Merge pull request #51451 from rohitwaghchaure/fixed-removed-forecasting_method

chore: removed forecasting method holt winter

erpnext/manufacturing/doctype/sales_forecast/sales_forecast.json

@@ -9,7 +9,6 @@
   "naming_series",
   "company",
   "posting_date",
-  "forecasting_method",
   "column_break_xdcy",
   "from_date",
   "frequency",
@@ -146,13 +145,6 @@
    "options": "Planned\nMPS Generated\nCancelled",
    "read_only": 1
   },
-  {
-   "default": "Holt-Winters",
-   "fieldname": "forecasting_method",
-   "fieldtype": "Select",
-   "label": "Forecasting Method",
-   "options": "Holt-Winters\nManual"
-  },
   {
    "default": "Monthly",
    "fieldname": "frequency",
@@ -166,7 +158,7 @@
  "index_web_pages_for_search": 1,
  "is_submittable": 1,
  "links": [],
- "modified": "2025-12-24 11:40:44.263700",
+ "modified": "2026-01-02 15:09:32.165242",
  "modified_by": "Administrator",
  "module": "Manufacturing",
  "name": "Sales Forecast",

erpnext/manufacturing/doctype/sales_forecast/sales_forecast.py

@@ -2,13 +2,10 @@
 # For license information, please see license.txt
 
 import frappe
-import pandas as pd
 from frappe import _
 from frappe.model.document import Document
 from frappe.model.mapper import get_mapped_doc
-from frappe.query_builder.functions import DateFormat, Sum, YearWeek
+from frappe.utils import add_to_date
-from frappe.utils import add_to_date, cint, date_diff, flt
-from frappe.utils.nestedset import get_descendants_of
 
 
 class SalesForecast(Document):
@@ -25,7 +22,6 @@ class SalesForecast(Document):
 		amended_from: DF.Link | None
 		company: DF.Link
 		demand_number: DF.Int
-		forecasting_method: DF.Literal["Holt-Winters", "Manual"]
 		frequency: DF.Literal["Weekly", "Monthly"]
 		from_date: DF.Date
 		items: DF.Table[SalesForecastItem]
@@ -39,46 +35,6 @@ class SalesForecast(Document):
 	def on_discard(self):
 		self.db_set("status", "Cancelled")
 
-	def validate(self):
-		self.validate_demand_qty()
-
-	def validate_demand_qty(self):
-		if self.forecasting_method == "Manual":
-			return
-
-		for row in self.items:
-			demand_qty = row.forecast_qty + flt(row.adjust_qty)
-			if row.demand_qty != demand_qty:
-				row.demand_qty = demand_qty
-
-	def get_sales_data(self):
-		to_date = self.from_date
-		from_date = add_to_date(to_date, years=-3)
-
-		doctype = frappe.qb.DocType("Sales Order")
-		child_doctype = frappe.qb.DocType("Sales Order Item")
-
-		query = (
-			frappe.qb.from_(doctype)
-			.inner_join(child_doctype)
-			.on(child_doctype.parent == doctype.name)
-			.select(child_doctype.item_code, Sum(child_doctype.qty).as_("qty"), doctype.transaction_date)
-			.where((doctype.docstatus == 1) & (doctype.transaction_date.between(from_date, to_date)))
-			.groupby(child_doctype.item_code)
-		)
-
-		if self.selected_items:
-			items = [item.item_code for item in self.selected_items]
-			query = query.where(child_doctype.item_code.isin(items))
-
-		if self.parent_warehouse:
-			warehouses = get_descendants_of("Warehouse", self.parent_warehouse)
-			query = query.where(child_doctype.warehouse.isin(warehouses))
-
-		query = query.groupby(doctype.transaction_date)
-
-		return query.run(as_dict=True)
-
 	def generate_manual_demand(self):
 		forecast_demand = []
 		for row in self.selected_items:
@@ -107,99 +63,8 @@ class SalesForecast(Document):
 
 	@frappe.whitelist()
 	def generate_demand(self):
-		from statsmodels.tsa.holtwinters import ExponentialSmoothing
-
 		self.set("items", [])
-
+		self.generate_manual_demand()
-		if self.forecasting_method == "Manual":
-			self.generate_manual_demand()
-			return
-
-		sales_data = self.get_sales_data()
-		if not sales_data:
-			frappe.throw(_("No sales data found for the selected items."))
-
-		itemwise_data = self.group_sales_data_by_item(sales_data)
-
-		for item_code, data in itemwise_data.items():
-			seasonal_periods = self.get_seasonal_periods(data)
-			pd_sales_data = pd.DataFrame({"item": data.item, "date": data.date, "qty": data.qty})
-
-			resample_val = "M" if self.frequency == "Monthly" else "W"
-			_sales_data = pd_sales_data.set_index("date").resample(resample_val).sum()["qty"]
-
-			model = ExponentialSmoothing(
-				_sales_data, trend="add", seasonal="add", seasonal_periods=seasonal_periods
-			)
-
-			fit = model.fit()
-			forecast = fit.forecast(self.demand_number)
-
-			forecast_data = forecast.to_dict()
-			if forecast_data:
-				self.add_sales_forecast_item(item_code, forecast_data)
-
-	def add_sales_forecast_item(self, item_code, forecast_data):
-		item_details = frappe.db.get_value(
-			"Item", item_code, ["item_name", "stock_uom as uom", "name as item_code"], as_dict=True
-		)
-
-		uom_whole_number = frappe.get_cached_value("UOM", item_details.uom, "must_be_whole_number")
-
-		for date, qty in forecast_data.items():
-			if uom_whole_number:
-				qty = round(qty)
-
-			item_details.update(
-				{
-					"delivery_date": date,
-					"forecast_qty": qty,
-					"demand_qty": qty,
-					"warehouse": self.parent_warehouse,
-				}
-			)
-
-			self.append("items", item_details)
-
-	def get_seasonal_periods(self, data):
-		days = date_diff(data["end_date"], data["start_date"])
-		if self.frequency == "Monthly":
-			months = (days / 365) * 12
-			seasonal_periods = cint(months / 2)
-			if seasonal_periods > 12:
-				seasonal_periods = 12
-		else:
-			weeks = days / 7
-			seasonal_periods = cint(weeks / 2)
-			if seasonal_periods > 52:
-				seasonal_periods = 52
-
-		return seasonal_periods
-
-	def group_sales_data_by_item(self, sales_data):
-		"""
-		Group sales data by item code and calculate total quantity sold.
-		"""
-		itemwise_data = frappe._dict({})
-		for row in sales_data:
-			if row.item_code not in itemwise_data:
-				itemwise_data[row.item_code] = frappe._dict(
-					{
-						"start_date": row.transaction_date,
-						"item": [],
-						"date": [],
-						"qty": [],
-						"end_date": "",
-					}
-				)
-
-			item_data = itemwise_data[row.item_code]
-			item_data["item"].append(row.item_code)
-			item_data["date"].append(pd.to_datetime(row.transaction_date))
-			item_data["qty"].append(row.qty)
-			item_data["end_date"] = row.transaction_date
-
-		return itemwise_data
 
 
 @frappe.whitelist()

pyproject.toml

@@ -24,8 +24,6 @@ dependencies = [
 
     # MT940 parser for bank statements
     "mt-940>=4.26.0",
-    "pandas~=2.3.3",
-    "statsmodels~=0.14.6",
 ]
 
 [build-system]