Fix product performance grouped JSON build

svc/queries/product_performance.go

@@ -1901,6 +1901,7 @@ func applyProductPerformanceProductRowScores(rows []models.ProductPerformanceRow
 			rows[i].StockTurnoverTotal,
 			float64(rows[i].MarketCountTotal),
 			float64(rows[i].CustomerCountTotal),
+			productPerformanceProductRowTotalPeriodDays(rows[i]),
 		)
 		rows[i].PerformanceScore = rows[i].PerformanceScore90
 	}
@@ -1949,6 +1950,10 @@ func productPerformanceProductRowAverages(rows []models.ProductPerformanceRow) p
 	return out
 }
 
+func productPerformanceProductRowTotalPeriodDays(row models.ProductPerformanceRow) float64 {
+	return productPerformancePeriodDays(map[string]any{"kpi_date": row.KpiDate}, "total")
+}
+
 func productPerformanceMarginFromSales(salesUSD, salesQty, unitCost float64) float64 {
 	if salesUSD <= 0 {
 		return 0
@@ -2286,7 +2291,7 @@ SELECT
     WHEN sales_qty_total > 0 AND stock_qty <= 0 AND sales_index_total >= 1 THEN 'STOKSUZ_TALEP'
     WHEN sales_qty_total = 0 AND stock_qty > 0 THEN 'STOK_RISKI'
     WHEN gross_margin_total < 0 THEN 'FIYAT_BASKISI'
-    WHEN sales_index_total >= 1.4 AND gross_margin_total >= 0.25 THEN 'YILDIZ_URUN'
+    WHEN sales_index_total >= 1.4 AND gross_margin_total >= 0.45 THEN 'YILDIZ_URUN'
     WHEN gross_margin_total >= 0.35 AND sales_index_total < 0.8 THEN 'FIYAT_FIRSATI'
     ELSE 'TAKIP'
   END AS performance_bucket,
@@ -2294,7 +2299,7 @@ SELECT
     WHEN sales_qty_total > 0 AND stock_qty <= 0 THEN 'Talep var, stok yok. Uretim/satin alma onceligi ver.'
     WHEN sales_qty_total = 0 AND stock_qty > 0 THEN 'Satis yok, stok maliyeti tasiyor. Piyasa/fiyat aksiyonu gerekli.'
     WHEN gross_margin_total < 0 THEN 'Ciplak maliyet altinda satis var. Fiyat veya maliyet kontrol edilmeli.'
-    WHEN sales_index_total >= 1.4 AND gross_margin_total >= 0.25 THEN 'Genel donemde guclu urun. Stok ve fiyat korunmali.'
+    WHEN sales_index_total >= 1.4 AND gross_margin_total >= 0.45 THEN 'Genel donemde guclu urun. Stok ve fiyat korunmali.'
     WHEN gross_margin_total >= 0.35 AND sales_index_total < 0.8 THEN 'Karli ama yavas. Dogru piyasada satis firsati var.'
     ELSE 'Izleme ve piyasa bazli aksiyon.'
   END AS recommendation,
@@ -3581,6 +3586,19 @@ StockTotalStart AS (
   WHERE stock_date <= DATE '2022-01-01'
   ORDER BY product_code, color_code, yaka_kodu, stock_date DESC
 ),
+FirstSaleByProduct AS (
+  SELECT
+    product_code,
+    color_code,
+    yaka_kodu,
+    MIN(sales_date) FILTER (WHERE sales_date >= current_date - INTERVAL '89 days') AS first_sale_date_90d,
+    MIN(sales_date) FILTER (WHERE sales_date >= current_date - INTERVAL '179 days') AS first_sale_date_180d,
+    MIN(sales_date) FILTER (WHERE sales_date >= current_date - INTERVAL '359 days') AS first_sale_date_365d,
+    MIN(sales_date) FILTER (WHERE sales_date >= DATE '2022-01-01') AS first_sale_date_total
+  FROM mk_product_performance_sales_daily
+  WHERE sales_date >= DATE '2022-01-01'
+  GROUP BY product_code, color_code, yaka_kodu
+),
 Agg AS (
   SELECT
     $2::text AS breakdown,
@@ -3683,18 +3701,55 @@ Agg AS (
       COALESCE(pd.base_price_usd,0) AS base_price_usd,
       COALESCE(pd.cost_price_usd,0) AS cost_price_usd,
       COALESCE(st.stock_qty,0) AS stock_qty,
-      (COALESCE(s90.stock_qty,0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_90d,
-      (COALESCE(s180.stock_qty,0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_180d,
-      (COALESCE(s365.stock_qty,0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_365d,
-      (COALESCE(stotal.stock_qty,0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_total
+      (COALESCE(s90.stock_qty, st.stock_qty, 0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_90d,
+      (COALESCE(s180.stock_qty, st.stock_qty, 0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_180d,
+      (COALESCE(s365.stock_qty, st.stock_qty, 0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_365d,
+      (COALESCE(stotal.stock_qty, st.stock_qty, 0) + COALESCE(st.stock_qty,0)) / 2.0 AS avg_stock_total
     FROM mk_product_performance_sales_daily s
     LEFT JOIN mk_product_performance_price_dim pd ON pd.product_code = s.product_code
     LEFT JOIN LatestKPI k ON k.product_code = s.product_code AND k.color_code = s.color_code AND k.yaka_kodu = s.yaka_kodu
+    LEFT JOIN FirstSaleByProduct fs ON fs.product_code = s.product_code AND fs.color_code = s.color_code AND fs.yaka_kodu = s.yaka_kodu
     LEFT JOIN StockAgg st ON st.product_code = s.product_code AND st.color_code = s.color_code AND st.yaka_kodu = s.yaka_kodu
-    LEFT JOIN Stock90Start s90 ON s90.product_code = s.product_code AND s90.color_code = s.color_code AND s90.yaka_kodu = s.yaka_kodu
-    LEFT JOIN Stock180Start s180 ON s180.product_code = s.product_code AND s180.color_code = s.color_code AND s180.yaka_kodu = s.yaka_kodu
