Plots¶

Ready-made Matplotlib views of runs and comparisons. Guide: Plots.

Each picture below is drawn by the function above it on the tea shop from the Walkthrough: one run with an 80% target, and a comparison of the 80% and 95% targets on the same demand.

plot_inventory ¶

plot_inventory(
    result: Any,
    sku: Optional[Union[Hashable, List[Hashable]]] = None,
    ax: Optional[Axes] = None,
    figsize: Tuple[float, float] = (12, 5),
) -> Axes

Plot on_hand inventory over time with stockout highlighting.

Parameters:

Name	Type	Description	Default
`result`	`Any`	SimulationResult	required
`sku`	`Optional[Union[Hashable, List[Hashable]]]`	One SKU id, or a list of SKU ids summed per period. None sums all SKUs.	`None`
`ax`	`Optional[Axes]`	Optional matplotlib Axes.	`None`
`figsize`	`Tuple[float, float]`	Figure size if creating new figure.	`(12, 5)`

Returns:

Type	Description
`Axes`	matplotlib Axes

On-hand stock over eight weeks, with the two stockout days shaded — `plot_inventory(result, sku="tea_250g")`: on-hand stock, with stockout periods shaded

plot_demand_vs_orders ¶

plot_demand_vs_orders(
    result: Any,
    sku: Optional[Union[Hashable, List[Hashable]]] = None,
    ax: Optional[Axes] = None,
    figsize: Tuple[float, float] = (12, 5),
) -> Axes

Plot demand and orders over time.

Parameters:

Name	Type	Description	Default
`result`	`Any`	SimulationResult	required
`sku`	`Optional[Union[Hashable, List[Hashable]]]`	One SKU id, or a list of SKU ids summed per period. None sums all SKUs.	`None`
`ax`	`Optional[Axes]`	Optional matplotlib Axes.	`None`
`figsize`	`Tuple[float, float]`	Figure size if creating new figure.	`(12, 5)`

Returns:

Type	Description
`Axes`	matplotlib Axes

Daily demand and the orders placed every four days — `plot_demand_vs_orders(result, sku="tea_250g")`: daily demand and the order placed at each review

plot_simulation_dashboard ¶

plot_simulation_dashboard(
    result: Any,
    sku: Optional[Union[Hashable, List[Hashable]]] = None,
    figsize: Tuple[float, float] = (13, 10),
    show_target: bool = True,
) -> ndarray

Multi-panel dashboard for a single simulation run.

Creates a synchronized 4-panel figure

Inventory position — on_hand with the target level
Demand and order decisions — demand line with order bars at decision points
Shortage per period — bar chart of unmet demand
Cumulative shortage — running total showing impact over time

Panels share the x-axis for easy cross-referencing.

Parameters:

Name	Type	Description	Default
`result`	`Any`	SimulationResult	required
`sku`	`Optional[Union[Hashable, List[Hashable]]]`	One SKU id, or a list of SKU ids summed per period. None sums all SKUs.	`None`
`figsize`	`Tuple[float, float]`	Overall figure size.	`(13, 10)`
`show_target`	`bool`	Show target inventory level (S) as horizontal reference.	`True`

Returns:

Type	Description
`ndarray`	Array of matplotlib Axes (one per panel).

Four-panel dashboard of one run — `plot_simulation_dashboard(result, sku="tea_250g")`: stock with the target level, demand and orders, shortage, and cumulative shortage

plot_comparison ¶

plot_comparison(
    comparison_result: Any,
    metric: str = "on_hand",
    sku: Optional[Union[Hashable, List[Hashable]]] = None,
    ax: Optional[Axes] = None,
    figsize: Tuple[float, float] = (12, 6),
) -> Axes

Overlay a metric across multiple policies from a ComparisonResult.

Parameters:

Name	Type	Description	Default
`comparison_result`	`Any`	ComparisonResult	required
`metric`	`str`	Column name from history to plot (default: 'on_hand').	`'on_hand'`
`sku`	`Optional[Union[Hashable, List[Hashable]]]`	One SKU id, or a list of SKU ids summed per period. None sums all SKUs.	`None`
`ax`	`Optional[Axes]`	Optional matplotlib Axes.	`None`
`figsize`	`Tuple[float, float]`	Figure size if creating new figure.	`(12, 6)`

Returns:

Type	Description
`Axes`	matplotlib Axes

On-hand stock of two policies on the same demand — `plot_comparison(comparison, metric="on_hand", sku="tea_250g", ax=ax)`, with a title set on `ax`

plot_summary_comparison ¶

plot_summary_comparison(
    comparison_result: Any,
    metrics: Optional[List[str]] = None,
    figsize: Tuple[float, float] = (10, 6),
) -> Axes

Grouped bar chart comparing summary metrics across policies.

Parameters:

Name	Type	Description	Default
`comparison_result`	`Any`	ComparisonResult	required
`metrics`	`Optional[List[str]]`	List of metric names to plot. None = all numeric metrics.	`None`
`figsize`	`Tuple[float, float]`	Figure size.	`(10, 6)`

Returns:

Type	Description
`Axes`	matplotlib Axes

Bars of fill rate and demand-period service level for two policies — `plot_summary_comparison(comparison, metrics=["fill_rate", "demand_period_service_level"])`, with the legend moved outside

plot_comparison_dashboard ¶

plot_comparison_dashboard(
    comparison_result: Any,
    sku: Optional[Union[Hashable, List[Hashable]]] = None,
    figsize: Tuple[float, float] = (13, 8),
) -> ndarray

Side-by-side multi-metric comparison across policies.

Creates a 3-panel figure

Inventory levels overlaid per policy
Cumulative shortage per policy
Fill rate evolution over time per policy

Parameters:

Name	Type	Description	Default
`comparison_result`	`Any`	ComparisonResult	required
`sku`	`Optional[Union[Hashable, List[Hashable]]]`	One SKU id, or a list of SKU ids summed per period. None sums all SKUs.	`None`
`figsize`	`Tuple[float, float]`	Overall figure size.	`(13, 8)`

Returns:

Type	Description
`ndarray`	Array of matplotlib Axes.

Three-panel comparison of two policies — `plot_comparison_dashboard(comparison, sku="tea_250g")`: stock, cumulative shortage, and fill rate over time