Merchandisers, buyers, and planners have a lot to think about.
How much does your customer want? When do they want it? How does all of this compare to the inventory levels and promotional plans?
From analyzing and forecasting financial trends to planning seasonal sales, production requirements, and controlling the flow of purchase for fabric, stock, volume, etc. There are a plethora of things to keep in mind when working with fashion buyers, management, manufacturing, marketing, and all the works.
As their job title entails, planning involves a lot of foresight, and in order to ensure the most optimal amount of orders, and a lack of financial loss, merchandise planners need to have a level of smart risk in their system.
While there is a lot of their job that involves dealing with a multitude of players and frequent travel, trying to keep the head intact throughout the process can be quite daunting.
How Predictive Analytics Can Assist Buyers and Planners
One of the biggest reasons why merchandisers, buyers, and planners need to harness the power of predictive analytics is so they can lighten the pressure that comes from unbelievable foresight.
It can be easy for a merchandise/inventory planner to look past modern solutions, as to them, forecasting is something they are already familiar with. Yet, so much of the techniques used until now are all about the past and leave a large portion of the interpretation up to the planner. Many of the software that helps to forecast models are based around traditional statistical models.
Effective big data & predictive analytics technologies assist planners to efficiently identify leftover stock ahead of time. This, in turn, can help you improve inventory management, which results in fewer cases of unsold product, saving costs.
Moreover, with the right analytics and recommendable actions, planners have the ability to interpret sales by specific locations. You can also receive more accurate recommendations of product assortments along with purchase orders, which can help optimize the planning process.
With machine learning-based solutions like Chain of Demand, planners have the ability to examine the relationship between sales and inventory for every SKU, across multiple stores and channels, all in half the time you’d do it on your own. Additionally, it can also help with setting the most top pricing and marketing strategy, as it drills everything down to an SKU level.
As evident from companies like H&M and Under Armor that reported multi-billion dollars of unsold inventory, more and more retailers (big or small) are beginning to realize the sudden change in climate within the customer journey.
For a lot of these reasons, planners play a huge part in optimizing businesses, as even big brands within the industry are suffering. To do so, learning how to leverage big data and work with predictive analytics/machine learning solutions such as Chain of Demand is highly important.
To survive in this evolving technological world, planners must do what they’ve always done – plan for the future. Just as they had done up till now, they also need to learn to integrate these results together with the people they must always have in mind: management, marketing, and designers.buyerschain of demandmachine learningmerchandisersplannerspredictive analytics