Error in forecasting using arima with multiple regressors

I'm trying to do a multi variate time series forecasting using dynamic regression. The data was collected for 148 weeks with two main variables- Shipment Qty(dependent variable) and Net.Production.Qty(independent variable).

The training data is first 118 weeks and the test data is remaining 30 weeks.

  1. First, I'm going to forecast the Net Production Qty- test set 30 weeks information using Arima with Fourier term
  2. Second, I'm going to generate the Fourier values and best fit model for Shipment test data set
  3. Third, I'm going to forecast the Shipment Qty using Net.Production.Qty forecasted values(test set 30 weeks) and Fourier values of Shipment data set as co-variates.

And this is the error I get - Error in forecast.forecast_ARIMA(bestfit.Shipment, xreg = cbind(final.prod, : Number of regressors does not match fitted model

Could anyone please help me resolve this error

Thanks much

library(tidyverse)
library(dplyr)
library(lubridate)
library(ISOweek)
library(feasts)
library(fable)

Shipment.df<-structure(list(YearWeek = c("201901", "201902", "201903", "201904", 
"201905", "201906", "201907", "201908", "201909", "201910", "201911", 
"201912", "201913", "201914", "201915", "201916", "201917", "201918", 
"201919", "201920", "201921", "201922", "201923", "201924", "201925", 
"201926", "201927", "201928", "201929", "201930", "201931", "201932", 
"201933", "201934", "201935", "201936", "201937", "201938", "201939", 
"201940", "201941", "201942", "201943", "201944", "201945", "201946", 
"201947", "201948", "201949", "201950", "201951", "201952", "202001", 
"202002", "202003", "202004", "202005", "202006", "202007", "202008", 
"202009", "202010", "202011", "202012", "202013", "202014", "202015", 
"202016", "202017", "202018", "202019", "202020", "202021", "202022", 
"202023", "202024", "202025", "202026", "202027", "202028", "202029", 
"202030", "202031", "202032", "202033", "202034", "202035", "202036", 
"202037", "202038", "202039", "202040", "202041", "202042", "202043", 
"202044", "202045", "202046", "202047", "202048", "202049", "202050", 
"202051", "202052", "202053", "202101", "202102", "202103", "202104", 
"202105", "202106", "202107", "202108", "202109", "202110", "202111", 
"202112", "202113", "202114", "202115", "202116", "202117", "202118", 
"202119", "202120", "202121", "202122", "202123", "202124", "202125", 
"202126", "202127", "202128", "202129", "202130", "202131", "202132", 
"202133", "202134", "202135", "202136", "202137", "202138", "202139", 
"202140", "202141", "202142", "202143"), Shipment = c(418, 1442, 
1115, 1203, 1192, 1353, 1191, 1411, 933, 1384, 1362, 1353, 1739, 
1751, 1595, 1380, 1711, 2058, 1843, 1602, 2195, 2159, 2009, 1812, 
2195, 1763, 821, 1892, 1781, 2071, 1789, 1789, 1732, 1384, 1435, 
1247, 1839, 2034, 1963, 1599, 1596, 1548, 1084, 1350, 1856, 1882, 
1979, 1021, 1311, 2031, 1547, 591, 724, 1535, 1268, 1021, 1269, 
1763, 1275, 1411, 1847, 1379, 1606, 1473, 1180, 926, 800, 840, 
1375, 1755, 1902, 1921, 1743, 1275, 1425, 1088, 1416, 1168, 842, 
1185, 1570, 1435, 1209, 1470, 1368, 1926, 1233, 1189, 1245, 1465, 
1226, 887, 1489, 1369, 1358, 1179, 1200, 1226, 1066, 823, 1913, 
2308, 1842, 910, 794, 1098, 1557, 1417, 1851, 1876, 1010, 160, 
1803, 1607, 1185, 1347, 1700, 981, 1191, 1058, 1464, 1513, 1333, 
1169, 1294, 978, 962, 1254, 987, 1290, 758, 436, 579, 636, 614, 
906, 982, 649, 564, 502, 274, 473, 506, 902, 639, 810, 398, 488
