Problem Statement

Defining a problem statement is critical to the success of a research project or thesis. Ensure that the problem is specific enough to address effectively within the scope of your document. Avoid investigating multiple problems or overly broad, ambitious topics, as these often lead to vague and unmanageable documents. Simply naming a topic is not equivalent to defining a problem.

Key Elements

Problem statements often consist of three core elements:

  1. The Problem: Clearly articulate the problem, providing sufficient contextual detail to explain its importance.
  2. The Approach: Outline the method of addressing the problem, often stated as a claim or a working thesis.
  3. The Purpose: Define the purpose, scope, and objectives of the document clearly.

These elements should be concise, ensuring the reader stays engaged and focused.

Cheat Sheet for Writing a Problem Statement

  1. Introduce the Problem Statement:
    Begin with a clear signal:

    “The problem that this study addresses is…”*

    Example:

    The problem that this study addresses is the inefficiency of resource allocation in automated sorting facilities during seasonal demand spikes.

  2. Explain the Consequences:
    Highlight who or what is affected if the problem remains unsolved. Describe the broader impact.

    Example:

    Without an efficient allocation system, facilities face increased sorting errors, longer processing times, and higher labor costs. This impacts service levels and customer satisfaction.

  3. Conclude with Research Needs:
    Specify the type of research required to address the problem. Cite relevant authors or studies suggesting the necessity of such research.

    Example:

    This study will investigate optimization models for resource allocation and test their feasibility in real-time sorting operations.

This structure helps transition into your research objectives and work plan, demonstrating how your study addresses a specific research gap.

Tips for an Effective Problem Statement

  • Be specific: Define the problem in clear and measurable terms.
  • Focus on one problem: Avoid diluting your work by addressing multiple issues.
  • Highlight relevance: Justify the importance of the problem to your field of study.
  • Be concise: Aim to communicate your problem within 150-250 words.

Example 1: Last-Mile Logistics and Congestion

Problem and Context:
A recent trend in warehousing is the adoption of autonomous mobile robots (AMRs) for order picking. While AMRs improve efficiency during normal operations, they face challenges in high-density storage environments. Specifically, AMRs experience navigation delays during peak operations and increased battery drain due to route congestion. In a study by XYZ Logistics [Ref. 2], AMRs in a multi-zone warehouse reported 25% slower picking rates during peak demand periods.

Research Approach:
This study analyzes the impact of navigation algorithms on AMR performance in three different multi-zone configurations:

  1. Standard storage layout with fixed zones,
  2. Dynamic re-zoning during peak demand, and
  3. High-density storage with dedicated fast-pick zones.

The objective is to identify the most effective strategies for minimizing navigation delays and improving battery performance during peak demand periods.

Purpose and Scope:

This study develops simulation models of AMR navigation and compares their performance metrics across different warehouse layouts under peak operational conditions.

Example 2: Last-Mile Logistics and Congestion

Problem and Context:
Urban delivery networks are increasingly strained by rising e-commerce demands, leading to inefficiencies in last-mile logistics. Delivery vehicles face delays due to traffic congestion and poor route planning, resulting in higher fuel consumption and missed delivery windows.

Research Approach:

This study evaluates the effectiveness of dynamic routing algorithms in mitigating delivery delays under varying traffic conditions. Simulated scenarios will compare:

  1. Static routing methods,
  2. Dynamic routing with real-time traffic updates, and
  3. Predictive routing using historical traffic patterns.

Purpose and Scope:
The objective is to identify the most effective routing approach for improving delivery reliability while minimizing fuel consumption and operational costs.

Example 3: Warehouse Automation and Productivity

Problem and Context:
The adoption of automated sorting systems in warehouses has accelerated, but the integration of these systems often leads to bottlenecks in high-volume environments. Specifically, conveyor systems struggle to match sorting speeds during peak operations, leading to delays and backlogs.

Research Approach:
This study investigates how queue management algorithms can optimize sorting performance in automated facilities. Experiments will focus on:

  1. Standard conveyor systems,
  2. Conveyor systems with dynamic sorting zones, and
  3. Hybrid systems combining manual and automated processes.

Purpose and Scope:
The study aims to propose algorithmic improvements for reducing bottlenecks and enhancing throughput in high-volume sorting operations.

Example 4: Inventory Management in Multi-Zone Warehouses

Problem and Context:
In multi-zone warehouses, inaccuracies in inventory data often result in delayed order fulfillment. These errors typically arise from manual restocking processes and outdated inventory tracking systems.

Research Approach:
This study evaluates the impact of RFID-based tracking systems on inventory accuracy and order fulfillment speed. Experiments will compare:

  1. Manual tracking systems,
  2. Barcode scanning systems, and
  3. RFID-enabled systems.

Purpose and Scope:
The study will develop an implementation framework for RFID systems in multi-zone warehouses to minimize inventory discrepancies and improve fulfillment efficiency.

Tips for Adapting Problem Statements to Industrial Engineering Topics

  • Focus on Systems and Operations: Address inefficiencies or challenges within logistics, warehousing, or manufacturing processes.
  • Highlight Quantifiable Outcomes: Emphasize metrics like cost savings, throughput, accuracy, or time reduction.
  • Incorporate Modern Technologies: Use examples involving automation, AI, or IoT to align with industry trends.
  • Specify the Context: Clearly define the environment (e.g., urban delivery, multi-zone warehouse, manufacturing plant).

References