# Laporan Akhir Eksplorasi Data

## 1. Executive Summary

**1. Executive Summary**

This exploratory data analysis provides a comprehensive overview of sales, operational efficiency, and potential bottlenecks within the company's order fulfillment process. Analyzing a dataset of over 130,000 transactions, the report aims to uncover key performance indicators, identify areas for improvement, and inform strategic decisions.

**Overall Landscape:**
The dataset comprises 131,315 order line items, involving 4,619 unique outlets and 1,439 distinct products across 8 operational divisions. A total of 129,575 orders were fully served, generating a substantial total sales value of **IDR 55.08 Billion** and a quantity of **1.44 Million units**. However, 1,740 orders were cancelled, representing a small but critical area for investigation.

**Key Performance Insights:**

*   **Dominant Divisions:** **PHARMANET 2** emerges as the primary revenue driver, contributing the largest share to both total quantity and value of fully served orders. This division also shows a strong reliance on Pharos products, which account for over 55% of its quantity and value. Divisions like VIBRANT PBF also show significant Pharos product contribution (over 95%), while others such as ETCKLINIK, PHARMANET B2B, OTC, and NUTRISAINS rely more on non-Pharos products.
*   **Top Performers:** Outlets such as **TEKNOLOGI MEDIKA PRATAMA, PT** and **SAKAJAJA MAKMUR ABADI, PT** consistently rank as top performers in terms of both sales value and quantity for fully served orders. Leading products by value include **POLYSILANE TAB. KUNYAH** and **PRORIS SUSP 60 ML RASA JERUK**, while **MICROLAX GEL OBAT PENCAHAR 5ML** leads in quantity. These top performers represent significant strengths and potential best practices to emulate.

**Areas of Concern & Anomalies:**

*   **Cancellation Black Hole:** A significant anomaly is the complete absence of cancellation reasons in the `Keterangan` column for all 1,740 cancelled orders. This lack of data severely hinders any in-depth analysis into *why* orders are being cancelled, making it challenging to implement targeted solutions.
*   **Cancellation Hotspots:** While the overall cancellation rate is low, some divisions exhibit significantly higher rates. **VIBRANT 3** shows the highest cancellation rate at 19.64%, followed by **ETCKLINIK** at 1.74%, and **NUTRINDO** at 1.53%. Specifically within **VIBRANT PBF** (1.48% cancellation rate), products like **MEDISIO 36% CONCENTRATE 5ML** and outlets like **GEMINI MANDIRA LESTARI, PT** are frequently associated with cancellations.
*   **Internal Processing Delays and Cancellations:** A critical finding reveals that cancelled orders experience a longer average processing time from Sales Order creation to Delivery Order issuance (**SP_to_DO_days**) compared to fully served orders (0.55 days vs. 0.41 days). This suggests that delays in internal order fulfillment, particularly in preparing the Delivery Order after the Sales Order is confirmed, might be a contributing factor to cancellations. The initial `Order_to_SP_days` (order placement to sales order creation) is consistently short (0.02 days) for both, indicating the bottleneck is downstream in the DO creation process.
*   **Delivery Efficiency Disparities:** The overall average total delivery duration is 2.13 days. While internal expedition companies like **EXPD INTERNAL JAKSEL** show impressive average delivery times of 0.3 days, external logistics partners like **PT. TUNAS ANTARNUSA MUDA KARGO** (3.42 days), **Media Transportasi Logistic** (2.91 days), and **INDAH LOGISTIK** (2.87 days) are significantly slower. This variation suggests inconsistencies in last-mile delivery and potential areas for optimizing logistics partnerships.

**Recommendations:**

1.  **Mandate Cancellation Reason Capture:** Immediately implement a process to capture specific, standardized reasons for order cancellations in the `Keterangan` column or a dedicated new field. Without this, root cause analysis and effective problem-solving for cancellations will remain speculative.
2.  **Investigate SP-to-DO Bottleneck:** Prioritize investigation into the internal process between Sales Order creation (`Tgl. SP`) and Delivery Order issuance (`Tgl DO`), especially for divisions with higher cancellation rates or higher cancellation counts. Identify reasons for delays in this stage for cancelled orders and implement process improvements to expedite this critical step.
3.  **Targeted Intervention for High-Cancellation Areas:**
    *   For **VIBRANT PBF**, analyze product availability, inventory management, and customer communication related to products like **MEDISIO 36% CONCENTRATE 5ML** and outlets like **GEMINI MANDIRA LESTARI, PT**.
    *   Review operational processes and product availability for **VIBRANT 3** and **ETCKLINIK** divisions, given their higher cancellation rates.
4.  **Optimize Logistics Partnerships:**
    *   Benchmark and learn from the fastest expedition companies (e.g., EXPD INTERNAL JAKSEL, EXPD JAKTIM) to understand best practices.
    *   Engage with slower expedition partners (e.g., PT. TUNAS ANTARNUSA MUDA KARGO, INDAH LOGISTIK) to understand their operational challenges and negotiate service level improvements or consider alternative partners where performance is consistently low.
5.  **Leverage Top Performer Insights:** Analyze the operational models, product strategies, and customer engagement practices of top-performing outlets and divisions to identify scalable strategies that can be applied across the organization.

This executive summary provides a foundation for deeper dives into specific issues, paving the way for data-driven strategic and operational improvements.

## 2. Introduction & Data Overview

## 2. Introduction & Data Overview

This chapter sets the stage for our comprehensive analysis by introducing the dataset under scrutiny and providing a high-level overview of its structure, content, and initial statistical properties. The data captures the intricate journey of product orders through various fulfillment stages, from initial order placement to final receipt by the customer, encompassing critical operational metrics and key business entities.

Our primary objective in this section is to establish a foundational understanding of the data's landscape, identify key business dimensions, and pinpoint any immediate anomalies or interesting patterns that warrant further investigation.

### 2.1 Data Source and Scope

The dataset, sourced from the `omg_10003_b1_sheet1` table, represents a detailed log of order transactions. Each row in this dataset corresponds to a specific product line item within an order, providing granular insights into the ordering, processing, and delivery lifecycle. It includes information about the products themselves, the outlets placing orders, the divisions responsible for fulfillment, and the logistics partners involved in delivery.

The dataset comprises **131,315 individual order line items**, offering a substantial volume of transactions for analysis. These transactions involve:
*   **4,619 unique outlets** identified by `Nama Outlet`. Interestingly, there are **5,062 unique `OutCode`** values, which suggests that some outlets might be associated with multiple internal codes or that there are naming inconsistencies in the `Nama Outlet` field. This discrepancy highlights a potential data quality issue that could impact analyses requiring unique outlet identification.
*   **1,439 unique products**, consistently identified by both `Procod` and `Prodes`, indicating a clear product catalog.
*   **8 distinct operational divisions** (e.g., PHARMANET 2, ETCKLINIK, NUTRISAINS), each playing a role in the fulfillment process.
*   **25 different expedition companies** (`Nama Ekspedisi`), reflecting the diverse logistics network supporting deliveries.

### 2.2 Key Data Attributes

The dataset is rich with attributes, spanning identification codes, descriptive names, quantities, monetary values, timestamps, and various status flags. Key columns include:

*   **Identifiers**: `KodePI`, `OutCode`, `Cmc`, `Procod`, `No. Web`, `No. RO`, `No. PO`, `No. SP`, `No. DO`, `No. PL`, `Nomor Resi`.
*   **Descriptions**: `Nama Outlet`, `Prodes` (Product Description), `Divisi`, `Nama Supplier`, `Nama Ekspedisi`, `Keterangan`.
*   **Quantities & Values**: `Qty RO` (Quantity Ordered), `Qty SP` (Quantity Sales Order), `Qty DO` (Quantity Delivery Order), `Qty PL` (Quantity Packing List - implied by `Value PL`), `Value RO`, `Value PL`.
*   **Timestamps**: `Tgl. Order`, `Tgl. PO`, `Tgl. SP`, `Tgl DO`, `Tgl. PL`, `Tgl Logbook`, `Tgl Ekspedisi`, `Tgl Receiving AMS`, `Tgl RN`, `Tgl Diterima`, `Lastupdate`. These are crucial for tracking process durations.
*   **Status Flags**: `Pharos YN` (Yes/No for Pharos product), `Sts SP Header`, `Sts SP Detail`, `Status DO`, `Status RO` (Status Request Order).
*   **Other**: `Asal Gudang` (Origin Warehouse), `Stok Avaiable` (Available Stock), `Ronaldo YN`, `Kel. Class`, `Nama Penerima` (Recipient Name), `AO YN`, `Distributor`.

### 2.3 Initial Data Exploration: Categorical Distributions

A preliminary examination of key categorical columns reveals fundamental aspects of the business operations:

#### Distribution of 'Divisi':
The `Divisi` column is pivotal, indicating the operational unit responsible for the order.
| Divisi        | count   |
|:--------------|:--------|
| PHARMANET 2   | 88887   |
| ETCKLINIK     | 17827   |
| PHARMANET B2B | 11532   |
| NUTRISAINS    | 4986    |
| OTC           | 3310    |
| VIBRANT PBF   | 2559    |
| NUTRINDO      | 2151    |
| VIBRANT 3     | 56      |

**Insight**: **PHARMANET 2** clearly dominates the order volume, accounting for the vast majority of line items. This signifies its central role in the company's operations and suggests it will be a primary driver of overall performance metrics. Divisions like VIBRANT 3, with only 56 entries, might represent niche operations or newer segments.

#### Distribution of 'Pharos YN':
This column distinguishes between proprietary ('Pharos') and non-proprietary products.
| Pharos YN   | count   |
|:------------|:--------|
| N           | 72014   |
| Y           | 59301   |

**Insight**: The split between Pharos (Y) and non-Pharos (N) products is relatively balanced, with non-Pharos products slightly more prevalent in terms of raw line item count. This indicates a diverse product portfolio, blending own-brand products with others, an important aspect for market strategy.

