INVENTORY MANAGEMENT, LOADING STRATEGY AND WAREHOUSE

CATEGORIZATION - GOLCHHA LUBRICANTS

MIHIR SANGODKAR [B15029] | PULKIT AGARWAL [B15037]

SEPTEMBER 11, 2016

Table of Contents

Introduction .………………………………………………………………………………1

Project Approach …………………………………………………………………………1

Data Collection ..…………………………………………………………………………1

Data Cleaning and Formatting ..…………………………………………………………1

Methodology and Analysis for Inventory Management Strategy ……………………..2

Forecasting………………………………………………………………………...2

Safety Stock and Re-order Point ………………………………………………….3

Cost Analysis ……………………………………………………………………..4

User Interface – Summary………………………………………………………...5

Methodology and Analysis for Loading Mechanism and Warehouse Categorization 5

Problem ...…………………………………………………………………………6

Docking Strategy …………………………………………………………………6

Warehouse Categorization (SKU –Wise) ....……………………………………..7

Future Scope ……………………………………………………………………………...8

References ………………………………………………………………………………...9

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Introduction Golchha Lubricants is currently handling a portfolio

of 1600 -1700 KL of lubricants across Shell, Castrol,

Servo and Mak. The distributor is facing with issues

regarding excess inventory for different SKUs of

different brands. The current inventory management

strategy is constrained with regards to SKU level

planning and only provides a brand level strategy.

Golchha lubricants is shifting its storage to a new

warehouse in Adityapur, capable of supporting 120-

3500 KL of lubricants. The old warehouse housed a

serial queuing model for loading. The system caused

delays of 24-48 hours in delivery of lubricants

causing a loss of sale ranging from 5-8 KL a month

per brand.

The project aims at designing an inventory

management strategy that helps the distributor with

SKU level planning for a month order. The strategy

will also encompass product level categorization

with respect storage to ensure efficient loading. The

construction for the docking system will begin in late

October this year and the distributor wants a docking

system that is scalable and easy to implement with

the constraint of minimal product loss while loading.

Project Approach The project will be divided into 4 stages.

Stage 1: Collection and analysis of inventory and

demand data for different SKUs for the last 2 years.

Stage 2: Detailed analysis of the warehouse facility

with respect to specifications. (This stage will

involve a feasibility study of the docking and

loading mechanisms that can be implemented)

Stage 3: Designing a product level categorization

strategy for storage at the new warehouse.

Stage 4: Designing a coherent SKU level inventory

management strategy and docking mechanism

meeting the prescribed criteria

Project Evaluation Metric

Docking and loading Strategy: Loss of sale should

be minimal: 80 % reduction in loss of sale due to

loading. (In terms of KL sale)

Inventory Management Strategy: No SKU should

remain at the warehouse for more than 3 months.

(Apart from cases where demand fluctuations are

more than 20% for the SKU)

Data Collection The data collection involved gathering data through

the distributor owner, sales officer or distributor

manager. For the purpose of inventory management

the sales force data was collected. SKU wise files

were generated together. The order quantities and On

hand inventory reports were generated. Price for

cases was obtained from the brand brochures

provided to the distributor by the respective area

managers.

The ordering cost data, lead time and ordering time

period was obtained from the order portal for the

respective brands. The holding cost data was

obtained from hard records maintained by the

distributor manager.

The data for the current loading mechanism was

obtained from the loaders, inspectors and distributor

manager at the ware house. Certain warehouse

dimensions were readily available, others had to be

measured. The investment quote was obtained from

the usual contractor that was often employed by the

distributor.

Data Cleaning and formatting The main issue with the data collected was the

structure. It was not available in a format that can be

easily used for analysis. The sales force report

generated a separate PDF for each month for every

SKU. The data for all the 18 months for all SKUs had

to be entered in an array fashion which was time

consuming. The pricing data had to be mapped to the

SKU order data. Demand data had to be calculated

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for all SKUs for each brand for the all the 18 months

using order data and On Hand Inventory data.

The data for wages was ambiguous and had to be

derived from salary quotes by inspectors and loaders.

The scaling factor for overtime was obtained from

the Factories Act 1948: Section 51, 55 to 56 and 59.

