Merge pull request #21724 from rohitwaghchaure/production-forecasting

feat: production forecasting using exponential smoothing method

erpnext/accounts/report/financial_statements.py

@@ -19,7 +19,7 @@ from six import itervalues
 from erpnext.accounts.doctype.accounting_dimension.accounting_dimension import get_accounting_dimensions, get_dimension_with_children
 
 def get_period_list(from_fiscal_year, to_fiscal_year, period_start_date, period_end_date, filter_based_on, periodicity, accumulated_values=False,
-	company=None, reset_period_on_fy_change=True):
+	company=None, reset_period_on_fy_change=True, ignore_fiscal_year=False):
 	"""Get a list of dict {"from_date": from_date, "to_date": to_date, "key": key, "label": label}
 		Periodicity can be (Yearly, Quarterly, Monthly)"""
 
@@ -67,8 +67,9 @@ def get_period_list(from_fiscal_year, to_fiscal_year, period_start_date, period_
 			# if a fiscal year ends before a 12 month period
 			period.to_date = year_end_date
 
-		period.to_date_fiscal_year = get_fiscal_year(period.to_date, company=company)[0]
+		if not ignore_fiscal_year:
-		period.from_date_fiscal_year_start_date = get_fiscal_year(period.from_date, company=company)[1]
+			period.to_date_fiscal_year = get_fiscal_year(period.to_date, company=company)[0]
+			period.from_date_fiscal_year_start_date = get_fiscal_year(period.from_date, company=company)[1]
 
 		period_list.append(period)
 

erpnext/manufacturing/desk_page/manufacturing/manufacturing.json

@@ -43,10 +43,11 @@
  "docstatus": 0,
  "doctype": "Desk Page",
  "extends_another_page": 0,
+ "hide_custom": 0,
  "idx": 0,
  "is_standard": 1,
  "label": "Manufacturing",
- "modified": "2020-05-19 14:05:59.100891",
+ "modified": "2020-05-20 11:50:20.029056",
  "modified_by": "Administrator",
  "module": "Manufacturing",
  "name": "Manufacturing",
@@ -88,6 +89,17 @@
    "stats_filter": "{ \n    \"status\": [\"not in\", [\"Completed\"]]\n}",
    "type": "DocType"
   },
+  {
+   "label": "Dashboard",
+   "link_to": "Manufacturing",
+   "restrict_to_domain": "Manufacturing",
+   "type": "Dashboard"
+  },
+  {
+   "label": "Forecasting",
+   "link_to": "Exponential Smoothing Forecasting",
+   "type": "Report"
+  },
   {
    "label": "Work Order Summary",
    "link_to": "Work Order Summary",
@@ -95,10 +107,14 @@
    "type": "Report"
   },
   {
-   "label": "Dashboard",
+   "label": "BOM Stock Report",
-   "link_to": "Manufacturing",
+   "link_to": "BOM Stock Report",
-   "restrict_to_domain": "Manufacturing",
+   "type": "Report"
-   "type": "Dashboard"
+  },
+  {
+   "label": "Production Planning Report",
+   "link_to": "Production Planning Report",
+   "type": "Report"
   }
  ]
 }

erpnext/manufacturing/report/exponential_smoothing_forecasting/exponential_smoothing_forecasting.js

@@ -0,0 +1,97 @@
+// Copyright (c) 2016, Frappe Technologies Pvt. Ltd. and contributors
+// For license information, please see license.txt
+/* eslint-disable */
+
+frappe.query_reports["Exponential Smoothing Forecasting"] = {
+	"filters": [
+		{
+			"fieldname":"company",
+			"label": __("Company"),
+			"fieldtype": "Link",
+			"options": "Company",
+			"reqd": 1,
+			"default": frappe.defaults.get_user_default("Company")
+		},
+		{
+			"fieldname":"from_date",
+			"label": __("From Date"),
+			"fieldtype": "Date",
+			"default": frappe.datetime.get_today(),
+			"reqd": 1
+		},
+		{
+			"fieldname":"to_date",
+			"label": __("To Date"),
+			"fieldtype": "Date",
+			"default": frappe.datetime.add_months(frappe.datetime.get_today(), 12),
+			"reqd": 1
+		},
+		{
+			"fieldname":"based_on_document",
+			"label": __("Based On Document"),
+			"fieldtype": "Select",
+			"options": ["Sales Order", "Delivery Note", "Quotation"],
+			"default": "Sales Order",
+			"reqd": 1
+		},
+		{
+			"fieldname":"based_on_field",
+			"label": __("Based On"),
+			"fieldtype": "Select",
+			"options": ["Qty", "Amount"],
+			"default": "Qty",
+			"reqd": 1
+		},
+		{
+			"fieldname":"no_of_years",
+			"label": __("Based On Data ( in years )"),
+			"fieldtype": "Select",
+			"options": [3, 6, 9],
+			"default": 3,
+			"reqd": 1
+		},
+		{
+			"fieldname": "periodicity",
+			"label": __("Periodicity"),
+			"fieldtype": "Select",
+			"options": [
+				{ "value": "Monthly", "label": __("Monthly") },
+				{ "value": "Quarterly", "label": __("Quarterly") },
+				{ "value": "Half-Yearly", "label": __("Half-Yearly") },
+				{ "value": "Yearly", "label": __("Yearly") }
+			],
+			"default": "Yearly",
+			"reqd": 1
+		},
+		{
+			"fieldname":"smoothing_constant",
+			"label": __("Smoothing Constant"),
+			"fieldtype": "Select",
+			"options": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
+			"reqd": 1,
+			"default": 0.3
+		},
+		{
+			"fieldname":"item_code",
+			"label": __("Item Code"),
+			"fieldtype": "Link",
+			"options": "Item"
+		},
+		{
+			"fieldname":"warehouse",
+			"label": __("Warehouse"),
+			"fieldtype": "Link",
+			"options": "Warehouse",
+			get_query: () => {
+				var company = frappe.query_report.get_filter_value('company');
+				if (company) {
+					return {
+						filters: {
+							'company': company
+						}
+					};
+				}
+			}
+		}
+	]
+};

