DDMRP Dynamic Buffer Management Dashboard

Buffer Zone Overview — Net Flow Position by SKU

R Code

# DDMRP Buffer Zone Visualization in R (ggplot2)
library(ggplot2)
library(dplyr)
library(tidyr)

# Define SKU data
skus <- data.frame(
  sku  = c("PCB-100","DSP-200","BAT-300","HSG-400","CAM-500","CHG-600","SPK-700","ANT-800"),
  adu  = c(45, 40, 60, 50, 35, 55, 42, 38),
  dlt  = c(12, 18, 8, 10, 20, 6, 9, 15),
  ltf  = c(0.35, 0.40, 0.30, 0.30, 0.45, 0.25, 0.30, 0.40),
  vf   = c(0.50, 0.60, 0.40, 0.35, 0.55, 0.30, 0.40, 0.55),
  moq  = c(200, 150, 500, 300, 100, 1000, 200, 100),
  oh   = c(380, 120, 850, 520, 95, 1200, 280, 60),
  oo   = c(500, 400, 0, 300, 350, 0, 200, 0),
  qd   = c(210, 195, 180, 240, 175, 165, 130, 190)
)

# Calculate buffer zones
skus <- skus %>% mutate(
  red_base   = adu * dlt * ltf * vf,
  red_safety = red_base * ltf,
  total_red  = red_base + red_safety,
  yellow     = adu * dlt,
  green      = pmax(adu * dlt * ltf, moq),
  tog        = total_red + yellow + green,
  toy        = total_red + yellow,
  tor        = total_red,
  nfp        = oh + oo - qd,
  status     = case_when(nfp <= tor ~ "Red", nfp <= toy ~ "Yellow", TRUE ~ "Green")
)

# Reshape for stacked bar chart
zones <- skus %>%
  select(sku, total_red, yellow, green, nfp, oh) %>%
  pivot_longer(cols = c(total_red, yellow, green),
               names_to = "zone", values_to = "value") %>%
  mutate(zone = factor(zone, levels = c("green","yellow","total_red")))

ggplot(zones, aes(x = sku, y = value, fill = zone)) +
  geom_col(position = "stack", width = 0.6) +
  geom_point(data = skus, aes(x = sku, y = nfp, fill = NULL),
             shape = 23, size = 3, fill = "black") +
  scale_fill_manual(values = c(
    total_red = "#E74C3C", yellow = "#F0AD4E", green = "#2ECC71"
  )) +
  labs(title = "Buffer Zone Overview", y = "Units") +
  theme_minimal() +
  theme(panel.grid.major.x = element_blank(),
        legend.position = "top")

Buffer Detail — Zone Calculations & Replenishment Signals

R Code

# Buffer detail table with kableExtra
library(kableExtra)

skus %>%
  mutate(
    order_qty = ifelse(nfp < toy, ceiling(tog - nfp), 0),
    pct_of_tog = round(nfp / tog * 100, 1)
  ) %>%
  select(sku, adu, dlt, red_base, red_safety, total_red,
         yellow, green, tog, toy, tor, oh, oo, qd, nfp,
         status, order_qty) %>%
  kbl(col.names = c("SKU","ADU","DLT","Red Base","Red Safety",
                     "Total Red","Yellow","Green","TOG","TOY",
                     "TOR","On-Hand","On-Order","Qual.Dem",
                     "NFP","Status","Order Qty"),
      digits = 0) %>%
  kable_styling(bootstrap_options = c("striped","condensed")) %>%
  row_spec(which(skus$status == "Red"), background = "#FDF2F0") %>%
  row_spec(which(skus$status == "Yellow"), background = "#FFF9F0")

Dynamic Buffer Adjustment — Normal vs. Seasonal Peak (DAF = 1.4)

R Code

# Dynamic Adjustment Factor comparison
library(ggplot2)
library(patchwork)

daf <- 1.4
selected <- skus %>% filter(sku %in% c("PCB-100","DSP-200","CAM-500"))

