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Making the Future Foreseeable | RIS News

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At one point in time, traditional prediction methods worked. But the global market disruptions and fast-moving trend targets can keep retailers guessing – even though sales data is ample. Today, most retail processes still struggle to directly connect consumer demand with ordering, purchasing and replenishing in a way that can react quickly to the changes in shopping behavior. 

The typical approach is to look back at past sales data to build annual and quarterly plans. Then you set those plans in motion, purchasing goods and making sourcing decisions. The next step: hope. You hope your predictions correctly interpret reality. And you hope that your execution systems can make efficient changes as the season arrives and adjustments are needed. 

As consumer habits and supplier patterns shift and evolve, traditional retailer challenges remain – but they become more difficult to overcome with traditional solutions. Here are just some of the ongoing retail demand-planning pursuits:

  • Understand and predict local consumer behavior. 
  • Understand and predict product mix preferences.
  • Understand and predict apparel sizes applicable to different markets. 
  • Understand and predict trends and seasonality patterns. 
  • Understand and predict consumer-demand shopping patterns across different retail formats. 
  • Understand and predict price sensitivities in local markets.

What’s the obvious common denominator? Understand and predict. This is the job of big data. 

Retailers have data. Lots of it. But is it the right data to address the complexities of the modern marketplace and ongoing global supply chain issues? What are the breakdowns in process or technology that are stopping you from getting the data you need? To get from mere data points to insights – that is, to understand what’s happening and predict your responses – you need analytics solutions. Sophisticated capabilities like machine learning can consider hundreds or thousands of input variables to solve your perpetually moving target. By directly connecting consumer demand into a faster cycle, you can adjust down to monthly and weekly levels at a moment’s notice.

For example, new product forecasting and introduction are always difficult challenges. Highly “fashionable” retailers are dealing with a significant number of new SKUs each season. Figuring out how to predict the performance of those new SKUs is a space where analytics-driven demand planning can help greatly. 

Supply Chain Planning

Even if you get a predictive handle on shopper patterns, online buying behaviors and trends for your own products, today’s material shortages and transportation disruptions are making supply chain visibility a key factor for providing successful on-shelf availability. Collaboration and data connectivity throughout your supply chain allow all players within the network flow to assess what’s happening and respond accordingly, pivoting quickly – together. 

But it’s not enough to focus on your own supply chain. You must be aware of the adjacent supply chains that ultimately affect your business; you’re only as resilient as they are. If there’s a shortage of personal protective equipment (PPE), for instance, and all the PPE is going to hospitals, then the people who clean out your food production lines can’t get it. Suddenly, you’ve inherited someone else’s supply chain challenges. Situational awareness of your partners’ risks is becoming an imperative aspect of supply chain visibility.


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