The utilization is assumed to be 100% as the breaks

are not measured and ad-hoc in nature. The time

taken for loading and inspection is an average time

quoted by the distributor manager. The warehouse

layout was not readily available. The layout was

created by the team using

http://planner.roomsketcher.com/. Overtime figures

were assumed to be constant.

Methodology and Analysis for

Inventory Management Strategy The estimated order quantity and inventory levels

were based on sales estimate coming from the sales

personnel as there was no particular inventory

management strategy adopted by the lubricant

distributor of Shell, Castrol and Servo. Since the

inventory management was sales driven, it often lead

to stock outs of slow moving goods and often bull-

whip effect was observed due to minor seasonality in

demand. Therefore, there was a need to determine the

forecasting for the lubricants to better understand the

demands of the product. Also, it was essential to

determine the safety stock and re order point in order

to maintain desired inventory level to keep the in-

stock ratio and the fill rate at satisfactory levels. In

order to formulate the inventory management

strategy, following steps were taken:

Step 1: Data Selection – This included taking data of

ordered quantity, closing and opening stocks, price

of the lubricant from the data dump received from the

distributor

Step 2: Data cleaning and Formatting – The data

received was not in the desired format to perform

analysis and calculate the desired metrics. Therefore,

data was cleaned and was brought to required format

through transposing etc. to perform calculations

Step 3: Forecasting – Demand forecasting was

performed using three methods in order to accurately

estimate the demand of the product. This will enable

the distributor to take calculated judgements on the

order quantity

Step 4: Safety Stock and Reorder point – Since the

inventory management is based on fixed time period

model, there was no need to calculate the EOQ while

calculation of safety stock level and reorder point

was required. Fill rate and in stock ratio was also

calculated in order for both actual demand as well as

forecasted demand for comparative purposes.

Step 5: Cost Analysis – There are two costs involved

in the distributors operations i.e. ordering cost and

inventory holding cost. There is no transportation

cost involved of the distributor since the prices

margins in the data are discounted for the

transportation cost.

Since data for sales in volume was not easy to

calculate, average slab cost has been considered for

ordering cost calculation of Castrol branded

lubricants.

Forecasting

In order to forecast the demand for lubricant which

have continuous demand with low seasonality, three

methods were employed. The three methods used

were weighted moving average method, simple

exponential smoothing method and lastly linear

regression method. Quantity sold was taken as the

proxy for demand which was calculated by using

data of orders made and opening stock of this month

versus opening stock of next month

Quantity Sold = Opening stock of month 1 +

Orders made in month 1 – Opening stock of month 2

In order to determine the forecast accuracy, Mean

Square Error (MSE) was used for all the three

techniques. It was observed that linear regression

technique proved to be most accurate for all the three

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brands and hence was chosen as the forecasting

technique to be used for the lubricant category. An

assumption here is that the demand distribution over

a period of time will not alter drastically. For SES

method, optimum alpha was calculated using the

Solver Analysis in Excel but it can be altered by the

distributor.

Safety Stock and Reorder Point

In order to calculate the Safety Stock and Reorder

Point, mean and standard deviation of the demand

across 17 months data point was considered. Here

approximations for the standard deviation were

utilized due to paucity of data as well as

unavailability of the demand distribution type.

Normal distribution was assumed for he lubricant

demand while standard deviation was calculated

using average daily demand for 17 month time

interval. The expected service level was considered

to be 99% and the lead time and order interval was

15 days and 30 days respectively for Castrol and

Page | 4

Shell lubricants while it was 15 days and 15 days for

Servo lubricants. The formulae that were utilized to

calculate the Safety Stock and Reorder Point are as

follows:

ROP = Average Daily Demand * (Lead time + Order

Interval)

Safety Stock = z* Std. Dev. of daily demand *

√(Lead time +Order interval)

The quantity available for a particular month was

calculated by summing up the ordered quantities of a

particular month with the opening stock of the same

month. This along with quantity sold was used to

calculate the in-stock ratio and the fill rate. The fill

rate and in-stock ratio was calculated considering

both the actual quantity sold and forecasted demand.

It was observed that for slow moving goods

(SLOBS) the in stock ratio improved when we

considered the forecasted demand in comparison to

the actual quantity sold while for the continuous

moving goods, the fill rate as well as in-stock ratio

was quite similar.