# Calculate peak zones with DAF applied to ADU
selected_peak <- selected %>% mutate(
  adu_peak     = adu * daf,
  red_base_p   = adu_peak * dlt * ltf * vf,
  red_safety_p = red_base_p * ltf,
  total_red_p  = red_base_p + red_safety_p,
  yellow_p     = adu_peak * dlt,
  green_p      = pmax(adu_peak * dlt * ltf, moq),
  tog_p        = total_red_p + yellow_p + green_p
)

# Side-by-side stacked bars (Normal vs Peak)
# Use facet_wrap with scenario column
bind_normal <- selected %>%
  select(sku, total_red, yellow, green) %>%
  mutate(scenario = "Normal")
bind_peak <- selected_peak %>%
  transmute(sku, total_red = total_red_p,
            yellow = yellow_p, green = green_p,
            scenario = "Peak (DAF=1.4)")
combined <- bind_rows(bind_normal, bind_peak)

ggplot(combined %>% pivot_longer(total_red:green),
       aes(x = scenario, y = value, fill = name)) +
  geom_col(width = 0.5) +
  facet_wrap(~sku, scales = "free_y") +
  scale_fill_manual(values = c(
    total_red="#E74C3C", yellow="#F0AD4E", green="#2ECC71")) +
  theme_minimal()

Net Flow Position Timeline — PCB-100 Main Circuit Board (30 Days)

R Code

# NFP Timeline for PCB-100 over 30 days
library(ggplot2)

# Simulate daily NFP with consumption and order arrivals
set.seed(42)
days <- 1:30
nfp_series <- numeric(30)
oh <- 670  # starting NFP for PCB-100
tor <- 127.6; toy <- 667.6; tog <- 867.6
orders_placed <- c()
orders_arrive <- c()

for (d in days) {
  consumption <- rpois(1, 45)  # ~ADU
  arrival <- ifelse(d %in% orders_arrive,
                    tog - oh, 0)  # simplified
  oh <- oh - consumption + arrival
  if (oh < toy) {
    orders_placed <- c(orders_placed, d)
    orders_arrive <- c(orders_arrive, d + 12)
  }
  nfp_series[d] <- oh
}

df <- data.frame(day = days, nfp = nfp_series)

ggplot(df, aes(x = day, y = nfp)) +
  annotate("rect", xmin=0, xmax=31, ymin=0,
           ymax=tor, fill="#E74C3C", alpha=0.2) +
  annotate("rect", xmin=0, xmax=31, ymin=tor,
           ymax=toy, fill="#F0AD4E", alpha=0.2) +
  annotate("rect", xmin=0, xmax=31, ymin=toy,
           ymax=tog, fill="#2ECC71", alpha=0.15) +
  geom_line(color = "#2C3E50", size = 0.8) +
  geom_hline(yintercept = c(tor, toy, tog),
             linetype = "dashed", color = "#666") +
  geom_point(data = df[df$day %in% orders_placed,],
             shape = 24, size = 3, fill = "#C0392B") +
  theme_minimal() +
  labs(title = "PCB-100 NFP Over 30 Days",
       x = "Day", y = "Net Flow Position")

Buffer Penetration Heat Map — Last 4 Weeks

R Code

# Buffer Penetration Heatmap
library(ggplot2)
library(reshape2)

# penetration = (TOG - NFP) / TOG * 100
# 0% = full buffer, 100% = empty buffer
pen_data <- data.frame(
  sku = rep(skus$sku, each = 4),
  week = rep(paste("Wk", 1:4), 8),
  penetration = c(
    18, 22, 20, 23,   # PCB-100
    55, 62, 68, 74,   # DSP-200
    28, 30, 35, 36,   # BAT-300
    25, 28, 30, 33,   # HSG-400
    60, 65, 72, 79,   # CAM-500
    15, 18, 20, 24,   # CHG-600
    38, 42, 44, 45,   # SPK-700
    72, 78, 88, 113   # ANT-800 (past 100% = negative NFP)
  )
)

