The omnichannel expansion of the apparel world has been quick for retailers, with technology taking quantum leaps to bridge physical and digital touchpoints. But it has been a rather painful one, considering that it has complicated the demand forecasting journey. An apparel business lives and breathes on customer insights. So, every retailer must have near-accurate information about how much demand there could be for a particular product.
Knowing which product is likely to generate the most sales or how many customers are willing to purchase, however, is only half the job done. In today’s omnichannel climate, demand forecasting has evolved – along with customer purchasing preferences – from moving to digital from brick-and-mortar to now being ready for both worlds. The emergence of various fulfillment options like BOPIS, curbside delivery, or doorstep delivery means that there’s no going back. It also means that retailers must be better-positioned to tackle omnichannel challenges in demand forecasting such as:
- Irregularity in predicting inventory positioning during peak holiday seasons
- Unable to optimize inventory management during seasonal variations
- Unreliable fulfillment due to lack of channel-based insights
- More risks involved in new product introductions
- More complicated to forecast the profitability of offers and promotions
- More difficulty in getting a unified view of inventory across all touchpoints
The fact is that time is not on the retailer’s side. If you are looking to future-proof your inventory management and avoid unexpected stock outages or overstocking, there is no room for you to adjust your demand forecasts. As can be evidenced in the above challenges, channel expansion has complicated risk management, as well.
What does it mean?
A more critical need for more accurate demand forecasting
With “omnichannel” becoming a bigger priority for retailers, there’s no separating demand forecasting, irrespective of the channel. With multiple shipping points, managing inventory levels has become fully dependent on the level of available customer and product insights. It improves supply chain dynamics that, in turn, leads to better inventory and replenishment planning strategies across brick-and-mortar stores and e-commerce stores.
It’s also why Artificial Intelligence and Machine Learning technologies have grown to play a major role in demand forecasting, inventory allocation. Whether planning SKU assortments or finalizing pricing strategies that span all channels, they help bring in more operational efficiency while ensuring consistent customer experiences.
However, in the e-commerce apparel industry, this can be tricky. Constantly changing fashion trends and unpredictable customer preferences make it difficult for you to accurately predict demand forecasting. It may result in a significant decrease in revenue and uncontrolled spurts in product overstocking.
Understanding channel-based customer demand
For apparel retailers, it is important to attribute customer demand to specific channels. While unified commerce allows for more shopping flexibility that customers find attractive, it may cause confusion to retailers while demand forecasting. But when armed with deep-dive insights about all digital and physical channels, it can significantly improve replenishment fulfillment efforts.
Find the right inventory balance based on experiential metrics
Bottom-line sales may seem like an obvious way to manage SKU management, but cumulative experiences of customers tell a clearer and more long-term story about what apparel retailers need to do. The focus must be on reducing shipping/servicing time for customers by offering omnichannel retail experiences, instead of merely meeting the existing demands of a particular channel.
Forecasting impact for non-recurring sales triggers
Given how much apparel businesses are impacted by social media events like influencer posting, demand forecasting should be interlinked with sales and marketing strategies. It may be too expensive a promotion to be non-recurring, but the impact can be so huge that your SKUs better be ready for it!
In a recent collaboration with the Oracle Data Cloud, Oracle Retail Cloud solutions bring together an undefeated mix of business insights that combines interoperability, security, cost efficiency, reliability along with embedded Machine Learning and Artificial Intelligence. This power-packed solution enables retailers to build a strong relationship with their existing customers and also attract new customers through effective acquisition campaigns.
How to maximize forecast accuracy with Oracle
Create user-defined forecast profiles: Get predefined forecasting profiles to generate accurate omnichannel demand forecasts. They are based on consumption history, shipping history, or booking history across physical and digital channels. While predefined profiles can’t be edited, you can modify the copies or duplicates of these profiles as per your liking and create user-defined forecasting profiles that suit your business perfectly.
Ample methods for demand forecasting: Leverage 15 different forecasting methods that can be used individually or as a combination of more. These methods can be controlled through forecasting profiles and maximizing forecast accuracy for the products’ entire lifecycle with tailor-made approaches for both long-term and short-term trends (micro and macro trends). You can drive ideal strategies in omnichannel inventory planning, reduce operational costs and increase productivity in inventory for supply chains.
Predict demand for new products: Harness adaptive intelligence while applying best-suited forecasting methods. By doing this, retailers can adjust and build forecasts for products with no previous historic data and can also keep up with seasonality and different trends.
Expedite business growth with retail science: Maximize the value of all past data and information by applying retail science drawn from AI and ML. It transforms raw data into business and marketing intelligence that can be used to make timely decisions around campaign management, inventory restocking, and more.
Boost customer engagement: Adapt to the most recent trends, promotions, seasonality, and out-of-stocks and cater to the biggest demand drivers. It helps deliver superior customer engagement – right from awareness to post-purchasing support. Retailers can also leverage from the dashboard to support forecasting workflows such as forecast scorecards, forecast approvals, overviews, and exceptions.
Increase inventory productivity: Gain transparency across the supply chain enabling end-users to engage in the forecasts and analytical processes. It goes a long way to increase inventory productivity in the long run.
Oracle’s Retail Demand Forecasting cloud services help e-commerce fashion retailers take immediate advantage of both new and old product updates to ensure minimal business risks. With an integrated and complete platform for all retail solutions, hardware, and cloud solutions, retailers can pivot to consumers and deliver superior customer experiences – anytime and anywhere.
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