Syntax Literate : Jurnal Ilmiah Indonesia p–ISSN: 2541-0849

  e-ISSN : 2548-1398

  Vol. 6, No. 1, Januari 2021

 

INVENTORY MANAGEMENT EVALUATION AND INVENTORY FORECAST USING EOQ

 

Rorim Panday dan Dovina Navanti

Universitas Bhayangkara Jakarta Raya, Indonesia

Email: indripan@gmail.com dan dovina_navanti@yahoo.com

 

Abstract

The fashion industry that is gamis in Indonesia is growing rapidly because the majority of the population is Moslem. Elzatta is a company that does business on Moslem clothing, one of its products is the gamis. The company is experiencing problems with stockpiling in warehouses, because of models that were not sold, as well as outdated models. With the accumulation of products in warehouses in 2017 and 2018, many products will be damaged. For this reason, the company runs a buy one get one business strategy and sells products at low prices. As a result, the company suffered a substantial loss. For this reason, it is necessary to evaluate the inventory management that has been carried out using the EOQ model. For 2019, it is necessary to plan the number of products to be sold and apply the EOQ model. The results of evaluations in 2017 and 2018, by using EOQ the company could save 64.78% for 2017 and 63.40% for 2018. Whereas for 2019, after forecasting the number of sales using the seasonal model, sales projections are similar to the number of sales in the previous years, so that the number of products needed for a single order is 1364 pcs.   

 

Keywords: economic order quantity; forecasting; seasonal model

 

Coresponden Author

Email: indripan@gmail.com

Artikel dengan akses terbuka dibawah lisensi

 

Introduction

The development of the moslem fashion industry that is gamis in Indonesia is growing rapidly because the majority of the population is Moslem. This dress is used to cover genitalia, especially Moslem women, has the potential to continue to grow. Moslem fashion companies in Indonesia are diverse and many, one of which is Elzatta, a subsidiary of Elcorps, which produces hijab, gamis, tunic, socks, cuffs, ciput, skirt, pants, mukena, and koko clothes. One of the targets of this company is how consumers can easily get the clothes they need. For that the company must be supported by a good distribution and marketing system, so the company can compete with other companies in capturing new customers. Elzatta is a brand that has been developing for more than 20 years in Moslem fashion retail, built-in 2012. In 2015 Elzatta has established one hundred and five stores throughout Indonesia building togetherness with partners for mutual benefit.

One of Elzatta Gallery is located in Pondok Ungu Permai, Bekasi City, using an online system through Instagram and Facebook and a conventional distribution system, as well as providing opportunities for customers who want to become agents. Elzatta Gallery changes catalog every four months or always releases the latest models of Moslem clothing such as gamis, hijab, tunic, and sturdy clothes. For ordering products done once a year with the delivery of goods more than 1 time, submitted to the head office in Bandung.

Each product is required to order for each model at least 6 pcs for the gamis, tunic, koko, and hijab products. From these orders, every year there is excess product stock, resulting in a buildup of products in the warehouse. This has an impact on finding the products needed to be difficult and requires a relatively long time. Another impact can damage the product because it is too dense in the existing product warehouse. Due to the buildup, Elzatta Gallery had to sell products at low prices, even sold by way of Buy One Get One Free or by holding promos to give customers gifts. The following is the actual excess stock data from the Elzatta Pondok Ungu Permai Gallery.


 

Table 1

Elzatta Gallery Over-Stock Products in 2017 and 2018

Product

Stock (pcs)

2017

2018

Gamis

162

185

 

               Resource : Galeri Elzatta Pondok Ungu Permai

 


Gamis products have excess stock in 2017 amounted to 162 pcs and in 2018 amounted to 185 pcs, this resulted in the product having to be sold as a promo at a discount of 30% - 40%. From the above background, it can be proposed the formulation of the problem that how to prevent the inventory of Moslem fashion products at the Elzatta Pondok Ungu Permai Gallery not to pile up? The purpose of this study was to find out how to optimize the inventory of Moslem fashion products at Elzatta Pondok Ungu Permai Gallery. In this study focused on the inventory of Gamis Moslem fashion.

 

Literature Review

Inventory management is part of production and operations management, therefore managing inventory is closely related to production costs (Panday, Rachmat, & Navanti, 2020).

Inventory management is a management system related to order, reorder time, number of items to be ordered, average inventory level. The purpose of inventory management is to maximize service to customers in anticipation of meeting demand, maximizing the efficiency of purchases in production, minimizing stock investment, maximizing profits in activities that produce goods or services. Too much inventory will cause increased inventory costs, while too little inventory will result in increased ordering costs and excessive ordering frequency. (Panday et al., 2020) Type of inventory according to (Heizer, J., & Render, 2014) (Hillier & Lieberman, 2010) (Blumenfeld, 2009)) as raw material inventory, work-in-process inventory, maintenance repair operating, finish-good inventory.

