| 4 | | == 1. == |
| | 4 | == 1. ABC Анализа на производи (ABC Product Analysis) == |
| | 5 | |
| | 6 | Овој извештај ги дели производите во сегменти A (80%), B (15%) и C (5%) според нивниот придонес во вкупниот промет, користејќи ги овие проценти за да се прикаже кои производи се најважни за приходот и да се олесни управувањето со залихите. |
| | 7 | |
| | 8 | {{{#!sql |
| | 9 | WITH ProductRevenue AS ( |
| | 10 | SELECT |
| | 11 | p.name AS product_name, |
| | 12 | SUM(si.quantity * si.unit_price_at_sale) AS total_revenue |
| | 13 | FROM product p |
| | 14 | JOIN sale_item si ON p.product_id = si.product_id |
| | 15 | GROUP BY p.name |
| | 16 | ), |
| | 17 | RevenueCalculations AS ( |
| | 18 | SELECT |
| | 19 | product_name, |
| | 20 | total_revenue, |
| | 21 | SUM(total_revenue) OVER (ORDER BY total_revenue DESC) / SUM(total_revenue) OVER () AS cumulative_share |
| | 22 | FROM ProductRevenue |
| | 23 | ) |
| | 24 | SELECT |
| | 25 | product_name, |
| | 26 | ROUND(total_revenue, 2) AS revenue_mkd, |
| | 27 | CASE |
| | 28 | WHEN cumulative_share <= 0.80 THEN 'A (High Value)' |
| | 29 | WHEN cumulative_share <= 0.95 THEN 'B (Medium Value)' |
| | 30 | ELSE 'C (Low Value)' |
| | 31 | END AS abc_classification |
| | 32 | FROM RevenueCalculations; |
| | 33 | }}} |
| | 34 | |
| | 35 | '''Релациона алгебра:''' |
| | 36 | {{{ |
| | 37 | ProductRevenue ← γ name; SUM(quantity × unit_price_at_sale) → total_revenue (product ⨝ product_id sale_item) |
| | 38 | |
| | 39 | RevenueCalculations ← π product_name, total_revenue, |
| | 40 | (RunningSum(total_revenue) / TotalSum(total_revenue)) → cumulative_share |
| | 41 | (τ total_revenue DESC (ProductRevenue)) |
| | 42 | |
| | 43 | Result ← π product_name, |
| | 44 | ROUND(total_revenue, 2) → revenue_mkd, |
| | 45 | CASE(cumulative_share ≤ 0.80 → 'A', |
| | 46 | cumulative_share ≤ 0.95 → 'B', |
| | 47 | ELSE → 'C') → abc_classification |
| | 48 | (RevenueCalculations) |
| | 49 | }}} |
| | 50 | |
| | 51 | ---- |
| | 52 | |
| | 53 | == 2. Месечен тренд на продажба (Monthly Sales Growth Trend) == |
| | 54 | |
| | 55 | Ги споредува приходите од тековниот месец со претходниот за да го пресмета процентот на раст или пад на бизнисот. |
| | 56 | |
| | 57 | {{{#!sql |
| | 58 | WITH MonthlySales AS ( |
| | 59 | SELECT |
| | 60 | DATE_TRUNC('month', date_time) AS sales_month, |
| | 61 | SUM(total_amount) AS revenue |
| | 62 | FROM sale |
| | 63 | GROUP BY 1 |
| | 64 | ) |
| | 65 | SELECT |
| | 66 | sales_month, |
| | 67 | revenue AS current_month_revenue, |
| | 68 | LAG(revenue) OVER (ORDER BY sales_month) AS previous_month_revenue, |
| | 69 | ROUND(((revenue - LAG(revenue) OVER (ORDER BY sales_month)) / |
| | 70 | NULLIF(LAG(revenue) OVER (ORDER BY sales_month), 0)) * 100, 2) AS growth_percentage |
| | 71 | FROM MonthlySales; |
| | 72 | }}} |
| | 73 | |
| | 74 | '''Релациона алгебра:''' |
| | 75 | {{{ |
| | 76 | MonthlySales ← γ DATE_TRUNC('month', date_time) → sales_month; |
| | 77 | SUM(total_amount) → revenue (sale) |
| | 78 | |
