A manufacturer wishes to change from the ordinary production control system to just-in-time
production, in which timely delivery of products to the suppliers is a major goal of the
manufacturer. To do so, the historical/planned production data for 6 years were gathered and
summarized in Table below.
2013 2014 2015 2016 2017 2018
Jan. 260 259 259 279 302 314
Feb. 192 230 233 278 313 350
Mar. 263 295 296 349 386 425
Apr. 253 312 318 383 431 483
May 200 265 265 334 388 450
June 287 301 309 378 431 485
July 199 262 262 321 366 422
Aug. 234 284 293 357 397 446
Sept. 231 310 310 369 413 467
Oct. 235 269 272 334 381 428
Nov. 257 308 314 366 410 467
Dec. 255 285 288 361 416 476
a) Use a three-period moving average to establish a forecast for each month over the six-year
b) Use the simple exponential smoothing method to determine the monthly forecasts for the
six-year period. Assume that α=0.18.
c) Use the double exponential smoothing method to determine the monthly forecasts for the
six-year period. Assume that overall history smooth factor α=0.18 and trend smooth factor γ
d) Use the triple exponential (Holt-Winter's) smoothing method to determine the monthly
forecasts for the six-year period. Assume that overall history smoothing factor α=0.20, trend
smoothing factor β = 0.25, and seasonal index smoothing factor γ = 0.15.
• Print out actual demands, forecasted demands and square errors on one table.
• One table per method.
• Calculate the errors for each month and the cumulated error throughout the six-year
• Commenting on which method is better.
• Submit your PHP code named "[login to view URL]" online
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