-    LEFT JOIN Stock365Start s365 ON s365.product_code = s.product_code AND s365.color_code = s.color_code AND s365.yaka_kodu = s.yaka_kodu
-    LEFT JOIN StockTotalStart stotal ON stotal.product_code = s.product_code AND stotal.color_code = s.color_code AND stotal.yaka_kodu = s.yaka_kodu
+    LEFT JOIN LATERAL (
+      SELECT x.stock_qty
+      FROM mk_product_performance_stock_daily x
+      WHERE x.product_code = s.product_code
+        AND x.color_code = s.color_code
+        AND x.yaka_kodu = s.yaka_kodu
+        AND x.stock_date <= COALESCE(fs.first_sale_date_90d, current_date - INTERVAL '89 days')
+      ORDER BY x.stock_date DESC
+      LIMIT 1
+    ) s90 ON TRUE
+    LEFT JOIN LATERAL (
+      SELECT x.stock_qty
+      FROM mk_product_performance_stock_daily x
+      WHERE x.product_code = s.product_code
+        AND x.color_code = s.color_code
+        AND x.yaka_kodu = s.yaka_kodu
+        AND x.stock_date <= COALESCE(fs.first_sale_date_180d, current_date - INTERVAL '179 days')
+      ORDER BY x.stock_date DESC
+      LIMIT 1
+    ) s180 ON TRUE
+    LEFT JOIN LATERAL (
+      SELECT x.stock_qty
+      FROM mk_product_performance_stock_daily x
+      WHERE x.product_code = s.product_code
+        AND x.color_code = s.color_code
+        AND x.yaka_kodu = s.yaka_kodu
+        AND x.stock_date <= COALESCE(fs.first_sale_date_365d, current_date - INTERVAL '359 days')
+      ORDER BY x.stock_date DESC
+      LIMIT 1
+    ) s365 ON TRUE
+    LEFT JOIN LATERAL (
+      SELECT x.stock_qty
+      FROM mk_product_performance_stock_daily x
+      WHERE x.product_code = s.product_code
+        AND x.color_code = s.color_code
+        AND x.yaka_kodu = s.yaka_kodu
+        AND x.stock_date <= COALESCE(fs.first_sale_date_total, DATE '2022-01-01')
+      ORDER BY x.stock_date DESC
+      LIMIT 1
+    ) stotal ON TRUE
   ) s
   WHERE sales_date >= DATE '2022-01-01'
     AND upper(translate(btrim(COALESCE(urun_ilk_grubu,'')), U&'\0130\015E\011E\00DC\00D6\00C7\0131\015F\011F\00FC\00F6\00E7', 'ISGUOCisguoc')) NOT IN ('MALZEMELI FASON', 'MALZEMESIZ FASON', 'MAZLEMELI FASON', 'MAZEMESIZ FASON', 'DIGER')
@@ -3813,7 +3868,7 @@ SELECT
   CASE WHEN NOT has_cost THEN 0 WHEN sales_usd_90d <= 0 THEN 1 ELSE
     ROUND((
       LEAST(35, sales_usd_90d / 25000 * 35)
-      + LEAST(30, GREATEST(gross_margin_cost_90d,0) / 0.40 * 30)
+      + LEAST(30, GREATEST(gross_margin_cost_90d,0) / 0.45 * 30)
       + LEAST(20, product_group_count_90d / 8.0 * 20)
       + LEAST(15, sales_qty_90d / 500 * 15)
     )::numeric, 4)
@@ -3821,7 +3876,7 @@ SELECT
   CASE WHEN NOT has_cost THEN 0 WHEN sales_usd_180d <= 0 THEN 1 ELSE
     ROUND((
       LEAST(35, sales_usd_180d / 50000 * 35)
-      + LEAST(30, GREATEST(gross_margin_cost_180d,0) / 0.40 * 30)
+      + LEAST(30, GREATEST(gross_margin_cost_180d,0) / 0.45 * 30)
       + LEAST(20, product_group_count_180d / 8.0 * 20)
       + LEAST(15, sales_qty_180d / 1000 * 15)
     )::numeric, 4)
@@ -3829,7 +3884,7 @@ SELECT
   CASE WHEN NOT has_cost THEN 0 WHEN sales_usd_365d <= 0 THEN 1 ELSE
     ROUND((
       LEAST(35, sales_usd_365d / 100000 * 35)
-      + LEAST(30, GREATEST(gross_margin_cost_365d,0) / 0.40 * 30)
+      + LEAST(30, GREATEST(gross_margin_cost_365d,0) / 0.45 * 30)
       + LEAST(20, product_group_count_365d / 8.0 * 20)
       + LEAST(15, sales_qty_365d / 2000 * 15)
     )::numeric, 4)
@@ -3837,18 +3892,18 @@ SELECT
   CASE WHEN NOT has_cost THEN 0 WHEN sales_usd_total <= 0 THEN 1 ELSE
     ROUND((
       LEAST(35, sales_usd_total / 300000 * 35)
-      + LEAST(30, GREATEST(gross_margin_cost_total,0) / 0.40 * 30)
+      + LEAST(30, GREATEST(gross_margin_cost_total,0) / 0.45 * 30)
       + LEAST(20, product_group_count_total / 8.0 * 20)
       + LEAST(15, sales_qty_total / 6000 * 15)
     )::numeric, 4)
   END AS customer_score_total,
   CASE WHEN NOT has_cost THEN 0 WHEN sales_usd_90d <= 0 THEN 1 ELSE
     ROUND(
-      LEAST(30, GREATEST(gross_margin_cost_90d,0) / 0.40 * 30)
+      LEAST(30, GREATEST(gross_margin_cost_90d,0) / 0.45 * 30)
       + LEAST(20, (CASE WHEN avg_stock_90d > 0 THEN sales_qty_90d / NULLIF(avg_stock_90d,0) * 4.0 ELSE 0 END) / 4.0 * 20)
-      + LEAST(20, sales_usd_90d / 10000 * 20)
-      + LEAST(15, market_count_90d / 8.0 * 15)
-      + LEAST(15, customer_count_90d / 25.0 * 15),
+      + LEAST(20, sales_usd_90d / 25000 * 20)
+      + LEAST(5, market_count_90d / 3.0 * 5)
+      + LEAST(25, customer_count_90d / 8.0 * 25),
       4
     )
   END AS performance_score,
@@ -6249,6 +6304,7 @@ func productPerformanceSalesPeriodScore(row map[string]any, suffix string) float
 		stockTurnover,
 		productPerformancePeriodCount(row, "market_count", suffix),
 		productPerformancePeriodCount(row, "customer_count", suffix),
+		productPerformancePeriodDays(row, suffix),
 	)
 }
 