erpnext/manufacturing/report/exponential_smoothing_forecasting/exponential_smoothing_forecasting.json

@@ -0,0 +1,40 @@
+{
+ "add_total_row": 0,
+ "creation": "2020-05-15 05:18:55.838030",
+ "disable_prepared_report": 0,
+ "disabled": 0,
+ "docstatus": 0,
+ "doctype": "Report",
+ "idx": 0,
+ "is_standard": "Yes",
+ "letter_head": "",
+ "modified": "2020-05-15 05:18:55.838030",
+ "modified_by": "Administrator",
+ "module": "Manufacturing",
+ "name": "Exponential Smoothing Forecasting",
+ "owner": "Administrator",
+ "prepared_report": 0,
+ "ref_doctype": "Sales Order",
+ "report_name": "Exponential Smoothing Forecasting",
+ "report_type": "Script Report",
+ "roles": [
+  {
+   "role": "Manufacturing User"
+  },
+  {
+   "role": "Stock User"
+  },
+  {
+   "role": "Manufacturing Manager"
+  },
+  {
+   "role": "Stock Manager"
+  },
+  {
+   "role": "Sales Manager"
+  },
+  {
+   "role": "Sales User"
+  }
+ ]
+}

erpnext/manufacturing/report/exponential_smoothing_forecasting/exponential_smoothing_forecasting.py