| | 79 | Result ← π sales_month, |
| | 80 | revenue → current_month_revenue, |
| | 81 | LAG(revenue) → previous_month_revenue, |
| | 82 | ROUND(((revenue - LAG(revenue)) / NULLIF(LAG(revenue), 0)) × 100, 2) → growth_percentage |
| | 83 | (τ sales_month ASC (MonthlySales)) |
| | 84 | }}} |
| | 85 | |
| | 86 | ---- |
| | 87 | |
| | 88 | == 3. Состојба на залихи и критични точки (Inventory Health & Reorder Levels) == |
| | 89 | |
| | 90 | Ги идентификува производите што се под минимумот во магацините и дава аларм за итна набавка. |
| | 91 | |
| | 92 | {{{#!sql |
| | 93 | SELECT |
| | 94 | p.name AS product_name, |
| | 95 | w.name AS warehouse_location, |
| | 96 | ws.quantity_on_hand, |
| | 97 | p.reorder_level, |
| | 98 | CASE |
| | 99 | WHEN ws.quantity_on_hand <= p.reorder_level THEN 'CRITICAL: REORDER' |
| | 100 | WHEN ws.quantity_on_hand <= p.reorder_level * 1.5 THEN 'WARNING: LOW' |
| | 101 | ELSE 'HEALTHY' |
| | 102 | END AS stock_status |
| | 103 | FROM product p |
| | 104 | JOIN warehouse_stock ws ON p.product_id = ws.product_id |
| | 105 | JOIN warehouse w ON ws.warehouse_id = w.warehouse_id; |
| | 106 | }}} |
| | 107 | |
| | 108 | '''Релациона алгебра:''' |
| | 109 | {{{ |
| | 110 | Temp1 ← product ⨝ product_id=product_id warehouse_stock |
| | 111 | |
| | 112 | Temp2 ← Temp1 ⨝ warehouse_id=warehouse_id warehouse |
| | 113 | |
| | 114 | Result ← π product.name → product_name, |
| | 115 | warehouse.name → warehouse_location, |
| | 116 | quantity_on_hand, |
| | 117 | reorder_level, |
| | 118 | CASE(quantity_on_hand ≤ reorder_level → 'CRITICAL: REORDER', |
| | 119 | quantity_on_hand ≤ reorder_level × 1.5 → 'WARNING: LOW', |
| | 120 | ELSE → 'HEALTHY') → stock_status |
| | 121 | (Temp2) |
| | 122 | }}} |
| | 123 | |
| | 124 | ---- |
| | 125 | |
| | 126 | == 4. Перформанси на добавувачите (Supplier Performance Matrix) == |
| | 127 | |
| | 128 | Го мери доцнењето на испораките од добавувачите и ја пресметува нивната доверливост во исполнување на роковите. |
| | 129 | |
| | 130 | {{{#!sql |
| | 131 | SELECT |
| | 132 | s.name AS supplier_name, |
| | 133 | COUNT(po.po_id) AS total_orders, |
| | 134 | AVG(po.actual_delivery_date - po.expected_delivery_date) AS average_delay_days, |
| | 135 | ROUND(COUNT(CASE WHEN po.actual_delivery_date <= po.expected_delivery_date THEN 1 END) * 100.0 / COUNT(*), 2) AS on_time_delivery_rate |
| | 136 | FROM supplier s |
| | 137 | JOIN purchase_order po ON s.supplier_id = po.supplier_id |
| | 138 | WHERE po.status = 'DELIVERED' |
| | 139 | GROUP BY s.name; |
| | 140 | }}} |
| | 141 | |
| | 142 | '''Релациона алгебра:''' |
| | 143 | {{{ |
| | 144 | Temp1 ← supplier ⨝ supplier_id=supplier_id purchase_order |
| | 145 | |
| | 146 | Temp2 ← σ status='DELIVERED' (Temp1) |
| | 147 | |
| | 148 | Result ← γ supplier.name → supplier_name; |
| | 149 | COUNT(po_id) → total_orders, |
| | 150 | AVG(actual_delivery_date - expected_delivery_date) → average_delay_days, |
| | 151 | ROUND(COUNT(actual_delivery_date ≤ expected_delivery_date) × 100.0 / COUNT(*), 2) → on_time_delivery_rate |