@@ -6292,16 +6348,17 @@ func productPerformanceCustomerSalesPeriodScore(row map[string]any) float64 {
 	return productPerformanceCustomerScore(suffix, salesUSD, margin, productCount, salesQty)
 }
 
-func productPerformanceProductScore(suffix string, salesUSD, salesIndex, margin, stockTurnover, marketCount, customerCount float64) float64 {
+func productPerformanceProductScore(suffix string, salesUSD, salesIndex, margin, stockTurnover, marketCount, customerCount float64, periodDays ...float64) float64 {
 	if salesUSD <= 0 {
 		return 1
 	}
-	revenueScore := productPerformanceRevenueScore(suffix, salesUSD, salesIndex, productPerformanceProductRevenueTarget(suffix))
+	days := productPerformanceScorePeriodDays(suffix, periodDays...)
+	revenueScore := productPerformanceRevenueScore(suffix, salesUSD, salesIndex, productPerformanceProductRevenueTargetForDays(suffix, days))
 	score := 0.30*productPerformanceMarginComponentScore(margin) +
 		0.20*productPerformanceRatioScore(stockTurnover, productPerformanceStockTurnoverTarget(suffix)) +
 		0.20*revenueScore +
 		0.05*productPerformanceRatioScore(marketCount, productPerformanceMarketSpreadTarget(suffix)) +
-		0.25*productPerformanceRatioScore(customerCount, productPerformanceCustomerSpreadTarget(suffix))
+		0.25*productPerformanceRatioScore(customerCount, productPerformanceCustomerSpreadTargetForDays(suffix, days))
 	return productPerformanceRoundScore(score)
 }
 