Some of the benefits of inventory in meeting company need, namely: 1. Minimizing the risk of late delivery of raw materials or goods. 2. Reducing the risk if the raw materials or goods ordered are not good, 3. Reducing the risk of rising goods prices or inflation, 4. To save raw materials produced seasonally, 5. Get profit from purchases based on quantity discounts and 6. Provide services to customers with the availability of goods needed. According to (Heizer, J., & Render, 2014) (Hillier & Lieberman, 2010) (Blumenfeld, 2009), There are three types of costs in inventory, among others:

1.     Holding cost.

2.    Ordering cost 

3.    Setup cost

 

EOQ (Economic Order Quantity)

EOQ is one of the inventory management models, to determine the number of goods / raw materials with minimal costs (Heizer, J., & Render, 2014) (Hillier & Lieberman, 2010) (Blumenfeld, 2009) (Kalaiarasi, 2011). The aim of the EOQ method is to achieve the efficiency of inventory levels by low cost and easily adjusted according to company needs. (Yuliana, Topowijono, & Sudjana, 2016) (Yopan Maulana, 2018) (Saragi & Setyorini, 2014) (Fahmi Sulaiman, 2015).

According to (Heizer, J., & Render, 2014)  Economic Order Quantity (EOQ) is one of the oldest and most widely known inventory control techniques, this inventory control method answers 2 (two) important questions, when to order and how much to order. The EOQ must meet the following assumptions (Maisuriya & Bhathawala, 2013) (Heizer, J., & Render, 2014) (Hillier & Lieberman, 2010):

1.    The number of requests known to be relatively constant and independent.

2.    The waiting time between order and receipt of an order is known and is constant.

3.    Supplies are immediately received and complete in full. In other words, ordered supplies arrive in one group at a time.

4.    Quantity discounts are not available.

5.    Variable costs are just the cost of ordering and the cost of storing inventory for a certain time.

6.    Out of stock can be completely avoided if the order is made promptly.

According to (Heizer, J., & Render, 2014) (Hillier & Lieberman, 2010) (Blumenfeld, 2009) (Onawumi, Oluleye, 2011) (Birbil, Bulbnul, J.B.G.Frenk, n.d.) (Tibrewala & Kleinstein, 2000) to determine the amount of economic order, use the following formulas:


 


Table 2

The formula of Economic Order Quantity

EOQ or Q⃰: Optimal order amount (in units)

D: Number of demand per year (in units).

S: Ordering cost.

H: Holding cost per unit.

N: Frequency of orders for one year

TCC: total holding cost

TOC: total ordering cost

Q: EOQ

H: ordering cost

D: demand

S: holding cost

TC: Total cost

ROP: Reorder point

L: Lead time

d: rate of need per day

SS: safety stock

X: The number of goods needed

x ̅: Average amount of goods needed

n: Amount of data

SD: Standard deviation

Z: Safety factor

 or


Forecasting Seasonal Model

Seasonal models are used for time-series data that have seasonal properties. Seasonal data is data that has the value of changing data at certain times / certain months so that seasonal data has an index called the seasonal index. Seasonal data can be said to be abnormal data in certain months, therefore the data must be removed from the seasonality, which is called deseasonalization. To become a data of normalization using the formula:

St  =  a (Dt/It-L) + (1-a) St-1

Then the seasonal index is calculated using the formula:

It   =  g (Dt/St) + (1 – g) It-L

Then the forecasting is done using the following formula:

 

Ft+1  = St It-L

Where:

L = Length of season

I  = Seasonal Index

a = deseasonalization Constanta

g = Reducer constants for seasonal indexes

 

Prior research

Research using EOQ to optimize inventory and costs has been used in research (Wahyudi, 2015) to sandals inventory at Samarinda's New Era store, (Nurhasanah, 2012) conducting Solar Inventory Analysis Using the Economic Order Quantity Method (EOQ) at PT. Anugrah Bara Kaltim, (Fahmi Sulaiman, 2015) researching Raw Material Inventory Control Using the EOQ Method in a furniture company, (Gede Agus Darmawan, Wayan Cipta, 2015) the application of Economic Order Quantity (EOQ) in the management of flour raw material inventories at the Pia Ariawan business in Banyuning Village, (Asrori, 2013) on Sengon wood raw material inventory PT. Abhirama Kresna with the EOQ method, and (Indrayati, 2007) in the analysis of raw material inventory control by the EOQ method at PT. Tipota Furnishings Jepara. (Rorim Panday; Hernawati, 2015) use EOQ in the inventory of raw materials for Akadril cough syrup.  From the previous research above it can be concluded that the studies are still in line with the research conducted by the author.

