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A.I. Powered Optimization for Distribution Centers. Next Generation Optimization as an Alternative to Automation


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ACTOR introduce with Cart (Center for Advancing Retail & Technology - US) the optimization approach by a real case chosen as good example of the benefit can be reached in food retail.

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A.I. Powered Optimization for Distribution Centers. Next Generation Optimization as an Alternative to Automation

  1. 1. 1 CONNECTING RETAIL AND INNOVATION. A.I. Powered Optimization for Distribution Centers. Next Generation Optimization as an Alternative to Automation © 2017 Center for Advancing Retail & Technology, LLC. All Rights Reserved. January 2018 Promoted By
  2. 2. A.I. Powered Optimization for Distribution Centers: Executive Summary Distribution centers are a vital link in the supply chain, enabling retail companies to aggregate the thousands of products sold at store-level in regional warehouses to efficiently supply retail locations within a geographic locale. While necessary, executives have long focused on minimizing distribution center related costs while ensuring order picking accuracy that impacts store service levels. The massive size of typical warehouses filled with racks containing different size slotting modules has long represented an optimization exercise. While optimization solutions have been in the market for years, it is only recently that leading solutions have incorporated artificial intelligence and machine learning to produce results far beyond those previously available. Using these new technologies, optimization has become more sophisticated in its ability to handle increasing complexity. A division of a national grocery retailer was recently challenged to take on servicing an increasing number of stores in its market area as the parent company moved to realign its distribution. In response, the division significantly expanded its existing warehouse and wanted to ensure it would maximize efficiency across the entire distribution center by optimizing racking, slot size, and product placement. The regional division worked with ACT Operations Research (ACTOR), a company based in Rome, Italy with offices in London, UK and Charlotte, NC. and a long history of providing industry leading optimization capabilities. Following a proven implementation process, the ACTOR and the retailer’s teams identified all the data and other information required to model and simulate key variables across the distribution center. Together, optimal designs were arrived at for racking, slotting module heights, numbers, and positions, and slotting location of specific products. Significant results were achieved in reducing travel time in putting away and picking stock, increasing service levels to the stores, and realizing substantial improvements in productivity. 2 Key Take Aways: • Applying optimization to distribution center layout and operations provides significant operational savings and benefits. • Artificial intelligence is powering optimization to new levels of effectiveness • A.I. powered optimization is an alternative to investment-intensive automation • Not all optimization solutions are the same; benefit of partnering with best-in-class solution providers. ! A.I. Powered Optimization for Distribution Centers.2
  3. 3. Distribution Center Challenges & Opportunities As online shopping grows it is bringing increasing focus to the role distribution centers play in the supply chain as they support brick & mortar stores and eCommerce operations. Executives have long sought to control or reduce distribution center related costs while improving productivity and order accuracy. Fulfilling orders accurately is perhaps even more important than productivity given that it directly impacts the service level at the store which dictates the shopping experience. “Order picking has long been identified as the most labor intensive and costly activity and it also determines the service seen by customers. It is estimated that order picking accounts for up to 40% to 50% of the total warehouse and distribution operating costs due in large part to the involvement of costly human order pickers.” The cost and importance of this activity makes it a1 prime area of focus for increasing efficiency and focus. A growing number of companies operating distribution centers are exploring automation, the use of robotics to put away and pick orders. While the use of automation is proven to lower costs it requires significant up front capital investment and the area is further challenged by rapidly evolving technology. An alternative approach to gaining efficiency is the use of a new generation of optimization tools that use artificial intelligence and machine learning. The Challenge As a division of a national grocery retailer, the operating division found itself in the position of having to service a growing number of stores in surrounding markets as the parent company consolidated and reorganized the company’s distribution strategy. The division’s CIO with responsibility over distribution was faced with the challenge of efficiently servicing nearly twice the number of stores out of a dated warehouse. To address the challenge, the company built a significant addition, adding 200,000 square feet to the existing 500,000square foot warehouse. But creating more space was only part of the answer. The division’s challenge was two-fold: to layout and optimize the new space and to then focus on the older part of the distribution center and optimize that. And accomplish all this while the distribution center was operating The retailer partnered with ACT Operations Research to provide the optimization solutions to accomplish the goals. Once engaged, executive sponsors, key stake holders, project leads and teams were identified and a kickoff meeting scheduled. Key elements for successful optimization projects like this include: 1) A deep understanding of the processes to be simulated and optimized See for another reference coronado/ 3! A.I. Powered Optimization for Distribution Centers.3
