A
Background
Latar Belakang
A warehouse operation handling goods across multiple product categories with varying turnover rates. The warehouse management system recorded quantities accurately but had no concept of time — it knew how much was there, but not how long it had been there.
Sebuah operasi gudang yang menangani barang di berbagai kategori produk dengan tingkat perputaran yang bervariasi. Sistem manajemen gudang mencatat kuantitas dengan akurat tetapi tidak memiliki konsep waktu — ia tahu berapa banyak yang ada, tetapi tidak tahu sudah berapa lama barang itu di sana.
B
Operational Problem
Masalah Operasional
Warehouse inventory was tracked by quantity only. Items sitting for extended periods had no automatic flagging, no aging classification, and no visibility into which SKUs were consuming space without moving.
Inventaris gudang dilacak hanya berdasarkan kuantitas. Barang yang tersimpan lama tidak memiliki penanda otomatis, tidak ada klasifikasi penuaan, dan tidak ada visibilitas SKU mana yang menggunakan ruang tanpa bergerak.
C
Existing Workflow
Alur Kerja Saat Ini
Inventory records existed in the main system but without aging calculation. Stock reviews happened during physical counting or when storage space pressure arose. Slow-moving items were discovered only when space became critical.
Catatan inventaris ada di sistem utama tetapi tanpa perhitungan penuaan. Tinjauan stok terjadi saat penghitungan fisik atau ketika tekanan ruang penyimpanan muncul. Barang lambat ditemukan hanya ketika ruang sudah kritis.
1
Daily stock reports exported from WMS to Excel
2
No aging field in standard WMS export
3
Physical counts used to identify slow movers — quarterly or annual
4
Space management reactive: clear only when storage pressure reaches critical
5
Write-off identification happens at year-end audit
1
Laporan stok harian diekspor dari WMS ke Excel
2
Tidak ada field penuaan dalam ekspor WMS standar
3
Penghitungan fisik digunakan untuk mengidentifikasi barang lambat — kuartalan atau tahunan
4
Manajemen ruang bersifat reaktif: dibersihkan hanya ketika tekanan penyimpanan mencapai titik kritis
5
Identifikasi write-off terjadi saat audit akhir tahun
D
Bottleneck & Risk
Bottleneck & Risiko
Without aging visibility, the warehouse team could not distinguish between items that arrived yesterday and items that had been sitting for 120 days. Every clearance decision required physical investigation. Holding costs were invisible until they became write-offs.
Tanpa visibilitas penuaan, tim gudang tidak dapat membedakan antara barang yang baru tiba kemarin dan barang yang sudah tersimpan selama 120 hari. Setiap keputusan clearance membutuhkan investigasi fisik. Biaya penyimpanan tidak terlihat hingga menjadi write-off.
Aging inventory increases holding costs, reduces space for fast-moving products, and creates write-off risk that only surfaces at year-end audit — long after the optimal intervention window has passed.
Inventaris yang menua meningkatkan biaya penyimpanan, mengurangi ruang untuk produk cepat bergerak, dan menciptakan risiko write-off yang hanya muncul saat audit akhir tahun — jauh setelah jendela intervensi optimal terlewati.
E
Why the Existing System Failed
Mengapa Sistem Lama Gagal
The WMS was configured for inventory control, not aging management. Customizing the WMS was expensive and risky. Manual aging calculation in Excel was error-prone and abandoned quickly. Nobody had built the operational habit of monitoring aging because the data was not visible.
WMS dikonfigurasi untuk pengendalian inventaris, bukan manajemen penuaan. Kustomisasi WMS mahal dan berisiko. Perhitungan penuaan manual di Excel rawan kesalahan dan cepat ditinggalkan. Tidak ada yang membangun kebiasaan operasional memantau penuaan karena datanya tidak terlihat.
F
Solution Approach
Pendekatan Solusi
Build a lightweight analytical layer on top of exported inventory data rather than modifying the main system. Focus strictly on aging visibility, location occupancy, and slow-moving detection as operational decision-support tools.
