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The “Amazon effect” has already changed shipping, logistics, employment and consumer behavior, not to mention giving a good kicking to brick-and-mortar stores. As a result, Demand Planning and Forecasting is very different to how it was just a few years ago, and our jobs have evolved drastically. We must understand that Big Data and shifting consumer demand have changed everything.

New Opportunities And Challenges in Demand Forecasting

When it comes to Demand planning and Forecasting, Amazon and – e-Commerce generally – has been a phenomenally disruptive force. This new world is impacting what we forecast, how we forecast it, and when we forecast it. This creates challenges but there is also a key opportunity presented by the new e-Planning environment, and that lies in the abundance and availability of data. Big Data is driving the following fundamental changes in Demand Planning:

  • Machine Learning and Neural Networks are replacing traditional time series methods
  • Web crawlers are replacing syndicated data
  • We are using more real-time or daily forecast at a more granular level
  • We are using more analytical decision making and demand shifting techniques

Adapt Your Techniques

In the new e-Planning world, we are forecasting orders less and looking more at a wider variety of inputs. The most well-established forecasting techniques are based on historical demand and they have served us well, but with e-Commerce, we have a new wealth of information that tell us not only about the future, but also the present. Predictive analytics encompasses a variety of statistical techniques like predictive modeling, Machine Learning and data mining that analyze current and historical facts to make predictions about the future – all in a way that we could never hope to replicate with time series forecasts.

Once you understand the drivers, you can  influence demand like never before.

Instead of looking at just shipments or sales history, we have access to website clicks, rankings and the number of customer reviews. Using a multivariate regression or recurrent neural networks, for example, you can isolate variables and determine their impact to predict future sales. The greatest benefit of looking at these attributes and variables is that we may not only be able to predict sales by week going forward, but also understand what will change and by how much.

Prediction is becoming more about behavior than history. This is powerful because once you understand the drivers, you can  influence demand like never before. In the age of Big Data, therefore, we can be proactive instead of reactive. Amazon are a perfect example of this; when shopping on the Amazon website, you will have seen the recommendation engine. It recommends an item based on what you purchased before on the same website. Your recommendations are filtered by a variety of drivers such as genre, price, brand and interests etc.

Be More Agile

In today’s business environment, changes in the marketplace are swift and sudden and may not follow the historical pattern, meaning time series models cannot always be relied on for accurate forecasts.  The new e-Planning environment is not only dynamic, it operates on the power of technology and innovation. If it is still taking you a month to gather data, create a forecast, add assumptions, and develop a forecast, I’ll tell you now that you won’t be able to keep up. But you may know that already.

The winners in this new era will be the ones that can see, interpret, and act on data the most efficiently.

In many industries, forecasting actual product sales is often a lengthy business process. With e-Commerce, however, you are competing almost in real time with price, features, and delivery promises. And feedback comes just as quickly in the form of reviews and competitive responses. To be more agile, companies are looking at demand sensing techniques to translate the drivers into rules based or machine-learned responses. This brings us closer to not only to the level of demand, but also closer to demand intent.

Even more importantly, the e-Business environment provides an opportunity to readily collect information about potential or future buyers. Along the different channels of communication between an organization and its potential customers, most companies now routinely log every visit to the product webpages, every call made to an inquiry response center and every email that was received. Many organizations also use every customer touch point as an opportunity to perform a brief customer survey to collect information about their customers and comments on their products.

Where traditional demand sensing focuses purely on Point Of Sale (POS) data from retailers aggregated weekly, you are now absorbing sales on an hourly basis or even quicker. Where third-party syndicated data from Neilsen and others could help you better understand markets and competitors, now you have web crawlers that traverse multiple sites and bring you relevant data whenever you want. We have more data than ever, and we’re getting it faster than ever before – the winners in this new era will be the ones that can see, interpret, and act on it the most efficiently.

Get Detailed On Promotions

Targeted marketing has created complications in the analysis of promotional effects. The traditional way of applying a general “lift factor” to nominal demand when a certain promotion is performed may not be adequate.  Add to this the changes we are seeing with dynamic pricing models, and time series forecasting reveals its limitations.

Online reviews correlate to sales so much that you can even use them in modeling.

Changes in the selling price and the presence of product promotions are known to have a significant effect on demand in many industries. Today, in large part due to analytics and the proliferation of data, price changes and promotions are cheaper to do and have greater impact. Price changes on the web or a pop up on your website shifting demand incur little incremental cost. Even in traditional retail stores, the day will soon come when a button on a computer is pressed to issue a price change, and new prices will be reflected on an electronic label in a physical store a few seconds later. Such opportunities imply that price changes and promotion actions may be used very frequently, and so they can no longer be analyzed separately from “normal” demand. This requires a disciplined process to capture the information in a timely manner.

Offer What the Customer Wants

One of the biggest challenges we have seen in demand forecasting over the past few years is shorter and shorter product life cycles. This is absolutely necessary to meet consumer demand. In the e-Planning realm, this will be compounded by a whole new set of challenges. Why? Because constant new products do little for building up the necessary reviews in the e-Commerce marketplace. Think of your experience shopping online – you have one item with 2000 reviews and 4-star rating and another with just 5 reviews and a rating of 4. It is actually more beneficial to keep older items with good rankings than introduce new items every 18 months. Online reviews correlate to sales so much that you can even use them in modeling.

This works well for items with multiple reviews but we still need to replace poorer performing products.  With e-Commerce going directly to consumers, speed to market is crucial. What’s more it makes it very difficult to plan. Because individual products are phased out and new products come in constantly over time, and the fact that the hierarchy might be reorganized fairly frequently to reflect the fast-changing business environment (e.g. gaining or losing major customers or markets), the product hierarchy is dynamic.

And there you have it. Amazon has totally revolutionized the marketplace, and with it demand forecasting and Demand Planning. If there’s one there’s one concept that all forecast analysts and Demand Planners must understand, is that companies will live and die by their ability to gather, interpret and act on data.