| | 152 | (Temp2) |
| | 153 | }}} |
| | 154 | |
| | 155 | ---- |
| | 156 | |
| | 157 | == 5. RFM Анализа на клиенти (Customer RFM Segmentation) == |
| | 158 | |
| | 159 | Ги сегментира купувачите според тоа кога последен пат купувале, колку често се враќаат и колку пари потрошиле. |
| | 160 | |
| | 161 | {{{#!sql |
| | 162 | SELECT |
| | 163 | c.name AS customer_name, |
| | 164 | EXTRACT(DAY FROM CURRENT_TIMESTAMP - MAX(s.date_time)) AS recency_days, |
| | 165 | COUNT(s.sale_id) AS frequency_count, |
| | 166 | SUM(s.total_amount) AS monetary_total, |
| | 167 | NTILE(5) OVER (ORDER BY MAX(s.date_time)) AS r_score, |
| | 168 | NTILE(5) OVER (ORDER BY COUNT(s.sale_id)) AS f_score, |
| | 169 | NTILE(5) OVER (ORDER BY SUM(s.total_amount)) AS m_score |
| | 170 | FROM customer c |
| | 171 | JOIN sale s USING (customer_id) |
| | 172 | GROUP BY c.customer_id, c.name; |
| | 173 | }}} |
| | 174 | |
| | 175 | '''Релациона алгебра:''' |
| | 176 | {{{ |
| | 177 | Temp1 ← customer ⨝ customer_id=customer_id sale |
| | 178 | |
| | 179 | Result ← γ customer_id, name → customer_name; |
| | 180 | EXTRACT(DAY FROM CURRENT_TIMESTAMP - MAX(date_time)) → recency_days, |
| | 181 | COUNT(sale_id) → frequency_count, |
| | 182 | SUM(total_amount) → monetary_total, |
| | 183 | NTILE(5, MAX(date_time)) → r_score, |
| | 184 | NTILE(5, COUNT(sale_id)) → f_score, |
| | 185 | NTILE(5, SUM(total_amount)) → m_score |
| | 186 | (Temp1) |
| | 187 | }}} |
| | 188 | |
| | 189 | ---- |
| | 190 | |
| | 191 | == 6. Искористеност на капацитетот на магацините (Warehouse Utilization Rate) == |
| | 192 | |
| | 193 | Пресметува колкав процент од физичкиот простор во секој магацин е моментално пополнет. |
| | 194 | |
| | 195 | {{{#!sql |
| | 196 | SELECT |
| | 197 | w.name AS warehouse_name, |
| | 198 | w.capacity AS total_unit_capacity, |
| | 199 | SUM(ws.quantity_on_hand) AS units_in_stock, |
| | 200 | ROUND((SUM(ws.quantity_on_hand)::NUMERIC / w.capacity) * 100, 2) AS occupancy_percentage |
| | 201 | FROM warehouse w |
| | 202 | LEFT JOIN warehouse_stock ws ON w.warehouse_id = ws.warehouse_id |
| | 203 | GROUP BY w.warehouse_id, w.name, w.capacity; |
| | 204 | }}} |
| | 205 | |
| | 206 | '''Релациона алгебра:''' |
| | 207 | {{{ |
| | 208 | Temp1 ← warehouse ⟕ warehouse_id=warehouse_id warehouse_stock |
| | 209 | |
| | 210 | Result ← γ warehouse_id, name → warehouse_name, capacity → total_unit_capacity; |
| | 211 | SUM(quantity_on_hand) → units_in_stock, |
| | 212 | ROUND((SUM(quantity_on_hand) / capacity) × 100, 2) → occupancy_percentage |
| | 213 | (Temp1) |
| | 214 | }}} |
| | 215 | |
| | 216 | ---- |
| | 217 | |
| | 218 | == 7. Анализа на застојни производи == |
| | 219 | |
| | 220 | Ги прикажува производите што не се продале во последните 90 дена и само зафаќаат простор и капитал. |
| | 221 | |
| | 222 | {{{#!sql |
| | 223 | SELECT |
| | 224 | p.name AS product_name, |
| | 225 | p.sku, |
| | 226 | ws.quantity_on_hand, |
| | 227 | COALESCE(MAX(s.date_time)::TEXT, 'NO SALES RECORDED') AS last_sold_date |
