as well as real-time COVID cases, mobility and restrictions for each postcode in most major countries. This data can instantly be prepared as inputs to be combined with sales data for specific products, channels or store locations. We have templates available which can plug into sales data at a store level and create the base models instantly (including COVID, weather and human mobility). From there, your analysts can iterate using other data or assumptions they have and get feedback in seconds on which assumptions or data are good predictors and which ones are not.
Talk to our specialist about adaptive retail sales forecasting
Watch a video taking you through this use case below:
Anyone who has ever sat in an S&OP meeting or in budget review will have heard this before: “We would have hit our target if it wasn’t for the (rain, wind, snow, sun, cold, hot…).” Very rarely are these comments backed up with real facts – but you will get the occasional nod around the room. Ah yes… I do recall it rained on the second Saturday in July – that must be why we missed our target by 12%. Or the line in a seasonal review meeting that says, “Our winter boot sales are up this year over last year because of the early winter storm this year and the late Indian summer in 2017, remember???”
The problem with these statements is that though they may be true, mostly they are easy excuses that cover true problems within the business. Maybe there are supply issues, or merchandising issues or a general decline or growth of our brand. Instead of comparing actuals to budget, what if a third line could be added called “Event adjusted budget”. This way, managers, planners and executives can understand the impact that events, such as weather, may have played on our performance, but most importantly, this will help us to understand and think about what else might be happening in our business. Sounds interesting?
The good news is that with advances in Machine Learning and tools like the TIM engine, this is entirely possible to do. By comparing actual sales history at different levels of your organization and bringing in weather conditions on the day as features, a company can start to understand the importance of these features on driving actual sales. Using this data, we can start to see what categories are heavily impacted (ice cream, umbrellas, or the heating bill) and what locations get impacted (outdoor shopping malls, online sales, restaurant delivery). As the TIM engine begins to understand what event variables are driving volatility, we can create historical forecasts based on simulated conditions. (This is what we would have predicted this year based on the same conditions as last year as an example.) This means that the next time your sales rep argues they would have made their target if it wasn’t for the cold front, your business can support this with real evidence. Not only that, but you can start to use live weather forecasts or long-term weather trends to drive predictive interventions – like adding an extra employee at your beach café this weekend.
With cloud storage being so cheap, detailed datasets containing years of history for hundreds of thousands of weather data points are readily available. Not to mention other event data like football games, traffic patterns, holidays and much more. This data is relatively easy to find and there are many services that make this data available historically and as forward-looking projections. With this data and your historical sales and historical budgets, the TIM engine can train models to produce forecasts based on simulated conditions (or after removing them entirely).
Watch a video taking you through this use case below:
Looking for a more in-depth view into this use case? Check out this video:
Launching a new product successfully can be a very stressful but extremely rewarding task if executed correctly. After weeks or months of planning, your new product or line extension is ready to go to market and in the initial few weeks or days of launch, speed and agility is critical. There are several complexities to consider with new product introductions (NPIs). First is that your initial forecast is a “cold start”, meaning that you must make many assumptions and usually have little data on how a product will perform. This makes it extremely difficult to anticipate how your product is going to be adopted in the market and how much inventory to buy or produce. Second is that usually, capacity is finite on an item that has not been made before and the inventory risk is very high – so rarely will your CFO want to take large inventory positions on an unproven item.
TIM’s InstantML can be an excellent solution to managing a new product forecast. Unlike other demand modeling tools, which require history to build models and then are inflexible to change models quickly, TIM can completely rebuild models as soon as the first set of sales data comes trickling in. With TIM, you can rebuild your models daily or even multiple times per day in the first several days of trading. Through storing the projections from each model, you can quickly evaluate how quickly the models converge or know immediately if your original projections are too high or too low. This enables the planning and launch team to react immediately and swiftly to rapidly changing conditions. It has been proven in the fashion industry that the market signal in the first few days of a new item trading is the single largest predictor of the overall long-term margin for that product. Isn’t this something you should be watching constantly and adapting to during those critical first few days?
A new product introduction will start with of course your initial forecast. If you have launched similar products in the past, you can use common features of the product to build an expected demand profile. In launching a new clothing item for example, you can use color, cut, fabric and price points as well as category to help build this. As we move into the launch phase, sales data is critical and getting as much detail as fast as possible is best. Sales by each transaction at each store or sales channel is ideal.
The output of TIM in this case is a timestamped forecast so you can see how the forecast is changing over the next several weeks hour by hour as new data comes in. Unique to the TIM product, is that every forecast can be built based on an entirely new model and each forecast can be compared to previous forecasts. When the forecasts converge and are consistent you can safely assume that demand has stabilized, however if they fluctuate or trend heavily up or down you can immediately adjust your production plan, distribution footprint or inventory policies.
Accurate forecasts of sales and demand in the retail industry can make a huge difference in allowing a company to adapt to changing times. Furthermore, sales planners frequently rely on forecast-driven tools to adjust levers such as product pricing and the timing of promotions. There is frequently also a need to apply these techniques dynamically across different levels of product hierarchy, geography, or other dimensions. However, many companies are still using relatively rudimentary forecasting techniques, which can affect the accuracy of these forecasts. Many enterprise-facing tools are also designed with inefficient workflows, reducing the ability to do effective analysis for end-users such as FP&A and sales planning teams.
Machine learning techniques provide the most up-to-date approach for accurate forecasting, but are time-consuming to implement from scratch. This is where TIM’s highly automated, lightning-fast, ML-driven capabilities can make the difference. Automation means that analysts can use the tool without needing experience in ML theory or in programming. Lightning-fast means that end-users will be easily able to apply forecasting to any data that requires it, and to quickly iterate on scenario planning. It also means that TIM’s forecasting abilities can be applied at scale, on any data that needs to be forecasted. It can also be used in interactive BI applications. For example, planning teams could use the same BI tools that they use every day, select a particular slice of data, adjust some inputs such as pricing and promotions, and use a TIM BI tool integration to get a forecast result – all without leaving the BI tool.
Typical inputs for a sales forecasting application might include historical sales (usually split across product hierarchy, geography etc.), regional store information, local demographics, level of competition, and indicators of consumer demand, industry performance, and economic performance. Sales planning applications might also include pricing and promotion start/end dates as adjustable inputs, allowing planners to see their effect on the sales forecast.
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