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- Power Your Distribution Center With Data Science
Power Your Distribution Center With Data Science
Power Your Distribution Center With Data Science
Shabab Mofidi
2 September 2020
Reduce order fulfillment costs with advanced decision intelligence software
The average cost to fulfill an order is roughly 70 percent of that order’s value.[1] To succeed, e-commerce retailers need every advantage to drive down distribution center (DC) costs in order to grow profitability.
Decision intelligence creates that advantage, replacing traditional “first-in, first-out” wave strategies with real-time order priorities and optimal order release sequencing. That’s critical in order for DC operators to better manage and orchestrate a growing number of automation systems, workstations, processes and SKUs.
From a human capabilities perspective, the interplay among these factors can become nearly impossible to decipher. As order volumes rise, the permutations of possible order sequencing also increase and have far-reaching impacts throughout the DC. As a result, the number of calculations needed to determine optimal fulfillment outcomes grows exponentially — and well beyond human capabilities. That’s why more e-commerce retailers are applying powerful data science techniques to the increasing complexity of warehouse automation and fulfillment scenarios.
Machine-learning algorithms, advanced data-driven optimization techniques and artificial intelligence (AI) are the data science; when combined with sophisticated warehouse execution system (WES) software, they create decision intelligence. With it, DC operators can streamline operations and improve their bottom line.
Transformative benefits from optimizing fulfillment
Modern WES software designed with decision intelligence can parse myriad data points and DC execution decisions to achieve specific fulfillment outcomes. This advanced capability to select the optimal fulfillment path among millions of possible scenarios becomes a force multiplier for effective warehouse execution — and ultimately, fulfillment success. What’s more, with decision intelligence, DC operators can make these decisions at literally any point of the fulfillment cycle, not only the day before or at the start of a shift, but also mid-shift and in real time in response to evolving customer demands and supply chain requirements.
Sophisticated WES software with decision intelligence provides the following transformative benefits to DC operations of all sizes and across industries:
- Real-time visibility into order status, inventory allocation and fulfillment operations, including a comprehensive dashboard of all warehouse activities
- Dynamic, automated decision-making based on order priorities, workload, labor availability and automation system status
- Improved accuracy rates, on-time delivery targets and process rigor
- Proactive decisions based on accurate predictions to avoid congestion and provide optimal balance among all the warehouse activities impacting order fulfillment
Beyond automation: Efficiency that grows profits
Design intelligence is based on data-driven optimization algorithms that look at current orders in the queue and select from a variety of order release sequences to determine which one will yield the most efficient and productive results. This calculation evaluates various dynamic factors of the fulfillment ecosystem, such as:
- Conveyor status
- Inventory locations
- Number of orders
- Picking travel and dwell times
- Put wall availability for order consolidation
- Time to complete tasks
By sequencing orders and releasing them based on the most optimal scenario, DC operations significantly can reduce unnecessary and inefficient over-processing and achieve substantial financial benefits.
Based on assumptions of a typical e-commerce operation, simulation environments can compare traditional warehouse execution methods — with no data science or decision-making capabilities — to data-driven optimization techniques that automate the entire operation via intelligent sequencing of orders. Even with conservative estimates of KPI improvements, the results indicate the potential for substantial annual financial gains. In one estimate, smart order sequencing delivers annual distribution and fulfillment cost savings of more than $1.6 million.[2]
As the number of orders rise, the amount of possible execution permutations increase exponentially. But these advanced, self-learning algorithms are smart enough to detect familiar patterns in data sets and quickly find an optimal fulfillment scenario — essentially giving them a head start and reducing the amount of calculations needed.
Tools for performance execution
Sophisticated distribution software creates business value through a combination of increased throughput, improved utilization and on-time shipment.
The Momentum software suite from Honeywell Intelligrated is equipped with a powerful machine-learning engine — Decision Intelligence — that delivers the operational efficiencies required to optimize today’s e-commerce environments. With Momentum software, you can combine advanced DC automation, robotics, automated storage and retrieval systems (AS/RS), voice, labor management, machine control, real-time asset monitoring and a wide variety of order fulfillment technologies — all within a connected infrastructure seamlessly.
Our Decision Intelligence technology architecture is agile and extensible, so you can scale up quickly to meet current and future requirements. In addition, Momentum integrates with robust WES capabilities and comprehensive modular functionality to equip e-commerce retailers with the tools to accelerate their digital transformations and empower fulfillment performance execution. Download our Leveraging Data Science for “Decision Intelligence” in the DC white paper to learn more.
To subscribe to our blog and receive exclusive communications and updates from Honeywell Intelligrated, click here.
Source
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[1] eFulfillment Service, “The State of Ecommerce Order Fulfillment & Shipping,” September 2017, (accessed March 17, 2020).
[2] Honeywell Intelligrated internal research.
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