On the Unique Features and Benefits of On-Demand Distribution Models

To close the gap between current distribution operations and today’s customer expectations, firms need to think differently about how resources are acquired, managed and allocated to fulfill customer requests. Rather than optimize planned resource capacity acquired through ownership or long- term partnerships, this work focuses on a specific supply-side innovation – on-demand distribution platforms. On-demand distribution systems move, store, and fulfill goods by matching autonomous suppliers\u27 resources (warehouse space, fulfillment capacity, truck space, delivery services) to requests on-demand. On-demand warehousing systems can provide resource elasticity by allowing capacity decisions to be made at a finer granularity (at the pallet-level) and commitment (monthly versus yearly), than construct or lease options. However, such systems are inherently more complex than traditional systems, as well as have varying costs and operational structures (e.g., higher variable costs, but little or no fixed costs). New decision- supporting models are needed to capture these trade-offs

Modeling the Inventory Requirement and Throughput Performance of Picking Machine Order-fulfillment Technology

Picking machines, also known as remote-order-picking systems, are an example of a stock-to-picker piece-level order-fulfillment technology that consists of two or more pick stations and a common storage area. An integrated closed-loop conveyor decouples the pick stations from the storage area by transporting the needed totes to and from the storage area and the pick stations. We develop a probabilistic model capable of quantifying the inventory differences between order-fulfillment technologies that pool inventory with technologies that do not pool inventory. To determine the throughput of a picking machine, we develop a methodology that incorporates existing analytical models for the picking machine’s subsystems. We present a case study comparing a picking machine to a carousel-pod system to illustrate how a manager could use our methodology to answer system design questions. Finally, we present conclusions and future research

A Frame Work and Analysis to Inform the Selection of Piece-level Order-fulfillment Technologies

Thepiece-levelorder-fulfillmenttechnologyselectionproblemisanimportantstrategicproblemthatsignificantlyimpactsdistributioncentercosts andoperations,andistypicallysolvedbasedonempiricalexperiences.Given ademandcurveandasuiteofavailablepiece-levelorder-fulfillmenttechnologies, weanalyzewhereinthedemandcurvedifferentorder-fulfillment technologiesshouldbeapplied. Todoso, wedevelopaframeworkthat jointlydeterminesthebestcombinationofpiece-levelorder-fulfillmenttechnologiesandtheassignmentofSKUstothesetechnologies, whichrelaxes thesequential-modelingapproachofpreviousresearch. Wevalidateour methodologywithindustrydataandshowthatour modelprovidestechnologyrecommendationsandSKUassignmentsthatareconsistent with successfulimplementations. Throughasetofnumericalexperimentsand statisticalanalysis,weidentifykeyfactorsinimplementingmanualversus automatedorder-fulfillmenttechnologiesandprovideobservationsintothe applicationofdifferentorder-fulfillmenttechnologystrategies.Finally,we presentconclusionsandfutureresearchdirections

A Measurement Tool for Circular Economy Practices: A Case Study in Pallet Supply Chains

A circular economy (CE) is an economic system where products and services are traded in closed loops or ‘cycles’. This work develops a framework for assessing the extent to which product supply chains incorporate circular economy principles, and applies this framework to a specific material handling application, the wooden pallet supply chain. The main decisions affecting circularity and the most common decision alternatives for the wooden pallet supply chain are identified for the Pre-manufacturing, manufacturing, product delivery, customer use, and end-of-life phases. A streamlined life cycle assessment tool is developed for supporting a quick analysis about how the level of adoption of CE strategies could support environmental sustainability in pallet supply chains. A questionnaire, scoring, and assessment are presented for each phase of a pallet supply chain to reduce input and use of natural resources, reduce emission levels, reduce valuable materials losses, increase share of renewable and recyclable resources, and increase the value of durability of products. A case study is used to test the proposed method and present a contrast between two scenarios

Dynamic Optimization in Peer-To-Peer Transportation with Acceptance Probability Approximation

Crowdsourced transportation by independent suppliers (or drivers) is central to urban delivery and mobility platforms. While utilizing crowdsourced resources has several advantages, it comes with the challenge that suppliers are not bound to assignments made by the platforms. In practice, suppliers often decline offered service requests, e.g., due to the required travel detour, the expected tip, or the area a request is located. This leads to inconveniences for the platform (ineffective assignments), the corresponding customer (delayed service), and also the suppliers themselves (non-fitting assignment, less revenue). In this work, we show how approximating suppliers’ acceptance behavior by analyzing their past decision making can alleviate these inconveniences. To this end, we propose a dynamic matching problem where suppliers’ acceptances or rejections of offers are uncertain and depend on a variety of request attributes. Suppliers who accept an offered request from the platform are assigned and reenter the system after service looking for another offer. Suppliers declining an offer stay idle to wait for another offer, but leave after a limited time if no acceptable offer is made. Every supplier decision reveals partial information about the suppliers’ acceptance behavior, and in this paper, we present a corresponding mathematical model and a solution approach that translates supplier responses into the probability of a specific supplier to accept a specific future offer and uses this information to optimize subsequent offering decisions. We show that our approach leads to overall more successful assignments, more revenue for the platform and most of the suppliers, and less waiting for the customers to be served. We also show that considering individual supplier behavior can lead to unfair treatment of more agreeable suppliers

