Supply Chain and Logistics Problems for Emergent and Personalized - PowerPoint PPT Presentation

Supply Chain and Logistics Problems for Emergent and Personalized Requests Jennifer Pazour, Ph.D. Assistant Professor of Industrial and Systems Engineering Rensselaer Polytechnic Institute (RPI) pazouj@rpi.edu http://jenpazour.wordpress.com/ 1‐518‐276‐6486 1

Overview 1. Thank you! 2. My Research Interests 3. Emergent and Personalized Requests 4. A model to determine proactive versus reactive strategy through the lens of military logistics 5. Other Research interests and applications 2

Fundamentally, I am a Modeler • Develop mathematical representations of real‐world systems and processes. • Study the structure of the model and develop solution approaches . • Use models to study and understand the dynamics and properties of the system and processes. • Recommend strategies and policies that optimize performance measures. 3

Research Interests • Development and use of analytical models to guide decision making in service industries . • Primary focus on applying operations research methodologies to logistics challenges in: Distribution Transportation Healthcare Military Centers • Emerging focus on: Disaster Peer‐to‐Peer Resource Sharing Systems Response 4

A wide variety of requests are made with little warning and are expected to be fulfilled quickly to a number of different locations. Emergent and Personalized Requests 5 Jennifer Pazour, Ph.D.

Characteristics of Emergent and Personalized Requests 1.Occur as a random event in time. (requests highly stochastic and time‐varying) 2.Are highly personalized. (variability in what’s requested, how its requested, and its delivery location) 3.To locations not necessarily known a priori (distributed demand) 4.Are expected to be fulfilled quickly. (short lead time expectations) 6 Jennifer Pazour, Ph.D.

Seabasing: Maritime platforms are used for logistics, delivery, and at‐sea transfer of cargo stored on ships. 7 Jennifer Pazour, Ph.D.

Seabased Distribution Network Scenarios Iron Mountain Policies & Vessels were Designed For Skin‐to‐Skin Replenishment Emergent & Tailored Resupply Personalized Packages Requests 8 Jennifer Pazour, Ph.D.

Iron Mountain Skin‐to‐Skin Replenishment Tailored Resupply Packages Demand‐Level Mission‐Level Vessel‐Level Individual‐Level Demand Pushed Plannable Emergent Characteristics No requests, instead Bulk requests for standard Types of Personalized requests everything loaded is items needed for Requests on demand. offloaded. replenishment. Container, Vehicle, Handling Unit Pallet, Case Case, Piece Pallet Maximize storage density Maximize Storage Key Performance and minimizing transfer Responsiveness and Density and Product Indicators time of cargo between Storage Density Assortment ships. Functional Dense Storage and Product Selective Offloading Dense Storage Requirements Segmentation and Dense Storage Order‐Fulfillment Type of System Transportation System Unit‐Load Storage System System Decision Less‐than‐Truckload E‐Commerce Order Knapsack Problem 9 Problem Jennifer Pazour, Ph.D. Loading and Routing Fulfillment Problem

The Optimal Assortment of Items to Apply a Proactive Strategy Submitted Manuscript Seyed Shahab Mofidi Ph.D. Student Mofids@rpi.edu Jennifer Pazour, Ph.D. Assistant Professor Pazouj@rpi.edu Debjit Roy, Ph.D. Associate Professor debjit@iimahd.ernet.in 10 Jennifer Pazour, Ph.D.

Tradeoff between Responsiveness & Additional Costs Reactive Proactive Wait until demand materializes Conduct some operations in advance. More agile and responsive Time to respond longer Online Request No expedite or rush processes Expedite options may be more expensive May increase inventory levels Extra space and labor costs Centralized pooling benefits Action in response to uncertain Action in response to demand known demand t 11

Assortment of Items Tied Together via a Demand Profile 1 0.8 𝑕�𝑗� � 𝜕𝑗 �� 0.6 𝐻 𝑗 � 𝑗 ��� � 𝛿 g ( i ) A B C 𝑂 ��� � 𝛿 0.4 G ( i ) 0.2 0 1 10 20 30 40 50 60 70 80 90 10 i 0 𝐽 � � �𝑗 ∈ ℝ ∶ 0 � 𝑗 � 𝑂� . Stochastic demand for the sets of items follows the ABC demand curve (Bender, 1981) Bender, P. S. (1981). Mathematical modeling of the 20/80 rule: theory and practice. Journal of Business Logistics , 2 (2), 139-157. 12

Unit costs have “economies of scale” Higher demanded items  lower unit costs 𝑫 𝒋 � 𝜷 � 𝜸𝒋 𝜹 lower demanded items  higher unit costs. Two‐stage item order‐fulfillment cost functions � � 𝛽 � � 𝛾 � 𝑗 � Different values of 𝛽 and 𝛾 𝐷 � � � 𝐷 � � 𝐷 � for some items � � 𝛽 � � 𝛾 � 𝑗 � 𝐷 � � � 𝐷 � � and some other 𝐷 � i 13

Research Questions Given multiple item types with skewed, stochastic demand and varying unit costs: Which items should be handled using a proactive strategy, rather than a reactive strategy? What quantity of the items should be proactively handled? Assumption: All demand fulfilled via either proactive or reactive strategy. 14 Jennifer Pazour, Ph.D.