@@ -6323,6 +6380,10 @@ func productPerformanceMarketSpreadTarget(suffix string) float64 {
 }
 
 func productPerformanceCustomerSpreadTarget(suffix string) float64 {
+	return productPerformanceCustomerSpreadTargetForDays(suffix, productPerformanceScorePeriodDays(suffix))
+}
+
+func productPerformanceCustomerSpreadTargetForDays(suffix string, periodDays float64) float64 {
 	switch suffix {
 	case "90d":
 		return 8
@@ -6331,7 +6392,7 @@ func productPerformanceCustomerSpreadTarget(suffix string) float64 {
 	case "365d":
 		return 50
 	default:
-		return 50
+		return 20 * productPerformanceTotalPeriodMultiplier(periodDays)
 	}
 }
 
@@ -6358,7 +6419,7 @@ func productPerformanceRelativeIndex(value, average float64) float64 {
 }
 
 func productPerformanceMarginComponentScore(margin float64) float64 {
-	return productPerformanceRatioScore(maxFloat(0, margin), 0.40)
+	return productPerformanceRatioScore(maxFloat(0, margin), 0.45)
 }
 
 func productPerformanceRatioScore(value, target float64) float64 {
@@ -6379,18 +6440,49 @@ func productPerformanceRoundScore(score float64) float64 {
 }
 
 func productPerformanceProductRevenueTarget(suffix string) float64 {
+	return productPerformanceProductRevenueTargetForDays(suffix, productPerformanceScorePeriodDays(suffix))
+}
+
+func productPerformanceProductRevenueTargetForDays(suffix string, periodDays float64) float64 {
 	switch suffix {
 	case "180d":
-		return 20000
+		return 50000
 	case "365d":
-		return 40000
+		return 100000
 	case "total":
-		return 120000
+		return 50000 * productPerformanceTotalPeriodMultiplier(periodDays)
 	default:
-		return 10000
+		return 25000
 	}
 }
 
+func productPerformanceScorePeriodDays(suffix string, periodDays ...float64) float64 {
+	if len(periodDays) > 0 && periodDays[0] > 0 {
+		return periodDays[0]
+	}
+	switch suffix {
+	case "90d":
+		return 90
+	case "180d":
+		return 180
+	case "365d":
+		return 360
+	case "total":
+		return productPerformancePeriodDays(map[string]any{
+			"kpi_date": time.Now().Format("2006-01-02"),
+		}, "total")
+	default:
+		return 0
+	}
+}
+
+func productPerformanceTotalPeriodMultiplier(periodDays float64) float64 {
+	if periodDays <= 0 {
+		periodDays = 180
+	}
+	return maxFloat(1, periodDays/180.0)
+}
+
 func productPerformanceCustomerRevenueTarget(suffix string) float64 {
 	switch suffix {
 	case "180d":

