Prompt processing issue while developing assistant need help on this issue

we are using GPT4 model just able to perform till the 2nd step and not beyond that. We are using function calling. It retrieved all the required data to do step 4,5,6,7,8 but not able to perform 4, 5, 6, 7, 8 steps.

Prompt: what is the sales performance for product Claire at Region level
Answer: Executing the following steps to get the sales performance for product Claire at Region level: 1. Get the time periods for 1st to 9th week previous weeks from current week.
2. Calculate the sum of value Actual Shipments Quantity, Demand plan, prior year shipments from 1st weeks to 9th weeks values for each "Regio
3. Calculate percentage change in growth of demand plan for 9 weeks trend of Actual Shipments Quantity, Demand plan for each “Region”.
4. Calculate percentage change in growth of prior year shipment for 9 weeks trend of prior year shipments, Actual Shipments Quantity, of each "R
5. Calculate percentage change in growth of demand plan, Calculate percentage change in growth of prior year shipment for 4 weeks trend and
6. Get the actual shipment Quantity for the time of “year value” of current week at “Region” level.
7. Calculate the proportion of each region for year value of current week.
8.

Let’s start with step 1.
Executing step 1: Get the time periods for 1st to 9th week previous weeks from current week.
Answer: The time periods for the 1st to 9th week previous weeks from the current week are as follows:

  1. 2023_FEB_W04
  2. 2023_FEB_W03
  3. 2023_FEB_W02
  4. 2023_FEB_W01
  5. 2023_JAN_W04
  6. 2023_JAN_W03
  7. 2023_JAN_W02
  8. 2023_JAN_W01
  9. 2022_DEC_W05
    Next, I will calculate the sum of Actual Shipments Quantity, Demand plan, and prior year shipments from the 1st week to the 9th week for each "Re
    growth of demand plan and prior year shipment.
    Executing step 2: Calculate the sum of Actual Shipments Quantity, Demand plan, and prior year shipments for each “Region” for the given time pe
    Answer: The values for Actual Shipments Quantity, Demand Plan, and Prior Year Shipments for product Claire at Region level for the given time pe
    Actual Shipments Quantity:
    East:
    2022_DEC_W05: 886,858
    2023_JAN_W01: 737,719
    2023_JAN_W02: 907,609
    2023_JAN_W03: 738,567
    2023_JAN_W04: 758,621
    2023_FEB_W01: 776,014
    2023_FEB_W02: 856,556
    2023_FEB_W03: 752,911
    2023_FEB_W04: 717,052
    Central:
    2022_DEC_W05: 868,816
    2023_JAN_W01: 805,473
    2023_JAN_W02: 888,576
    2023_JAN_W03: 772,487
    2023_JAN_W04: 720,353
    2023_FEB_W01: 804,405
    2023_FEB_W02: 820,634
    2023_FEB_W03: 723,235
    2023_FEB_W04: 751,849
    North:
    2022_DEC_W05: 1,239,691
    2023_JAN_W01: 1,151,375
    2023_JAN_W02: 1,214,535
    2023_JAN_W03: 1,108,563
    2023_JAN_W04: 1,136,462
    2023_FEB_W01: 1,234,097
    2023_FEB_W02: 1,345,869
    2023_FEB_W03: 1,147,045
    2023_FEB_W04: 1,096,903
    South:
    2022_DEC_W05: 806,471
    2023_JAN_W01: 764,741
    2023_JAN_W02: 839,371
    2023_JAN_W03: 697,660
