Demand forecasting: 5 best practices to deal with volatile demand

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As consumer sentiment (and buying behaviour) now shifts more quickly than ever before, it has become practically impossible for fashion retailers to produce a meaningful demand forecast.

Collections are still typically produced many months in advance, meaning the potential margin of forecast error widens over time with increasing uncertainty about real demand. It can become a titanic problem if demand unexpectedly changes during those long months – and it often does. Even when things go perfectly to plan, this way of doing things limits profitability with inevitable understock or overstock at the various points of sale.

The solution to this problem is already here – in fact, it is right under our noses: data.

Retail businesses routinely collect vast amounts of data as a part of their regular sales processes, and there is much more available for collection and analysis if the strategy is in place to handle it. If you don’t collect much data aside from sales figures, it is also possible to buy data and insights in a thriving market of data and analytics tools.

Data is worth serious money if you can wield it effectively. Used properly, data can focus your efforts – like a laser – in the exact points where they will have the most impact. Using a rationally designed analytical process, raw data is turned into something meaningful – like an understanding of the relationship between real demand and external factors. These influences might include uncontrollable factors, like the seasons, weather or sporting events; or they may be factors that you control, such as promotions or special sales events.

By harnessing the insights from your available data, it is possible to make better decisions that manage the supply process more effectively and reduce the reliance on faulty forecasting.

demand forecasting

How to deal with volatile consumer demand?

Even without using more complex data insights that can actually predict trends, you can make an immediate positive impact on operations by using data for simple but effective decisions that respond to real demand signals.

How to deal with volatile demand, step-by-step:

  • Step 1: Move inventory only when needed. By holding more inventory closer to the source, excess inventory is minimised.
  • Step 2: Only supply what is actually needed. With the adoption of a more short-term perspective, the supply chain can react more accurately to real demand changes. Data can define replenishment quantities that match a ‘reasonable expectation’ of demand, incorporating variables like average replenishment time, supply constraints and a safety margin (Paranoia Quotient).
  • Step 3: Replenish frequently. More frequent adjustments enable retailers to closely mirror actual sales instead of using a guess/forecast.
  • Step 4: Constant monitoring. With a close eye being kept on actual consumption rates, the inventory safety buffers can be adjusted to ensure perfect supply levels – even when demand suddenly changes.
  • Step 5: Make your inventory more flexible. By pooling or trading inventory with other retailers, partners within the fashion retail sector can mitigate risk and create new opportunities. Cash generation is maximized with a flexible supply strategy that can actually cater to unpredictable demand.
  • Step 6: Embed the process. Training and long-term support can help new methodologies take root and flourish. Use the consulting service supplied by Retailisation to ensure effective data-driven processes are incorporated into the daily routine.
demand forecasting

Say goodbye to bad demand forecasting

First the question needs to be asked: “Why do we feel that collections need to be made at least six months in advance?”

This long-standing methodology has served the fashion industry reasonably well for many years, but the tactic has now outlived its usefulness. As global development and prosperity grows, we cannot expect to find a new source ‘cheap foreign labour’ every few years – the world is only so big.

As ‘manufacturing countries’ become thriving consumer markets, the rational move is to divide collections into two parts. The bulk of the collection will come from local, near-shored responsive production (or ‘make-to-order’ items), while a safety buffer of ‘make-to-stock’ and ‘evergreen’ items can be sourced from further afield or from consolidated mass-production facilities.

This flexible supply model is more suited to the requirement for flexibility that is demanded by the consumer. The intelligent use of data can further empower a more agile supply chain which is focused on just the next replenishment cycle, and which offers more accurate guides for the next two or three replenishments down the line (using dynamic modelling based on real-time data). This is a more realistic goal than guessing what the customer will want to buy (and how much) in six months’ time.

Anticipating demand changes due to planned events and promotions

With this set-up, replenishments are generally conducted based on ‘reasonably expected’ levels of consumption over the following period (before the next replenishment), using data from the previous period/s. However, consumer demand can also be affected by seasonal promotions or planned sales events.

Thankfully, a well-designed retail software solution can incorporate this kind of data into the decision-making process. This will ensure that decisions about what to make, buy, order or ship will always reflect the real demand, and factor-in the effect of known influences that will affect the real demand.

The only thing that is needed is for the user to add this data into the system, simply by defining the scope and duration of any planned promotional events. This can even be defined right down to the details of which products and locations are to be included in the sales event.

At Retailisation, we’re dedicated to improving the fashion supply chain by delivering good supply chain decisions. This means ensuring that the supply chain can also respond effectively to promotional events by making it easy for users to add this data into the equation. This way, the supply of merchandise will continue to perfectly meet the demand of the consumer, wherever and whenever they come to buy.

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