svc/queries/product_performance_sql.go

@@ -489,6 +489,10 @@ SalesAgg AS (
         COUNT(DISTINCT NULLIF(customer_code, '-')) FILTER (WHERE sales_date >= $1::date - INTERVAL '179 days' AND COALESCE(sales_usd, 0) > 0) AS customer_count_180d,
         COUNT(DISTINCT NULLIF(customer_code, '-')) FILTER (WHERE sales_date >= $1::date - INTERVAL '359 days' AND COALESCE(sales_usd, 0) > 0) AS customer_count_365d,
         COUNT(DISTINCT NULLIF(customer_code, '-')) FILTER (WHERE sales_date >= DATE '2022-01-01' AND COALESCE(sales_usd, 0) > 0) AS customer_count_total,
+        MIN(sales_date) FILTER (WHERE sales_date >= $1::date - INTERVAL '89 days') AS first_sale_date_90d,
+        MIN(sales_date) FILTER (WHERE sales_date >= $1::date - INTERVAL '179 days') AS first_sale_date_180d,
+        MIN(sales_date) FILTER (WHERE sales_date >= $1::date - INTERVAL '359 days') AS first_sale_date_365d,
+        MIN(sales_date) FILTER (WHERE sales_date >= DATE '2022-01-01') AS first_sale_date_total,
         MAX(sales_date) AS last_sale_date,
         MAX(last_ref_number) AS last_ref_number
     FROM mk_product_performance_sales_daily
@@ -503,7 +507,9 @@ Scope AS (
         item_description, kategori, seri, yas_grubu, askili_yan, urun_ilk_grubu, urun_ana_grubu, urun_alt_grubu,
         sales_qty_30d, sales_qty_90d, sales_qty_180d, sales_qty_365d, sales_qty_730d, sales_qty_total,
         sales_usd_30d, sales_usd_90d, sales_usd_180d, sales_usd_365d, sales_usd_total,
-        customer_count_90d, customer_count_180d, customer_count_365d, customer_count_total, last_sale_date, last_ref_number
+        customer_count_90d, customer_count_180d, customer_count_365d, customer_count_total,
+        first_sale_date_90d, first_sale_date_180d, first_sale_date_365d, first_sale_date_total,
+        last_sale_date, last_ref_number
     FROM SalesAgg
     UNION ALL
     SELECT
@@ -534,6 +540,10 @@ Scope AS (
         0 AS customer_count_180d,
         0 AS customer_count_365d,
         0 AS customer_count_total,
+        NULL::date AS first_sale_date_90d,
+        NULL::date AS first_sale_date_180d,
+        NULL::date AS first_sale_date_365d,
+        NULL::date AS first_sale_date_total,
         NULL::date AS last_sale_date,
         '' AS last_ref_number
     FROM LatestStock ls
@@ -576,10 +586,10 @@ Base AS (
         COALESCE(s.sales_qty_180d, 0) / 180.0 AS avg_daily_sales_180d,
         COALESCE(s.sales_qty_365d, 0) / 360.0 AS avg_daily_sales_365d,
         COALESCE(s.sales_qty_total, 0) / GREATEST(1, ($1::date - DATE '2022-01-01') + 1) AS avg_daily_sales_total,
-        (COALESCE(s90.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_90d,
-        (COALESCE(s180.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_180d,
-        (COALESCE(s365.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_365d,
-        (COALESCE(stotal.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_total,
+        (COALESCE(s90.stock_qty, ls.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_90d,
+        (COALESCE(s180.stock_qty, ls.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_180d,
+        (COALESCE(s365.stock_qty, ls.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_365d,
+        (COALESCE(stotal.stock_qty, ls.stock_qty, 0) + COALESCE(ls.stock_qty, 0)) / 2.0 AS avg_stock_total,
         CASE WHEN COALESCE(s.sales_qty_90d, 0) = 0 THEN 0 ELSE COALESCE(s.sales_usd_90d, 0) / NULLIF(s.sales_qty_90d, 0) END AS avg_price_usd_90d,
         CASE WHEN COALESCE(s.sales_qty_180d, 0) = 0 THEN 0 ELSE COALESCE(s.sales_usd_180d, 0) / NULLIF(s.sales_qty_180d, 0) END AS avg_price_usd_180d,
         COALESCE(s.sales_usd_90d, 0) - (COALESCE(s.sales_qty_90d, 0) * COALESCE(pd.cost_price_usd, 0)) AS gross_profit_usd_90d,
@@ -596,11 +606,47 @@ Base AS (
         CASE WHEN COALESCE(s.sales_qty_180d, 0) = 0 THEN 0 ELSE (COALESCE(s.sales_usd_180d, 0) / NULLIF(s.sales_qty_180d, 0)) - COALESCE(pd.base_price_usd, 0) END AS unit_profit_base_180d
     FROM Scope s
     LEFT JOIN LatestStock ls ON ls.product_code=s.product_code AND ls.color_code=s.color_code AND ls.yaka_kodu=s.yaka_kodu
-    LEFT JOIN Stock90Start s90 ON s90.product_code=s.product_code AND s90.color_code=s.color_code AND s90.yaka_kodu=s.yaka_kodu
-    LEFT JOIN Stock180Start s180 ON s180.product_code=s.product_code AND s180.color_code=s.color_code AND s180.yaka_kodu=s.yaka_kodu
-    LEFT JOIN Stock365Start s365 ON s365.product_code=s.product_code AND s365.color_code=s.color_code AND s365.yaka_kodu=s.yaka_kodu