Let Shoppers shop: why click and collect is not the solution

Buy-Online-Pickup-In-Store (BOPIS), also known as Click-and-Collect, are services where customers place their orders online and then travel to pick up their requested items at a pre-determined local location. This provides convenience to shoppers and eliminates the retailer’s last-mile delivery costs associated with home delivery. Yet, such omnichannel services shift the responsibility for the order fulfillment process to the retailer, whereas with in-store shoppers, the order fulfillment task is completed by shoppers themselves. Further, because of customers’ short response time expectations, it is typically infeasible for a retailer to conduct this order fulfillment process for click-and-collect orders at distribution centers located far from customers. This has led companies to deploy different distribution strategies to support such omnichannel services, yet many are struggling to find a cost-effective solution. This research quantifies and explores why omnichannel supply chains are expensive. We adopt the Distribution Center Reference Model (DCRM) to systematically identify differences in the type of processes between the in-store customer model and the micro-fulfillment model for click-and-collect online customers. To quantify the material handling cost of fulfilling a customer’s request, we build analytical models that capture the interactions among different DCRM process steps and the different supporting supply chain facilities. Using benchmark data collected from operating warehouses to estimate the average cost per process activity, we quantify where the material handling costs vary for in-store versus click-and-collect customers. We observe that click-and-collect online orders are much more costly than supporting in-store shoppers: even without last-mile transportation costs, the average per unit cost of fulfilling a clock-and-collect request, rather than having shoppers shop, is 0.52 euros more per unit. Thus, from a material handling perspective, retailers should let shoppers shop. Then we analyze the benefit of replacing a company’s central distribution center with a crossdock and decentralizing the storage function, in addition to the fulfillment function, and discuss in what scenarios this could be a useful strategy

Analytical Models For An Automated Storage And Retrieval System With Multiple In-The-Aisle Pick Positions

An automated storage and retrieval system with multiple in-the-aisle pick positions (MIAPP-AS/RS) is a case-level order fulfillment technology that enables order picking via multiple pick positions (outputs) located in the aisle. This article develops expected travel time models for different operating policies and different physical configurations. These models can be used to analyze MIAPP-AS/RS throughput performance during peak and non-peak hours. Moreover, closed-form approximations are derived for the case of an infinite number of pick positions, which enable the optimal shape configuration that minimizes expected travel times to be derived. The expected travel time models are compared with a simulation model of a discrete rack, and the results validate that the proposed models provide good estimates. Finally, a numerical experiment is conducted to illustrate the trade-offs between performance of operating policies and design configurations. It is found that MIAPP-AS/RS with a dual picking floor and input point is a robust configuration due to the single command operating policy having a comparable throughput performance to a dual-command operating policy. Copyright © 2014 IIE

Analyzing Rental Vehicle Threshold Policies That Consider Expected Waiting Times For Two Customer Classes

Vehicle rental providers, which operate in an uncertain environment, offer differentiated services to priority and non-priority customers. In this research, we study one such service differentiation strategy, a vehicle threshold policy, which is to hold vehicles for priority class customers in anticipation of their future arrivals. To consider the impact that vehicle threshold policies have on priority and non-priority customer waiting times, we model a rental depot as a multi-class non-work-conserving semi-open queue with stochastic inputs. To analyze the effect of vehicle rental period distributions, we identify and analyze the optimal threshold quantity for stationary customer arrivals with exponential and deterministic service time distributions. For non-stationary customer arrivals, we develop different threshold policies and analyze their performance using a detailed simulation model to conduct numerical experiments. We find that a maximum threshold policy is recommended when the average arrival rate of the priority customers is larger than the average arrival rate of the non-priority customers, and a stationary independent period by period policy is recommended when the average arrival rates of the customer classes are equal and the average best case utilization is less than 0.60. Finally, we analyze the impact that allowing priority customers to upgrade to a higher class of vehicles has on threshold policies

Product Allocation Problem For An As/Rs With Multiple In-The-Aisle Pick Positions

An automated storage/retrieval system with multiple in-the-aisle pick positions is a semi-automated case-level order fulfillment technology that is widely used in distribution centers. We study the impact of product to pick position assignments on the expected throughput for different operating policies, demand profiles, and shape factors. We develop efficient algorithms of complexity O(nlog(n)) that provide the assignment that minimizes the expected travel time. Also, for different operating policies, shape configurations, and demand curves, we explore the structure of the optimal assignment of products to pick positions and quantify the difference between using a simple, practical assignment policy versus the optimal assignment. Finally, we derive closed-form analytical travel time models by approximating the optimal assignment\u27s expected travel time using continuous demand curves and assuming an infinite number of pick positions in the aisle. We illustrate that these continuous models work well in estimating the travel time of a discrete rack and use them to find optimal design configurations

An Analytical Model For A-Frame System Design

An A-frame system is a highly automated piece-level order-fulfillment technology. A systematic analysis is performed in this article in order to understand the design decisions of using an A-frame system in a distribution center. A math-programming-based approach to determine the amount of A-frame infrastructure investment is presented along with the assignment and allocation of Stock Keeping Units (SKUs) to the A-frame. Then throughput considerations are explicitly considered by developing analytical models for the throughput of an A-frame and heuristics to adjust the allocation and assignment of SKUs in order for the A-frame to meet a throughput constraint. The proposed heuristic approach performs well as compared to the exact solution approaches for small problems. Since the proposed methodology is capable of solving industrial-sized problems it is applied to a case study from the pharmaceutical industry. Design testing indicates that A-frame systems provide the greatest potential in labor savings when a distribution center has high item commonality, small order sizes, and high skewness levels and in throughput when many small orders have low item commonality and low skewness levels. Copyright © 2011 IIE