#### Distribution of Order Statuses:
Several columns track the status of an order at different stages (`Status RO`, `Sts SP Header`, `Sts SP Detail`, `Status DO`). Their distributions are remarkably consistent:

| Status RO       | count   |
|:----------------|:--------|
| TERLAYANI PENUH | 129575  |
| CANCEL          | 1740    |

| Sts SP Header   | count   |
|:----------------|:--------|
| P               | 129575  |
| C               | 1740    |

| Sts SP Detail   | count   |
|:----------------|:--------|
| P               | 129575  |
| C               | 1740    |

| Status DO   | count   |
|:------------|:--------|
| P           | 129575  |
| C           | 1740    |

**Insight**: The near-identical counts for 'TERLAYANI PENUH' (or 'P' for Processed) and 'CANCEL' (or 'C') across `Status RO`, `Sts SP Header`, `Sts SP Detail`, and `Status DO` indicate a coherent and consistent tracking mechanism for order states. This consistency simplifies analysis, as a cancellation at one stage seems to propagate effectively through subsequent stages. While the overall cancellation rate is low (1,740 out of 131,315 orders), these consistent flags mean we can reliably identify cancelled orders for deeper investigation.

### 2.4 Initial Data Exploration: Numerical Statistics

An initial look at the quantitative data provides a sense of scale, variability, and potential data quality issues:

#### Basic Statistics for Quantity and Value Columns:
|       | Qty RO   | Value RO    | Qty SP   | Value PL   | Qty DO   | Stok Avaiable   |
|:------|:---------|:------------|:---------|:-----------|:---------|:----------------|
| count | 131315   | 131315      | 131315   | 129575     | 131315   | 130581          |
| mean  | 11.0437  | 423485      | 10.9373  | 425089     | 10.9373  | 0               |
| std   | 71.6998  | 1.92652e+06 | 71.4741  | 1.9353e+06 | 71.4741  | 0               |
| min   | 0        | 0           | 0        | 0          | 0        | 0               |
| 25%   | 1        | 71068       | 1        | 71706      | 1        | 0               |
| 50%   | 3        | 144590      | 3        | 145203     | 3        | 0               |
| 75%   | 6        | 317193      | 6        | 318442     | 6        | 0               |
| max   | 7200     | 1.6627e+08  | 7200     | 1.6627e+08 | 7200     | 0               |

**Insights and Anomalies**:

*   **Quantity and Value Consistency**: `Qty RO`, `Qty SP`, and `Qty DO` show very similar statistics, as do `Value RO` and `Value PL`. This suggests that the quantities ordered, sales ordered, and delivery ordered are largely consistent. The slight differences (e.g., `Qty RO` mean 11.04 vs `Qty SP`/`Qty DO` mean 10.93) are likely due to the cancelled orders where `Qty SP` and `Qty DO` might revert to 0 or are not fully processed.
*   **Presence of Zero Values**: The minimum values for all quantity and value columns are 0. This indicates that some order line items might have zero quantity or value, potentially due to promotional items, adjustments, or data entry specifics, which will need to be considered in calculations.
*   **Wide Distribution and Outliers**: The standard deviations (`std`) are very high compared to the means, and the maximum values are orders of magnitude larger than the 75th percentile. This suggests a highly skewed distribution with a few very large orders or high-value items, which is common in sales data and means careful handling of outliers might be necessary for certain analyses.
*   **Critical Anomaly: `Stok Avaiable`**: The `Stok Avaiable` column has a `count` of 130,581 but its `mean`, `std`, `min`, `25%`, `50%`, `75%`, and `max` values are all **0**. This is a significant anomaly. It implies that this column, despite being present, contains no meaningful information about available stock for any order line item within this dataset.

**Recommendations based on initial overview**:

1.  **Address `OutCode` vs `Nama Outlet` Discrepancy**: Investigate why `OutCode` has more unique values than `Nama Outlet`. This could indicate data entry issues or a many-to-one relationship that needs clarification to ensure accurate outlet-level analysis. Data cleaning or standardization may be required.
2.  **Investigate `Stok Avaiable`**: Determine the intended purpose of the `Stok Avaiable` column. If it is meant to reflect actual inventory, its consistent zero value across all records renders it useless for inventory analysis and suggests a fundamental data capture issue that needs to be addressed at the source. If it's a placeholder or irrelevant, this should be confirmed.

This initial overview provides a solid foundation, highlighting the dataset's strengths in tracking order statuses consistently while also pointing out critical data quality issues and areas for deeper exploration. The next chapters will delve into more specific analyses based on these findings.

## 3. Sales Performance Analysis

## 3. Sales Performance Analysis

This chapter delves into the core of the business: its sales performance. We will analyze the overall sales volume and value, identify top-performing outlets and products, scrutinize divisional contributions, and, crucially, investigate the instances and potential causes of lost sales due to cancellations. Understanding these dynamics is paramount for optimizing sales strategies, resource allocation, and operational efficiency.

### 3.1 Overall Sales Performance

The dataset reveals a robust sales operation, with the vast majority of orders being fully served. For all fully served orders:

*   **Total Quantity (Qty RO):** 1,439,303 units
*   **Total Value (Value RO):** IDR 55,080,971,689

These figures represent the aggregate success of the sales and fulfillment process, forming the baseline for evaluating specific performance areas.

### 3.2 Top Performers: Outlets and Products

Identifying key contributors to sales helps in understanding market strengths and potential areas for strategic focus.

#### 3.2.1 Top 10 Outlets by Sales Value

The following outlets are the powerhouse revenue generators, leading in total sales value for fully served orders:

| Nama Outlet | Value RO |
|:---|---:|
| TEKNOLOGI MEDIKA PRATAMA, PT | 1594845216 |
| SAKAJAJA MAKMUR ABADI, PT | 799078128 |
| BAHTERA SEHAT SEJAHTERA, PT | 659882436 |
| CARMELLA GUSTAVINDO, PT | 624706276 |
| EVA SURYA PRATAMA PT | 597144492 |
| SEMPURNA MITRA PERKASA, PT | 524622659 |
| LESTARI JAYA SEJAHTERA, PT | 373540778 |
| PODO MEKAR JAYA SENTOSA, PT. | 365289348 |
| IKON SURABAYA FARMAMART, PT | 359560284 |
| BIMA SAKTI MEDICA, PT | 333473893 |

**Insight:** These top outlets represent significant business partnerships. Understanding their purchasing patterns, product preferences, and engagement strategies could yield valuable insights for expanding relationships with other clients.

#### 3.2.2 Top 10 Outlets by Sales Quantity

When looking at the sheer volume of products ordered, a slightly different picture emerges, though many top value generators also feature prominently here:

| Nama Outlet | Qty RO |
|:---|---:|
| TEKNOLOGI MEDIKA PRATAMA, PT | 49864 |
| SAKAJAJA MAKMUR ABADI, PT | 36361 |
| BAHTERA SEHAT SEJAHTERA, PT | 24533 |
| CARMELLA GUSTAVINDO, PT | 21578 |
| EVA SURYA PRATAMA PT | 20746 |
| SEMPURNA MITRA PERKASA, PT | 18967 |
| PODO MEKAR JAYA SENTOSA, PT. | 14210 |
| IKON SURABAYA FARMAMART, PT | 13738 |
| INTI TERAFARMA INDONESIA, PT | 12349 |
| LESTARI JAYA SEJAHTERA, PT | 11799 |

**Insight:** High overlap between top value and quantity outlets indicates consistent, large-volume purchases. "INTI TERAFARMA INDONESIA, PT" appears in the top 10 by quantity but not by value, suggesting they may focus on higher-volume, lower-value products.

#### 3.2.3 Top 10 Products by Sales Value

Identifying the most profitable products is crucial for inventory management, marketing, and product development:

| Prodes | Value RO |
|:---|---:|
| POLYSILANE TAB. KUNYAH 5X8TAB | 4035621351 |
| PRORIS SUSP 60 ML RASA JERUK | 3345577543 |
| MICROLAX GEL OBAT PENCAHAR 5ML | 3292285798 |
| MICROLAX 3 X 5 ML | 2686758593 |
| POLYSILANE SUSPENSI 100 ML | 1930169781 |
| PRORIS FORTE 200MG SUSP 50ML | 1635649720 |
| CATAFLAM 50MG TAB 50`S | 1414415775 |
| MEFINAL 500MG CAP 100`S | 1117221995 |
| NEBACETIN POWDER 5GR | 1017719735 |
| ALBUSMIN 3 BLISTER @ 10 KAPSUL | 945403138 |

**Insight:** Products like `POLYSILANE TAB. KUNYAH` and `PRORIS SUSP` are significant revenue drivers. This suggests strong market demand and brand loyalty for these particular items.

#### 3.2.4 Top 10 Products by Sales Quantity

Understanding high-volume products can inform logistics and inventory planning:

| Prodes | Qty RO |
|:---|---:|
| MICROLAX GEL OBAT PENCAHAR 5ML | 173810 |
| PRORIS SUSP 60 ML RASA JERUK | 148718 |
| POLYSILANE TAB. KUNYAH 5X8TAB | 110296 |
| POLYSILANE SUSPENSI 100 ML | 94472 |
| PRORIS FORTE 200MG SUSP 50ML | 61032 |
| MICROLAX 3 X 5 ML | 52885 |
| NEBACETIN POWDER 5GR | 52625 |
| SANMOL PARACETAMOL SIROP 60ML | 44039 |
| NEBACETIN OINT 5GR | 43893 |
| POLYSILANE SUSP 180 ML | 25876 |

**Insight:** While `MICROLAX GEL` leads in quantity, its value position is slightly lower than `POLYSILANE TAB. KUNYAH`, indicating a potentially lower price point or margin. This highlights the importance of analyzing both quantity and value for a holistic view of product performance.

