Supply and demand analysis at Palestine poultry company (AZIZA) Prepared by: Ahmad Shahrori Bilal Ghazi Osma Dawabshh Waleed Abbas Supervisor Dr. Mohammad Othman Outline Background project goals Problem statement literature review Methodology CURRENT SITUATION Demand forecasting centralization and decentralization Transportation Conclusion Recommendation Background Palestine Poultry Company (AZIZA) was established as a public shareholding company in Nablus – Palestine in 1997 Palestine Industrial Investment Company (PIIC) The leader in the Palestinian poultry sector More than 250 employees Vision pioneer in the poultry industry, achieving Palestinian food security Mission high- quality national products--international quality standards project goals Identify the optimum way for fulfilling the customer order. Reduce the amount of poultry waste. Determine the optimal distance for each cluster. Obtain the best way to deal with retailers. Problem statement Determine the proper demand for each segment by subjective methods. Centralization technique not adopted impacted in CSL. literature review perishable products: value of the products deteriorate dramatically with time. literature review Methodology CURRENT SITUATION Current situation Demand forecasting Forecast types Forecasting Method Applicability Moving average No trend or seasonality Simple exponential smoothing No trend or seasonality Holt’s method Trend but no seasonality Winter’s method Trend and seasonality Demand Month Demand(chicken) January 243,209 February 283,620 March 110,144 April 291,321 May 209,462 June 309,364 July 324,586 August 300,839 September 233,612 October 195,096 November 341,484 December 210,851 January 309440 February 296767 March 188755 April 105304 May 409655 June 112845 July 317174 august 228381 Period New demand (chicken) 1 337749 2 262509.3 3 328035.7 4 312583.3 5 422633.3 6 409034.3 7 298644 8 357720 9 324679 10 408362.3 11 323681.3 12 168222.3 13 447270 14 286679.3 15 334105.7 Demand for 40 days New demand for 40 days period 337749 262509.33333333331 328035.66666666663 312583.33333333331 422633.33333333331 409034.33333333326 298644 357720 324679 408362.33333333331 323681.33333333331 168222.33333333331 447270 286679.33333333331 334105.66666666663 Moving average Moving average Period time Forecast(units or chicken) 16 309,069 17 309,069 18 309,069 19 309,069 moving average figure Demand Dt 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 422633.33333333331 409034.33333333326 298644 357720 324679 408362.33333333331 323681.33333333331 168222.33333333331 447270 286679.33333333331 334105.66666666663 Forecast Ft 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 310219.33333333331 331440.41666666663 368071.66666666663 360723.75 372007.91666666663 347519.33333333331 347351.33333333331 353610.66666666663 306236.24999999994 336884 306463.25 309069.33333333326 309069.33333333326 309069.33333333326 309069.33333333326 Simple exponential Simple EXPONENTISAL Period Forecast (units or chicken) 16 333,262 17 333,262 18 333,262 19 333,262 α=0.1 Simple Exponential smoothing Demand Dt 337749 262509.33333333331 328035.66666666663 312583.33333333331 422633.33333333331 409034.33333333326 298644 357720 324679 408362.33333333331 323681.33333333331 168222.33333333331 447270 286679.33333333331 334105.66666666663 Forecast Ft 334793.93333333335 335089.44000000006 327831.42933333339 327851.85306666669 326325.00109333335 335955.83431733336 3432 63.6842189334 338801.71579704009 340693.54421733611 339092.08979560254 346019.11414937564 343785.33606777142 326229.03579432762 338333.13221489487 333167.75232673873 333261.54376073153 333261.54376073153 333261.54376073153 333261.54376073153 Trend-corrected exponential smoothing (holt’s model) Deseasonalized demand Lₒ=343844 Tₒ=-858.13 trend-exponential smoothing Period time Units 16 328,384 17 327,401 18 326,419 19 325,437 α=0.1 and β=0.1 Trend-exponential smoothing Demand Dt 337749 262509.33333333331 