Welcome to my research tab!
User Guide:
This chapter is a gentle introduction of my research navigation during PhD studies and reveals some stories 'behind-the-scene'. For a quick snapshot on titles, abstracts and status of my papers, please skip this chapter and directly refer to the tab Papers.
My research initially drew inspiration from numerous business reports about high return processing costs, lenient return policies and resulting environmental impacts in 2019. These prompted a lead from the other side of the coin,
"Efficient product return management might enhance profitability while reducing the carbon footprint."
Brilliant though it seemed at first glance, this idea led to more questions during my third year of PhD studies: "What are the focal decisions, key trade-offs, and major pain points in the lengthy return handling process that involves 15-25 labor touch points?", "How do store managers, logistics associates, and warehouse managers view return management?" More fundamentally, "Should I pursue this research direction? How can I ensure my proposed research solves the right problems?"
To dive deeper into these questions, in 2022, I chose to deviate slightly from the conventional PhD path and undertook a hands-on investigation of the product return handling process at Nordstrom. To gain firsthand knowledge of the entire supply chain operations, I took on roles as a cashier at the return service desk, a logistics assistant in store backrooms, and a return inspector in the fulfillment center.
In many ways, this experience has shaped both my current and future research agenda. In what follows, I will introduce my papers as well as their practical relevance. If you are interested in any aspect of my research, please don't hesitate to reach out at jiaxinl@umich.edu
Research on Product Return Management
Let's start with background info about returns:
There are four challenges imposed by high-volume product returns:
1. High Return Handling Cost: Per return handling costs 17$-50$
2. Managerial Challenges in Decision Coordination:
Return handling process involves coordination of 15-25 labor touch points (validated by my working experiences in a fulfillment center in Iowa).
3. Technical Complexity Introduced by 'Return-Restock-Resale' Cycle ---- Increased Dimensionality
4. Environmental Impacts
Example: For pricing decision in t.
Let Xs(t) be the purchases in day s which have not been returned in t
Without return:
State variables = (current inventory, pipeline orders)
With return:
State variables = (current inventory, pipeline orders, Xt-1(t), Xt-2(t),...,X2(t), X1(t))
In light of these facts, two primary issues emerge:
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Survival Priority:
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How can we address the high return handling cost and secure the base profit margin amidst fierce market competition?
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Could we achieve this with little environmental compromise?
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Sustainable Business Growth:
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What are the root causes of increased landfill and emission?
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How can we efficiently adapt to various sustainability constraints at scale?
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The papers I completed during my PhD focus on the first priority. My ongoing research, motivated by the cross-team projects I undertook at Nordstrom, aims to shed light on the second issue.
Part 1: Tackle "Survival Priority" at the Moment
Industry Background:
Return as a cost center
How can we manage high-volume product returns to secure base profit margin?
To address the challenges at scale....
We need clarity on the current situation:
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How do returns influence fundamental business decisions (i.e. pricing, inventory, assortment)?
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Why do prevailing strategies fail?
We aim for "Hassle-Free" enhancements:
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No changes on decision types and business tenets:
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Could minor adjustments on existing decisions be effective?
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Can we maintain "lenient return policy" without compromising sustainability
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No extra burden on team coordination:
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Fact: Basic return handling process already requires coordinating 15-25 labor touch points!
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Working as a cashier in the flagship store. Photo by Ziqi Ding.
Research Question in Response:
How to effectively tune ''returns'' into existing decision framework?
Combining a Smart Pricing Policy with a Simple Replenishment Policy: Managing Uncertainties in the Presence of Stochastic Purchase Returns [Link] with Stefanus Jasin and Joline Uichanco, under major revision at Mathematics of Operations Research
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Selected for MSOM SIG, 2023
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Second prize, POMS-HK Best Student Paper Competition, 2022
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Winner, EURO Pricing and Revenue Management Student Video Presentation Award, 2022
Assortment and Inventory Planning under Dynamic (Stockout-based) Substitution in the Presence of Customer Returns: A Fluid Analysis [Link] with Stefanus Jasin and Xiuli Chao, under major revision at Operations Research
Part 2: Navigate Long-term "Sustainable Business Growth"
Industry Background:
Returns incur three types of carbon footprint :
Landfill Contribution: Off-season returns and 'over-replenishment'
Emission and Waste: Repacking, small parcel retrieving
Pollutions: Hazmat return (e.g., cosmetics) incineration
A Loss-Loss Status Quo: High Return Handling Cost + High Carbon Footprint
One root cause: Rigid return handling rule (FIFS) vs. Heterogenous Product Life-Cycle Patterns
A Win-Win Solution? Life-Cycle Adaptive Strategy
Illustrative Example:
Early September,
300 shorts are returned, followed by 300 hoodies,
workers start with processing shorts......
Mid October,
shorts: restocked, but low market demand
(emission +processing cost + landfill contribution)
hoodies: waiting to be processed, but store inventory already runs out
(lost sales)
Research Question in Response:
How to design life-cycle adaptive return handling process?
Multi-product Return Management through Life Cycle Aware Logistics Optimization with Sheng Liu, Layla Martin and Yeqing Zhou, in progress
Sponsored by TSL Cross-region Collaborative Grant (North America & Europe), 2023
Multi-product Inventory and Return Processing Rationing in the Presence of Heterogeneous Product Life Cycle Patterns under Waste Regulations (with Stefanus Jasin and Joline Uichanco), in progress
Dynamic Return Disposition in the Presence of Heterogeneous Product Expiration Dates: Reconciliation between Profitability and Waste Reduction (with Xiuli Chao and Murray Lei), in progress
Research on Joint Decision-Making Strategies
Industry Background:
Opportunities and challenges within joint decision-making strategies
Autonomy in Business Decisions
The integration of traditionally segregated functions like marketing (pricing or assortment) and logistics has been a notable trend among retailers over the past decade. This autonomy is amplified by the availability of abundant data, granting real-time system visibility.
Challenges in Coordination
While autonomy can empower decision-making, it simultaneously introduces multiple coordination challenges.
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Theoretically:
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Joint decision-making problems (e.g., joint inventory and pricing strategies) are challenging due to their high-dimensional nature.
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Practically:
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Implementing these strategies raises questions about how to minimize cross-team communication costs and how to accurately assess the contributions of individual teams.
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Research Question in Response:
What is the value of pursuing joint-decision making?
How to design a simple, scalable strategy backed by comprehensive performance analysis?
Assortment and Inventory Planning Under Dynamic Substitution with MNL Model: An LP Approach and an Asymptotically Optimal Policy with Stefanus Jasin and Joline Uichanco, under major revision at Operations Research, [Link]
Presented at INFORMS Revenue Management and Pricing Section Conference, 2023
Expected Profit of the Best Fixed Price Policy Decays Exponentially in Lead Time with Stefanus Jasin, Murray Lei and Lai Wei, under review at Operations Research, [Link]
Presented at Cornell OTIM Symposium on New Frontiers in Revenue Management, 2023
Papers Teaching Industry Immersions Research Exploration Curriculum Vitae