### 3.3 Divisional Performance Deep Dive

Divisional analysis provides a strategic perspective on which operational units drive sales and how they manage their product portfolios and potential challenges.

#### 3.3.1 Overall Sales by Divisi (Fully Served Orders)

| Divisi | Total_Qty_RO | Total_Value_RO |
|:---|---:|---:|
| PHARMANET 2 | 662346 | 28660973075 |
| VIBRANT PBF | 496144 | 14318817088 |
| ETCKLINIK | 102591 | 4113114777 |
| PHARMANET B2B | 67832 | 3274695052 |
| NUTRISAINS | 45690 | 2096180418 |
| OTC | 36082 | 1413094512 |
| NUTRINDO | 26496 | 1108096610 |
| VIBRANT 3 | 2046 | 77188021 |
| nan | 76 | 18812136 |

**Insight:** **PHARMANET 2** is the undisputed leader, contributing the largest share to both quantity and value, affirming its role as the primary revenue driver. **VIBRANT PBF** also shows substantial contribution, particularly in quantity. The 'nan' entries for `Divisi` represent a small portion of orders where division information is missing, which should be addressed for data completeness.

#### 3.3.2 Pharos Product Contribution by Divisi (Fully Served Orders)

Understanding the sales mix of proprietary ('Pharos YN=Y') versus non-proprietary ('Pharos YN=N') products within each division offers insights into their strategic focus.

| Divisi | Non_Pharos_Qty | Pharos_Qty | Pharos_Qty_Pct | Non_Pharos_Value | Pharos_Value | Pharos_Value_Pct |
|:---|---:|---:|---:|---:|---:|---:|
| ETCKLINIK | 42368 | 60223 | 58.7 | 1.70553e+09 | 2.40758e+09 | 58.53 |
| NUTRINDO | 10723 | 15773 | 59.53 | 6.1674e+08 | 4.91357e+08 | 44.34 |
| NUTRISAINS | 18002 | 27688 | 60.6 | 1.19509e+09 | 9.01088e+08 | 42.99 |
| OTC | 12512 | 23570 | 65.32 | 6.30036e+08 | 7.83058e+08 | 55.41 |
| PHARMANET 2 | 292650 | 369696 | 55.82 | 1.56828e+10 | 1.29782e+10 | 45.28 |
| PHARMANET B2B | 29006 | 38826 | 57.24 | 1.89587e+09 | 1.37883e+09 | 42.11 |
| VIBRANT 3 | 9 | 2037 | 99.56 | 2.01035e+06 | 7.51777e+07 | 97.4 |
| VIBRANT PBF | 4552 | 491592 | 99.08 | 6.13138e+08 | 1.37057e+10 | 95.72 |

**Insight:**
*   **PHARMANET 2** and **VIBRANT PBF** clearly prioritize Pharos products, with Pharos products accounting for over 55% of quantity in PHARMANET 2 and over 99% in VIBRANT PBF. This signifies a strong focus on own-brand products in these key divisions.
*   Conversely, divisions like **NUTRINDO, NUTRISAINS, PHARMANET B2B, and ETCKLINIK** show lower contributions from Pharos products (around 42-59% by value), indicating a more diverse portfolio or a higher reliance on third-party products.
*   **VIBRANT 3** is almost exclusively focused on Pharos products, with 97.4% of its value coming from them, albeit with a smaller overall volume.

### 3.4 The Shadow of Cancellations: Lost Sales Analysis

While overall sales are strong, cancelled orders represent lost revenue opportunities and potential customer dissatisfaction. A total of **1,740 orders** were cancelled.

#### 3.4.1 Cancellation Rates by Divisi

Analyzing cancellation rates by division helps pinpoint areas with operational challenges or higher risks.

| Divisi | CANCEL | TERLAYANI PENUH | Total_Orders | Cancellation_Rate_Pct |
|:---|---:|---:|---:|---:|
| VIBRANT 3 | 11 | 45 | 56 | 19.64 |
| ETCKLINIK | 310 | 17517 | 17827 | 1.74 |
| NUTRINDO | 33 | 2118 | 2151 | 1.53 |
| VIBRANT PBF | 38 | 2521 | 2559 | 1.48 |
| PHARMANET 2 | 1154 | 87733 | 88887 | 1.3 |
| PHARMANET B2B | 131 | 11401 | 11532 | 1.14 |
| OTC | 28 | 3282 | 3310 | 0.85 |
| NUTRISAINS | 35 | 4951 | 4986 | 0.7 |

**Insight:**
*   **VIBRANT 3** exhibits an alarmingly high cancellation rate of 19.64%, significantly higher than any other division. While its total order volume is small, this rate warrants immediate investigation.
*   **ETCKLINIK, NUTRINDO, and VIBRANT PBF** also show cancellation rates above 1%, which is higher than the overall average.
*   **PHARMANET 2**, despite having the highest number of cancelled orders (1,154), maintains a relatively low cancellation rate (1.3%) due to its overwhelmingly large volume of served orders.

#### 3.4.2 Top 10 Outlets by Number of Cancelled Orders

Identifying outlets with frequent cancellations can indicate specific issues, such as poor communication, frequent stock-outs, or unreliable demand.

| Nama Outlet | Cancelled Order Count |
|:---|---:|
| MITRA HUSADA, APT | 42 |
| PURWONEGORO, APT | 24 |
| AL MUGHNI FARMA, APT | 22 |
| ALDERAN, APT | 17 |
| SCHOMED HEALTHMART, APT | 15 |
| CIPTO KEDUNGWARINGIN, APT | 13 |
| ASYIFA, APT (DURI) | 13 |
| GAMA CIRACAS, APT | 11 |
| Apotek Dede Farma | 11 |
| SETIA MEDIKA, APT | 9 |

**Insight:** `MITRA HUSADA, APT` leads in cancelled orders. These outlets should be prioritized for review, potentially through direct engagement to understand their challenges and improve order fulfillment.

#### 3.4.3 Top 10 Products by Number of Cancelled Orders

Certain products may be more prone to cancellations, perhaps due to inconsistent stock, sudden demand shifts, or pricing issues.

| Prodes | Cancelled Order Count |
|:---|---:|
| MEDISIO 36% CONCENTRATE 5ML | 90 |
| VOMARIN FLASH TAB 50`S | 62 |
| ALTADIS 2% KRIM 10GR | 53 |
| MEFINAL 500MG CAP 100`S | 52 |
| CATAFLAM 50MG TAB 50`S | 39 |
| KANTONG DELIVERY S APT. ONLINE | 35 |
| MOXAM 7.5MG TAB 30`S | 33 |
| MICROLAX GEL OBAT PENCAHAR 5ML | 26 |
| OZEN 5MG SIRUP 60ML | 26 |
| KOLTON 300MG TAB 50`S | 22 |

**Insight:** `MEDISIO 36% CONCENTRATE 5ML` stands out with a significantly high number of cancellations. This product, and others on this list, might be facing chronic stock issues, reorder problems, or other supply chain inefficiencies.

#### 3.4.4 The Missing "Why": Analysis of `Keterangan` for Cancelled Orders

A critical anomaly hindering deeper cancellation analysis is the complete absence of information in the `Keterangan` column for cancelled orders.

**Distribution of 'Keterangan' for Cancelled Orders:**
| Keterangan | count |
|:---|:---|
| | 1740 |

*   Total cancelled orders: 1740
*   Number of cancelled orders with non-null Keterangan: 0
*   Number of cancelled orders with null Keterangan: 0

**Insight:** The `Keterangan` column is entirely empty for all cancelled orders. This represents a significant data gap, preventing any meaningful root cause analysis of why orders are being cancelled. Without this information, efforts to reduce cancellations are largely based on speculation.

**Recommendation:** It is imperative to implement a process to capture specific, standardized reasons for order cancellations. This could involve mandatory fields in the order management system.

#### 3.4.5 Case Study: Cancellation Analysis for `VIBRANT PBF` Division

Given its higher cancellation rate compared to the overall average, a closer look at `VIBRANT PBF` reveals specific products and outlets contributing to this issue.

**Top 10 Products by Number of Cancelled Orders in VIBRANT PBF:**
| Prodes | Cancelled_Count |
|:---|---:|
| MEDISIO 36% CONCENTRATE 5ML | 6 |
| NOURISH SKIN ULTIMATE 30`S | 5 |
| CLINOVIR CREAM 5GR | 5 |
| NOURISH SKIN ULTIMATE @15 TABS | 4 |
| VEGEBLEND 21JR VEG.EXTR.30`S | 2 |
| NEBACETIN OINT 5GR | 2 |
| MICROLAX GEL OBAT PENCAHAR 5ML | 2 |
| NERIPROS 1MG TAB 30`S | 1 |
| OMEPROS 60 SOFT KAPSUL | 1 |
| NOURISH BC ACNE FCL FOAM 100ML | 1 |

**Top 10 Outlets by Number of Cancelled Orders in VIBRANT PBF:**
| Nama Outlet | Cancelled_Count |
|:---|---:|
| GEMINI MANDIRA LESTARI, PT | 4 |
| GUNA ABDI WISESA, PT | 4 |
| PODO MEKAR JAYA SENTOSA, PT. | 4 |
| TEKNOLOGI MEDIKA PRATAMA, PT | 3 |
| SEJAHTERA MULIA FARMA, PT | 3 |
| LANCAR JAYA SEHAT, PT | 2 |
| SEHAT BERSAMA SEMPURNA, PT | 2 |
| MULIA ABDI SENTOSA, PT (SINGARAJA) | 2 |
| FERTO MULIA PRATAMA, PT | 2 |
| GRAHA SEJATI MEDIKA, PT | 1 |

**Insight:** Within VIBRANT PBF, `MEDISIO 36% CONCENTRATE 5ML` continues to be a high-cancellation product, echoing the overall product cancellation trend. Outlets like `GEMINI MANDIRA LESTARI, PT` and `GUNA ABDI WISESA, PT` are recurring sources of cancellations for this division. This targeted information can guide interventions for `VIBRANT PBF`.

