Multi-criteria approach to adjust demand forecast for products: application of analytic hierarchy process

Oliveira, Lidiane Cristina de; Pacheco, Bruna Cristine Scarduelli; Piratelli, Claudio Luis

doi:10.1590/0103-6513.20220006

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Research Article • Prod. 32 • 2022 • https://doi.org/10.1590/0103-6513.20220006 copy

Multi-criteria approach to adjust demand forecast for products: application of analytic hierarchy process

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

Investigate whether the results of time series models can be adjusted with the AHP method towards a more assertive forecast.

Originality

Considering demand forecasting as a complex decision-making situation, this research investigated the use of the AHP as a complement to traditional forecasting methods.

Research method

This applied research employed, as main procedures, literature review and mathematical modeling.

Main findings

Two models were proposed that presented satisfactory results: model I reduced the forecast error by 16% in January, 25% in February, 37% in March, 3% in April, and 7% in May; model II reduced it by 17% in January, 21% in February, 29% in March, 2% in April, and 5% in May.

Implications for theory and practice

We conclude that the AHP has the potential to correct the results of time series in the textile industry by allowing the incorporation of quantitative and qualitative variables.

Keywords
Demand forecasting; Analytic hierarchy process; Textile industry

Degree of Importance	Definition	Brief Explanation
1	Equally important	The elements contribute equally to the objective, criterion, and subcriterion.
2	Weakly or slightly more important
3	Moderate importance	Experience and judgment moderately favor one element over the other.
4	Moderate to strong importance
5	Strong importance.	Experience and judgment strongly favor one element over the other.
6	Strong to very strong importance
7	Very strong importance	One element is very strongly favored over the other.
8	Very, very strong importance
9	Extreme	The evidence favoring one element over the other is of the highest possible order of assertion.

1	$a_{12}$	$a_{13}$	. . .	$a_{1 n}$
$\frac{1}{a_{12}}$	1	$a_{23}$	. . .	$a_{2 n}$
$\frac{1}{a_{13}}$	$\frac{1}{a_{23}}$	1	. . .	$a_{3 n}$
. . .	. . .	. . .	. . .	. . .
$\frac{1}{a_{1 n}}$	$\frac{1}{a_{2 n}}$	$\frac{1}{a_{3 n}}$	. . .	1

n	1	2	3	4	5	6	7	8	9	10
IR	0.00	0.00	0.52	0.89	1.11	1.25	1.35	1.40	1.45	1.49

Internal factors	External factors
Product Technology	Seasonality
Promotional Actions	Commemorative Dates
Marketing Activities	Exchange Variation
Product Quality	Fashion Trends
Sales Price	Economy
Punctuality in delivery	Competition
Sales Team Motivation	Unexpected events
Prompt Delivery	Unemployment Level
Customer Relationship	Inflation
Customer Relationship Strengthening	Seasons / Weather
	Customer loss and gain
	Franchise Renovations/ Inaugurations

Name	E1	E2	E3	E4	E5
Sales
Marketing Actions	1.67%	5.11%	1.70%	2.41%	3.03%
Promotional Actions	2.10%	2.84%	2.35%	2.12%	1.51%
Strengthening Channels	2.62%	4.87%	2.63%	1.72%	3.03%
Team Motivation	2.96%	6.04%	2.63%	2.28%	3.03%
On-Time Delivery	4,69%	7.24%	4.71%	5.58%	3.03%
Customer Relationship	2.62%	7.24%	2.63%	2.56%	3.03%
Seasonality
Commemorative Dates	6.57%	3.58%	6.91%	3.78%	3.66%
Seasons of the Year	2.19%	1.79%	2.30%	3.78%	1.83%
Products
Cost	5.56%	8.66%	5.56%	5.56%	3.33%
Quality	5.56%	10.92%	5.56%	5.56%	6.67%
Technology	5.56%	13.75%	5.56%	5.56%	6.67%
Economic Indicators
Inflation	4.82%	0.58%	4.07%	3.99%	4.01%
Unemployment Level	4.82%	1.46%	4.07%	3.99%	4.01%
Exchange rate variation	4.82%	0.92%	4.07%	3.99%	4.01%
Competition	8.08%	2.43%	10.92%	11.98%	12.03%
Unexpected Events	14.46%	7.98%	12.21%	11.98%	12.03%
Openings and Renovations Franchises	6.07%	6.55%	6.17%	5.03%	7.57%
Customer Gains and Losses	9.88%	3.62%	10.92%	11.98%	12.03%
Fashion Trends	4.96%	4.41%	5.03%	6.16%	5.48%

Month	Forecast
January	286,003
February	397,900
March	320,000
April	218,202
May	445,003