  4. 4. 2) The clear definition of goals and the process to obtain them 3) Comprehensive data needed to accomplish the project At the outset of the project the following challenges and opportunities were identified: Challenges: • Modify an existing, operational warehouse • Estimate the performance gains made possible • Change some established processes to realize the potential gains Opportunities: • Improve the stock capacity by reducing wasted space and guaranteeing the flexibility needed to manage variation in inbound pallet heights • Reduce or eliminate the break-down operations related to inbound pallets; break-down involves manually reducing the pallet height by moving cases to another pallet to make the pallet compatible with a stock location • Optimize the slotting layout in order to increase service levels and reduce case-picking costs • Improve overall warehouse productivity • Assess and project the workforce needed to handle the estimated increased volume for servicing the new stores utilizing the new layout The Project The project evolved through three distinct phases: Discovery Phase: This included an understanding of all operational processes and workflow, constraints related to human workers and products, and identifying all data required to accomplish the project. All this information and data is used in building the simulation model and includes: Processes • Delivery truck arrivals • Unloading process • Pallet quality control • Pallet break down • Put away process • Order picking • Full pallet picking • Case picking • Replenishment strategy Workforce • Operator availability based on shifts • Skill level of each operator • Any pertinent work policy rules and regulations Warehouse physical characteristics • Rack configuration (dimensions and position) 4! A.I. Powered Optimization for Distribution Centers.4
  5. 5. • Distances between different positions in the warehouse • Buffer areas • Slotting configuration Parameterization • Basic timing of operational activities (ex. time to unload pallets) • Forklift speeds modeled on real data Other constraints • Product slotting policy such as not locating cleansers or chemicals over food products or locating fresh chicken over fresh beef • Fragility of products / packages The Simulation and Optimization Modeling Phase: Once all the data and other requisite information was collected and made available, the ACTOR team worked to ingest the data into their systems and create a simulation model. The simulator provided the key performance KPIs for the performance of the warehouse. They included: • Operator utilization by resource • Productivity of each key process • Working time of each process • Number of pallets and cases handled • Travel distances by process types (ex. put away vs. order picking) With all the data provided, ACTOR used a math optimization engine called OPT Racking to define the optimal slot heights of the pick and stock modules with a goal of minimizing waste of volume while providing proper flexibility. Using the OPT Racking engine, the team was able to: 1) Identify the optimal heights of the rack modules (2, 3, and 4 module types) 2) Design the new rack configuration of the whole warehouse testing different options in terms of the number of module types (2, 3, and 4) 3) ACTOR devised three options and evaluated the quality of the solution by measuring the wasted space and the flexibility through simulating one year’s inbound and outbound orders 5! A.I. Powered Optimization for Distribution Centers.5
  6. 6. Once the warehouse racking layout and slotting spaces were optimized work commenced on slotting optimization, determining which products go into which slots. In this stage, ACTOR used its OPT Slotting engine to determine the optimal slotting configuration seeking to maximize the picking efficiency while considering in parallel all the physical constraints (ex. Picking position height). The OPT Slotting engine enables the user to establish goals while respecting rules and constraints such as product storage constraints (ex. do not store chemicals above food products) and finds the slotting arrangement that maximizes the stated goals. The Validation and Deployment Phase: After the simulation models are built and optimization is run, the results were analyzed in collaboration with the retail division’s team and ACTOR’s engineers with decisions made to deploy the suggested racking and slotting recommendations. 6 Slotting top down view - BEFORE the Optimization Red dots identify inefficiency Slotting top down view- AFTER the Optimization Red dots identify inefficiency ! A.I. Powered Optimization for Distribution Centers.6
