Refining your order-fulfillment strategy is an ongoing process for most organizations, especially in light of the increasingly complex supply chains that companies find themselves in today. Satisfying demand for a wider range of products and ever-faster delivery is enough to keep most shippers busy evaluating new technology and equipment, and considering process changes that can help them whisk orders through the pipeline to the end-consumer. And although technology plays a larger role than ever in that process, experts caution that companies looking to make improvements should take a fundamentals-based approach to evaluating their fulfillment strategies in order to make the right moves.
Adrian Kumar, global head of operations science and analytics for third-party logistics service provider (3PL) DHL Supply Chain, explains that there are an infinite number of "solution sets" available to companies seeking to improve the fulfillment process, and he says it takes patience and a detailed approach to analyzing a company's needs and goals in order to chart the best course.
"We all hear what the industry leaders are doing," he says, pointing to the highly automated fulfillment centers of Amazon.com and other large e-commerce players that have made big investments in warehouse robotics. "[But for] someone whose warehouse has 30 or 40 people, that type of automation wouldn't pay off. [We say] 'Where are you on that spectrum and let's figure out what options are available to you.'"
Kumar and other supply chain experts offer three tips for evaluating your order-fulfillment strategy in 2020: start with data, be honest about your automation requirements, and embrace the flexibility of the technology solutions on the market today.
1. DRILL DOWN TO THE BEST DATA
The first step in evaluating your order-fulfillment strategy is to create a company profile. This can be done internally or as you are working with a 3PL or consultant. Essentially, management and operations personnel should answer a series of questions regarding the type and variety of orders the company receives (large retail or wholesale orders, e-commerce orders, or a combination), the size of items being handled (can a human pick it up?), how those orders are picked (batch, wave, or zone; manually, automatically, or some combination of the two), and how they are packaged and shipped. Answers to these questions can rule out many design options, according to Kumar.
"We need to collect data and look at the [customer's] profile to see what [its] warehouse should look like," Kumar explains. "Understanding the profile will make you go one way or the other."
Kumar points to storage requirements as an example. A warehouse that primarily ships pallets of a particular high-demand item will require a large storage area to accommodate the pallets. In comparison, a fashion retailer may have a large inventory that contains a limited number of each particular item, requiring a more segmented approach to storage. Each will require a different combination of material handling equipment and technology as well as the development of a tailored slotting strategy to maximize the efficiency of its fulfillment process. The data collected at the evaluation stage feeds all of those efforts, Kumar says.
Companies should also factor in their short- and long-term objectives. For example, is e-commerce a small but growing portion of the business? How quickly are you expecting to ramp up e-commerce sales? Also consider how seasonal peaks affect your fulfillment process; this is especially important in developing training programs that can get temporary employees up to speed with equipment and processes as quickly as possible.
"[You need to] understand all those different things as well," Kumar says.
On an even more fundamental level, what are the larger, "big picture" goals the company is trying to achieve? For some businesses, minimizing operating costs and/or capital investments is most important. For others, maximizing throughput capacity while maintaining the best service level may outweigh the high cost of investing in advanced automated equipment and systems. And for many companies, doing all of these things simultaneously may be the ultimate goal—creating a need to strike a balance between competing objectives, Kumar adds.
Gathering and processing data is a key part of providing analytics solutions, adds Arnaud Morvan, senior engagement director for Aera Technology, which uses machine learning and artificial intelligence (AI) to develop cognitive automation software solutions for supply chain operations. Aera works with large brands in the consumer packaged goods (CPG), pharmaceutical, and medical-device industries, among others, and counts Johnson & Johnson, Merck, and Unilever among its customers. Morvan says the data-collection phase consists of gathering information from a company's various IT systems—enterprise resource planning (ERP), warehouse management software (WMS), and transportation management software (TMS), for example—and analyzing it to understand patterns and business performance. In Aera's case, combining analytics, AI, and process modeling allows the firm to deploy solutions that "understand" how a business works so that they can make recommendations, predict outcomes, and ultimately, act autonomously. Whether or not a company uses such advanced solutions, the data-gathering and analytics process opens the door to a critical component in developing a better fulfillment strategy: visibility.
"When we work with companies, we ask them 'What visibility do you have?'" Morvan explains, adding that organizations often are very "siloed" and lack visibility across their end-to-end supply chain. Drilling down and analyzing fulfillment-process data creates a "holistic" visibility that allows for better decision-making.
2. SET REALISTIC AUTOMATION GOALS
Determining the appropriate level of automation for a warehouse or fulfillment center is an important next step in the evaluation process. The ultimate strategy will depend on the data gathered in the profile stage, but experts say companies should be careful to look before they leap. For example, it's easy to get carried away with the idea of a fully automated goods-to-person picking system that will boost throughput and allow you to meet same- or next-day delivery goals, but if only a small portion of your business demands hyper-fast delivery, it may not make sense to invest in such a system, Kumar observes.
"You hear about [demand for] same-day, next-day delivery ... but depending on what you're selling, that might not be what matters most for your company. Every company may not need to set up a warehouse to accommodate that," Kumar explains.
That said, advanced solutions are beginning to make sense for a wider variety of companies, especially as businesses continue to deal with low unemployment rates and the resulting tight labor market, and as the cost of technology drops. November statistics from the Robotics Industries Association (RIA) help support that argument. The group said robot orders in North America rose 5.2% in the first three quarters of 2019 compared with the same period in 2018. Automakers drove the growth, increasing their orders by 47%, but the association says it is also seeing growing interest in robotics from a wide range of other companies, including those that have never invested the technology before.
"Orders from non-automotive customers remain near record numbers, a healthy sign for the long-term growth of the robotics industry," the association said in mid-November.
3. LEVERAGE TECHNOLOGICAL FLEXIBILITY
Companies should also consider the flexibility of today's automation and technology solutions—understanding that they don't need to start from scratch or reinvent the wheel when upgrading or revamping all or part of their fulfillment systems.
"The technology just gets better and better, and a lot more flexible," Kumar explains. "If you have a warehouse operation, you don't necessarily need to scrap the whole thing and put in some new automation and get rid of your entire layout. A lot of this technology is more collaborative in nature [today]."
Kumar points to a new breed of warehouse execution systems (WES) that can optimize and prioritize work to avoid congestion and shortages, and improve productivity. These systems require limited additional hardware and they work behind the scenes, directing work based on real-time feedback. For example, new high-priority orders can be inserted into operators' task queues without having to wait for the next wave to be released.
He points to robots that can work within a company's existing aisles and those that incorporate machine learning to improve navigation as further examples of how technology is becoming more collaborative.
"It's getting better and better all the time," Kumar says.