Month	Error margin 2020	Arbitrated Variation	Scenarios
January	-69%	-53%	High decline
February	-56%
March	-33%
April	-24%	-14%	Low decline
May	-11%
June	-7%
July	-3%	17%	Low growth
August	19%
September	36%
October	37%	70%	High growth
November	86%
December	89%

Name	E1	VA	E2	VA	E3	VA	E4	VA	E5	VA
Sales
Marketing Actions	1.67%	17%	5.11%	-14%	1.70%	70%	2.41%	17%	3.03%	17%
Promotional Actions	2.10%	17%	2.84%	-14%	2.35%	17%	2.12%	17%	1.51%	17%
Strengthening of Channels	2.62%	17%	4.87%	70%	2.63%	70%	1.72%	17%	3.03%	17%
Team Motivation	2.96%	17%	6.04%	70%	2.63%	17%	2.28%	70%	3.03%	70%
On-Time Delivery	4.69%	-14%	7.24%	-14%	4.71%	-14%	5.58%	-14%	3.03%	-14%
Customer Relationship	2.62%	17%	7.24%	70%	2.63%	70%	2.56%	70%	3.03%	70%
Seasonality
Commemorative Dates	6.57%	17%	3.58%	17%	6.91%	17%	3.78%	17%	3.66%	17%
Seasons of the Year	2.19%	17%	1.79%	17%	2.30%	17%	3.78%	17%	1.83%	17%
Products
Cost	5.56%	-14%	8.66%	-14%	5.56%	-14%	5.56%	-14%	3.33%	-14%
Quality	5.56%	70%	10.92%	17%	5.56%	70%	5.56%	70%	6.67%	70%
Technology	5.56%	17%	13.75%	17%	5.56%	70%	5.56%	17%	6.67%	17%
Economic Indicators
Inflation	4.82%	-14%	0.58%	17%	4.07%	-14%	3.99%	-14%	4.01%	-14%
Unemployment Level	4.82%	-14%	1.46%	-14%	4.07%	-14%	3.99%	-14%	4.01%	-14%
Exchange rate variation	4.82%	-14%	0.92%	17%	4.07%	-14%	3.99%	-14%	4.01%	-14%
Competition	8.08%	17%	2.43%	-14%	10.92%	17%	11.98%	70%	12.03%	17%
Unexpected Events	14.46%	70%	7.98%	-53%	12.21%	17%	11.98%	17%	12.03%	70%
Openings and Renovations Franchises	6.07%	17%	6.55%	17%	6.17%	17%	5.03%	-14%	7.57%	-14%
Customer Gains and Losses	9.88%	17%	3.62%	17%	10.92%	17%	11.98%	17%	12.03%	17%
Fashion Trends	4.96%	17%	4.41%	17%	5.03%	17%	6.16%	17%	5.48%	17%

Month	E1	E2	E3	E4	E5
January	19.95%	12.43%	19.62%	20.13%	22.07%
February	-6.15%	10.23%	2.81%	-0.51%	-21.52%
March	-7.96%	11.35%	-5.02%	-6.10%	-19.46%
April	-1.19%	16.71%	3.37%	-0.21%	-3.28%
May	-0.37%	8.99%	3.37%	0.62%	-5.77%

Expert	Initial forecast (IF)	Adjustment percentage (AP)	Adjusted forecast (AF)	Sales in the Period (SP)	Error margin (IF / SP)	Error margin (AF / SP)
January
E1	286,003	20%	343,204	392,486	-27%	-12.56%
E2	286,003	12%	320,323	392,486	-27%	-18.39%
E3	286,003	20%	343,204	392,486	-27%	-12.56%
E4	286,003	20%	343,204	392,486	-27%	-12.56%
E5	286,003	22%	348,924	392,486	-27%	-11.10%
February
E1	397,900	-6%	374,026	343,810	16%	8.79%
E2	397,900	10%	437,690	343,810	16%	27.31%
E3	397,900	3%	409,837	343,810	16%	19.20%
E4	397,900	-1%	393,921	343,810	16%	14.58%
E5	397,900	-22%	310,362	343,810	16%	-9.73%
March
E1	320,000	-8%	294,400	165,124	94%	78.29%
E2	320,000	11%	355,200	165,124	94%	115.11%
E3	320,000	-5%	304,000	165,124	94%	84.10%
E4	320,000	-6%	300,800	165,124	94%	82.17%
E5	320,000	-19%	259,200	165,124	94%	56.97%
April
E1	218,202	-1%	216,020	211,054	3%	2.35%
E2	218,202	17%	255,296	211,054	3%	20.96%
E3	218,202	3%	224,748	211,054	3%	6.49%
E4	218,202	0%	218,202	211,054	3%	3.39%
E5	218,202	-3%	211,656	211,054	3%	0.29%
May
E1	445,003	0%	445,003	379,624	17%	17.22%
E2	445,003	9%	485,053	379,624	17%	27.7%
E3	445,003	3%	458,353	379,624	17%	20.74%
E4	445,003	1%	449,453	379,624	17%	18.39%
E5	445,003	-6%	418,303	379,624	17%	10.19%