 

Methodology

This research is a quantitative study, conducted at the Elzatta Pondok Ungu Permai Gallery, Bekasi, from March 2019 to May. The calculation method uses Economic Order Quantity, and forecasting. The data needed is inventory data which includes sales data, ordering costs, storage costs, and product ordering data. After collecting all data, a calculation is made using the Economic Order Quantity formula. Then a comparative analysis is carried out on the implementation of inventory management and inventory forecasting for the next year using a seasonal model.

 

Result and Discussion

 Actual sales of gamis products in 2017 and 2018.

 


Table 3

Data Sales Gamis Products 2017 and 2018

Month

Gamis Sales 2017

Gamis Sales 2018

January

146

106

February

128

98

March

231

128

April

254

179

May

342

382

June

495

486

July

260

427

August

123

146

September

203

216

October

197

203

November

186

163

December

218

172

Jumlah

2.783

2.706

Sources : Galeri Elzatta Pondok Ungu Permai

 


At table 3, the sale of gamis products in 2017 amounted to 2,783 and in 2018 amounted to 2,706. Holding costs are costs incurred concerning holding an inventory of goods, by formula according to (Heizer, J., & Render, 2014) is:

H = (Total holding cost/Total Sales of product)

Ordering costs are costs incurred for the activities of ordering goods, since ordering until the availability of goods in the warehouse. For ordering products, the company makes an order to Bandung using a private car. The formula of the booking fee according to (Heizer, J., & Render, 2014) is:

S= (Total ordering cost/Frequency of ordering)

 Order frequency = 1 time in one year. The following are the 2017 ordering and holding costs for 2018.


 

Table 4

Data holding and ordering costs

Types of holding costs

 The year 2017

 The year 2018

Electricity cost

9.600.000 IDR

9.865.000 IDR

Number of Gamis

3.015 pcs

2.950 pcs

Holding cost

3.184 IDR/pcs

3.344 IDR/pcs

Type of ordering cost

 

 

Transportation cost

750.000 IDR

850.000 IDR

Sum

10.350.000 IDR

10.715.000 IDR

Source: Data processed, 2019

The computation EOQ, dan ROP

The computation of EOQ gamis product in the year 2017 and  year 2018

Table 5

EOQ calculation results

 

EOQ 2017

EOQ 2018

EOQ

1.145 pcs

1.173 pcs

Frequency

2,4  3 kali

2,3  ≈ 3 kali

Interval ordering time (T)

104 days

104 days

Ordering cost/ year

Rp 1.822.883

Rp 1.961.063

Holding cost /year

Rp 1.822.883

Rp 1.961.063

Total cost/year

Rp 3.645.766

Rp 3.922.126

231,92  232

225,5225

Standard deviation

98,75

125,45

Safety stock

162,94 163 pcs

206,99  207 pcs

L= Lead time

30 days

30 days

d =Average sale per day

7,73 ≈ 8 pcs per-day

7,51  8 pcs per-day

ROP= Reorder Point

403 pcs

447 pcs

Source: Data processed, 2019

Comparison of actual inventory with 2017 EOQ inventory and 2018 EOQ inventory

 

Table 6

Comparison of Actual Inventories and EOQ Inventories

 

EOQ Inventory 2017

EOQ Inventory 2018

Actual

EOQ

Actual

EOQ

Sales

3.015 pcs

1.145 pcs

2.950 pcs

1.173 pcs

Frequency order

1 time

3 time

1 time

3 time

Time inteval

-

104 days

-

104 days

Total Cost

10.350.000IDR

3.645.766IDR

10.715.000IDR

3.922.126IDR

Lead Time

30 days

30 days

30 days

30 days

Safety Stock

-

163 pcs

-

207 pcs

ROP

-

403Pcs

-

447 pcs

Source: Data processed, 2019


For year 2017 EOQ total cost become more efficient at   6,704,234 IDR or 64.78%, for the order frequency of 3 times, where each order is 1145 pcs. For year 2018 EOQ total cost becomes more efficient at 6,792,874 IDR or 63.40%, for the frequency of ordering 3 times, where each order is 1173 pcs.