For in-stock ratio, if the quantity available was less

than the quantity sold, it was considered as an out-of-

stock situation. For the fill rate, average of the ratio

of quantity sold to quantity available of all the

months was considered.

Cost Analysis

In order to calculate the costs, data extracted from the

dump included ordering cost and holding cost

components along with the price per case of each

product. The data for price per unit was mentioned

which was converted to price per case using the

number of units per case for a lubricant. Ordering

cost was fixed for Shell and Servo but it was variable

on both product category and ordered quantity front

for Castrol. For simplicity in calculation and owing

to data unavailability, the slabs for ordered quantity

were discounted using an average price for a

particular product category. The holding cost had

two primary components i.e. cost of damaged goods

and insurance cost which were obtained as a

percentage of price of a lubricant. For calculation of

holding cost, linear consumption of goods across the

time period was considered and thus average

inventory was quantity available in a month divided

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by two. Since the inventory was managed through

fixed order time interval model, the number of

annual orders were fixed to 12 in case of Shell and

Castro and 24 in case of servo lubricants.

Total inventory cost was calculated by summing up

the ordering cost and holding cost for a particular

month. Mean annual total cost was determined. The

safety stock holding cost for each product was

calculated separately in order to understand the cost

associated with keeping the buffer stock at the

warehouse.

User Interface - Summary

A user interface has been created in which the user

needs to enter the SKU code of the respective brand

and the sheets of that brand will be automatically

calculated to give the summary of the results in the

user interface page itself. Some of the summary

statistics include reorder point, safety stock, mean

total cost etc. This sheet will enable the distributor to

have a look at the inventory management strategy at

an overall level and maintain stocks at the desired

level. Only the incremental data needs to be updated

in the working sheets.

Methodology and Analysis for

Loading Mechanism and Warehouse

Categorization The current loading mechanism at the Golchha

Lubricants warehouse operates through 2 loading

bays. The loading bay dimensions are 5m*4m. The

distributor uses Ashok Leyland Boss 1212 LE

Distribution trucks. The trucks have a width of 2.24

m. The docking width assigned for these trucks

currently is 2.5 m. Consequently only 2 trucks can

dock at a single bay with restricted turning circle.

The front and rear loading at the warehouse prevents

any further entry of vehicles while the trucks are

docked. Consequently the other 4 trucks along with

visiting vehicles have to be parked outside the

compound causing traffic disruptions. The

warehouse also has similar gates at the sides. The

ramp and other docking facilities are not present at

the side of the warehouse.

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Each truck is currently loaded by 2 loaders. The

loaders operate on a 10 hour shift. Each truck on an

average takes 3 hours to be loaded. The trucks and

the products at each bay are inspected by an

inspector. The inspection process takes on an average

30 minutes per bay per loading batch (comprising of

the 2 trucks). During the data collection, the

distributor stated that the inspection process for up to

5 trucks can be completed in 30 minutes.

Problem

The major cost center in the loading operations is

overtime. The distributor wants a faster loading

mechanism for loading to save on overtime costs.

The distributor has an investment cap of 10 Lakhs.

Docking Strategy

The docking strategies considered for the analysis

were:

Flush Dock: The vertical face of the dock is

flush with the outside wall of the warehouse.

To prevent wall damage, often the

foundation/dock bumper extends 4” beyond the

outside wall. Risk of building damage is still high

and hence was rejected.

Enclosed Dock: These are used for climate

control, product protection, security and

overhead lift capabilities are required. The

truck is parked inside the warehouse during

loading. The space is limited and requires

high construction cost. Since this does not

solve any problems and has high risks and

costs, this is rejected.

195 m 195 m

108 m 108 m

108 m 108 m

15 m

150 m

65 m

5 m

4 m

25 m 25 m

7 m

Max Loading: 1 Truck at a time

Max Loading: 1 Truck at a time

Max Loading:

8 Truck at a time

Max Loading:

8 Truck at a time

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Depressed Dock: These are used when the

there is a need to eliminate basement/ dock

level floors. This does not meet our

requirement due to the sloped nature and

hence is rejected.