Name	E1	E2	E3	E4	E5	Geometric Average	Normalized Result
Sales
Marketing Actions	1.67%	5.11%	1.70%	2.41%	3.03%	2.54%	2.71%
Promotional Actions	2.10%	2.84%	2.35%	2.12%	1.51%	2.14%	2.28%
Strengthening of Channels	2.62%	4.87%	2.63%	1.72%	3.03%	2.81%	2.99%
Team Motivation	2.96%	6.04%	2.63%	2.28%	3.03%	3.18%	3.39%
On-Time Delivery	4.69%	7.24%	4.71%	5.58%	3.03%	4.86%	5.17%
Customer Relationship	2.62%	7.24%	2.63%	2.56%	3.03%	3.29%	3.51%
Seasonality
Commemorative Dates	6.57%	3.58%	6.91%	3.78%	3.66%	4.68%	4.99%
Seasons of the Year	2.19%	1.79%	2.30%	3.78%	1.83%	2.29%	2.43%
Products
Cost	5.56%	8.66%	5.56%	5.56%	3.33%	5.48%	5.84%
Quality	5.56%	10.92%	5.56%	5.56%	6.67%	6.59%	7.03%
Technology	5.56%	13.75%	5.56%	5.56%	6.67%	6.91%	7.36%
Economic Indicators
Inflation	4.82%	0.58%	4.07%	3.99%	4.01%	2.83%	3.02%
Unemployment Level	4.82%	1.46%	4.07%	3.99%	4.01%	3.41%	3.63%
Exchange rate variation	4.82%	0.92%	4.07%	3.99%	4.01%	3.11%	3.31%
Competition	8.08%	2.43%	10.92%	11.98%	12.03%	7.91%	8.43%
Unexpected Events	14.46%	7.98%	12.21%	11.98%	12.03%	11,52%	12.28%
Openings and Renovations Franchises	6.07%	6.55%	6.17%	5.03%	7.57%	6.22%	6.63%
Customer Gains and Losses	9.88%	3.62%	10.92%	11.98%	12.03%	8.91%	9.50%
Fashion Trends	4.96%	4.41%	5.03%	6.16%	5.48%	5.18%	5.51%
	100.00%	100.00%	100.00%	100.00%	100.00%	93.87%	100.00%

Month	E1	E2	E3	E4	E5
January	20.73%	4.95%	23.00%	20.29%	22.33%
February	-1.30%	0.75%	7.48%	1.51%	-17.90%
March	-4.40%	5.63%	-3.51%	-4.40%	-15.40%
April	2.61%	12.02%	4.53%	0.56%	-1.97%
May	3.50%	12.02%	4.53%	1.45%	-3.93%

Expert	Initial forecast (IF)	Adjustment percentage (AP)	Adjusted forecast (AF)	Sales in the Period (SP)	Error margin (PI / VOP)	Error margin (PA / VOP)
January
E1	286,003	21%	346,064	392,486	-27%	-11.83%
E2	286,003	5%	300,303	392,486	-27%	-23.49%
E3	286,003	23%	351,784	392,486	-27%	-10.37%
E4	286,003	20%	343,204	392,486	-27%	-12.56%
E5	286,003	22%	348,924	392,486	-27%	-11.10%
February
E1	397,900	-1%	393,921	343,810	16%	14.58%
E2	397,900	1%	401,879	343,810	16%	16.89%
E3	397,900	7%	425,753	343,810	16%	23.83%
E4	397,900	2%	405,858	343,810	16%	18.05%
E5	397,900	-18%	326,278	343,810	16%	-5.10%
March
E1	320,000	-4%	307,200	165,124	94%	86.04%
E2	320,000	6%	339,200	165,124	94%	105.42%
E3	320,000	-4%	307,200	165,124	94%	86.04%
E4	320,000	-4%	307,200	165,124	94%	86.04%
E5	320,000	-15%	272,000	165,124	94%	64.72%
April
E1	218,202	3%	224,748	211,054	3%	6.49%
E2	218,202	12%	244,386	211,054	3%	15.79%
E3	218,202	5%	229,112	211,054	3%	8.56%
E4	218,202	1%	220,384	211,054	3%	4.42%
E5	218,202	-2%	213,838	211,054	3%	1.32%
May
E1	445,003	4%	462,803	379,624	17%	21.91%
E2	445,003	12%	498,403	379,624	17%	31.29%
E3	445,003	5%	467,253	379,624	17%	23.08%
E4	445,003	1%	449,453	379,624	17%	18.39%
E5	445,003	-4%	427,203	379,624	17%	12.53%

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