  7. 7. Since the distribution center was operating throughout this time and the retail division’s management was under pressure to service an increasing number of stores while this project was evolving, deployment of the recommended rack configurations and product slotting was phased in over time. Results As the retail division deploys the recommendations developed by ACTOR in a phased approach while maintaining operations, positive results are already apparent. Optimizing the racking configuration and product slotting has produced significant productivity gains. The new Optimized Racking Configuration generated the following improvements at the warehouse: • Reduction of the average workload (inbound/put to stock) by 35% • Reduction of the miles related to put to stock by 30% • Productivity improved by 46% for the inbound operation • Replenishment and full pallet picking operator workload reduced by 25% The Slotting Optimization has increased the productivity and the quality level: • Productivity improved by 21% • Reduction of the average workload by 16% • Reduction of the miles related to picking by 34% • Service level to stores (in terms of pallet quality) increased by 20% It is important to note that slotting optimization is not a ‘once and done’ project. Due to seasonal variation in product assortment, ongoing delisting of slow movers and the entry of new products into the marketplace, slotting optimization is best viewed as an ongoing process. Seasonal product variances along with holiday-specific products suggest at least a quarterly review of slotting to maintain and continue to increase the efficiencies and product gains realized. Conclusion While optimization solutions have been available for a number of years, the space has taken on renewed interest with the application of artificial intelligence and machine learning as used by leading solution providers. The application of powerful new optimization tools like those provided by ACTOR to distribution center operations is a more appropriate approach for companies than deploying costly robotics and automation solutions. Successfully implementing optimization at a distribution center initially requires a great deal of data and information gathering and, more importantly, a cultural mindset to leave legacy and preconceived notions behind as data objectively leads to more effective solutions. The most successful implementations do not use a ‘cookie-cutter’ optimization approach but seek to apply 7! A.I. Powered Optimization for Distribution Centers.7
  8. 8. the most powerful and flexible capabilities - specifically advanced optimization engines tuned to specific tasks - to the specific project. It is also important to clearly identify specific goals early on in the project, giving attention to clearly identifying what specific activities are to be optimized. Its easy to say ‘improve efficiency’ but optimizing against one dimension or one activity involves reducing efficiency in other dimensions or other related activities. The secret lies in working with solution partners like ACTOR that can draw on vast experience to help in defining a project’s goals. Successful optimization projects many times lead to additional efforts as executives wish to extend the gains across other parts of the supply chain. For example, an initial effort to improve efficiency in the warehouse can then lead to a project focused on forecasting optimization to improve the quality and quantity of products flowing into and out of the distribution center. Many of ACTOR’s clients have gone on to implement promotion optimization, price optimization, and replenishment optimization solutions to increase realized gains. Most importantly, retail companies implementing optimization solutions must be open to change and have a willingness to evolve the way they design and operate processes across their organizations, establishing a data-driven culture as a strategy for decision making. About ACT Operations Research ACT Operations Research (ACTOR) is a privately held group of companies with offices in the USA and Europe. ACTOR has more than 20 years of experience helping notable customers to improve their operations, their margin and risk profile, using advanced analytics. The company’s core business is providing strategic custom software solutions based on math-optimization, statistics, predictive models, dynamic simulation and artificial intelligence. ACTOR’s clients include many multinational companies in various industrial areas, from retail to big manufactures to logistics operators. ACTOR has a deep relationship with the Sapienza University, Rome Italy, which provides intensive research capability and ensures ACTOR’s technologies remain leading edge. Core solutions include Transport and Warehouse optimization and simulation, Demand Forecast, Inventory & replenishment Optimization, Capacity & Revenue Optimization, Price & Promotion optimization, Planning, Scheduling and Workforce optimization. ABOUT CART CART is Advancing Retail by connecting the industry to innovation. Retailers, wholesalers and brands utilize CART to find, research and connect with solutions appropriate for their businesses. Solution providers use CART as a go-to-market tool that connects them directly to their target retail audience, all the way into the brick and mortar store itself. CART has unparalleled insight into what’s next in retail and shares this information regularly through multiple channels. For questions or comments please contact 8! A.I. Powered Optimization for Distribution Centers.8