### 3.5 Operational Bottlenecks Impacting Sales (Cancellations)

A key hypothesis is that internal processing delays might contribute to cancellations. By comparing the `Order to DO` processing time for served versus cancelled orders, we can test this.

#### 3.5.1 Overall Average Order to DO Processing Time

The duration from `Tgl. Order` (order placement) to `Tgl DO` (Delivery Order issuance) is a critical internal metric.

| Status RO | Avg_Order_to_DO_days | Total_Orders |
|:---|---:|---:|
| CANCEL | 0.56984 | 1375 |
| TERLAYANI PENUH | 0.433934 | 129575 |

**Insight:** Cancelled orders, on average, take significantly longer (0.57 days) to process internally from order placement to delivery order issuance compared to fully served orders (0.43 days). This suggests that delays in this initial processing phase contribute to a higher likelihood of cancellation.

#### 3.5.2 Average Order to DO Processing Time by Key Divisions

This pattern holds true when examining specific divisions:

| Divisi | Status RO | Avg_Order_to_DO_days | Total_Orders |
|:---|:---|---:|---:|
| PHARMANET 2 | CANCEL | 0.61369 | 880 |
| PHARMANET 2 | TERLAYANI PENUH | 0.451236 | 87733 |
| VIBRANT PBF | CANCEL | 0.814221 | 37 |
| VIBRANT PBF | TERLAYANI PENUH | 0.443412 | 2521 |

**Insight:** Both `PHARMANET 2` and `VIBRANT PBF` show a similar trend: cancelled orders experience longer `Order to DO` times. Notably, `VIBRANT PBF` cancelled orders have an average `Order to DO` time of 0.81 days, almost double that of its fully served orders, strongly indicating internal processing delays as a factor in its higher cancellation rate.

#### 3.5.3 Analysis of Internal Processing Time Breakdown (Order to SP and SP to DO)

To pinpoint *where* these delays occur, we break down the `Order to DO` time into two stages: `Order to SP` (Order to Sales Order creation) and `SP to DO` (Sales Order to Delivery Order issuance).

| Status RO | Order_to_SP_days | SP_to_DO_days |
|:---|---:|---:|
| CANCEL | 0.02 | 0.55 |
| TERLAYANI PENUH | 0.02 | 0.41 |

**Insight:** The time from customer order placement to Sales Order (`Order_to_SP_days`) is consistently fast (0.02 days) for both cancelled and served orders. However, the bottleneck lies in the `SP_to_DO_days` stage:
*   Cancelled orders take **0.55 days** from Sales Order to Delivery Order issuance.
*   Fully served orders take **0.41 days** for the same stage.

This clearly indicates that delays in generating the Delivery Order *after* the Sales Order has been created are a significant contributing factor to order cancellations. This internal process, often involving inventory checks, picking, and packing confirmation, is where intervention is most needed to reduce lost sales.

### 3.6 Chapter Summary

The sales performance analysis reveals a strong overall picture, driven by key outlets and products, particularly within the **PHARMANET 2** division, which also shows a healthy reliance on proprietary Pharos products. However, the analysis also uncovers critical areas of concern:

*   **Lost Sales due to Cancellations:** While overall rates are low, **VIBRANT 3**, **ETCKLINIK**, and **VIBRANT PBF** exhibit higher cancellation rates.
*   **Specific Problem Areas:** `MITRA HUSADA, APT` and `MEDISIO 36% CONCENTRATE 5ML` are recurring themes in cancellation lists, requiring targeted investigation.
*   **Critical Data Gap:** The lack of cancellation reasons in the `Keterangan` column severely hampers root cause analysis.
*   **Operational Bottleneck:** Delays in the internal process from Sales Order creation to Delivery Order issuance (`SP_to_DO_days`) are strongly correlated with order cancellations. This bottleneck needs immediate attention to prevent further lost sales.

These insights form a foundation for strategic adjustments, process improvements, and focused interventions to enhance overall sales performance and minimize revenue leakage.

## 4. Divisional Performance Insights

## 4. Divisional Performance Insights

Understanding the performance of individual divisions is critical for strategic resource allocation, identifying best practices, and pinpointing areas that require targeted intervention. This chapter delves into a comprehensive analysis of each division's sales contributions, their product portfolio strategies (specifically concerning Pharos products), and their efficiency as measured by cancellation rates.

### 4.1 Overall Divisional Sales Performance

The diverse operational landscape of the company is channeled through several divisions, each contributing to the overall sales volume and value. A breakdown of fully served orders by division reveals distinct leaders and varying scales of operation.

| Divisi        |   Total_Qty_RO |   Total_Value_RO |
|:--------------|---------------:|-----------------:|
| PHARMANET 2   |         662346 |      28660973075 |
| VIBRANT PBF   |         496144 |      14318817088 |
| ETCKLINIK     |         102591 |       4113114777 |
| PHARMANET B2B |          67832 |       3274695052 |
| NUTRISAINS    |          45690 |       2096180418 |
| OTC           |          36082 |       1413094512 |
| NUTRINDO      |          26496 |       1108096610 |\
| VIBRANT 3     |           2046 |         77188021 |\
| nan           |             76 |         18812136 |

**Key Insights:**

*   **PHARMANET 2 Dominance:** With 662,346 units sold and a staggering IDR 28.66 billion in value, **PHARMANET 2** unequivocally stands as the primary revenue and volume driver for the company. Its performance significantly dwarfs that of other divisions, indicating its central role in the business.
*   **VIBRANT PBF as a Strong Contributor:** **VIBRANT PBF** is another substantial contributor, particularly in quantity (496,144 units) and value (IDR 14.32 billion). While trailing PHARMANET 2, it demonstrates a strong operational scale.
*   **Mid-Tier Divisions:** **ETCKLINIK** and **PHARMANET B2B** represent the next tier of contributors, with ETCKLINIK showing a notably higher quantity relative to its value compared to PHARMANET B2B.
*   **Niche Operations:** Divisions like **NUTRISAINS, OTC, and NUTRINDO** contribute smaller but significant volumes, likely catering to specific market segments or product categories.
*   **VIBRANT 3:** This division, with only 2,046 units and IDR 77 million in value, appears to be a smaller or more specialized operation.
*   **Data Anomaly (`nan`):** A small number of orders (76 units, IDR 18.8 million) lack a defined `Divisi`. While minor, addressing these `nan` values is essential for complete data integrity.

### 4.2 Product Portfolio Strategy: Pharos vs. Non-Pharos Products

Understanding the proportion of proprietary (Pharos) products versus third-party (non-Pharos) products sold by each division sheds light on their strategic focus and market positioning.

| Divisi        |   Non_Pharos_Qty |   Pharos_Qty |   Pharos_Qty_Pct |   Non_Pharos_Value |   Pharos_Value |   Pharos_Value_Pct |
|:--------------|-----------------:|-------------:|-----------------:|-------------------:|---------------:|-------------------:|\
| ETCKLINIK     |            42368 |        60223 |            58.7  |        1.70553e+09 |    2.40758e+09 |              58.53 |\
| NUTRINDO      |            10723 |        15773 |            59.53 |        6.1674e+08  |    4.91357e+08 |              44.34 |\
| NUTRISAINS    |            18002 |        27688 |            60.6  |        1.19509e+09 |    9.01088e+08 |              42.99 |\
| OTC           |            12512 |        23570 |            65.32 |        6.30036e+08 |    7.83058e+08 |              55.41 |\
| PHARMANET 2   |           292650 |       369696 |            55.82 |        1.56828e+10 |    1.29782e+10 |              45.28 |\
| PHARMANET B2B |            29006 |        38826 |            57.24 |        1.89587e+09 |    1.37883e+09 |              42.11 |\
| VIBRANT 3     |                9 |         2037 |            99.56 |        2.01035e+06 |    7.51777e+07 |              97.4  |\
| VIBRANT PBF   |             4552 |       491592 |            99.08 |        6.13138e+08 |    1.37057e+10 |              95.72 |

**Key Insights:**

*   **Strong Pharos Focus (VIBRANT PBF, VIBRANT 3):** **VIBRANT PBF** and **VIBRANT 3** exhibit a highly concentrated portfolio, with Pharos products contributing over 95% of both quantity and value. This indicates a strategic emphasis on proprietary brands within these divisions.
*   **Balanced or Moderate Pharos Contribution (PHARMANET 2, ETCKLINIK, OTC):** **PHARMANET 2** shows a substantial contribution from Pharos products (55.82% of quantity, 45.28% of value), suggesting a healthy mix but still a significant reliance on non-Pharos products for value. **ETCKLINIK** and **OTC** also fall into this category, with Pharos products contributing around 55-65% by quantity and value.
*   **Higher Reliance on Non-Pharos (NUTRINDO, NUTRISAINS, PHARMANET B2B):** Divisions like **NUTRINDO**, **NUTRISAINS**, and **PHARMANET B2B** see Pharos products contributing less than 45% of their total value. This implies that these divisions likely focus on distributing a wider range of third-party products, which could be part of their distinct business models (e.g., B2B distribution, specific nutritional products).

This breakdown highlights diverse strategies across divisions, ranging from heavily promoting own-brand products to acting as distributors for a broader range of goods.