| | 228 | FROM product p |
| | 229 | JOIN warehouse_stock ws ON p.product_id = ws.product_id |
| | 230 | LEFT JOIN sale_item si ON p.product_id = si.product_id |
| | 231 | LEFT JOIN sale s ON si.sale_id = s.sale_id |
| | 232 | GROUP BY p.product_id, p.name, p.sku, ws.quantity_on_hand |
| | 233 | HAVING MAX(s.date_time) < CURRENT_DATE - INTERVAL '90 days' OR MAX(s.date_time) IS NULL; |
| | 234 | }}} |
| | 235 | |
| | 236 | '''Релациона алгебра:''' |
| | 237 | {{{ |
| | 238 | Temp1 ← product ⨝ product_id=product_id warehouse_stock |
| | 239 | |
| | 240 | Temp2 ← Temp1 ⟕ product_id=product_id sale_item |
| | 241 | |
| | 242 | Temp3 ← Temp2 ⟕ sale_id=sale_id sale |
| | 243 | |
| | 244 | Temp4 ← γ product_id, name, sku, quantity_on_hand; |
| | 245 | MAX(date_time) → last_sold_date (Temp3) |
| | 246 | |
| | 247 | Result ← π name → product_name, sku, quantity_on_hand, |
| | 248 | COALESCE(last_sold_date::TEXT, 'NO SALES RECORDED') → last_sold_date |
| | 249 | (σ last_sold_date < CURRENT_DATE - 90 ∨ last_sold_date IS NULL (Temp4)) |
| | 250 | }}} |
| | 251 | |
| | 252 | ---- |
| | 253 | |
| | 254 | == 8. Маржа на профит по категорија (Category Profit Margin) == |
| | 255 | |
| | 256 | Ја пресметува разликата помеѓу продажната и набавната цена за секоја категорија производи. |
| | 257 | |
| | 258 | {{{#!sql |
| | 259 | WITH ProductCosts AS ( |
| | 260 | SELECT product_id, AVG(unit_cost) AS average_cost FROM purchase_order_item GROUP BY 1 |
| | 261 | ) |
| | 262 | SELECT |
| | 263 | c.name AS category_name, |
| | 264 | SUM(si.quantity * si.unit_price_at_sale) AS gross_revenue, |
| | 265 | SUM(si.quantity * pc.average_cost) AS total_cost_of_goods, |
| | 266 | ROUND(((SUM(si.quantity * si.unit_price_at_sale) - SUM(si.quantity * pc.average_cost)) / |
| | 267 | NULLIF(SUM(si.quantity * si.unit_price_at_sale), 0)) * 100, 2) AS profit_margin_pct |
| | 268 | FROM category c |
| | 269 | JOIN product p USING (category_id) |
| | 270 | JOIN sale_item si USING (product_id) |
| | 271 | JOIN ProductCosts pc USING (product_id) |
| | 272 | GROUP BY c.name; |
| | 273 | }}} |
| | 274 | |
| | 275 | '''Релациона алгебра:''' |
| | 276 | {{{ |
| | 277 | ProductCosts ← γ product_id; AVG(unit_cost) → average_cost (purchase_order_item) |
| | 278 | |
| | 279 | Temp1 ← category ⨝ category_id=category_id product |
| | 280 | |
| | 281 | Temp2 ← Temp1 ⨝ product_id=product_id sale_item |
| | 282 | |
| | 283 | Temp3 ← Temp2 ⨝ product_id=product_id ProductCosts |
| | 284 | |
| | 285 | Result ← γ category.name → category_name; |
| | 286 | SUM(quantity × unit_price_at_sale) → gross_revenue, |
| | 287 | SUM(quantity × average_cost) → total_cost_of_goods, |
| | 288 | ROUND(((SUM(quantity × unit_price_at_sale) - SUM(quantity × average_cost)) / |
| | 289 | NULLIF(SUM(quantity × unit_price_at_sale), 0)) × 100, 2) → profit_margin_pct |
| | 290 | (Temp3) |
| | 291 | }}} |
| | 292 | |
| | 293 | ---- |
| | 294 | |
| | 295 | == 9. Анализа на „пазарна кошничка" (Product Affinity / Market Basket) == |
| | 296 | |
