Data Analyst in Manufacturing & Supply Chain India 2026

SELECT
  production_date,
  line_id,
  shift,
  planned_minutes,
  downtime_minutes,
  (planned_minutes - downtime_minutes)              AS run_time_minutes,
  ROUND(100.0 * (planned_minutes - downtime_minutes)
        / NULLIF(planned_minutes, 0), 1)            AS availability_pct,
  actual_units_produced,
  ROUND(60.0 / cycle_time_minutes)                  AS theoretical_rate_per_hour,
  ROUND(100.0 * actual_units_produced /
        NULLIF(
          (planned_minutes - downtime_minutes) * (60.0 / cycle_time_minutes) / 60
        , 0), 1)                                    AS performance_pct,
  good_units,
  ROUND(100.0 * good_units
        / NULLIF(actual_units_produced, 0), 1)      AS quality_pct,
  -- OEE = A × P × Q / 10000 (since each is in %)
  ROUND(
    (100.0 * (planned_minutes - downtime_minutes) / NULLIF(planned_minutes, 0))
    * (100.0 * actual_units_produced /
        NULLIF(
          (planned_minutes - downtime_minutes) * (60.0 / cycle_time_minutes) / 60
        , 0))
    * (100.0 * good_units / NULLIF(actual_units_produced, 0))
    / 10000, 1
  )                                                 AS oee_pct
FROM production_log
WHERE production_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY)
ORDER BY production_date DESC, line_id, shift;

WITH item_revenue AS (
  SELECT
    material_code,
    material_name,
    SUM(consumption_value)     AS annual_value,
    SUM(consumption_qty)       AS annual_qty,
    AVG(unit_cost)             AS avg_unit_cost
  FROM inventory_movements
  WHERE movement_type = 'consumption'
    AND posting_date >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
  GROUP BY material_code, material_name
),
total_value AS (SELECT SUM(annual_value) AS grand_total FROM item_revenue),
ranked AS (
  SELECT *,
    SUM(annual_value) OVER (ORDER BY annual_value DESC) AS running_total,
    (SELECT grand_total FROM total_value)               AS grand_total
  FROM item_revenue
)
SELECT
  material_code, material_name, annual_value, annual_qty, avg_unit_cost,
  ROUND(100.0 * annual_value / grand_total, 2)         AS value_pct,
  ROUND(100.0 * running_total / grand_total, 1)        AS cumulative_pct,
  CASE
    WHEN 100.0 * running_total / grand_total <= 80 THEN 'A'  -- top 80% value
    WHEN 100.0 * running_total / grand_total <= 95 THEN 'B'  -- next 15%
    ELSE 'C'                                                  -- bottom 5%
  END                                                  AS abc_class
FROM ranked
ORDER BY annual_value DESC;

-- ── Stockout risk: items below reorder point ─────────────
SELECT
  m.material_code, m.material_name, m.abc_class,
  i.current_stock_qty,
  m.reorder_point,
  m.avg_daily_consumption,
  ROUND(i.current_stock_qty / NULLIF(m.avg_daily_consumption, 0), 0)
                                                       AS days_of_stock
FROM inventory_master m
JOIN current_stock i USING (material_code)
WHERE i.current_stock_qty <= m.reorder_point
ORDER BY abc_class, days_of_stock ASC;

What does a data analyst do in a manufacturing company in India?

Data analysts in Indian manufacturing companies work across production, quality, supply chain, and sales functions. Key responsibilities: production analytics — tracking daily output vs planned production, downtime analysis, line efficiency, shift-wise performance; quality analytics — defect rates by line, machine, shift, and material batch, root cause analysis for quality failures; supply chain analytics — raw material inventory levels, supplier on-time delivery rates, procurement cost analysis; demand planning — using historical sales and seasonality to forecast production requirements; and MIS reporting — daily, weekly, monthly performance dashboards for plant managers and leadership. Most manufacturing analysts use Excel heavily, SQL for querying ERP systems (SAP, Oracle), and Power BI for dashboards.

What is OEE and why is it important for manufacturing data analysts in India?

OEE stands for Overall Equipment Effectiveness — the single most important manufacturing KPI. OEE = Availability × Performance × Quality. Availability: what % of planned production time was the machine actually running (downtime reduces this). Performance: what % of theoretical maximum output was achieved while running (slow running reduces this). Quality: what % of output was good (defects reduce this). OEE of 85% is considered world-class in discrete manufacturing; most Indian factories target 65-80%. Data analysts calculate OEE from machine logs and production records, identify which factor (availability, performance, or quality) is dragging OEE down, and help maintenance and production teams prioritise improvements. OEE analysis in SQL and Power BI is a specific skill that manufacturing analytics job descriptions frequently mention.