### 4.3 Identifying Operational Challenges: Divisional Cancellation Rates

While sales volume is crucial, high cancellation rates can erode profitability and customer satisfaction. Analyzing these rates by division helps pinpoint operational bottlenecks or specific market challenges.

| Divisi        |   CANCEL |   TERLAYANI PENUH |   Total_Orders |   Cancellation_Rate_Pct |
|:--------------|---------:|------------------:|---------------:|------------------------:|\
| VIBRANT 3     |       11 |                45 |             56 |                   19.64 |\
| ETCKLINIK     |      310 |             17517 |          17827 |                    1.74 |\
| NUTRINDO      |       33 |              2118 |           2151 |                    1.53 |\
| VIBRANT PBF   |       38 |              2521 |           2559 |                    1.48 |\
| PHARMANET 2   |     1154 |             87733 |          88887 |                    1.3  |\
| PHARMANET B2B |      131 |             11401 |          11532 |                    1.14 |\
| OTC           |       28 |              3282 |           3310 |                    0.85 |\
| NUTRISAINS    |       35 |              4951 |           4986 |                    0.7  |

**Key Insights & Anomalies:**

*   **Alarming Rate in VIBRANT 3:** **VIBRANT 3** exhibits an exceptionally high cancellation rate of 19.64%. Despite its small total order volume, this rate is a significant anomaly and demands immediate investigation into its operational processes, stock management, or specific customer base.
*   **Above Average Cancellations (ETCKLINIK, NUTRINDO, VIBRANT PBF):** These divisions show cancellation rates ranging from 1.48% to 1.74%, which are higher than the lowest performers. While not as extreme as VIBRANT 3, these rates indicate areas where process improvements could significantly reduce lost revenue.
*   **PHARMANET 2's Efficiency:** Despite processing the highest volume of orders, **PHARMANET 2** maintains a relatively low cancellation rate of 1.3%. This suggests robust operational efficiency within its high-volume environment.
*   **Lowest Cancellation Rates (NUTRISAINS, OTC):** **NUTRISAINS** and **OTC** demonstrate the best performance in terms of minimizing cancellations, with rates below 1%. Their practices could serve as benchmarks for other divisions.

### 4.4 Deep Dive into High-Cancellation Divisions: VIBRANT PBF Case Study

Given its higher-than-average cancellation rate, a focused examination of **VIBRANT PBF** reveals specific products and outlets contributing to this challenge. While the overall cancellation analysis for all divisions points to internal processing delays (specifically `SP_to_DO_days`) as a general factor, understanding the specifics within VIBRANT PBF is crucial.

#### Top 10 Products by Number of Cancelled Orders in VIBRANT PBF

| Prodes                         |   Cancelled_Count |
|:-------------------------------|------------------:|\
| MEDISIO 36% CONCENTRATE 5ML    |                 6 |\
| NOURISH SKIN ULTIMATE 30`S     |                 5 |\
| CLINOVIR CREAM 5GR             |                 5 |\
| NOURISH SKIN ULTIMATE @15 TABS |                 4 |\
| VEGEBLEND 21JR VEG.EXTR.30`S   |                 2 |\
| NEBACETIN OINT 5GR             |                 2 |\
| MICROLAX GEL OBAT PENCAHAR 5ML |                 2 |\
| NERIPROS 1MG TAB 30`S          |                 1 |\
| OMEPROS 60 SOFT KAPSUL         |                 1 |\
| NOURISH BC ACNE FCL FOAM 100ML |                 1 |

`MEDISIO 36% CONCENTRATE 5ML` appears as a consistently problematic product, not only within VIBRANT PBF but also across the entire dataset (as highlighted in Chapter 3). The appearance of multiple "NOURISH SKIN" products also indicates potential issues specific to this brand or product line within the division.

#### Top 10 Outlets by Number of Cancelled Orders in VIBRANT PBF

| Nama Outlet                        |   Cancelled_Count |
|:-----------------------------------|------------------:|\
| GEMINI MANDIRA LESTARI, PT         |                 4 |\
| GUNA  ABDI WISESA,   PT            |                 4 |\
| PODO MEKAR JAYA SENTOSA, PT.       |                 4 |\
| TEKNOLOGI MEDIKA PRATAMA, PT       |                 3 |\
| SEJAHTERA MULIA FARMA, PT          |                 3 |\
| LANCAR JAYA SEHAT, PT              |                 2 |\
| SEHAT BERSAMA SEMPURNA, PT         |                 2 |\
| MULIA ABDI SENTOSA, PT (SINGARAJA) |                 2 |\
| FERTO MULIA PRATAMA, PT            |                 2 |\
| GRAHA SEJATI MEDIKA, PT            |                 1 |

Several outlets, such as `GEMINI MANDIRA LESTARI, PT`, `GUNA ABDI WISESA, PT`, and `PODO MEKAR JAYA SENTOSA, PT.`, consistently experience cancelled orders within the VIBRANT PBF division. This could be due to issues specific to these outlets' ordering habits, their location, or how they interact with VIBRANT PBF's fulfillment process.

### 4.5 Recommendations for Divisional Performance Improvement

Based on the divisional performance insights, the following recommendations are proposed:

1.  **Investigate VIBRANT 3's High Cancellation Rate:** Conduct an immediate, in-depth audit of VIBRANT 3's entire order fulfillment process. This includes reviewing inventory management, order processing steps, customer communication, and potential data quality issues that might inflate the cancellation rate for this small division.
2.  **Targeted Intervention for VIBRANT PBF's Cancellations:**
    *   **Product-Specific Review:** Investigate `MEDISIO 36% CONCENTRATE 5ML` and the `NOURISH SKIN` product line for consistent stock availability issues, supplier problems, or forecasting inaccuracies within VIBRANT PBF.
    *   **Outlet Engagement:** Proactively engage with outlets like `GEMINI MANDIRA LESTARI, PT` and `GUNA ABDI WISESA, PT` to understand the specific reasons behind their high cancellation counts. This could uncover issues related to delivery expectations, product quality, or order accuracy.
    *   **Address Internal Processing Delays:** Reinforce the finding from Chapter 3 that delays in `SP_to_DO_days` contribute to cancellations. VIBRANT PBF should prioritize streamlining this internal stage, particularly for orders containing frequently cancelled products or destined for high-cancellation outlets.
3.  **Benchmarking and Best Practices Sharing:** Study the operational practices of **PHARMANET 2** and divisions with lower cancellation rates (e.g., NUTRISAINS, OTC) to identify replicable strategies for efficiency, inventory management, and customer satisfaction that can be adopted across other divisions.
4.  **Strategic Review of Product Portfolios:** For divisions with lower Pharos product contribution (NUTRINDO, NUTRISAINS, PHARMANET B2B), evaluate if the current mix aligns with overall business goals. Are these divisions effectively leveraging third-party products, or could there be opportunities to increase Pharos market share where strategically beneficial?

By acting on these divisional insights, the company can optimize operations, reduce revenue leakage from cancellations, and align product strategies with divisional strengths.

## 5. Logistics and Delivery Efficiency

## 5. Logistics and Delivery Efficiency

The journey of a product from order placement to customer receipt is a critical determinant of customer satisfaction and operational cost. This chapter scrutinizes the company's logistics and delivery pipeline, breaking down the process into key stages to identify bottlenecks, evaluate expedition partner performance, and uncover opportunities for enhanced efficiency.

### 5.1 Overall Delivery Timelines

Our analysis of fully served orders provides a clear picture of the average time taken for products to reach their destination. The entire delivery process, from the moment an order is placed (`Tgl. Order`) to when it is received by the customer (`Tgl Diterima`), averages **2.13 days**.

Let's break down the average duration of each critical stage across all expeditions:

*   **Average Order to DO (Internal Processing) Duration:** 0.43 days
    *   *This represents the time from customer order placement to the issuance of the internal Delivery Order (DO), encompassing initial sales order processing and preparation.*
*   **Average DO to Ekspedisi (Handover to Logistics) Duration:** 0.40 days
    *   *This measures the time from DO issuance to when the package is handed over to the chosen expedition company.*
*   **Average Ekspedisi to Received (Shipping Time) Duration:** 1.31 days
    *   *This is the actual transit time, from the logistics partner receiving the package to the customer confirming receipt.*
*   **Average Total Delivery Duration:** 2.13 days

**Insight:** The "Ekspedisi to Received" stage, representing the actual shipping time by the logistics partner, constitutes the largest portion of the total delivery duration (1.31 days out of 2.13 days). While internal processing and handover times are relatively efficient, the last-mile delivery remains the longest segment of the journey.

### 5.2 Expedition Partner Performance Analysis

The choice of logistics partners significantly impacts delivery speed. Our data reveals a wide disparity in performance among the various expedition companies utilized, highlighting opportunities for optimization.

Below is a detailed breakdown of the average durations for each delivery stage, categorized by the expedition company for fully served orders:

| Nama Ekspedisi                 |   Avg_Order_to_DO_days |   Avg_DO_to_Ekspedisi_days |   Avg_Ekspedisi_to_Received_days |   Avg_Total_Delivery_days |   Total_Orders |
|:-------------------------------|-----------------------:|---------------------------:|---------------------------------:|--------------------------:|---------------:|
| EXPD INTERNAL SEMARANG         |                   0.08 |                       0.04 |                             0.04 |                      0.16 |             63 |
| EXPD INTERNAL JAKSEL           |                   0.23 |                       0.06 |                             0    |                      0.3  |            354 |
| EXPD JAKTIM                    |                   0.3  |                       0.08 |                             0.24 |                      0.62 |           2330 |
| EXPD JAKBAR                    |                   0.38 |                       0.05 |                             0.2  |                      0.63 |           1810 |
| EXPD SURABAYA 1                |                   0.26 |                       0.16 |                             0.36 |                      0.78 |            643 |
| EXPD JAKSEL                    |                   0.36 |                       0.35 |                             0.26 |                      0.97 |           1195 |
| EXPD BANJARMASIN               |                   0.3  |                       0.3  |                             0.37 |                      0.98 |            710 |
| EXPD BEKASI                    |                   0.39 |                       0.13 |                             0.49 |                      1.01 |           5028 |
| EXPD INTERNAL BOGOR            |                   0.29 |                       0.51 |                             0.35 |                      1.15 |            129 |
| EXPD Bogor (Sierra)            |                   0.48 |                       0.22 |                             0.5  |                      1.2  |            169 |
| Anteraja                       |                   0.35 |                       0.29 |                             0.63 |                      1.27 |          24201 |
| EXPD MEDAN                     |                   0.43 |                       0.32 |                             0.87 |                      1.62 |            147 |
| Antaraja Same Day              |                   0.75 |                       0.66 |                             0.3  |                      1.71 |            201 |
| PT CENTURY                     |                   0.38 |                       0.2  |                             1.85 |                      2.42 |          18843 |
| GLOBALINDO 21 EXPRESS          |                   0.42 |                       0.35 |                             1.78 |                      2.56 |           5027 |
| PCP EXPRESS                    |                   0.42 |                       0.44 |                             1.75 |                      2.59 |          12232 |
| LION PARCEL                    |                   0.48 |                       0.73 |                             1.52 |                      2.73 |          17289 |
| ANDALAN 21 EXPRESS             |                   0.81 |                       0.79 |                             1.2  |                      2.79 |           8355 |
| INDAH LOGISTIK                 |                   0.45 |                       0.39 |                             2.03 |                      2.87 |           8366 |
| Media Transportasi Logistic    |                   0.26 |                       0.52 |                             2.13 |                      2.91 |            744 |
| PT. TUNAS ANTARNUSA MUDA KARGO |                   0.44 |                       0.66 |                             2.32 |                      3.42 |           1029 |