Membangun lapisan analitik ringan di atas data inventaris yang diekspor daripada memodifikasi sistem utama. Fokus ketat pada visibilitas penuaan, hunian lokasi, dan deteksi barang lambat sebagai alat pendukung keputusan operasional.
G
System Architecture
Arsitektur Sistem
Python-based warehouse aging analytics with stock age classification (0–30, 30–60, 60–90, 90+ days), slow-moving detection dashboard, location occupancy monitoring, and capacity trend analysis.
Analitik penuaan gudang berbasis Python dengan klasifikasi usia stok (0–30, 30–60, 60–90, 90+ hari), dashboard deteksi barang lambat, pemantauan hunian lokasi, dan analisis tren kapasitas.
Python pipeline reads daily WMS exports and cross-references with receipt records to calculate item age. Pandas aggregates by SKU, location, and age band. Streamlit renders a warehouse aging dashboard with drill-down capability. Threshold-based alerts exportable to Excel for warehouse team action.
Pipeline Python membaca ekspor WMS harian dan mencocokkan dengan catatan penerimaan untuk menghitung usia barang. Pandas mengagregasi berdasarkan SKU, lokasi, dan band usia. Streamlit merender dashboard penuaan gudang dengan kemampuan drill-down. Peringatan berbasis threshold dapat diekspor ke Excel untuk tindakan tim gudang.
H
Technologies Used
Teknologi yang Digunakan
Python
Pandas
Excel Integration
Analytics
I
Workflow Visualization
Visualisasi Alur Kerja
01
WMS Export
Ekspor WMS
Daily stock snapshot exported from WMS to shared folder
Snapshot stok harian diekspor dari WMS ke folder bersama
02
Age Calculation
Hitung Usia
Python cross-references receipt dates to calculate item age per SKU
Python mencocokkan tanggal penerimaan untuk menghitung usia barang per SKU
03
Classification
Klasifikasi
Items bucketed: 0–30, 30–60, 60–90, 90+ days aging bands
Barang dikelompokkan: band penuaan 0–30, 30–60, 60–90, 90+ hari
04
Clearance Queue
Antrian Clearance
Warehouse supervisor sees prioritized list of items to act on today
Supervisor gudang melihat daftar prioritas barang yang perlu ditindaklanjuti hari ini
J
Operational Impact
Dampak Operasional
Metric
Metrik
Before
Sebelum
After
Sesudah
Aging Visibility
Visibilitas Penuaan
Unknown until physical check
Daily dashboard by SKU
Intervention Timing
Waktu Intervensi
Reactive (space crisis)
Proactive (threshold alert)
Write-off Discovery
Penemuan Write-off
Year-end audit
90-day aging flag
Physical Count Scope
Cakupan Hitung Fisik
Full warehouse scan
Pre-screened priority list
Slow-moving items flagged automatically by configurable aging threshold
Warehouse team can prioritize clearance proactively, not reactively
Holding cost visibility enabled at SKU and location level
Physical stock review cycles reduced through data-driven pre-screening
Barang lambat ditandai otomatis berdasarkan threshold penuaan yang dapat dikonfigurasi
Tim gudang dapat memprioritaskan clearance secara proaktif, bukan reaktif
Visibilitas biaya penyimpanan tersedia di tingkat SKU dan lokasi
Siklus review stok fisik berkurang melalui pra-penyaringan berbasis data
K
Future Development
Pengembangan ke Depan
Holding cost calculator per SKU based on configurable storage cost rates. Automated clearance request workflow linked to procurement. Supplier performance analysis correlating aging with supplier lead times.
Kalkulator biaya penyimpanan per SKU berdasarkan tarif biaya yang dapat dikonfigurasi. Alur permintaan clearance otomatis yang terhubung ke pengadaan. Analisis performa supplier yang menghubungkan penuaan dengan lead time supplier.