@@ -0,0 +1,222 @@
+# Copyright (c) 2020, Frappe Technologies Pvt. Ltd. and contributors
+# For license information, please see license.txt
+
+from __future__ import unicode_literals
+import frappe, erpnext
+from frappe import _
+from frappe.utils import flt, nowdate, add_years, cint, getdate
+from erpnext.accounts.report.financial_statements import get_period_list
+from erpnext.stock.doctype.warehouse.warehouse import get_child_warehouses
+
+def execute(filters=None):
+	return ForecastingReport(filters).execute_report()
+
+class ExponentialSmoothingForecast(object):
+	def forecast_future_data(self):
+		for key, value in self.period_wise_data.items():
+			forecast_data = []
+			for period in self.period_list:
+				forecast_key = "forecast_" + period.key
+
+				if value.get(period.key) and not forecast_data:
+					value[forecast_key] = flt(value.get("avg", 0)) or flt(value.get(period.key))
+
+					# will be use to forecaset next period
+					forecast_data.append([value.get(period.key), value.get(forecast_key)])
+				elif forecast_data:
+					previous_period_data = forecast_data[-1]
+					value[forecast_key] = (previous_period_data[1] +
+						flt(self.filters.smoothing_constant) * (
+							flt(previous_period_data[0]) - flt(previous_period_data[1])
+						)
+					)
+
+class ForecastingReport(ExponentialSmoothingForecast):
+	def __init__(self, filters=None):
+		self.filters = frappe._dict(filters or {})
+		self.data = []
+		self.doctype = self.filters.based_on_document
+		self.child_doctype = self.doctype + " Item"
+		self.based_on_field = ("qty"
+			if self.filters.based_on_field == "Qty" else "amount")
+		self.fieldtype = "Float" if self.based_on_field == "qty" else "Currency"
+		self.company_currency = erpnext.get_company_currency(self.filters.company)
+
+	def execute_report(self):
+		self.prepare_periodical_data()
+		self.forecast_future_data()
+		self.data = self.period_wise_data.values()
+		self.add_total()
+
+		columns = self.get_columns()
+		charts = self.get_chart_data()
+		summary_data = self.get_summary_data()
+
+		return columns, self.data, None, charts, summary_data
+
+	def prepare_periodical_data(self):
+		self.period_wise_data = {}
+
+		from_date = add_years(self.filters.from_date, cint(self.filters.no_of_years) * -1)
+		self.period_list = get_period_list(from_date, self.filters.to_date,
+			from_date, self.filters.to_date, None, self.filters.periodicity, ignore_fiscal_year=True)
+
+		order_data = self.get_data_for_forecast() or []
+
+		for entry in order_data:
+			key = (entry.item_code, entry.warehouse)
+			if key not in self.period_wise_data:
+				self.period_wise_data[key] = entry
+
+			period_data = self.period_wise_data[key]
+			for period in self.period_list:
+				# check if posting date is within the period
+				if (entry.posting_date >= period.from_date and entry.posting_date <= period.to_date):
+					period_data[period.key] = period_data.get(period.key, 0.0) + flt(entry.get(self.based_on_field))
+
+		for key, value in self.period_wise_data.items():
+			list_of_period_value = [value.get(p.key, 0) for p in self.period_list]
+
+			if list_of_period_value:
+				value["avg"] = sum(list_of_period_value) / len(list_of_period_value)
+
+	def get_data_for_forecast(self):
+		cond = ""
+		if self.filters.item_code:
+			cond = " AND soi.item_code = %s" %(frappe.db.escape(self.filters.item_code))
+
+		warehouses = []
+		if self.filters.warehouse:
+			warehouses = get_child_warehouses(self.filters.warehouse)
+			cond += " AND soi.warehouse in ({})".format(','.join(['%s'] * len(warehouses)))
+
+		input_data = [self.filters.from_date, self.filters.company]
+		if warehouses:
+			input_data.extend(warehouses)
+
+		date_field = "posting_date" if self.doctype == "Delivery Note" else "transaction_date"
+
+		return frappe.db.sql("""
+			SELECT
+				so.{date_field} as posting_date, soi.item_code, soi.warehouse,
+				soi.item_name, soi.stock_qty as qty, soi.base_amount as amount
+			FROM
+				`tab{doc}` so, `tab{child_doc}` soi
+			WHERE
+				so.docstatus = 1 AND so.name = soi.parent AND
+				so.{date_field} < %s AND so.company = %s {cond}
+		""".format(doc=self.doctype, child_doc=self.child_doctype, date_field=date_field, cond=cond),
+			tuple(input_data), as_dict=1)
+
+	def add_total(self):
+		total_row = {
+			"item_code": _(frappe.bold("Total Quantity"))
+		}
+
+		for value in self.data:
+			for period in self.period_list:
+				forecast_key = "forecast_" + period.key
+				if forecast_key not in total_row:
+					total_row.setdefault(forecast_key, 0.0)
+
+				if period.key not in total_row:
+					total_row.setdefault(period.key, 0.0)
+
+				total_row[forecast_key] += value.get(forecast_key, 0.0)
+				total_row[period.key] += value.get(period.key, 0.0)
+
+		self.data.append(total_row)
+
+	def get_columns(self):
+		columns = [{
+			"label": _("Item Code"),
+			"options": "Item",
+			"fieldname": "item_code",
+			"fieldtype": "Link",
+			"width": 130
+		}, {
+			"label": _("Warehouse"),
+			"options": "Warehouse",
+			"fieldname": "warehouse",
+			"fieldtype": "Link",
+			"width": 130
+		}]
+
+		width = 150 if self.filters.periodicity in ['Yearly', "Half-Yearly", "Quarterly"] else 100
+		for period in self.period_list:
+			if (self.filters.periodicity in ['Yearly', "Half-Yearly", "Quarterly"]
+				or period.from_date >= getdate(self.filters.from_date)):
+
+				forecast_key = 'forecast_' + period.key
+
+				columns.append({
+					"label": _(period.label),
+					"fieldname": forecast_key,
+					"fieldtype": self.fieldtype,
+					"width": width,
+					"default": 0.0
+				})
+
+		return columns
+
+	def get_chart_data(self):
+		if not self.data: return
+
+		labels = []
+		self.total_demand = []
+		self.total_forecast = []
+		self.total_history_forecast = []
+		self.total_future_forecast = []
+
+		for period in self.period_list:
+			forecast_key = "forecast_" + period.key
+
+			labels.append(_(period.label))
+
+			if period.from_date < getdate(self.filters.from_date):
+				self.total_demand.append(self.data[-1].get(period.key, 0))
+				self.total_history_forecast.append(self.data[-1].get(forecast_key, 0))
+			else:
+				self.total_future_forecast.append(self.data[-1].get(forecast_key, 0))
+
+			self.total_forecast.append(self.data[-1].get(forecast_key, 0))
+
+		return {
+			"data": {
+				"labels": labels,
+				"datasets": [
+					{
+						"name": "Demand",
+						"values": self.total_demand
+					},
+					{
+						"name": "Forecast",
+						"values": self.total_forecast
+					}
+				]
+			},
+			"type": "line"
+		}
+
+	def get_summary_data(self):
+		return [
+			{
+				"value": sum(self.total_demand),
+				"label": _("Total Demand (Past Data)"),
+				"currency": self.company_currency,
+				"datatype": self.fieldtype
+			},
+			{
+				"value": sum(self.total_history_forecast),
+				"label": _("Total Forecast (Past Data)"),
+				"currency": self.company_currency,
+				"datatype": self.fieldtype
+			},
+			{
+				"value": sum(self.total_future_forecast),
+				"indicator": "Green",
+				"label": _("Total Forecast (Future Data)"),
+				"currency": self.company_currency,
+				"datatype": self.fieldtype
+			}
+		]