What is the salary of a data analyst in manufacturing and supply chain in India in 2026?

Data analyst salaries in Indian manufacturing and supply chain in 2026: at large FMCG companies (HUL, ITC, Nestlé, Marico, Godrej) — ₹8-16 LPA for commercial and supply chain analytics roles; at automotive companies (Maruti Suzuki, Hero MotoCorp, Tata Motors) — ₹6-13 LPA for plant and supply chain analysts; at industrial manufacturers (L&T, Siemens India, ABB India) — ₹7-14 LPA; at logistics companies (Blue Dart, Delhivery, XpressBees) — ₹7-15 LPA for network and operations analytics; at FMCG companies in Greater Noida and Ghaziabad (consumer goods plants) — ₹5-10 LPA. Manufacturing analytics pays less than BFSI and tech on average, but offers strong domain expertise and stable employment. FMCG companies in particular offer excellent training programs.

What is demand forecasting and how do analysts do it in Indian manufacturing?

Demand forecasting in manufacturing is predicting future product demand to inform production planning, raw material procurement, and inventory management. Indian manufacturing analysts use several methods: simple moving average (average of last 3-6 months of sales — easy to implement in Excel or SQL); exponential smoothing (weighted average that gives more weight to recent data — better for products with trends); seasonal decomposition (splitting demand into trend, seasonality, and residual — important for FMCG products with festival seasonality like Diwali or Navratri); and causal forecasting (using external variables like monsoon, festival dates, GDP as predictors — done in Python with regression or Prophet). Most Indian manufacturing companies still use Excel-based forecasting with some manual judgement layered on top. Analysts who can implement even basic Python forecasting (Facebook Prophet is popular) are highly valued.

How does SAP data work for manufacturing analytics in India?

SAP is the dominant ERP system at large Indian manufacturing companies. SAP stores all production orders, inventory movements, procurement records, and quality data. Data analysts typically access SAP data in two ways: SAP transaction codes (T-codes) — running standard SAP reports like MB52 (warehouse stocks), ME2M (purchase orders by material), CO03 (production order details); or extracting data from SAP tables into Excel or a data warehouse using SE16N (table browser) or SQVI (quick viewer). Common SAP tables analysts use: MARA (material master), MSEG (goods movements), EKKO/EKPO (purchase orders), AUFK (production orders), QMEL (quality notifications). Analysts do not need to be SAP developers — reading, extracting, and cleaning SAP data is an analyst skill; building SAP modules is a developer skill.

Which manufacturing and supply chain companies hire data analysts near Noida and Greater Noida?

Major manufacturing and supply chain analytics employers in the Noida/Greater Noida belt in 2026: Greater Noida Industrial Area — automotive component manufacturers, pharma API plants, electronics assembly (Samsung Electronics plant), textile and garment manufacturers; Noida Sector 62 and nearby — HCL manufacturing analytics, tech hardware companies; Ghaziabad — Asian Paints plant analytics, FMCG contract manufacturers; Yamuna Expressway — automotive OEMs and tier-1 suppliers (Maruti Suzuki vendor plants, Honda assembly nearby); Delhi NCR broadly — HUL supply chain analytics team, ITC supply chain, Nestlé India analytics, Marico analytics, Britannia analytics. Many of these companies hire through campus recruitment at engineering colleges — analytics and industrial engineering graduates from colleges near Greater Noida have a direct recruitment pathway.

What Excel and data skills do manufacturing companies test in analytics interviews in India?

Manufacturing analytics interviews in India typically test: Excel — pivot tables and pivot charts (for production and quality MIS), VLOOKUP/INDEX-MATCH (for combining data from multiple sheets), basic VBA macros (for automating repetitive MIS reports, still common at mid-size manufacturers), conditional formatting and dashboards; SQL — basic to intermediate queries (SELECT, GROUP BY, JOIN, CASE WHEN), specifically querying ERP-style tables; domain knowledge — understanding OEE, cycle time, throughput, downtime categories, ABC inventory classification, FIFO vs FEFO; and problem-solving — given a dataset with production records, identify which line/shift/machine is underperforming. Power BI is increasingly tested, especially at FMCG companies. Python is a bonus but not yet standard in most manufacturing interviews outside large FMCG companies.