Operational Research Techniques: Network Analysis and
Decision Trees
Performance evaluation in stores management, often achieved through
benchmarking, relies on robust performance measures. To further enhance decision
making and optimize operations, operational research techniques like network
analysis and decision trees are invaluable tools. These techniques provide a
structured approach to analysing complex problems, identifying optimal solutions,
and improving overall efficiency.
Here is a breakdown of network analysis and decision trees:
Network Analysis:
Definition and Purpose:
Network analysis is a technique used to model and analyse complex
systems that involve interconnected activities or events. It visually
represents the relationships between these activities, allowing for the
identification of critical paths, bottlenecks, and dependencies. In stores
management, network analysis can be used to optimize material flow,
schedule deliveries, and manage projects related to warehouse layout
or process improvements. The goal is to improve the efficiency and
effectiveness of the overall system by identifying areas for optimization
and streamlining processes. This technique is particularly useful in
projects with many interconnected tasks and helps to identify the
shortest possible completion time.
Key Components:
Nodes: Represent activities or events within the system.
Arcs/Edges: Represent the relationships or dependencies between
activities.
Critical Path: The longest sequence of activities that determines the
shortest possible completion time for the project.
PERT/CPM: Project Evaluation and Review Technique (PERT) and
Critical Path Method (CPM) are common network analysis techniques
used for project scheduling and management. PERT uses probabilistic
time estimates, while CPM uses deterministic time estimates. These
methods help to identify the critical path, calculate project completion
time, and manage resources effectively. Within a warehouse
environment, these tools can be used to calculate the most efficient
path for order fulfilment.
Applications in Stores Management:
Material Flow Optimization: Network analysis can be used to map the
flow of materials within a warehouse, identifying bottlenecks and
optimizing routes.
Project Scheduling: It can be used to schedule projects related to
warehouse layout changes, equipment installations, or process
improvements.
Delivery Route Optimization: It can be used to optimize delivery
routes, minimizing transportation costs, and improving delivery times.
Resource Allocation: Network analysis can help in allocating
resources effectively by identifying critical activities and dependencies.
Supply Chain Analysis: Network analysis can be used to model and
analyse the entire supply chain, identifying areas for improvement and
optimizing overall efficiency.
Decision Trees:
Definition and Purpose:
Decision trees are a visual representation of decision-making
processes, showing possible outcomes and their associated
probabilities. They provide a structured approach to evaluating different
options and selecting the most favourable course of action. In stores
management, decision trees can be used to make decisions related to
inventory control, equipment selection, and process improvements.
The aim is to make informed decisions by considering all possible
outcomes and their associated risks. This technique is particularly
useful in situations with uncertainty, where multiple factors need to be
considered.
Key Components:
Decision Nodes: Represent points where a decision needs to be
made.
Chance Nodes: Represent points where there is uncertainty and
multiple possible outcomes.
Branches: Represent possible options or outcomes.
Leaf Nodes: Represent the final outcomes or results.
Probabilities: Represent the likelihood of each outcome occurring.
Payoffs: Represent the value or cost associated with each outcome. Decision trees allow for the easy visualization of complex problems and allow for the calculation of the expected value of each decision.
Applications in Stores Management:
Inventory Control Decisions: Decision trees can be used to
determine optimal inventory levels, considering factors such as
demand variability, lead times, and holding costs.
Equipment Selection: They can be used to evaluate different
equipment options, considering factors such as cost, performance, and
reliability.
Process Improvement Decisions: Decision trees can be used to
evaluate different process improvement options, considering factors
such as cost, efficiency, and risk.
Supplier Selection: Decision trees can assist in the supplier selection
process, considering factors like cost, delivery times, and quality.
Risk Management: Decision trees can be used to assess and manage
risks associated with different decisions, such as the risk of stockouts
or obsolescence.
Benchmarking and Measures of Performance
Benchmarking and measures of performance are essential tools for evaluating and
improving the efficiency and effectiveness of stores management operations.
Benchmarking involves comparing an organization's performance against industry
best practices or competitors to identify areas for improvement. Measures of
performance, also known as key performance indicators (KPIs), are metrics used to
track and assess the performance of specific processes or activities within the stores
function. Together, these tools provide a structured approach to identifying gaps,
setting targets, and driving continuous improvement.
Here is a breakdown of benchmarking and measures of performance:
Benchmarking:
Definition and Purpose:
Benchmarking is the process of comparing an organization's
performance, processes, or practices against those of industry leaders
or competitors. It involves identifying best practices, understanding the
factors that contribute to superior performance, and adapting those
practices to improve the organization's own performance. The goal of
benchmarking is to identify areas for improvement, set realistic targets,
and drive continuous improvement. It allows organizations to gain
valuable insights into their strengths and weaknesses, and to
understand how they compare to others in the industry. Benchmarking
is not about simply copying others; it is about learning from best
practices and adapting them to fit the organization's specific context.
Types of Benchmarking:
Internal Benchmarking: Comparing performance within different
departments or units of the same organization.
Competitive Benchmarking: Comparing performance against direct
competitors in the same industry.
Functional Benchmarking: Comparing performance against
organizations in different industries that excel in specific functions or
processes.
Generic Benchmarking: Comparing performance against best-in
class organizations regardless of industry.
Process of Benchmarking:
Planning: Define the scope of the benchmarking study, identify the
areas to be benchmarked, and select the benchmarking partners.
Data Collection: Gather data on the organization's own performance
and the performance of the benchmarking partners.
Analysis: Analyse the data to identify performance gaps and
understand the factors that contribute to superior performance.
Implementation: Develop and implement action plans to close the
performance gaps and improve the organization's performance.
Monitoring: Continuously monitor and evaluate the effectiveness of
the implemented changes.
Measures of Performance (KPIs):
Definition and Purpose:
Measures of performance, or KPIs, are metrics used to track and
assess the performance of specific processes or activities within the
stores function. They provide quantifiable data that can be used to
monitor progress, identify trends, and make informed decisions. The
purpose of KPIs is to provide a clear and objective assessment of
performance, allowing organizations to identify areas for improvement
and track the impact of improvement initiatives. Effective KPIs should
be relevant, measurable, achievable, and time-bound (SMART).
Key Performance Indicators (KPIs) in Stores Management:
Inventory Turnover: Measures how quickly inventory is sold or used.
Stockout Rate: Measures the percentage of time that a product is out
of stock.
Order Fulfilment Rate: Measures the percentage of orders that are
fulfilled on time and in full.
Warehouse Space Utilization: Measures the percentage of available
warehouse space that is being used.
Picking Accuracy: Measures the percentage of orders that are picked
correctly.
Put-Away Time: Measures the time taken to put away received
materials.
Cycle Count Accuracy: Measures the accuracy of cycle counts.
Inventory Holding Costs: Measures the costs associated with holding
inventory.
Order Cycle Time: Measures the time taken to process and fulfil an
order.
Supplier Lead Time: Measures the time taken for suppliers to deliver
materials.
Using KPIs for Performance Improvement:
Setting Targets: Establish realistic targets for each KPI based on
benchmarking data or industry best practices.
Monitoring Performance: Regularly track and monitor KPIs to identify
trends and deviations from targets.
Analysing Data: Analyse KPI data to identify the root causes of
performance issues.
Acting: Develop and implement action plans to address performance
issues and improve KPIs.
Continuous Improvement: Continuously monitor and evaluate the
effectiveness of improvement initiatives and adjust as needed.