In Response to Tailored Resupply Packages a Proactive Strategy is to Prestage cargo on the flight deck Reduces time of transferring cargo for receiving vehicle Requires additional costs • Double handling cargo • Extra labor Prestaging involves retrieving and storing • Higher risk of cargo on the flight deck of the supply ship damage/spoilage in anticipation of requested demand. 15

the effort required to retrieve an item is Inversely Proportional to its Demand: 𝑢 � ∝ 1/𝐸 � Direct Cargo Flow Process Prestaging Cargo Flow Process Strike‐Down (if not needed) 16

How Many to Prestage for Item i ? ∗ ) (Quantity for the Initial Order 𝑅 � The total payoff function for a prestaging policy Q when demand of x is realized � 𝑎 𝑅, 𝑦 � � 𝑎 � 𝑅 � , 𝑦 � ��� Decomposed for each item i : � 𝑅 � � 𝑤 𝑅 � � 𝑦 � , 𝑎 � 𝑅 � , 𝑦 � � �𝑞 � 𝑦 � � 𝐷 � 𝑅 � � 𝑦 � � 𝑦 � � 𝑅 � , � 𝑅 � � 𝐷 � 𝑞 � 𝑦 � � 𝐷 � 𝑅 � � 𝑦 � max 𝛲 � 𝑅 � ≡ 𝐹 𝑎 � 𝑅 � , 𝑦 � � � 𝑤 𝐹 𝑅 � � 𝑦 � � � 𝐷 � � � 𝐷 � � 𝐹 𝑦 � � 𝑅 � � � �𝜈 � � 𝛲 � 𝑅 � � �𝑞 � �𝐷 � 𝐷 � ∗ � min 𝑅 � ∈ 𝑋 𝐺 The optimal prestaged quantity of 𝑅 � � 𝑅 � � 𝐷𝑊 � ∗ can be found: each item i 𝑅 � � � 𝐷 � � 𝐷𝑊�𝑗� � 𝐷 � � � 𝑤 𝐷 � 𝐷𝑊�𝑗� � 𝛾 � � 𝛾 � 𝑗 � � 𝛽 � � 𝛽 � 𝛾 � 𝑗 � � 𝛽 � � 𝑤 17

Properties of the Critical Value 𝐷𝑊 � is a rational function of i • Potential negative values due to negative marginal 𝐷𝑊�𝑗� ∈ �∞, 1 shortage cost �→� 𝐷𝑊�𝑗� � 1 � 𝛾 � • As the value of i grows, 𝐷𝑊 � approaches a certain lim 𝛾 � value • 𝐷𝑊 � is a monotone function of i �� 𝐷𝑊 ∗ � �𝐺 � , if 0 � 𝐷𝑊 � � 1 � 𝑅 � 0 if 𝐷𝑊 � � 0

Critical Point & Proactive Candidates (CII) 𝜄 � �𝐷𝑊�𝜄� � 0|𝜄 ∈ 𝐽 � � � 𝛽 � � 𝛽 � � 𝜄 � 𝛾 � � 𝛾 � ∗ � 0 for 𝑗 ∈ CII �𝑅 � ∗ � 0 for 𝑗 ∉ CII 𝑅 � 𝛾 � � 𝛾 � 𝛾 � � 𝛾 � (a) (d) 𝛽 � � 𝛽 � 𝜄 � CII � 𝑂 CII ≡ ∅ (c) (b) CII ≡ 𝐽 � 𝛽 � � 𝛽 � 0 � 𝐷𝐽𝐽 � 𝜄

Prestaging Problem: Given fulfilling requested demand by prestaging an item requires additional labor efforts by the delivery ship, negative marginal shortage costs can occur. is the decision maker’s willingness to pay for responsiveness. 𝛿 ∈ �0,1� Overestimating : Prestaging too many units, Extra Labor ( 𝑅 � � 𝑦 � ) � � 𝑢 � � 𝑣 � 𝑠 � 𝑛 𝐷 � Underestimating: Prestaging too few units, Low responsiveness ( 𝑅 � � 𝑦 � ) � � 𝑚 � 𝑙 � 𝑠 � 𝜹 𝐷 � 20

Lower Bound Table 1. Different recommendations for the lower bound with respect to three different conditions 𝛾 𝜀 � 𝐦 � 𝐬 � 𝐥 𝐵 � 𝐷𝑊 Case Recommendation CII interval � 0 � 0 � 0 � 0 No prestaging for ∀𝑗 ∈ 𝐽 ∅ 1 � 0 0 � 0 � 0 Candidate Items for Prestaging 𝑗 � 1 �1, 𝑉� 2 � 0 � 0 ��∞, 𝐶 � � �∞, 1 No prestaging for 𝑗 � 𝑀 �𝑀, 𝑉� 3 21 Jennifer Pazour, Ph.D.

Policy Recommendation for Imperfect Location Visibility 𝐷𝐽𝑄 ∗ � 0 ∗ � 0 ∗ � 0 𝑅 � 𝑅 � 𝑅 � Candidate items for Prestaging 𝐷𝐽𝑄 ∈ �𝑀, 𝑉� 1 1 1 4 � 𝜀 � 𝛽𝛿 𝑽 � �𝑂𝑇 � 1 2 𝑴 � �𝑂𝑇 � 1 2 � � 4 � 𝑂𝑇 1 � 𝑇 � � 𝑂𝑇 1 � 𝑇 𝛿 𝛿𝑢̂ �� 1 � 1 � 𝛿 22