-    LEFT JOIN StockTotalStart stotal ON stotal.product_code=s.product_code AND stotal.color_code=s.color_code AND stotal.yaka_kodu=s.yaka_kodu
     LEFT JOIN PriceDim pd ON pd.product_code=s.product_code
+    LEFT JOIN LATERAL (
+        SELECT x.stock_qty
+        FROM mk_product_performance_stock_daily x
+        WHERE x.product_code=s.product_code
+          AND x.color_code=s.color_code
+          AND x.yaka_kodu=s.yaka_kodu
+          AND x.stock_date <= COALESCE(s.first_sale_date_90d, $1::date - INTERVAL '89 days')
+        ORDER BY x.stock_date DESC
+        LIMIT 1
+    ) s90 ON TRUE
+    LEFT JOIN LATERAL (
+        SELECT x.stock_qty
+        FROM mk_product_performance_stock_daily x
+        WHERE x.product_code=s.product_code
+          AND x.color_code=s.color_code
+          AND x.yaka_kodu=s.yaka_kodu
+          AND x.stock_date <= COALESCE(s.first_sale_date_180d, $1::date - INTERVAL '179 days')
+        ORDER BY x.stock_date DESC
+        LIMIT 1
+    ) s180 ON TRUE
+    LEFT JOIN LATERAL (
+        SELECT x.stock_qty
+        FROM mk_product_performance_stock_daily x
+        WHERE x.product_code=s.product_code
+          AND x.color_code=s.color_code
+          AND x.yaka_kodu=s.yaka_kodu
+          AND x.stock_date <= COALESCE(s.first_sale_date_365d, $1::date - INTERVAL '359 days')
+        ORDER BY x.stock_date DESC
+        LIMIT 1
+    ) s365 ON TRUE
+    LEFT JOIN LATERAL (
+        SELECT x.stock_qty
+        FROM mk_product_performance_stock_daily x
+        WHERE x.product_code=s.product_code
+          AND x.color_code=s.color_code
+          AND x.yaka_kodu=s.yaka_kodu
+          AND x.stock_date <= COALESCE(s.first_sale_date_total, DATE '2022-01-01')
+        ORDER BY x.stock_date DESC
+        LIMIT 1
+    ) stotal ON TRUE
     WHERE upper(translate(btrim(COALESCE(NULLIF(s.urun_ilk_grubu,''), pd.urun_ilk_grubu, '')), U&'\0130\015E\011E\00DC\00D6\00C7\0131\015F\011F\00FC\00F6\00E7', 'ISGUOCisguoc')) NOT IN ('MALZEMELI FASON', 'MALZEMESIZ FASON', 'MAZLEMELI FASON', 'MAZEMESIZ FASON', 'DIGER')
 ),
 Spread AS (
@@ -683,15 +729,15 @@ SELECT
     COALESCE(sales_index_90d, 0), COALESCE(sales_index_180d, 0), COALESCE(sales_index_365d, 0), COALESCE(sales_index_total, 0), COALESCE(price_index_90d, 0), COALESCE(margin_index_90d, 0),
     CASE WHEN COALESCE(sales_usd_90d, 0) <= 0 THEN 1 ELSE
       ROUND(
-          LEAST(35, COALESCE(sales_index_90d, 0) * 18)
-        + LEAST(25, GREATEST(COALESCE(gross_margin_90d, 0), 0) * 50)
-        + LEAST(20, COALESCE(customer_count_90d, 0) * 2)
-        + LEAST(10, COALESCE(market_count_90d, 0) * 3)
+          LEAST(30, GREATEST(COALESCE(gross_margin_90d, 0), 0) / 0.45 * 30)
         + LEAST(20, COALESCE(stock_turnover_90d, 0) / 4.0 * 20)
+        + LEAST(20, COALESCE(sales_usd_90d, 0) / 25000.0 * 20)
+        + LEAST(5, COALESCE(market_count_90d, 0) / 3.0 * 5)
+        + LEAST(25, COALESCE(customer_count_90d, 0) / 8.0 * 25)
       , 4)
     END AS performance_score,
     CASE
-      WHEN COALESCE(sales_index_90d,0) >= 1.25 AND COALESCE(gross_margin_90d,0) >= 0.25 THEN 'YILDIZ_URUN'
+      WHEN COALESCE(sales_index_90d,0) >= 1.25 AND COALESCE(gross_margin_90d,0) >= 0.45 THEN 'YILDIZ_URUN'
       WHEN COALESCE(stock_qty,0) <= 0 AND COALESCE(sales_qty_90d,0) > 0 THEN 'STOKSUZ_TALEP'
       WHEN COALESCE(stock_days_90d,0) > 180 AND COALESCE(sales_index_90d,0) < 0.75 THEN 'STOK_RISKI'
       WHEN COALESCE(price_index_90d,0) > 1.10 AND COALESCE(sales_index_90d,0) < 0.80 THEN 'FIYAT_BASKISI'
@@ -699,7 +745,7 @@ SELECT
       ELSE 'TAKIP'
     END AS performance_bucket,
     CASE
-      WHEN COALESCE(sales_index_90d,0) >= 1.25 AND COALESCE(gross_margin_90d,0) >= 0.25 THEN 'Piyasa ustu satis ve iyi marj: stok ve fiyat gucu takip edilmeli.'
+      WHEN COALESCE(sales_index_90d,0) >= 1.25 AND COALESCE(gross_margin_90d,0) >= 0.45 THEN 'Piyasa ustu satis ve iyi marj: stok ve fiyat gucu takip edilmeli.'
       WHEN COALESCE(stock_qty,0) <= 0 AND COALESCE(sales_qty_90d,0) > 0 THEN 'Talep var, stok yok: uretim/satin alma planina alinmali.'
       WHEN COALESCE(stock_days_90d,0) > 180 AND COALESCE(sales_index_90d,0) < 0.75 THEN 'Stok yuksek, satis piyasa alti: kampanya veya fiyat kontrolu gerekli.'
       WHEN COALESCE(price_index_90d,0) > 1.10 AND COALESCE(sales_index_90d,0) < 0.80 THEN 'Fiyat piyasa ustunde ve satis zayif: fiyat revizyonu degerlendirilmeli.'