), Production = c(0, 198, 1436, 1055, 1396, 1330, 1460, 1628, 
1513, 1673, 1737, 1274, 1726, 1591, 2094, 1411, 2009, 1909, 1759, 
1693, 1748, 1455, 2078, 1717, 1737, 1886, 862, 1382, 1779, 1423, 
1460, 1454, 1347, 1409, 1203, 1235, 1397, 1563, 1411, 1455, 1706, 
688, 1446, 1336, 1618, 1404, 1759, 746, 1560, 1665, 1317, 0, 
441, 1390, 1392, 1180, 1477, 1265, 1485, 1495, 1543, 1584, 1575, 
1609, 1233, 1420, 908, 1008, 1586, 1392, 1385, 1259, 1010, 973, 
1053, 905, 1101, 1196, 891, 1033, 925, 889, 1136, 1058, 1179, 
1047, 967, 900, 904, 986, 1014, 945, 1030, 1066, 1191, 1143, 
1292, 574, 1174, 515, 1296, 1315, 1241, 0, 0, 1182, 1052, 1107, 
1207, 1254, 1055, 258, 1471, 1344, 1353, 1265, 1444, 791, 1397, 
1186, 1264, 1032, 949, 1059, 954, 798, 956, 1074, 1136, 1209, 
975, 833, 994, 1127, 1153, 1202, 1234, 1336, 1484, 1515, 1151, 
1175, 976, 1135, 1272, 869, 1900, 1173), Net.Production.Qty = c(22, 
188, 1428, 1031, 1382, 1368, 1456, 1578, 1463, 1583, 1699, 1318, 
1582, 1537, 2118, 1567, 1961, 1897, 1767, 1603, 1666, 1419, 2186, 
1621, 1677, 1840, 698, 1290, 1411, 927, 1754, 1222, 1411, 1549, 
1491, 1359, 1179, 1945, 1463, 1465, 1764, 764, 810, 1308, 1830, 
1542, 1695, 544, 1482, 1673, 1659, 0, 445, 1358, 1364, 1224, 
1417, 1239, 1387, 1595, 1469, 1624, 1643, 1763, 1217, 1456, 568, 
1290, 1666, 1428, 1327, 773, 1118, 1231, 1143, 921, 1083, 1124, 
935, 903, 937, 849, 1132, 1032, 1143, 1081, 891, 886, 880, 1002, 
1072, 969, 1000, 996, 1243, 1183, 1306, 650, 1226, 553, 1306, 
1379, 1359, 0, 0, 1182, 988, 1099, 1173, 1244, 1039, 254, 1425, 
1318, 1385, 1221, 1364, 739, 1397, 1112, 1160, 924, 971, 1015, 
978, 828, 868, 994, 1090, 1165, 783, 887, 934, 1023, 1045, 1114, 
1052, 1186, 1456, 1401, 1249, 779, 430, 1625, 1498, 883, 1860, 
1101)), row.names = c(NA, 148L), class = "data.frame")
Shipment.df <- Shipment.df %>%
  mutate(isoweek = str_replace(YearWeek,
                               "^(\\d{4})(\\d{2})$",
                               "\\1-W\\2-1"),
         date = ISOweek::ISOweek2date(isoweek))
Shipment2.df<-Shipment.df[,c("Shipment","Production","Net.Production.Qty")]
Shipment.ts<-ts(Shipment2.df,frequency = 365.25/7,start = c(2019,1))
Shipment.df$date<-as.Date(Shipment.df$date)
Shipment.train.df<-with(Shipment.df,Shipment.df[(Shipment.df$date >= "2018-12-31" &
                                                   Shipment.df$date <= "2021-03-29"),])
Net.Production.df<-Shipment.train.df[,c("Net.Production.Qty")]
Net.Production.train.ts<-ts(Net.Production.df,frequency = 365.25/7,start = c(2019,1))
bestfit.Net.Prod <- list(aicc=Inf)
for(K in seq(25))
{
  fit.Net.Prod <- auto.arima(Net.Production.train.ts, xreg=fourier(Net.Production.train.ts, K=K), seasonal=FALSE)
  if(fit.Net.Prod$aicc < bestfit.Net.Prod$aicc)
  {
    bestfit.Net.Prod <- fit.Net.Prod
    bestK.Net.Prod <- K
  }
}
forecast.net.prod<- forecast(bestfit.Net.Prod,xreg = fourier(Net.Production.train.ts,K=bestK.Net.Prod,h=30))
final.prod<-forecast.net.prod$mean

Shipment4.df<-Shipment.train.df[,c("Shipment")]
Shipment.train.ts<-ts(Shipment4.df,frequency = 365.25/7,start = c(2019,1))
bestfit.Shipment <- list(aicc=Inf)
for(K in seq(25))
{
  fit.Shipment <- auto.arima(Shipment.train.ts, xreg=fourier(Shipment.train.ts, K=K), seasonal=FALSE)
  if(fit.Shipment$aicc < bestfit.Shipment$aicc)
  {
    bestfit.Shipment <- fit.Shipment
    bestK.Shipment <- K
  }
}
fourier.shipment<-fourier(Shipment.train.ts,K=bestK.Shipment,h=30)
forecast.shipment<-forecast(bestfit.Shipment,xreg =cbind(final.prod,fourier.shipment),h=30)