 


Ordering cost and holding cost of the year 2019


Table 7

Costs of 2019

Ordering cost of the product

Transportation cost

950.000 IDR

Holding cost

Electricity cost

9.875.000 IDR

Purchasing of Gamis

3.165 pcs

Holding cost (H) Per pcs Gamis

3.120 IDR/pcs

Source: Data processed, 2019


Sales Forecasting, EOQ and ROP, and Analyses of  Gamis in 2019

Sales forecasting for 2019 uses the Seasonal model, because the data for 2017 and 2018 show a similar graph (see Figure 1 and Figure 2), therefore the data is seasonal. Based on these reasons, for 2019, the data is forecast using the seasonal model (see Figure 3 and Table 8).

 

 


 

 

 


Table 8

result of the seasonal model computation

Gamis sales

 Month

Data

St

It

Ft

2017

January

146

 

0.629527

 

February

128

 

0.551914

 

March

231

 

0.996033

 

April

254

 

1.095205

 

May

342

 

1.474646

 

June

495

 

2.134357

 

July

260

 

1.121076

 

August

123

 

0.530355

 

September

203

 

0.875302

 

October

197

 

0.849431

 

November

186

 

0.802001

 

December

218

218

0.939979

 

2018

January

106

213.038

0.589938

137.237

February

98

209.4906

0.526681

117.5788

March

128

201.3925

0.887896

208.6596

April

179

197.5972

1.038409

220.5661

May

382

203.742

1.594729

291.3861

June

486

206.1382

2.201342

434.8582

July

427

223.6127

1.357619

231.0966

August

146

228.7802

0.562699

118.5942

September

216

230.5794

0.893743

200.2517

October

203

231.4198

0.85776

195.8612

November

163

228.602

0.775309

185.5988

December

172

224.0401

0.888301

214.8811

2019

January

330

257.5741

0.797312

132.1698

February

207

271.1195

0.597727

135.6593

March

132

258.8741

0.774497

240.7258

April

146

247.0467

0.90418

268.8171

May

531

255.6392

1.739454

393.9724

June

396

248.0643

2.019848

562.7494

July

343.5

248.5595

1.364922

336.7768

August

134.5

247.6062

0.55685

139.8642

September

209.5

246.2864

0.88081

221.2962

October

200

244.9743

0.845355

211.2545

November

174.5

242.984

0.758163

189.9309

December

195

240.6376

0.864915

215.8431

 

191.8633

 

Figure 1

 Sales data

Figure 2

Sales data in 2018


  Figure 3

Sales data forescasting in 2019

Table 9

Data Forecasting of Gamis Product at 2019 From July to December

Month

Forecast

 

January

330

Data

February

207

Data

March

132

Data

April

146

Data

May

531

Data

June

396

Data

July

337

Forecast

August

140

Forecast

September

221

Forecast

October

211

Forecast

November

190

Forecast

December

216

Forecast

Sum  (D)

3056.9656

 

                            Source: Data processed, 2019

 

EOQ computation of gamis at 2019


Using forecast data for 2019, the computation of EOQ gamis product as follows:


 

Table 10

The result of EOQ at 2019

EOQ

1364

Frequency

2.24~ 3 times

Interval ordering time (T)

104.00 days

Ordering cost/ year

2,128,500.61 IDR

Holding cost /year

2,128,500.61 IDR

Total cost/year

4,257,001.21 IDR

255 pcs

Standard deviation

115.3821794 pcs

Safety stock

190.380596 ≈ 190

L= Lead time

30 days

d =Average sale per day

8.49 ≈ 9 pcs per day

ROP= Reorder Point

460 pcs

 


From the calculation of 2019 product inventory using EOQ, it can be concluded as follows:

a. The amount of demand for 1 year for gamis products is 3057 pcs.

b.The optimal number of orders is 1364 pcs.

c. The frequency of ordering for 1 year is 3 times

d.The total cost for one year is 4,257,001.21 IDR

e. Safety Stock of gamis products as much as 190 pcs.

f.  The company will place an order again if there are only 460 pcs left of the gamis.

 

Conclusions

From the results of previous inventory calculations, the following conclusions can be made: 1) By using EOQ, the results of evaluations in 2017 and 2018, the number of purchases of goods becomes less, with more frequency, but provides efficiency in significant financing. 2) By implementing EOQ, a very safe Safety Stock is obtained for Inventory in 2017 and 2018. 3) Elzatta Gallery Management can reorder when the 2017 gamis stock is 403 pcs, and to know 2018 at 447 pcs. 4) The optimal number of orders in 12019 is 1364 pcs, with Safety Stock of gamis products as much as 190 pcs, and reorder point on 460 pcs left.

 

 

 

 

 

 

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