The fallout from the COVID-19 pandemic and the ongoing war in Ukraine has led to a significant economic slump in 2022. Prices for some common commodities are reaching record levels, retail sales are softening, overall economic growth is slowing, and general inflation is rising. According to the latest Bureau of Labor Statistics report, the annual inflation rate increased by 9.1% for the 12 months ending June, the highest since 1981. With this rise in inflation, retailers face a real conundrum. On one hand, everything from the cost of raw materials to the cost of commercial retail space is increasing dramatically. At the same time, consumers are becoming more price sensitive and resistant to price increases. As retailers look to minimize operating costs to maintain margins, inventory planning and allocation are critical steps that retailers simply cannot afford to overlook.
For the past two decades, retailers have been establishing better integration between their physical and online operations, including inventory management, product information, price matching, flexible delivery options (click-and-collect, ship from stores etc.) and omnichannel customer interactions. In fact, the growth of omnichannel retail is responsible for the evolving challenges retailers are facing, particularly in allocation and replenishment, which require proactive and integrated strategies across points of sale, responsive allocation, and timely replenishment.
When we step back and look at the rapid changes in core merchandising effectiveness, coupled with the demands from an uncertain economy, we focus on six steps for retailers to optimize allocations and drive profitability.
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Say “Goodbye” to traditional forecasting methods which are wrong half of the time, and get accurate forecasts to power your business.
Accurate demand forecasting is one of the most powerful ways to improve just about every part of your business, starting with the ability to better allocate products to meet demand across your distribution centers and channels. For years, the traditional forecasting algorithms have leaned very heavily on historical data. But with rapid changes in product preferences and consumption patterns, especially during this post COVID period, businesses need a more robust framework that includes factors beyond just historical data. We typically find that using recency as well as dozens of macro exogenous inputs (like fuel prices, consumer sentiment, etc.) are necessary to accurately predict demand.
Traditionally, forecasting down to the item/store/week level using legacy systems is challenging because of the sheer number of combinations requiring review and action. To optimize results, the inventory allocation effort must excel at the item store level. The modern machine learning merchandising solutions that leading retailers are deploying help merchants and planners drill deep into product and size level granularities to predict the exact style-size combination to serve customer demand across both offline and online channels. Retailers can leverage AI-powered demand forecasting systems for identifying recent trends, seasonality, and other unique demand drivers by leveraging internal and external data. Retailers that go this route generate accurate demand forecasts for all SKUs at the store, style or any hierarchy level across their entire lifecycle.
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Stop banging your head against the wall with manual allocation processes, and implement the right allocation software.
Traditional ways of running the allocation process involve a vast number of repetitive tasks, and constantly revisiting a large portfolio of SKUs and stores manually. This is one of the least productive activities for any retailer. An ideal allocation system should provide powerful, yet easy-to-configure automated workflows that eliminate repetitive user-system interactions.
Creating and assigning complex rules must not only be easy, but also free up many hours of an allocator’s time. Advanced retail allocation software not only improves ROI on end-to-end inventory planning and management through highly accurate forecasting, but also helps retailers cut inventory costs, decrease markdowns and increase margin dollars through better planning and allocation optimization. With auto-allocation functionality, the system selects products for allocation and automatically creates a set of allocation plans in the background reducing the time spent on product allocation and allowing the team the time to focus on high-value work.
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Focus on the things that actually need attention – by effectively managing exceptions.
Exceptions are a means of reporting exceptional situations, such as problems or errors, which may occur during the allocation calculation or when finalizing the allocation plans. Exceptions can occur for business reasons (for example, insufficient available quantity in the distribution center) or for technical reasons (for example, missing parameter settings). Mismanagement of exceptions can lead to out-of-stocks that create dissatisfied customers and mark-downs that erode margins. Allocators who try to tackle exceptions manually may not be able to take timely corrective action. In most retail settings waiting a few days just won’t cut it!
With the right retail allocation software, exception filters notify the allocator about the high-priority problem areas so they can be addressed before routine activities. Such systems must provide the ability to set up a range of constraints and business rules in accordance with the retail strategy.
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Stop guessing! Simulate and compare multiple allocation scenarios before finalizing.
Manually generating multiple plans and comparing their effectiveness is very labor and time intensive, so most allocators simply don’t do it.. They use an approach they are comfortable with, though typically not the optimal one. In fact, with manual observations, it is typically impossible to anticipate the different outcomes of implementing one sales plan versus another.
Advanced allocation systems can test allocation strategies prior to execution using robust what-if analysis. They have functionalities to model different rules and parameters and generate alternative sales and stock plans within the tool. For agile decision-making, the store plans must be calculated at the click of a button so that multiple versions can be easily compared and the best option for driving allocation and replenishment processes identified.
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Defend your turns by expediting the sale of slow movers
Markdown management is critical to expedite the sale of slow-moving stock during the season. Most retailers get their markdown strategy wrong and pack all their clearance messaging into just one clearance event each season. At the end of a season, retailers usually sell off the leftover stock at much higher discounts than would have been required to make room for a new season. Retail leaders must deploy a comprehensive end-to-end lifecycle pricing solution which considers seasonal timing and sales curves, price elasticity, price-points/markdown breaks, and frequency of markdowns to develop an optimized markdown solution. This will help retailers create careful clearance patterns that extract the most revenue, sell through the most product, and preserve margin throughout the season.
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Proactively manage in-store returns from Omnichannel sales.
The adoption of omnichannel commerce has increased the complexity of returns as customers who buy online often prefer to return the goods offline at your brick-and-mortar stores. Retailers, therefore, have to facilitate easy return management by putting in place quality checks and product sorting right at the store level, or at a nearby fulfillment center. Finally, in some cases, they are required to accommodate speedy reverse logistics to the warehouse so that the merchandise can be made available for resale as soon as possible. This is complicated and requires having powerful MFP tools in place to make this work seamlessly.
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Given the current retail climate, retailers must grab every opportunity they can, to drive profitability. Unsold inventory is one of the main causes of both margin pressure and waste. Optimizing inventory allocation processes is an important part of helping retailers minimize such avoidable costs. And by rationalizing their decision processes, retailers will also benefit from the increased sales that will result from achieving higher levels of availability and faster turns across channels.
How can Impact Analytics help?
IA’s robust predictive analytics can take into account millions of data points across myriad variables such as seasonality, trends, inflationary pressures, price elasticity, supply chain challenges, customer demand patterns, and many more, to provide actionable insights around assortment, allocation, pricing, and overall financial planning. Combining technology innovations—such as big data analytics and ML-based algorithms, IA also provides superior demand forecasting to tackle shifts in consumer demand patterns and rising inflation.
If you wish to improve your inventory turnover and reduce markdowns by leveraging accurate demand forecasts leading to the ability to execute automated retail allocation and replenishment strategies, Impact Analytics can provide the perfect solution.
