Predictive Ordering: Boosting Availability, Minimizing Inventory, Elevating Customer Satisfaction

Predictive ordering is the key to always having the right quantities in stock: enough for your customers but never too much. Learn how to efficiently predict demand for your products to optimise your operations and customer experience on an ongoing basis.  

With predictive ordering you look at future product demand based on historical data, a variety of trends and other factors. Unlike traditional forecasting, which only gives an estimate of demand, predictive ordering also predicts when exactly that demand will occur.

The added value of predictive ordering varies by sector:

  1. For goods with limited shelf life - such as food - predictive ordering reduces waste. Restaurants and food service providers, for example, can order their ingredients and supplies based on menu offerings and customer traffic patterns.
  2. Pharmaceutical companies face regulatory requirements they have to comply with. They have to guarantee the shelf life of their products and anticipate fluctuations in drug demand, for example during flu season.
  3. Retailers selling consumer electronics often have to anticipate product launches and technological advances. They order their inventory before product releases so that stock is available when consumers are looking for the latest gadgets.
  4. The fashion industry is all about seasonal trends, fashion cycles and changing consumer preferences. Again, retailers need to order their stock well in advance of the season to ensure they have the right products in stock during busy shopping seasons, such as school holidays, holidays and specific fashion weeks. With predictive ordering, these retailers can also predict declining demand so that they need to put fewer products on sale.

For most non-food companies, the value of predictive ordering is in lower inventory, which means you realise lower interest costs, space costs and risk costs. And the following applies to any business: you improve the customer experience by always keeping enough stock to meet demand. The challenge for all businesses is just how to put predictive ordering into practice. With the rapid breakthrough of AI in the field of language modelling, you might expect predictive ordering to be quickly set up with this as well, but that's not happening for now. Predictive ordering stands or falls with human insights.

Take the human factor as your starting point

Your people on the work floor know the market and customers better than anyone else. The insights of the sales, marketing and operations departments are invaluable when creating scenarios and hypotheses. Intuitively, your people know what is going on and what you should be looking at to determine future demand. 

Machine learning and AI only come into the picture when you are clear on what insights you are looking for in what data. They are then ideal tools for finding out key figures, statistics and trends. Take Black Friday as an example: predicting demand based on previous sales alone was not possible because a lot of consumer electronics production was down due to corona. You will have to identify these kinds of determining factors with your employees before you can effectively include them.

Setting up predictive ordering is done in four steps

  1. Collect data - Start by collecting the right data: historical sales data, customer segmentations, customer behaviour, sales promotions and marketing campaigns, market figures, seasonal influences, technology trends, etc. All the data you use should be accurate, up to date and clear.
  2. Prepare data - Before you can use the data for predictions, they need to be prepared by employees with knowledge. For example, they can recognise and remove inconsistencies and outliers, so they don't give a distorted picture.
  3. Modelling - Next, you start processing the prepared data into a prediction model. There are several models that work in different ways. Choose a model that fits the data you have available and the complexity of your product or market.
  4. Testing - Before you start actively using the prediction model, you will first need to determine its accuracy. Compare the predicted demand with the actual demand to learn to what extent you can trust the model.

Keep improving continuously with prediction model

No prediction model will continue to work well without continuous improvements. Create a feedback loop that allows you to continuously improve data quality, add more relevant data sources or remove irrelevant data and comprehensively identify patterns. The reality of the market is constantly changing: the challenge is to follow it so regularly that your predictions keep getting better.

Major organisational changes also have a big impact on the forecasting model, such as new product lines or new markets being entered. Make sure the model can scale with the organisation. Even then, it is crucial to keep human insights leading. Relying too much on new technological capabilities or external specialists with insufficient knowledge of the market is a pitfall.

Best practices for excellence

  1. Use quality data - The quality of the data is crucial to the accuracy of predictions. Make sure the data is clean, correct and consistent.
  2. Collaborate across departments - Involve departments such as sales, customer service, marketing and operations. Their insights and feedback are essential for establishing, testing, assessing and adjusting assumptions and forecasts.
  3. Keep adjusting - The world is constantly changing: assume change. Adjust the model continuously to seasonal influences, trends, new developments and circumstances.
  4. Look at your competitors - The customer relationship also includes the competitors your customers look at and compare you to. A sharp price cut or an improved customer experience can have a direct impact on your forecasts, so include benchmarks in your forecasting model.
  5. Ensure knowledge transfer - Knowledge of the market or how a forecasting model works should be as widely spread throughout the organisation as possible. Therefore, ensure knowledge transfer between departments and employees so that the operation does not remain a black box.

Finally, the most important best practice of all is to use the insights from predictive ordering to actually improve your operations. If you don't take action on your created predictions, then you are actually of little use. Make sure that an employee or department takes ownership of the realised predictions and translates them into a more effective procurement process, lower inventories, successful marketing campaigns, dynamic pricing, less overhead and, above all, a better customer experience.  
Are you going for a little better every day with predictive ordering? Then your long-term success can be predicted with ease.


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