#### 5.2.1 Top 5 Fastest Expedition Companies (minimum 100 orders)

| Nama Ekspedisi       |   Avg_Total_Delivery_days |   Total_Orders |
|:---------------------|--------------------------:|---------------:|
| EXPD INTERNAL JAKSEL |                      0.3  |            354 |
| EXPD JAKTIM          |                      0.62 |           2330 |
| EXPD JAKBAR          |                      0.63 |           1810 |
| EXPD SURABAYA 1      |                      0.78 |            643 |
| EXPD JAKSEL          |                      0.97 |           1195 |

**Insight:** Internal expedition teams, particularly `EXPD INTERNAL JAKSEL`, demonstrate exceptional speed, completing deliveries in less than a third of a day on average. Other regional "EXPD" branches also perform very strongly, indicating that localized, possibly dedicated, logistics operations are highly efficient.

#### 5.2.2 Top 5 Slowest Expedition Companies (minimum 100 orders)

| Nama Ekspedisi                 |   Avg_Total_Delivery_days |   Total_Orders |
|:-------------------------------|--------------------------:|---------------:|
| PT. TUNAS ANTARNUSA MUDA KARGO |                      3.42 |           1029 |
| Media Transportasi Logistic    |                      2.91 |            744 |
| INDAH LOGISTIK                 |                      2.87 |           8366 |
| ANDALAN 21 EXPRESS             |                      2.79 |           8355 |
| LION PARCEL                    |                      2.73 |          17289 |

**Insight:** There is a significant performance gap between the fastest internal expeditions and the slowest external partners. `PT. TUNAS ANTARNUSA MUDA KARGO` takes over 3.4 days on average, more than 10 times longer than `EXPD INTERNAL JAKSEL`. Companies like `INDAH LOGISTIK`, `ANDALAN 21 EXPRESS`, and `LION PARCEL` handle a substantial volume of orders (8k-17k), making their slower average delivery times a critical area of concern due to their broad impact.

### 5.3 Bottlenecks in the Delivery Chain

Delving deeper into the stage-by-stage performance of the slower expedition companies provides a clearer picture of where the delays occur:

*   **Last-Mile Bottleneck:** For most of the slower partners (e.g., `PT CENTURY`, `GLOBALINDO 21 EXPRESS`, `INDAH LOGISTIK`, `Media Transportasi Logistic`, `PT. TUNAS ANTARNUSA MUDA KARGO`), the `Avg_Ekspedisi_to_Received_days` is significantly higher, ranging from 1.75 to 2.32 days. This confirms that the actual transit and delivery to the customer is the primary factor contributing to their longer total delivery times.
*   **Handover Delays:** Some partners also show extended `Avg_DO_to_Ekspedisi_days`, indicating potential delays in the handover process from the company's warehouse to the logistics provider. Notably, `EXPD INTERNAL BOGOR` (0.51 days), `LION PARCEL` (0.73 days), and `ANDALAN 21 EXPRESS` (0.79 days) exhibit longer handover times compared to the overall average of 0.40 days. While their actual shipping time might not be the absolute slowest, these handover delays add to their total delivery duration.
*   **Internal Processing Consistency:** The `Avg_Order_to_DO_days` stage is relatively consistent across most expedition types, typically under 0.5 days, suggesting that the initial internal processing up to DO issuance is fairly standardized and efficient, irrespective of the final delivery partner. However, `Antaraja Same Day` (0.75 days) and `ANDALAN 21 EXPRESS` (0.81 days) stand out with longer internal processing times, which might be unique cases or suggest specific process variations for these partners.

### 5.4 Recommendations for Logistics Optimization

Based on the observed delivery performance, the following strategic actions are recommended to enhance logistics efficiency:

1.  **Conduct Performance Reviews with Underperforming Expedition Partners:** Engage directly with `PT. TUNAS ANTARNUSA MUDA KARGO`, `Media Transportasi Logistic`, `INDAH LOGISTIK`, `ANDALAN 21 EXPRESS`, and `LION PARCEL` to understand the root causes of their longer delivery times. This includes reviewing their internal processes, capacity, geographical coverage, and service level agreements (SLAs).
2.  **Focus on Last-Mile Delivery Optimization:** Given that `Ekspedisi to Received` is the longest stage for many partners, explore initiatives to improve this segment. This could involve:
    *   **Route optimization technologies** for frequently used partners.
    *   **Implementing real-time tracking and delivery updates** to improve customer visibility and manage expectations.
    *   **Incentivizing faster delivery** within contractual agreements.
    *   **Exploring alternative last-mile solutions** in areas served by consistently slow partners.
3.  **Investigate and Streamline Handover Processes:** For partners exhibiting longer `DO_to_Ekspedisi_days` (e.g., `LION PARCEL`, `ANDALAN 21 EXPRESS`), analyze the handover points and processes. Delays here could be due to inefficient pickup schedules, inadequate loading capacity, or procedural bottlenecks at the warehouse.
4.  **Benchmark Against Best Practices:** Study the operational models and advantages of the most efficient internal expedition teams (`EXPD INTERNAL JAKSEL`, `EXPD JAKTIM`, `EXPD JAKBAR`). Identify transferable best practices in route planning, dispatch management, and resource allocation that could be applied to other internal operations or shared with external partners.
5.  **Strategic Partner Evaluation:** Regularly review the performance of all logistics partners. For those consistently failing to meet acceptable delivery standards, evaluate the feasibility and benefits of negotiating improved terms, reallocating volume to better-performing partners, or exploring new logistics providers.
6.  **Consider Customer Impact:** Acknowledge that longer delivery times directly impact customer satisfaction. Communication strategies should be tailored to set realistic expectations, especially when using slower partners.

By systematically addressing these inefficiencies, the company can significantly enhance its delivery performance, leading to improved customer experience and potentially reduced operational costs over time.

## 6. Deep Dive into Order Cancellations

## 6. Deep Dive into Order Cancellations

Order cancellations, though a seemingly minor percentage of total transactions, represent a direct loss of revenue and can be a significant indicator of underlying operational inefficiencies or customer dissatisfaction. In this chapter, we peel back the layers of our cancellation data to understand its landscape, identify hotspots, and pinpoint the specific stages in our order fulfillment process where delays might be turning potential sales into lost opportunities.

Our analysis reveals a total of **1,740 cancelled orders** out of 131,315 line items. While this constitutes a relatively low overall cancellation rate of approximately 1.3%, a deeper investigation uncovers critical insights and areas demanding immediate attention.

### 6.1 The Silent Culprit: Missing Cancellation Reasons

Perhaps the most striking anomaly in our cancellation data is the complete absence of explanatory remarks. The `Keterangan` column, intended to capture reasons or notes, is entirely empty for all 1,740 cancelled orders.

**Distribution of 'Keterangan' for Cancelled Orders:**
| Keterangan   | count   |
|:-------------|:--------|
|              | 1740    |

*   Total cancelled orders: 1740
*   Number of cancelled orders with non-null Keterangan: 0
*   Number of cancelled orders with null Keterangan: 0

This data vacuum is a significant impediment. Without understanding *why* orders are being cancelled – whether due to customer changes, stock unavailability, pricing issues, or delivery problems – any efforts to mitigate these losses remain largely speculative. Addressing this data gap is paramount for meaningful root cause analysis.

### 6.2 Cancellation Hotspots: Divisions, Outlets, and Products

While the overall cancellation rate is low, it is not evenly distributed. Certain divisions, outlets, and products experience disproportionately higher cancellation incidents, signaling specific vulnerabilities.

#### 6.2.1 Divisional Cancellation Landscape

The cancellation rate varies significantly across divisions, highlighting operational disparities.

| Divisi        |   CANCEL |   TERLAYANI PENUH |   Total_Orders |   Cancellation_Rate_Pct |
|:--------------|---------:|------------------:|---------------:|------------------------:|
| VIBRANT 3     |       11 |                45 |             56 |                   19.64 |
| ETCKLINIK     |      310 |             17517 |          17827 |                    1.74 |
| NUTRINDO      |       33 |              2118 |           2151 |                    1.53 |
| VIBRANT PBF   |       38 |              2521 |           2559 |                    1.48 |
| PHARMANET 2   |     1154 |             87733 |          88887 |                    1.3  |
| PHARMANET B2B |      131 |             11401 |          11532 |                    1.14 |
| OTC           |       28 |              3282 |           3310 |                    0.85 |
| NUTRISAINS    |       35 |              4951 |           4986 |                    0.7  |

**Insights:**
*   **VIBRANT 3: The Alarm Bell.** Despite its small order volume, VIBRANT 3 stands out with an alarming 19.64% cancellation rate. This is a critical red flag that warrants immediate, in-depth investigation into its specific processes, inventory management, or customer base dynamics.
*   **Higher-than-Average Risks.** ETCKLINIK (1.74%), NUTRINDO (1.53%), and VIBRANT PBF (1.48%) also show cancellation rates above the overall average, indicating potential inefficiencies that need addressing.
*   **PHARMANET 2: Efficiency in Volume.** PHARMANET 2, despite having the highest raw number of cancelled orders (1,154), maintains a commendable low cancellation rate of 1.3% due to its massive order volume. This suggests robust operational processes within this high-throughput division.