328035.66666666663 312583.33333333331 422633.33333333331 409034.33333333326 298644 357720 324679 408362.33333333331 323681.33333333331 168222.33333333331 447270 286679.33333333331 334105.66666666663 Forecast Ft 342985.44230769231 341498.93827838835 331057.32588644698 328152.07488263748 323680.74081488949 332640.59200428514 340872.43290132226 336397.48771729594 338704.08729732624 337195.42517340707 345629.30075841164 344312.72943641414 324060.10732455482 336201.71194405702 330079.64186272788 329392.93261894374 328303.62089476571 327214.30917058769 326124.99744640966 325035.68572223163 Forecast Results Forecasting Method MAD Four-period moving average 72,595 Simple exponential smoothing 52,050 Holt's model 53,174 centralization and decentralization centralization and decentralization CM: manufacture cost/ Kg (AZIZA) CR: retailer cost (Price for AZIZA) P: price for retailer to the end costumer D: forecasted demand for year 2019 centralization and decentralization customer service level (CSL*) for retailers = = Probability (demand≤ O*) Co: Cost of overstocking by one unit Cu: Cost of under stocking by one unit CSL*: Optimal cycle service level O*: Corresponding optimal order size centralization and decentralization Month Forecasted Demand (in Kg) January 2,704 February 2,803 March 2,836 April 2,867 June 2,941 July 3,038 August 3,064 September 3,146 October 3,275 Normality test decentralization AZIZA Retailers Demand CM= 7NIS/Kg CR= 12NIS/Kg P = 14NIS/Kg Price of over stock = 13 NIS/Kg Overstock cost =1NIS/Kg D ~ N (3241, 294.7)   Cost of overstocking by one kilogram = 14-13 = 1NIS /Kg Cost of under stocking by one kilogram = 14 – 12 = 2 NIS/Kg   Cost of overstocking by one kilogram = 1NIS /Kg   decentralization result Profit of manufacture (AZIZA) = O* (CR – CM) = 16839.68 Profit of retailers = 6480.912NIS Total profit = 16839.68+6480.912 = 23320.592NIS Expected overstock = 191.7728 Kg Expected under stock = 64.84 Kg CSL =.66 By use Excel O* = NORMINV (CSL*, μ, σ,) = 3367.935 Centralization CR= 7NIS /Kg P = 14 NIS/Kg D ~ N (3241, 294.7)   AZIZA Retailers Demand centralization results Cost of overstocking by one kilogram = 1NIS /Kg Cost of under stocking by one kilogram = 14 – 7= 7 NIS/Kg   CSL = = Probability (demand≤ O*) By use Excel O* = NORMINV (CSL*, μ, σ,) = 3580.008 Profit of manufacture (AZIZA) = O* (P – CM) = 25060.06  Profit of retailer = 22685.35 NIS Total profit = 25060.06 + 22685.35 = 47745.41NIS Expected over stock = 357.297 Kg Expected under stock = 18.28 Kg Comparing between centralization Decentralization Centralization Comparison 0.666 0.875 CSL 23320.592 47745.41 Total Profit 3580.008 3367.935 Optimal Quantity Transportation Model Why is Transportation importance? Limitations and constrains Why using transportation in AZIZA? Model Objective Function: Min constrains Subjected t j = 2, …, n ……………………………… (2) i = 2, …, n ……………………………… (3) p = 1, …., n, j  = 1, …., NV…………. (4) k = 1, …, NV ……………………. (5) k = 1, …, NV………. (6) ∈ {0, 1} Conclusion Analyze the supply and the demand of AZIZA company by using different scientific models. Using scientific forecasting techniques to predict the demand, applied different mathematical models to increase AZIZA supply chain profitability and customers’ satisfaction. Applying the mathematical models, we used real data that collected from AZIZA company. Recommendation Increase customer service level decreasing the product cost as much as possible Implement the mode Take the consideration into forecast image3.png image4.jpg image5.emf Selecting project and company Developing project time frame planCollecting data Analyzing data Applying a proper model for each drivers of supply chain Definition the problem Microsoft_Visio_Drawing.vsdx Selecting project and company Developing project time frame plan Collecting data Analyzing data Applying a proper model for each drivers of supply chain Definition the problem /docProps/thumbnail.emf image2.png