At its essence, efficient picking is about fulfilling customer orders via your warehouse management system (WMS) with the least amount of material handling and labor output. It’s an important task to optimize as more than 50 percent of a DC’s labor force is typically involved in picking.
 
Many organizations today overlook easy ways to make picking more efficient and less costly. Below you’ll find four ways to tackle this problem followed by an in-depth look at various picking methods and associated technologies.

Learn more about this related supply chain topic: The Future of Warehouse Management Systems (WMS) Must Support Complex Warehouse Requirements for a New Era of Buyers

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Ask anyone in your warehouse how well they like to handle returns, and you’ll likely hear a sigh or groan in response. That’s because the returns process, with its time and space requirements and excess inventory handling, is a sizeable hassle for most distributors. Returns are not a tidy process to manage the way regular inbound and outbound activities are. Sometimes items are broken or in a box with other unrelated SKUs. Sometimes they’re out of stock or no longer sold, meaning there may be a subsequent return to a vendor. There’s also extra processing required to determine what to do with unusable products, like dispose of it or request a vendor return material authorization (RMA). The list goes on. The only thing predictable about returns is that they’re unpredictable. That said, there are ways to make life easier on the returns front. But first, let’s look at how these processes typically work.

Discover other ways to trim expenses in your warehouse with this valuable guide:
5 Ways to Start Cutting Supply Chain Costs

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Effective inventory management is always a delicate dance between the cost associated with carrying too much inventory and the risk of not carrying enough to fulfill a customer order at the right moment. With ongoing pressure to ship orders within compressed timeframes, there’s no room for error, and competitors are always on your heels. It’s critical to unearth new ways to generate savings and efficiency at every turn. Cross-docking often leads the opportunity list with its ability to minimize material handling costs and get inbound products prepped for shipment at lightning speed.

Although the practice of cross-docking has been around for quite a while, many organizations shy away from implementing it because they think it’s more work than it actually is. In doing this, they forfeit cost and efficiency benefits that can help them better meet customer demands, particularly for specialty items.

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A new era of buyers has arrived, and they are focused on the customer experience. It’s not enough to simply deliver a product into a buyer’s hands. No matter the industry, today’s savvy consumers expect a seamless, superior experience that delivers on a company’s brand promise. They want faster service, higher value, and 100% fulfillment. They want to feel good during the buying process. And they want this across all channels wherever they touch the brand.

With that in mind, everything a company does from its messaging to the sales process to what occurs after the sale is part of the customer experience. If a company has deficiencies in any area, it could result in a customer having unmet expectations and a poor experience. If they are left unsatisfied, the company – and brand – could suffer detrimental effects.

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A lot of focus has been placed on advanced and predictive analytics, and rightfully so. I have written many posts and have spoken publically on the merits of advanced analytics for several years now.

What I find disorienting and misleading are marketers harping on how important it is to adopt advanced analytics right now. The thing that they just don’t get (or maybe they don’t want to get?) is that an organization will need to transcend a series of analytical maturity levels before they can truly capitalize on the benefits of advanced and predictive analytics.

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Source: MIT Sloan Management Review

In my last post, I introduced the longitudinal study that MIT Sloan Management Review has been conducting over the past five years. From 2010 to 2012 they indicated that 67% of those surveyed believed that analytics gave their organizations a competitive edge. In 2013, that figure stabilized at 66% revealing the so called ‘Moneyball Effect’ where leaders lost their competitive edge that they once enjoyed because followers matured and made analytics core competencies. In 2014, that trend continued, falling to 61%.

But why?

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When supply chain professionals plan for future demand, their thoughts gravitate to meeting customer service levels while minimizing the amount of capital tied up in inventory.  Demand planning is about having the right product in the right place at the right time … right? Four occurrences of the same word would cause my old English teacher to shudder at this excessive use of a word in a single sentence. However, let us return to the important business of meeting consumer demand without incurring the cost of excess inventory.

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In the movie Big, Tom Hanks plays a child trapped in the body of a 30-year-old who challenges the status quo at a toy manufacturer. To the audience it all makes sense as the movie progresses – think like a kid when selling stuff to kids. How revolutionary! Yet the audience also relates to the adults who pretend to know what kids like.

For me, the big thinking in this movie is demonstrated by the creativity of a child whose mind is unencumbered by preconceptions.

Dr. David Schwartz, the author of a book entitled The Magic of Big Thinking, attempts to define big thinking. He explains that visualization adds value to everything and that thinking big means training oneself to see not just what is, but what can be. Author Malcolm Gladwell writes, “A visionary is a person that takes a white piece of paper and re-imagines the world”. The Internet is full of success stories of such visionaries.

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In 2013, Gartner conducted a survey on Big Data Adoption in Supply Chain Industries and found that adoption has been flat and is lagging behind the overall adoption rate of other industries such as banking, insurance, and retail to name a few. Gartner ascertained that these characteristics pertaining to the Supply Chain industry are attributable to an inherent lack of understanding of what Big Data truly is and a fundamental lack of the required skill sets. This, in essence, is the challenge facing the Supply Chain industry.

In parts one, two and three of this four part series on Big Data, we looked at what makes data “big”, how it can benefit organizations that apply the right analytics, and the implications of doing so, respectively. In the closing segment of this series, we focus our attention on examples of how elements of Big Data can be leveraged. The challenges identified by Gartner translate to opportunities regardless of your organization’s analytical maturity level.

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Big data and big data analytics pose a series of implications and challenges.  Organizations that seek to become analytical competitors must have an established analytics culture consisting of  well trained employees who are using the right enabling technologies.  However, these organizations face challenges maintaining consumer privacy while they collect and use sensitive information.

In parts one and two of this four part series on big data, we looked at what makes data ‘big’ and how it can benefit organizations that apply the right analytics.  In part three, we will look at the cultural, technological, and ethical implications of big data and advanced analytics.

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