#### 6.2.2 Outlets Prone to Cancellations

Understanding which outlets frequently cancel orders can point to issues with their ordering practices, communication, or specific service experiences.

**Top 10 Outlets by Number of Cancelled Orders:**
| Nama Outlet               |   Cancelled Order Count |
|:--------------------------|------------------------:|
| MITRA HUSADA, APT         |                      42 |
| PURWONEGORO, APT          |                      24 |
| AL MUGHNI FARMA, APT      |                      22 |
| ALDERAN, APT              |                      17 |
| SCHOMED HEALTHMART, APT   |                      15 |
| CIPTO KEDUNGWARINGIN, APT |                      13 |
| ASYIFA, APT (DURI)        |                      13 |
| GAMA CIRACAS, APT         |                      11 |
| Apotek Dede Farma         |                      11 |
| SETIA MEDIKA, APT         |                       9 |

**Insights:**
`MITRA HUSADA, APT` leads this list with 42 cancelled orders, significantly more than any other outlet. These outlets are prime candidates for direct engagement to understand their specific challenges, improve order accuracy, or address any recurring service issues they might face.

#### 6.2.3 Products Frequently Cancelled

Certain products are more susceptible to cancellations, often indicative of stock issues, forecasting inaccuracies, or competitive pressures.

**Top 10 Products by Number of Cancelled Orders:**
| Prodes                         |   Cancelled Order Count |
|:-------------------------------|------------------------:|
| MEDISIO 36% CONCENTRATE 5ML    |                      90 |
| VOMARIN FLASH TAB 50`S         |                      62 |
| ALTADIS 2% KRIM 10GR           |                      53 |
| MEFINAL 500MG CAP 100`S        |                      52 |
| CATAFLAM 50MG TAB 50`S         |                      39 |
| KANTONG DELIVERY S APT. ONLINE |                      35 |
| MOXAM 7.5MG TAB 30`S           |                      33 |
| MICROLAX GEL OBAT PENCAHAR 5ML |                      26 |
| OZEN 5MG SIRUP 60ML            |                      26 |
| KOLTON 300MG TAB 50`S          |                      22 |

**Insights:**
`MEDISIO 36% CONCENTRATE 5ML` experiences a remarkably high 90 cancellations, suggesting persistent availability issues, backorder problems, or perhaps a highly volatile demand. Other products like `VOMARIN FLASH TAB 50`S` and `ALTADIS 2% KRIM 10GR` also frequently appear on this list. These products should be prioritized for supply chain review, including stock levels, supplier reliability, and demand forecasting accuracy.

### 6.3 Unmasking the "Why": Operational Bottlenecks in Processing Time

Beyond "what" is being cancelled, a crucial question is "when" and "how" the cancellation occurs within our operational flow. We hypothesized that delays in internal processing might contribute to cancellations. Our analysis of the time taken from order placement (`Tgl. Order`) to delivery order issuance (`Tgl DO`) strongly supports this.

#### 6.3.1 Overall Processing Time for Cancelled vs. Served Orders

| Status RO       |   Avg_Order_to_DO_days |   Total_Orders |
|:----------------|-----------------------:|---------------:|
| CANCEL          |               0.56984  |           1375 |
| TERLAYANI PENUH |               0.433934 |         129575 |

**Insights:** Orders that are eventually cancelled take, on average, approximately **31% longer** to get processed from initial order placement to the issuance of the Delivery Order (0.57 days for cancelled vs. 0.43 days for served orders). This significant difference strongly suggests that internal processing delays are a key contributing factor to cancellations. Customers are likely abandoning orders that take too long to confirm.

#### 6.3.2 Divisional Processing Time Comparison

This trend is consistent even within our highest-volume and high-cancellation divisions:

| Divisi      | Status RO       |   Avg_Order_to_DO_days |   Total_Orders |
|:------------|:----------------|-----------------------:|---------------:|
| PHARMANET 2 | CANCEL          |               0.61369  |            880 |
| PHARMANET 2 | TERLAYANI PENUH |               0.451236 |          87733 |
| VIBRANT PBF | CANCEL          |               0.814221 |             37 |
| VIBRANT PBF | TERLAYANI PENUH |               0.443412 |           2521 |

**Insights:**
*   **PHARMANET 2**, despite its overall efficiency, sees cancelled orders take significantly longer (0.61 days) in this stage compared to served orders (0.45 days).
*   **VIBRANT PBF**, a division with an already higher cancellation rate, shows an even more pronounced difference: cancelled orders take a staggering 0.81 days, almost double the time of its served orders (0.44 days). This clearly links internal processing delays to its higher cancellation incidence.

#### 6.3.3 Pinpointing the Bottleneck: SP to DO Stage

To pinpoint exactly *where* these delays occur, we broke down the 'Order to DO' time into two stages: from order placement to Sales Order (SP) creation (`Order_to_SP_days`), and from Sales Order creation to Delivery Order (DO) issuance (`SP_to_DO_days`).

| Status RO       |   Order_to_SP_days |   SP_to_DO_days |
|:----------------|-------------------:|----------------:|\
| CANCEL          |               0.02 |            0.55 |\
| TERLAYANI PENUH |               0.02 |            0.41 |

**Insights:**
The `Order_to_SP_days` (time from customer order to Sales Order creation) is consistently fast for both cancelled and served orders (0.02 days). This indicates that the initial capture and logging of an order into the sales system is highly efficient.

However, the real bottleneck lies in the **`SP_to_DO_days`** stage:
*   Cancelled orders take **0.55 days** to progress from Sales Order creation to Delivery Order issuance.
*   Fully served orders complete this stage in **0.41 days**.

This is a critical finding: **delays in generating the Delivery Order *after* the Sales Order has been confirmed are strongly correlated with order cancellations.** This internal process, which typically involves inventory allocation, picking, packing, and final confirmation before dispatch, is where intervention is most urgently needed.

### 6.4 Case Study: VIBRANT PBF - Products and Outlets Driving Cancellations

Revisiting VIBRANT PBF, whose cancellation rate and `Avg_Order_to_DO_days` for cancelled orders were notably high, we examine the specific items and clients involved:

**Top 10 Products by Number of Cancelled Orders in VIBRANT PBF:**
| Prodes                         |   Cancelled_Count |
|:-------------------------------|------------------:|
| MEDISIO 36% CONCENTRATE 5ML    |                 6 |
| NOURISH SKIN ULTIMATE 30`S     |                 5 |
| CLINOVIR CREAM 5GR             |                 5 |
| NOURISH SKIN ULTIMATE @15 TABS |                 4 |
| VEGEBLEND 21JR VEG.EXTR.30`S   |                 2 |
| NEBACETIN OINT 5GR             |                 2 |
| MICROLAX GEL OBAT PENCAHAR 5ML |                 2 |
| NERIPROS 1MG TAB 30`S          |                 1 |
| OMEPROS 60 SOFT KAPSUL         |                 1 |
| NOURISH BC ACNE FCL FOAM 100ML |                 1 |

**Top 10 Outlets by Number of Cancelled Orders in VIBRANT PBF:**
| Nama Outlet                        |   Cancelled_Count |
|:-----------------------------------|------------------:|
| GEMINI MANDIRA LESTARI, PT         |                 4 |
| GUNA  ABDI WISESA,   PT            |                 4 |
| PODO MEKAR JAYA SENTOSA, PT.       |                 4 |
| TEKNOLOGI MEDIKA PRATAMA, PT       |                 3 |
| SEJAHTERA MULIA FARMA, PT          |                 3 |
| LANCAR JAYA SEHAT, PT              |                 2 |
| SEHAT BERSAMA SEMPURNA, PT         |                 2 |
| MULIA ABDI SENTOSA, PT (SINGARAJA) |                 2 |
| FERTO MULIA PRATAMA, PT            |                 2 |
| GRAHA SEJATI MEDIKA, PT            |                 1 |

**Insights:** `MEDISIO 36% CONCENTRATE 5ML` remains a problematic product, both overall and specifically within VIBRANT PBF. Similarly, outlets like `GEMINI MANDIRA LESTARI, PT`, `GUNA ABDI WISESA, PT`, and `PODO MEKAR JAYA SENTOSA, PT.` are repeat cancellers for this division. This granular view allows for highly targeted interventions – whether it’s investigating specific product stock issues, improving communication with these key outlets, or streamlining VIBRANT PBF's internal `SP_to_DO` process for orders involving these elements.

### 6.5 Recommendations for Reducing Order Cancellations

Based on this deep dive, the following recommendations are crucial for curbing cancellations and recovering lost revenue:

1.  **Mandate and Standardize Cancellation Reason Capture:**
    *   **Action:** Immediately implement a mandatory field in the order management system to capture specific, standardized reasons for *every* order cancellation. This could include categories like "Out of Stock," "Customer Request," "Pricing Error," "Delivery Delay," "Product Discontinued," etc.
    *   **Impact:** This will unlock the ability to conduct true root cause analysis, moving beyond correlation to causation, and enabling data-driven solutions.