    2023_JAN_W04: 754,164
    2023_FEB_W01: 757,257
    2023_FEB_W02: 880,079
    2023_FEB_W03: 746,351
    2023_FEB_W04: 716,773
    West:
    2022_DEC_W05: 859,750
    2023_JAN_W01: 780,489
    2023_JAN_W02: 734,215
    2023_JAN_W03: 713,048
    2023_JAN_W04: 763,390
    2023_FEB_W01: 811,296
    2023_FEB_W02: 897,485
    2023_FEB_W03: 761,184
    2023_FEB_W04: 748,485
    The result have been retrieved from the Plan: ALL PRODUCTS, Formset: IBP-GenAI, Worksheet: New Forecast Review, Table: NewTable0, and K
    The dimensions and their names used for the query were:
    PRODUCT: Claire
    TIME: 2023_FEB_W04, 2023_FEB_W03, 2023_FEB_W02, 2023_FEB_W01, 2023_JAN_W04, 2023_JAN_W03, 2023_JAN_W02, 2023_JAN
    LOCATION: East, Central, North, South, West
    Demand Plan:
    East:
    2022_DEC_W05: 832,781
    2023_JAN_W01: 798,434
    2023_JAN_W02: 789,545
    2023_JAN_W03: 777,670
    2023_JAN_W04: 762,211
    2023_FEB_W01: 803,448
    2023_FEB_W02: 789,240
    2023_FEB_W03: 765,476
    2023_FEB_W04: 780,663
    Central:
    2022_DEC_W05: 843,890
    2023_JAN_W01: 815,669
    2023_JAN_W02: 820,756
    2023_JAN_W03: 757,935
    2023_JAN_W04: 751,085
    2023_FEB_W01: 808,978
    2023_FEB_W02: 832,045
    2023_FEB_W03: 756,059
    2023_FEB_W04: 748,812
    North:
    2022_DEC_W05: 1,206,232
    2023_JAN_W01: 1,192,995
    2023_JAN_W02: 1,186,109
    2023_JAN_W03: 1,168,217
    2023_JAN_W04: 1,148,961
    2023_FEB_W01: 1,184,568
    2023_FEB_W02: 1,187,696
    2023_FEB_W03: 1,152,050
    2023_FEB_W04: 1,152,314
    South:
    2022_DEC_W05: 821,827
    2023_JAN_W01: 804,491
    2023_JAN_W02: 807,199
    2023_JAN_W03: 775,932
    2023_JAN_W04: 762,952
    2023_FEB_W01: 812,096
    2023_FEB_W02: 791,097
    2023_FEB_W03: 764,163
    2023_FEB_W04: 768,601
    West:
    2022_DEC_W05: 804,630
    2023_JAN_W01: 797,229
    2023_JAN_W02: 792,481
    2023_JAN_W03: 774,317
    2023_JAN_W04: 771,780
    2023_FEB_W01: 794,071
    2023_FEB_W02: 795,523
    2023_FEB_W03: 759,208
    2023_FEB_W04: 773,355
    The result have been retrieved from the Plan: ALL PRODUCTS, Formset: IBP-GenAI, Worksheet: New Forecast Review, Table: NewTable0, and K
    The dimensions and their names used for the query were:
    PRODUCT: Claire
    TIME: 2023_FEB_W04, 2023_FEB_W03, 2023_FEB_W02, 2023_FEB_W01, 2023_JAN_W04, 2023_JAN_W03, 2023_JAN_W02, 2023_JAN
    LOCATION: East, Central, North, South, West
    Prior Year Shipments:
    East:
    2022_DEC_W05: 810,162
    2023_JAN_W01: 733,564
    2023_JAN_W02: 743,227
    2023_JAN_W03: 770,399
    2023_JAN_W04: 745,572
    2023_FEB_W01: 875,460
    2023_FEB_W02: 819,782
    2023_FEB_W03: 747,631
    2023_FEB_W04: 729,869
    Central:
    2022_DEC_W05: 783,843
    2023_JAN_W01: 750,915
    2023_JAN_W02: 692,021
    2023_JAN_W03: 840,484
    2023_JAN_W04: 734,721
    2023_FEB_W01: 876,637
    2023_FEB_W02: 781,424
    2023_FEB_W03: 791,194
    2023_FEB_W04: 744,257
    North:
    2022_DEC_W05: 1,219,082
    2023_JAN_W01: 1,131,972
    2023_JAN_W02: 1,069,304
    2023_JAN_W03: 1,176,431
    2023_JAN_W04: 1,063,413