pen_data$zone <- cut(pen_data$penetration,
  breaks = c(-Inf, 33, 66, Inf),
  labels = c("Green", "Yellow", "Red"))

ggplot(pen_data, aes(x = week, y = sku, fill = zone)) +
  geom_tile(color = "white", size = 1) +
  geom_text(aes(label = paste0(penetration, "%")),
            size = 3.5) +
  scale_fill_manual(values = c(
    Green="#D5EDDB", Yellow="#FFF0CC", Red="#F8D7DA")) +
  theme_minimal() +
  labs(title = "Buffer Penetration Heatmap")

Replenishment Signal Board — Prioritized Actions

R Code

# Replenishment signal board
signals <- skus %>%
  mutate(
    order_qty = ifelse(nfp < toy, ceiling(tog - nfp), 0),
    priority  = case_when(
      nfp <= tor ~ "CRITICAL",
      nfp <= toy ~ "WARNING",
      TRUE       ~ "OK"
    ),
    delivery_date = Sys.Date() + dlt
  ) %>%
  filter(order_qty > 0) %>%
  arrange(desc(priority == "CRITICAL"), order_qty)

# Display as formatted table or card layout
signals %>%
  select(sku, priority, order_qty, nfp, tog,
         delivery_date) %>%
  kbl() %>%
  kable_styling()

How DDMRP Buffers Work

Red Base

ADU x DLT x Lead Time Factor x Variability Factor

Red Safety

Red Base x Lead Time Factor

Total Red Zone (TOR)

Red Base + Red Safety

Yellow Zone

ADU x DLT

Green Zone

MAX(ADU x DLT x LT Factor, MOQ, Imposed Order Cycle)

Top of Green (TOG) = Order-Up-To Level

Red + Yellow + Green

Net Flow Position (NFP)

On-Hand + On-Order - Qualified Demand

Replenishment Order

When NFP < TOY, order (TOG - NFP) units

Demand Driven MRP decouples the supply chain by placing strategic inventory buffers at key positions. Unlike traditional MRP which pushes forecasted demand through every level of the BOM, DDMRP uses actual demand signals to drive replenishment.

The buffer is divided into three zones. The Green Zone controls order frequency -- a wider green zone means less frequent but larger orders. The Yellow Zone represents the normal demand coverage during lead time. The Red Zone is safety stock that absorbs variability in both demand and supply.

When the Net Flow Position (on-hand + on-order - qualified demand) drops below the Top of Yellow, a replenishment order is generated to bring the NFP back to the Top of Green. This creates a natural pull-based replenishment system that responds to actual consumption.

R Code

# Annotated DDMRP buffer diagram
library(ggplot2)

# Single buffer diagram with annotations
zones <- data.frame(
  zone  = factor(c("Red Base","Red Safety","Yellow","Green"),
                  levels = c("Red Base","Red Safety","Yellow","Green")),
  value = c(95, 33, 540, 200)  # PCB-100 example
)

ggplot(zones, aes(x = "Buffer", y = value, fill = zone)) +
  geom_col(width = 0.4) +
  scale_fill_manual(values = c(
    "Red Base"   = "#C0392B",
    "Red Safety" = "#E74C3C",
    "Yellow"     = "#F0AD4E",
    "Green"      = "#2ECC71"
  )) +
  geom_hline(yintercept = cumsum(zones$value)[c(2,3,4)],
             linetype = "dashed") +
  annotate("text", x = 1.4,
           y = cumsum(zones$value)[c(2,3,4)],
           label = c("TOR","TOY (Order Point)","TOG (Order-Up-To)"),
           hjust = 0, size = 3.5) +
  coord_flip() +
  theme_minimal() +
  labs(title = "DDMRP Buffer Structure",
       y = "Units")