Saw Tooth Dock: This comes in handy when

the space is limited. However there should be

a provision for the trucks to leave in the

direction of the angle of dock, which does not

meet our criteria and hence is rejected.

Open Dock: The open dock has the truck

trailer open at the docking bay and provides

sufficient product protection. The dock is

ideal for multiple horizontal docks without

interference and hence is selected.

The new bays are proposed on the sides with the

current bays serving the purpose of emergencies and

breakdowns. The dimensions of the truck are 2.24 m.

Since the loading has to be done horizontally one

truck width has been given as tolerance to avoid

interference and product pile up. 1 m each from each

edge has been kept un-allotted due to congestion.

The total required width of the docking bay for 4

trucks comes out to be 19.92 m (Recommended 20

m). One truck width tolerance has been given for

further expansion which when utilized can support

20 trucks. The current suggestion will have 4 trucks

docking on each side. Since the distributor currently

has only 8 trucks, each horizontal docking wall will

see 2 trucks.

According to the analysis, the monthly overtime paid

for the current model amounts to ₹12,00,00 per

month. The new model reduces the overall time

required from 14 hours to 10 hours. The overtime to

be paid with this model essentially turns out to be

null. The construction of the new docking system

will cost ₹67,50,00. The breakeven period for this

investment is 5.625 months (~6 months).

Warehouse Categorization (SKU-Wise)

Currently the warehouse has 4 storage silos. The first

silo comprises of a space of 20m* 140m. The second

silo comprises of a space of 25m*140m. The other 2

silos, adjacent to the proposed loading bay2 are of

sizes 30m*13m each. The total average volume is

used to assign the SKUs to the storage silos.

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Castrol has an average volume of 655 KL. Servo has

an average 469 KL. Shell has an average volume of

80.9 KL. The Storage silos 3 and 4 are incapable of

holding the inventory for Servo and Castrol and

hence by default are assigned to Shell products. The

larger volume brand is given the space closer to the

loading bay. Thus the storage area 1 has been

assigned to Castrol.

Storage Areas 1 and 2 will be divided into 2 halves.

Each half would be further divided into 9 categories.

Thus brands would be divided into 18 categories

based on the volume. The highest selling SKUs for

Castrol would be placed to the leftmost category near

the midpoint. The arrow heads in the direction

indicate the direction of ranks. Rank 1 being the

bestselling SKU and Rank 18 being the worst.

The Highest selling Servo SKUs will be placed at the

left-top-most category and the right-bottom most

category.

The Shell SKUs are stored in 2 separate silos in either

direction of the loading bay 2. Each storage silo is

sub- divided into 9 categories.

The bestselling SKUs are placed closest to the bay 2

entrance. The Silo 3 has a clockwise rank

progression and the silo 4 has an anti-clockwise rank

progression.

Future Scope The project analysis was run for a period of two

months. The possible future commitments to the

project subject to the distributor willingness are:

Training the loaders with new schemes

suggested in the report for optimum impact

on warehouse activities.

Potential move to a fixed quantity – EOQ

model with a comparative study between

fixed quantity and fixed period inventory

models.

Developing an automated warning level with

sales force and Excel model collaboration.

Optimizing the delivery route for the trucks

using a Logware model.

Identifying low performing SKUs using

appropriate margin levels.

TQM application to the different

warehousing operations at Golcha

Distributor.

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References http://www.aalhysterforklifts.com.au/index.php/about/blog-

post/different_types_of_loading_docks [1]

http://www.asprova.jp/mrp/glossary/en/cat249/post-660.html

[2]

http://www.theoperationsmanagement.com/fixed-time-period-

model-4178 [3]

http://www.accountingtools.com/periodic-inventory-system

[4]

http://accountinginfocus.com/financial-

accounting/inventory/inventory-discounts/ [5]

Operations Management Along The Supply Chain, 6th Ed

By Robert S. Russell, Bernard W.Taylor-III [6]

http://mcu.edu.tw/~ychen/op_mgm/notes/inventory.html [7]

http://www.aalhysterforklifts.com.au/index.php/about/blog-

post/loading_dock_safety [8]

http://www.aalhysterforklifts.com.au/index.php/about/blog-

post/order_picking_in_the_warehouse