| | 297 | Открива кои производи купувачите најчесто ги купуваат заедно во иста трансакција. |
| | 298 | |
| | 299 | {{{#!sql |
| | 300 | SELECT |
| | 301 | p1.name AS product_a, |
| | 302 | p2.name AS product_b, |
| | 303 | COUNT(*) AS pair_occurrence_count |
| | 304 | FROM sale_item si1 |
| | 305 | JOIN sale_item si2 ON si1.sale_id = si2.sale_id AND si1.product_id < si2.product_id |
| | 306 | JOIN product p1 ON si1.product_id = p1.product_id |
| | 307 | JOIN product p2 ON si2.product_id = p2.product_id |
| | 308 | GROUP BY 1, 2 |
| | 309 | ORDER BY pair_occurrence_count DESC |
| | 310 | LIMIT 10; |
| | 311 | }}} |
| | 312 | |
| | 313 | '''Релациона алгебра:''' |
| | 314 | {{{ |
| | 315 | Temp1 ← ρ si1 (sale_item) |
| | 316 | |
| | 317 | Temp2 ← ρ si2 (sale_item) |
| | 318 | |
| | 319 | Temp3 ← σ si1.sale_id = si2.sale_id ∧ si1.product_id < si2.product_id (Temp1 × Temp2) |
| | 320 | |
| | 321 | Temp4 ← Temp3 ⨝ si1.product_id = p1.product_id (ρ p1 (product)) |
| | 322 | |
| | 323 | Temp5 ← Temp4 ⨝ si2.product_id = p2.product_id (ρ p2 (product)) |
| | 324 | |
| | 325 | Temp6 ← γ p1.name → product_a, p2.name → product_b; |
| | 326 | COUNT(*) → pair_occurrence_count (Temp5) |
| | 327 | |
| | 328 | Result ← δ 10 (τ pair_occurrence_count DESC (Temp6)) |
| | 329 | }}} |
| | 330 | |
| | 331 | ---- |
| | 332 | |
| | 333 | == 10. Финансиска вредност на залихите (Stock Asset Valuation) == |
| | 334 | |
| | 335 | Дава точен преглед на вкупната парична вредност на производите во магацините врз основа на последните набавни цени. |
| | 336 | |
| | 337 | {{{#!sql |
| | 338 | SELECT |
| | 339 | w.name AS warehouse_location, |
| | 340 | SUM(ws.quantity_on_hand * ( |
| | 341 | SELECT unit_cost FROM purchase_order_item poi |
| | 342 | WHERE poi.product_id = ws.product_id |
| | 343 | ORDER BY po_id DESC LIMIT 1 |
| | 344 | )) AS total_inventory_valuation_mkd |
| | 345 | FROM warehouse w |
| | 346 | JOIN warehouse_stock ws USING (warehouse_id) |
| | 347 | GROUP BY w.name; |
| | 348 | }}} |
| | 349 | |
| | 350 | '''Релациона алгебра:''' |
| | 351 | {{{ |
| | 352 | LatestCost(product_id) ← π product_id, unit_cost |
| | 353 | (δ 1 (τ po_id DESC (purchase_order_item))) |
| | 354 | |
| | 355 | Temp1 ← warehouse ⨝ warehouse_id=warehouse_id warehouse_stock |
| | 356 | |
| | 357 | Temp2 ← Temp1 ⨝ product_id=product_id LatestCost |
| | 358 | |
| | 359 | Result ← γ warehouse.name → warehouse_location; |
| | 360 | SUM(quantity_on_hand × unit_cost) → total_inventory_valuation_mkd |
| | 361 | (Temp2) |
| | 362 | }}} |
| | 363 | |
| | 364 | ---- |
| | 365 | |
| | 366 | '''Символи и операции:''' |
| | 367 | * π - проекција (SELECT) |
| | 368 | * σ - селекција (WHERE) |
| | 369 | * ⨝ - природен спој (JOIN) |
| | 370 | * ⟕ - left outer join (LEFT JOIN) |
| | 371 | * × - Декартов производ (CROSS JOIN) |
| | 372 | * γ - групирање (GROUP BY) |
| | 373 | * τ - сортирање (ORDER BY) |
| | 374 | * δ - лимит (LIMIT) |
| | 375 | * ρ - преименување (AS / alias) |
| | 376 | * ∧ - логичко И (AND) |
| | 377 | * ∨ - логичко ИЛИ (OR) |