svc/routes/product_performance_excel.go

@@ -447,7 +447,8 @@ func productPerformanceExcelPeriodColumns(label, suffix string, values func(*pro
 		}),
 		numberExcelColumn(label+" Ürün Skor", "number", func(v *productPerformanceExcelVariant) any {
 			qty, usd, markets, customers := values(v)
-			return productPerformanceExcelProductScore(suffix, usd, productPerformanceExcelStockTurnover(qty, productPerformanceExcelAvgStock(v, suffix), productPerformanceExcelPeriodDays(v, suffix)), float64(markets), float64(customers), productPerformanceExcelMargin(usd, qty, v.CostPriceUSD))
+			days := productPerformanceExcelPeriodDays(v, suffix)
+			return productPerformanceExcelProductScore(suffix, usd, productPerformanceExcelStockTurnover(qty, productPerformanceExcelAvgStock(v, suffix), days), float64(markets), float64(customers), productPerformanceExcelMargin(usd, qty, v.CostPriceUSD), days)
 		}),
 	}
 }
@@ -685,9 +686,11 @@ func productPerformanceExcelSortNumber(row *productPerformanceExcelVariant, sort
 	case "customer_count_total":
 		return float64(row.CustomerCountTotal)
 	case "performance_score", "performance_score_90d":
-		return productPerformanceExcelProductScore("90d", row.SalesUSD90, productPerformanceExcelStockTurnover(row.SalesQty90, productPerformanceExcelAvgStock(row, "90d"), productPerformanceExcelPeriodDays(row, "90d")), float64(row.marketCount90()), float64(row.CustomerCount90), productPerformanceExcelMargin(row.SalesUSD90, row.SalesQty90, row.CostPriceUSD))
+		days := productPerformanceExcelPeriodDays(row, "90d")
+		return productPerformanceExcelProductScore("90d", row.SalesUSD90, productPerformanceExcelStockTurnover(row.SalesQty90, productPerformanceExcelAvgStock(row, "90d"), days), float64(row.marketCount90()), float64(row.CustomerCount90), productPerformanceExcelMargin(row.SalesUSD90, row.SalesQty90, row.CostPriceUSD), days)
 	case "performance_score_total":
-		return productPerformanceExcelProductScore("total", row.totalUSD(), productPerformanceExcelStockTurnover(row.totalQty(), productPerformanceExcelAvgStock(row, "total"), productPerformanceExcelPeriodDays(row, "total")), float64(row.marketCountTotal()), float64(row.CustomerCountTotal), productPerformanceExcelMargin(row.totalUSD(), row.totalQty(), row.CostPriceUSD))
+		days := productPerformanceExcelPeriodDays(row, "total")
+		return productPerformanceExcelProductScore("total", row.totalUSD(), productPerformanceExcelStockTurnover(row.totalQty(), productPerformanceExcelAvgStock(row, "total"), days), float64(row.marketCountTotal()), float64(row.CustomerCountTotal), productPerformanceExcelMargin(row.totalUSD(), row.totalQty(), row.CostPriceUSD), days)
 	default:
 		return 0
 	}
@@ -803,7 +806,7 @@ func productPerformanceExcelStatus(row *productPerformanceExcelVariant) string {
 		return "Stok Riski"
 	case productPerformanceExcelMargin(row.totalUSD(), row.totalQty(), row.CostPriceUSD) < 0:
 		return "Fiyat Baskısı"
-	case productPerformanceExcelProductScore("total", row.totalUSD(), productPerformanceExcelStockTurnover(row.totalQty(), productPerformanceExcelAvgStock(row, "total"), productPerformanceExcelPeriodDays(row, "total")), float64(row.marketCountTotal()), float64(row.CustomerCountTotal), productPerformanceExcelMargin(row.totalUSD(), row.totalQty(), row.CostPriceUSD)) >= 70:
+	case productPerformanceExcelProductScore("total", row.totalUSD(), productPerformanceExcelStockTurnover(row.totalQty(), productPerformanceExcelAvgStock(row, "total"), productPerformanceExcelPeriodDays(row, "total")), float64(row.marketCountTotal()), float64(row.CustomerCountTotal), productPerformanceExcelMargin(row.totalUSD(), row.totalQty(), row.CostPriceUSD), productPerformanceExcelPeriodDays(row, "total")) >= 70:
 		return "Yıldız Ürün"
 	default:
 		return productPerformanceExcelBucketLabel(row.PerformanceBucket)
@@ -954,16 +957,17 @@ func productPerformanceExcelMargin(salesUSD, qty, unitCost float64) float64 {
 	return productPerformanceExcelGrossProfit(salesUSD, qty, unitCost) / salesUSD
 }
 