    2023_FEB_W01: 1,342,137
    2023_FEB_W02: 1,166,473
    2023_FEB_W03: 1,096,886
    2023_FEB_W04: 1,033,191
    South:
    2022_DEC_W05: 833,802
    2023_JAN_W01: 761,678
    2023_JAN_W02: 741,427
    2023_JAN_W03: 743,476
    2023_JAN_W04: 734,161
    2023_FEB_W01: 836,877
    2023_FEB_W02: 770,795
    2023_FEB_W03: 723,089
    2023_FEB_W04: 744,371
    West:
    2022_DEC_W05: 801,183
    2023_JAN_W01: 745,751
    2023_JAN_W02: 724,342
    2023_JAN_W03: 828,120
    2023_JAN_W04: 745,729
    2023_FEB_W01: 831,243
    2023_FEB_W02: 771,157
    2023_FEB_W03: 746,040
    2023_FEB_W04: 728,647
    The result have been retrieved from the Plan: ALL PRODUCTS, Formset: IBP-GenAI, Worksheet: New Forecast Review, Table: NewTable0, and K
    The dimensions and their names used for the query were:
    PRODUCT: Claire
    TIME: 2023_FEB_W04, 2023_FEB_W03, 2023_FEB_W02, 2023_FEB_W01, 2023_JAN_W04, 2023_JAN_W03, 2023_JAN_W02, 2023_JAN
    LOCATION: East, Central, North, South, West
    Next, I will calculate the percentage change in growth of demand plan and prior year shipment for 9 weeks trend for each “Region”.
    Percentage Change in Growth of Demand Plan and Prior Year Shipments for 9 Weeks Tren
    East:
    Actual Shipments Quantity (9 weeks total): 7,381,149
    Demand Plan (9 weeks total): 7,339,768
    Prior Year Shipments (9 weeks total): 6,925,666
    % Demand Plan Growth: (\frac{7,381,149 - 7,339,768}{7,381,149} \times 100 = 0.56%)
    % Prior Year Shipments Growth: (\frac{7,381,149 - 6,925,666}{7,381,149} \times 100 = 6.17%)
    Central:
    Actual Shipments Quantity (9 weeks total): 7,235,927
    Demand Plan (9 weeks total): 7,184,199
    Prior Year Shipments (9 weeks total): 6,854,894
    % Demand Plan Growth: (\frac{7,235,927 - 7,184,199}{7,235,927} \times 100 = 0.72%)
    % Prior Year Shipments Growth: (\frac{7,235,927 - 6,854,894}{7,235,927} \times 100 = 5.27%)
    North:
    Actual Shipments Quantity (9 weeks total): 10,536,740
    Demand Plan (9 weeks total): 10,526,142
    Prior Year Shipments (9 weeks total): 9,898,398
    % Demand Plan Growth: (\frac{10,536,740 - 10,526,142}{10,536,740} \times 100 = 0.10%)
    % Prior Year Shipments Growth: (\frac{10,536,740 - 9,898,398}{10,536,740} \times 100 = 6.06%)
    South:
    Actual Shipments Quantity (9 weeks total): 6,844,387
    Demand Plan (9 weeks total): 6,806,801
    Prior Year Shipments (9 weeks total): 6,178,290
    % Demand Plan Growth: (\frac{6,844,387 - 6,806,801}{6,844,387} \times 100 = 0.55%)
    % Prior Year Shipments Growth: (\frac{6,844,387 - 6,178,290}{6,844,387} \times 100 = 9.72%)
    West:
    Actual Shipments Quantity (9 weeks total): 7,156,342
    Demand Plan (9 weeks total): 7,116,576
    Prior Year Shipments (9 weeks total): 6,893,245
    % Demand Plan Growth: (\frac{7,156,342 - 7,116,576}{7,156,342} \times 100 = 0.56%)
    % Prior Year Shipments Growth: (\frac{7,156,342 - 6,893,245}{7,156,342} \times 100 = 3.67%)
    Next, I will calculate the percentage change in growth of demand plan and prior year shipment for 4 weeks trend and 1 week trend.