image6.png image7.emf Period DemandLevelForecastErrorAbsoluteMean Squared t D t L t F t E t ErrorError A t 1337,749 2262,509 3328,036 4312,583310,219 5422,633331,440310,219-112,414112,41412,636,907,396112,4142727-1 6409,034368,072331,440-77,59477,5949,328,861,65095,0041923-2 7298,644360,724368,07269,42869,4287,825,974,73386,4792323-1.39 8357,720372,008360,7243,0043,0045,871,736,67865,610117-1.79 9324,679347,519372,00847,32947,3295,145,394,61361,9541517-1.13 10408,362347,351347,519-60,84360,8434,904,807,28661,7691517-2.12 11323,681353,611347,35123,67023,6704,284,158,94556,326715-1.91 12168,222306,236353,611185,388185,3888,044,743,34472,459110271.08 13447270336,884306,236-141,034141,0349,360,940,59980,0783228-0.79 14286679306,463336,88450,20550,2058,676,897,39477,0911827-0.17 15334106309,069306,463-27,64227,6427,957,552,46872,595825-0.56 16309,069 17309,069 18309,069 19309,069 MAD t % ErrorMAPE t TS t image8.emf Period t Demand D t Level L t Forecast F t Error E t Absolute Error A t Mean Squared Error MSE t MAD t % ErrorMAPE t TS t 0334,794 1 337749 335,089334,794-2,9552,9558,732,4192,95511-1 2 262509 327,831335,08972,58072,5802,638,302,15137,76828141.84 3 328036 327,852327,831-2042041,758,882,00525,2460102.75 4 312583 326,325327,85215,26915,2691,377,443,42822,752583.72 5 422633 335,956326,325-96,30896,3082,957,013,71437,4632311-0.31 6 409034 343,264335,956-73,07873,0783,354,255,93143,3991812-1.95 7 298644 338,802343,26444,62044,6203,159,493,11543,5731513-0.92 8 357720 340,694338,802-18,91818,9182,809,294,16140,492512-1.46 9 324679 339,092340,69416,01516,0152,525,646,54637,772511-1.14 10 408362 346,019339,092-69,27069,2702,752,918,55540,9221712-2.74 11 323681 343,785346,01922,33822,3382,548,014,72839,232711-2.29 12 168222 326,229343,785175,563175,5634,904,210,82850,593104191.69 13 447270 338,333326,229 -121,041 121,0415,653,957,30456,0122720-0.63 14 286679 333,168338,33351,65451,6545,440,682,84955,70118190.29 15 334106 333,262333,168-9389385,078,029,30552,0500180.29 16 333,262  17333,262  18333,262  19333,262 image9.emf Period t Demand D t Deseasonalized Demand 1 337749 2 262509 297700.8 3 328036 307791 4 312583 343958.9 5 422633 391721.1 6 409034 384836.5 7 298644 341010.6 8 357720 334690.8 9 324679 353860.1 10 408362 366271.3 11 323681 305986.8 12 168222 276849 13 447270 337360.4 14 286679 338683.6 15 334106 image10.png image10.emf Period t Demand D t Level L t Trend T t Forecast F t Error E t Absolute Error A t Mean Squared Error MSE t MAD t % ErrorMAPE t TS t 0343844-858.1 1 337749 342,462-910342,9855,2365,23627,420,3285,236221 2 262509 333,647-1,701341,55179,04279,0423,137,526,62542,13930162 3 328036 331,555-1,740331,9463,9113,9112,096,781,81629,3961113 4 312583 328,092-1,912329,81517,23217,2321,646,819,96326,35569.594 5 422633 335,825-948326,180-96,45496,4543,178,120,45240,3752312.240.22 6 409034 342,293-206334,877-74,15774,1573,564,981,09746,0051813.22-1.42 7 298644 337,742-641342,08743,44343,4433,325,307,33345,6391513.41-0.48 8 357720 339,164-434337,102-20,61820,6182,962,783,00442,512612.45-1 9 324679 337,324-575338,72914,05014,0502,655,518,78939,349411.55-0.72 10 408362 343,910141336,749-71,61371,6132,902,812,31242,5761812.15-2.35 11 323681 342,015-63344,05220,37020,3702,676,642,76640,557611.62-1.96 12 168222 324,579-1,800341,952173,730173,7304,968,756,17051,65510319.251.82 13 447270 335,228-555322,779-124,491124,4915,778,694,70057,2572819.91-0.53 14 286679 329,874-1,035334,67347,99447,9945,530,461,22756,5961719.690.31 15 334106 329,366-982328,839-5,2675,2675,163,612,94253,174218.480.23 16328,384 17327,401 18326,419 19325,437 20324,455 image7.png image11.wmf 4 0 0 0 3 7 5 0 3 5 0 0 3 2 5 0 3 0 0 0 2 7 5 0 2 5 0 0 9 9 9 5 9 0 8 0 7 0 6 0 5 0 4 0 3 0 2 0 1 0 5 1 d e m a n d P e r c e n t M e a n 3 2 4 1 S t D e v 2 9 4 . 7 N 2 1 A D 0 . 2 7 3 P - V a l u e 0 . 6 3 2 P r o b a b i l i t y P l o t o f d e m a n d N o r m a l oleObject1.bin image12.png image13.png image14.png image15.png image1.png