6 Proven Ways AI Will Improve Your OMS

As we progress into the next fiscal year and roll through the daily motions required to meet a new set of annual objectives, retailers like you know — better than anyone — how fleeting the status quo in the retail world can be. Fulfillment models have evolved tremendously over the past decade, yet many of the systems merchants rely on to meet new delivery expectations have not. In today’s retail environment, the point of purchase can be wherever the consumer wants to be, adding a new level of complexity to the standard, rules-based order management system (OMS) running every e-commerce transaction coming through.

That being said, to be efficient, profitable, and fast for online delivery requires an ability to adapt to consumer’s ever-changing needs in advance while leveraging your stores. This means arming systems with predictive insights to anticipate demand and understand what/when/where your next customer will buy.

So, the question stands, how exactly have retailers tackled the challenges online fulfillment presents? Here are six proven ways AI will significantly improve your existing OMS:  

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Topics: ship-from-store, artificial intelligence, ecommerce, order delivery, brick-and-mortar retail, technology adoption, order fulfillment, OMS, order management systems

The Biggest Takeaway from NRF 2019? Optimize Everything.

The word “optimize” was littered across the expo floor at NRF 2019.

From shelf space to e-commerce basket sizes to your in-store workforce – every process across the retail value chain seems to be begging for optimization. “Frictionless,” “efficient,” and “seamless” were the all-encompassing descriptors used to entice interest and engagement among attendees perusing the aisles for technology solutions, ideas, or (let’s be real) free giveaway swag. 

The need for optimization is unequivocal as retail organizations are tasked with executing costly decisions on a daily basis at a tremendous scale. Yet how to approach this problem is less than certain when there are numerous opportunities to improve decisions at each stage of the retail process. Whether your current struggle hinges on production, inventory, store operations, or last mile fulfillment — whatever the challenge may be — the technology available today will enable retailers to glean insights into the mountainous amount of data they have. 

Based on our observations at the big show in NYC, it’s apparent retailers must prioritize investments in tools like artificial intelligence (AI) and advanced analytics to make the optimal decisions to meet the ever-evolving needs of their customers.

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Topics: allocation, retail technology, retail trends, artificial intelligence, inventory optimization, brick-and-mortar retail, fulfillment optimization, advanced analytics, demand prediction, NRF2019

4 Revealing Stats About AI in Retail You Need to Know

“Artificial intelligence (AI) offers the prospect of a frictionless existence, making us more efficient, helping us prevent mistakes, spotting the onset of potential problems before they become problems, and enabling us to spend more time on the things that really matter to us.” – Paul Clarke, CTO of UK-based online supermarket Ocado

Strategic planning for 2019 is underway as the new year unfolds. Findings from the holiday season, so far, appear cautiously optimistic as sales rose 5.1 percent to over $850 billion – the strongest in six years, according to a Mastercard report.

However, the itch for opportunity and disruption never subsides in an industry wrought with constant change. After years of navigating the tumultuous retail market, many brands are embracing advanced analytics and artificial intelligence (AI) technologies for a chance to resurface for a gasp of fresh air. Considered by Gartner “transformational” and “high-benefit” technologies, AI and advanced analytics will enable new ways for retailers to do business that will result in “major industry dynamic shifts” and “significant revenue increase or cost savings.” Here are four revealing findings about AI in retail you need to know going into 2019:

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Topics: retail technology, retail trends, artificial intelligence, brick-and-mortar retail, advanced analytics

3 Practical Reasons Retailers Should Adopt AI in 2019

In the world of retail, the goal is simple: sell product. 

However, reaching that goal is a completely different story. When you throw competition, customer expectations, stores, inventory, logistics, and fulfillment into the mix, things reach a whole new level of complexity. The challenge is really two-fold:

  1. ) First, demand is seemingly unpredictable, with a multitude of factors influencing a consumer’s desire to want to buy something. 
  2. ) Secondly, there’s the issue of optimization – how can retailers leverage their inventory more productively? 

Overcoming these challenges requires retailers to better predict demand and optimize their inventory decisions more effectively to ultimately maximize every opportunity for a sale. Artificial intelligence and advanced analytics technologies are growing to be the tools of choice for retailers when it comes to optimizing inventories as customer expectations force retailers to invest heavily in digital capabilities.

Many retailers, according to Gartner, are “simply not optimizing their most significant assets.” What asset is more important than a retailer’s inventory? Here are three reasons retailers should adopt AI for inventory optimization in 2019:

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Topics: allocation, merchandise planning, inventory, e-commerce, artificial intelligence, brick-and-mortar retail, data, advanced analytics, order fulfillment, demand prediction, markdowns

Why the Biggest Problem with AI is Human

Every brand in retail is talking about AI. We’ve read the reports, the articles, and the survey results about all the different pain points AI can solve for retailers. From all the chatter around groundbreaking innovations and the changing retail industry, brands understand this need for technology in order to adapt. 

And there isn’t a shortage of technology solutions out there either—retailers have a ton of options. 

Understanding this need, the need for tech, then why is it so difficult to actually adopt? Why are retailers still struggling to keep margins afloat and adapt to the digital consumer? 

What’s the biggest problem with AI in retail?

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Topics: artificial intelligence, inventory optimization, machine learning, data, advanced analytics, digital transformation

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