2.  **Investigate and Streamline the `SP_to_DO` Bottleneck:**
    *   **Action:** Prioritize a comprehensive audit and process improvement initiative for the `SP_to_DO` stage. This includes:
        *   Analyzing inventory allocation procedures.
        *   Reviewing picking and packing processes.
        *   Optimizing warehouse workflows.
        *   Assessing system integrations that facilitate DO generation.
    *   **Impact:** Accelerating this critical internal step will directly reduce the likelihood of cancellations, as quicker processing times correlate with higher fulfillment rates.

3.  **Targeted Intervention for High-Cancellation Hotspots:**
    *   **VIBRANT 3 Division:** Conduct an urgent, holistic review of all operational aspects of VIBRANT 3, from product procurement and inventory to customer service and order processing, to understand and address its exceptionally high cancellation rate.
    *   **Problematic Products:** Investigate the supply chain for `MEDISIO 36% CONCENTRATE 5ML`, `VOMARIN FLASH TAB 50`S`, `ALTADIS 2% KRIM 10GR`, and other frequently cancelled products. This should include forecasting accuracy, buffer stock levels, and supplier lead times.
    *   **High-Cancelling Outlets:** Engage directly with outlets like `MITRA HUSADA, APT` to understand their ordering patterns, any recurring issues they face, and how we can better support their needs to reduce cancellations.

4.  **Leverage Best Practices from Low-Cancellation Divisions:**
    *   **Action:** Study the internal processes, inventory management strategies, and customer communication protocols of divisions with consistently low cancellation rates (e.g., NUTRISAINS, OTC).
    *   **Impact:** Identify and disseminate these best practices across other divisions, particularly those struggling with higher cancellation rates, to foster a culture of efficiency and customer satisfaction.

By systematically addressing these identified issues, especially by enabling the capture of cancellation reasons and streamlining the `SP_to_DO` process, the company can significantly reduce lost sales, improve operational efficiency, and enhance overall customer satisfaction.

## 7. Recommendations

Our extensive exploration of the order fulfillment data has illuminated a complex interplay of sales drivers, operational efficiencies, and critical areas for improvement. While the company demonstrates robust sales performance driven by key divisions, products, and outlets, a deeper analysis has unveiled bottlenecks and blind spots that, if addressed, can significantly enhance profitability, reduce lost revenue, and elevate customer satisfaction. This chapter outlines a series of actionable recommendations, grounded in the insights from our analysis, to guide strategic decision-making and operational enhancements.

### 7.1 Address the Critical Data Blind Spot: Mandate Cancellation Reason Capture

**Insight:** The most significant impediment to understanding and resolving order cancellations is the complete lack of information in the `Keterangan` column for all 1,740 cancelled orders. Without knowing *why* an order was cancelled, efforts to prevent future occurrences are akin to shooting in the dark. Our analysis highlighted products like "MEDISIO 36% CONCENTRATE 5ML" and outlets like "MITRA HUSADA, APT" as frequent contributors to cancellations, but the underlying causes remain a mystery.

**Recommendation:**
**Immediately implement a mandatory, standardized process for capturing explicit cancellation reasons.** This should be a non-negotiable field in the order management system for every cancelled order. Categories could include: "Customer Request (e.g., changed mind, found cheaper elsewhere)", "Out of Stock (internal)", "External Supply Issue", "Pricing Discrepancy", "Delivery Delay (customer initiated)", "Delivery Problem (company initiated)", "Data Entry Error", or "Quality Issue".

**Impact:** This foundational step will transform our ability to perform genuine root cause analysis. It will enable targeted interventions based on factual reasons, rather than assumptions, leading to more effective strategies to reduce lost sales and improve customer retention.

### 7.2 Optimize Internal Processing: Eliminate the `SP_to_DO` Bottleneck

**Insight:** A striking finding from our deep dive into cancellations is the strong correlation between internal processing delays and order abandonment. Specifically, orders that are ultimately cancelled spend significantly longer (0.55 days) in the "Sales Order to Delivery Order issuance" (`SP_to_DO_days`) stage compared to fully served orders (0.41 days). This bottleneck is evident across the board and particularly pronounced in divisions with higher cancellation rates like VIBRANT PBF, where cancelled orders take almost double the time in this stage. The initial "Order to SP" stage, conversely, is consistently efficient.

**Recommendation:**
**Prioritize a comprehensive audit and re-engineering of the `SP_to_DO` process.** Focus on identifying and eliminating inefficiencies between the confirmation of a Sales Order and the generation of its corresponding Delivery Order. This initiative should investigate:
*   **Inventory Allocation & Availability Checks:** Are there delays in confirming product availability post-Sales Order?
*   **Picking & Packing Workflow:** How efficiently are items picked and prepared for shipment?
*   **System Integrations:** Are there manual handoffs or system lags that impede DO generation?
*   **Resource Allocation:** Is there adequate staff or automated systems to handle DO creation volume?

**Impact:** Streamlining this critical internal step will directly reduce the likelihood of cancellations, converting more confirmed sales into delivered orders. A faster `SP_to_DO` cycle will improve customer experience by expediting order fulfillment and signaling efficiency.

### 7.3 Targeted Interventions for High-Cancellation Hotspots

**Insight:** While internal processing delays are a systemic issue, certain divisions, products, and outlets consistently appear as "hotspots" for cancellations, demanding specific attention.
*   **VIBRANT 3** exhibits an alarmingly high 19.64% cancellation rate, despite its smaller volume.
*   **ETCKLINIK, NUTRINDO, and VIBRANT PBF** also have higher-than-average cancellation rates.
*   "MEDISIO 36% CONCENTRATE 5ML" is the most frequently cancelled product overall and within VIBRANT PBF, suggesting chronic stock or supply issues.
*   Outlets like "MITRA HUSADA, APT" are recurring cancellers, indicating potential specific service or communication challenges.

**Recommendation:**
**Implement targeted investigations and improvement plans for these identified hotspots:**
*   **VIBRANT 3 Division:** Launch an immediate, in-depth operational review covering inventory, order processing, and customer communication to pinpoint the exact drivers of its exceptionally high cancellation rate.
*   **Problematic Products:** Conduct a supply chain deep dive for "MEDISIO 36% CONCENTRATE 5ML" and other frequently cancelled items. This should include re-evaluating demand forecasting, inventory buffer levels, supplier reliability, and potential product lifecycle management issues.
*   **High-Cancelling Outlets:** Initiate direct engagement with top cancelling outlets (e.g., "MITRA HUSADA, APT", "GEMINI MANDIRA LESTARI, PT") to understand their specific pain points, gather feedback on service delivery, and work collaboratively to improve order accuracy and satisfaction.

**Impact:** Focused interventions will address specific, high-impact issues, leading to tangible reductions in cancellations from these identified areas. This also allows for the development of tailored solutions rather than broad-stroke changes.

### 7.4 Enhance Logistics Efficiency: Optimize External Delivery Partnerships

**Insight:** Our analysis of delivery times revealed a significant disparity among expedition partners. While internal logistics teams (e.g., "EXPD INTERNAL JAKSEL") deliver with remarkable speed (0.3 days total delivery), several external partners like "PT. TUNAS ANTARNUSA MUDA KARGO" (3.42 days), "INDAH LOGISTIK" (2.87 days), and "LION PARCEL" (2.73 days) are significantly slower, often handling substantial order volumes. The "Ekspedisi to Received" stage (actual shipping time) is the longest segment for many, suggesting last-mile challenges. Some partners also showed delays in the "DO to Ekspedisi" handover.

**Recommendation:**
**Develop a comprehensive logistics partner management program focused on performance optimization:**
*   **Performance Reviews & SLAs:** Conduct regular, data-driven performance reviews with underperforming external logistics partners. Establish clear Service Level Agreements (SLAs) with penalties for non-compliance and incentives for exceptional performance, especially for the "Ekspedisi to Received" (shipping) duration.
*   **Target Last-Mile Improvement:** Explore collaborative initiatives with partners to optimize last-mile delivery, such as route optimization, enhanced tracking technologies, and improved delivery slot management.
*   **Streamline Handover Processes:** For partners exhibiting longer "DO to Ekspedisi" times, investigate and streamline the handover points and procedures from our warehouses to their pickup points.
*   **Strategic Partner Portfolio Review:** Continuously evaluate the performance of all partners. Consider reallocating volume from consistently underperforming partners to more efficient ones, or exploring new logistics providers to diversify and improve overall delivery speed.

**Impact:** Improving logistics efficiency will directly translate to faster, more reliable deliveries, significantly boosting customer satisfaction, reducing delivery-related queries, and potentially enabling competitive advantages.

### 7.5 Leverage Best Practices and Data for Continuous Improvement

**Insight:** The analysis identified numerous areas of excellence. PHARMANET 2 consistently dominates sales with robust efficiency despite its high volume, maintaining a low cancellation rate. Divisions like NUTRISAINS and OTC achieve very low cancellation rates. The internal expedition teams set a high bar for delivery speed.

**Recommendation:**
**Establish mechanisms for internal knowledge transfer and data-driven decision-making:**
*   **Internal Benchmarking:** Conduct internal benchmarking studies of PHARMANET 2's order processing and inventory management practices, and NUTRISAINS' / OTC's cancellation prevention strategies. Disseminate these best practices across other divisions.
*   **Data Culture:** Foster a data-driven culture by providing regular, accessible reports on key performance indicators (KPIs) identified in this analysis (e.g., cancellation rates by product/outlet, stage-wise processing times, expedition performance) to relevant operational teams and management.
*   **Invest in Further Analytics:** With the `Keterangan` column populated, further analytical models can be developed to predict cancellation risks and identify more nuanced root causes, leading to proactive interventions.

**Impact:** By learning from internal successes and embedding data insights into daily operations, the company can drive a culture of continuous improvement, ensuring that strategic decisions are always informed by objective performance metrics.

By systematically implementing these recommendations, the organization can move beyond merely reacting to issues and instead build a more resilient, efficient, and customer-centric order fulfillment ecosystem, converting potential into tangible growth.