-func productPerformanceExcelProductScore(suffix string, salesUSD, stockTurnover, marketCount, customerCount, margin float64) float64 {
+func productPerformanceExcelProductScore(suffix string, salesUSD, stockTurnover, marketCount, customerCount, margin float64, periodDays ...float64) float64 {
 	if salesUSD <= 0 {
 		return 1
 	}
-	revenueScore := productPerformanceExcelRatioScore(salesUSD, productPerformanceExcelProductRevenueTarget(suffix))
+	days := productPerformanceExcelScorePeriodDays(suffix, periodDays...)
+	revenueScore := productPerformanceExcelRatioScore(salesUSD, productPerformanceExcelProductRevenueTargetForDays(suffix, days))
 	score := 0.30*productPerformanceExcelMarginScore(margin) +
 		0.20*productPerformanceExcelRatioScore(stockTurnover, 4) +
 		0.20*revenueScore +
 		0.05*productPerformanceExcelRatioScore(marketCount, productPerformanceExcelMarketSpreadTarget(suffix)) +
-		0.25*productPerformanceExcelRatioScore(customerCount, productPerformanceExcelCustomerSpreadTarget(suffix))
+		0.25*productPerformanceExcelRatioScore(customerCount, productPerformanceExcelCustomerSpreadTargetForDays(suffix, days))
 	return productPerformanceExcelRoundScore(score)
 }
 
@@ -981,6 +985,10 @@ func productPerformanceExcelMarketSpreadTarget(suffix string) float64 {
 }
 
 func productPerformanceExcelCustomerSpreadTarget(suffix string) float64 {
+	return productPerformanceExcelCustomerSpreadTargetForDays(suffix, productPerformanceExcelScorePeriodDays(suffix))
+}
+
+func productPerformanceExcelCustomerSpreadTargetForDays(suffix string, periodDays float64) float64 {
 	switch suffix {
 	case "90d":
 		return 8
@@ -989,28 +997,62 @@ func productPerformanceExcelCustomerSpreadTarget(suffix string) float64 {
 	case "365d":
 		return 50
 	default:
-		return 50
+		return 20 * productPerformanceExcelTotalPeriodMultiplier(periodDays)
 	}
 }
 
 func productPerformanceExcelProductRevenueTarget(suffix string) float64 {
+	return productPerformanceExcelProductRevenueTargetForDays(suffix, productPerformanceExcelScorePeriodDays(suffix))
+}
+
+func productPerformanceExcelProductRevenueTargetForDays(suffix string, periodDays float64) float64 {
 	switch suffix {
 	case "180d":
-		return 20000
+		return 50000
 	case "365d":
-		return 40000
+		return 100000
 	case "total":
-		return 120000
+		return 50000 * productPerformanceExcelTotalPeriodMultiplier(periodDays)
 	default:
-		return 10000
+		return 25000
 	}
 }
 
+func productPerformanceExcelScorePeriodDays(suffix string, periodDays ...float64) float64 {
+	if len(periodDays) > 0 && periodDays[0] > 0 {
+		return periodDays[0]
+	}
+	switch suffix {
+	case "90d":
+		return 90
+	case "180d":
+		return 180
+	case "365d":
+		return 360
+	case "total":
+		start := time.Date(2022, 1, 1, 0, 0, 0, 0, time.UTC)
+		now := time.Now()
+		if now.Before(start) {
+			return 0
+		}
+		return now.Sub(start).Hours()/24 + 1
+	default:
+		return 0
+	}
+}
+
+func productPerformanceExcelTotalPeriodMultiplier(periodDays float64) float64 {
+	if periodDays <= 0 {
+		periodDays = 180
+	}
+	return math.Max(1, periodDays/180.0)
+}
+
 func productPerformanceExcelMarginScore(margin float64) float64 {
 	if margin < 0 {
 		margin = 0
 	}
-	return productPerformanceExcelRatioScore(margin, 0.40)
+	return productPerformanceExcelRatioScore(margin, 0.45)
 }
 
 func productPerformanceExcelRatioScore(value, target float64) float64 {