Easter is on its way. And Easter, like Christmas, is a peak time for retail businesses and the supply chain. Demand intensifies, stock increases and then the seasonal time passes, leaving the retailer with surplus stock that is both perishable and no longer relevant to the consumer.

For any business with a consumer end product, anticipating stock levels is absolutely crucial. Ensuring you have enough items available to meet the demand, whilst at the same time ensuring you don’t over stock enables businesses to forecast their goods more effectively, therefore helping to save money.

Taking stock

The stock management process has predominantly been based on evaluating historical data relating to a particular item for a particular store or particular sales channel – how much was sold, where it was most or least popular and so on. However, with the rise in e-commerce and the ever changing customer landscape it can be difficult to accurately pinpoint the exact amount of stock needed based on just historical figures alone.

> See also: Tesco saves millions with supply chain analytics

While the factors affecting the supply chain and stock amounts may not have changed – the volume and quality of data available has certainly increased. Rather than anticipating demand and getting it wrong, predictive applications can be used to analyse historical sales data alongside other factors such as weather conditions, special events or price comparisons to accurately plan stock levels.

Now, more than ever, retailers and the supply chain are beginning to wise up to the benefits of analysing their big data streams. But identifying how data can be useful to your business – large or small – is often the trickiest part.

Connecting the data dots

Gathering information and insights from your data that will support the business in its decision-making is crucial, therefore, the first step is evaluating what you are looking for. Analyse your business processes and how you deal with data in your existing systems. It’s important to ask what the data you already have provides.

The next stage is to evaluate to what extent the data can be related to the defined goal variables. Your data structure needs to be measured and analysed. This includes understanding correlations on target variables, checking for sufficient statistics as well as the quality of the target variable and sufficient significance of the correlations.

Predicting pain points

One of the retail industry’s biggest challenges is order and returns fulfilment – it is the number one failure or success issue for online retailers as e-commerce increases. For the customer, online purchasing is all about comfort and convenience – with the freedom of ordering more than one item in search of the right size and knowing that you can return, often free of charge. For retailers however, the increase of e-commerce sales and returns equates to a costly re-entry into the supply chain.

This is exactly the type of pain point that predictive applications can help improve. For example, German online retailer OTTO uses predictive analytics to forecast the return rates on clothing saving between €10 – €15m and reducing return rates by over two million items per annum.

Analysts at OTTO are constantly identifying the causes for returns and developing measures to reduce them. If a dress is returned above an average number of times the system will raise a flag, the item can then be checked for processing defects and, if necessary, be temporarily removed from production.

Unlocking the intelligent business

As the Internet of Things era takes flight, organisations are realising that they can become so much smarter. The data you’re looking for has always been there – it may just be that it was never adequately mined and analysed.

> See also: How to build a holiday-proof supply chain with the Internet of Things

For retailers in particular, recognising they have access to this kind of big data can essentially help them to become more efficient, more customer-centric enterprises that understand how to achieve higher revenues and bigger profits. Highlighting new ways in which the business can interact with its customers to improve sales opportunities or ensure brand loyalty in an increasingly fast-paced and competitive market remains one of the retail industry’s core objectives.

Using the right technology to identify and analyse the right data holds the key to transforming the any business in real-time – whether the Easter Bunny is around the corner or not.

Sourced from Rakesh Harji, UK managing director, Blue Yonder