case study finished goods supply chain

7/30/2019 Case Study Finished Goods Supply Chain

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Case Study Finished Goods Supply Chain

The Great Indian Bazaar the rapidly growing and evolving Indian retail marketpresents

special and daunting distribution challenges for fast-moving consumer goods (FMCG)products, such as groceries, toiletries and other items that are used up and must be replaced in

a short amount of time. For a typical producer, about 100 stock keeping units (SKUs, or

product types) flow from 40 to 50 depots to 2,000 to 3,000 distributors, and then on to more

than 1 million retail outlets, varying in size from the tiniest corner shop to large

supermarkets.

Depending upon the product and the companys manufacturing strategy, production could be

sourced from a handful to as many as 50-plus plants spread throughout the country.

Inevitably, not all plants produce all the products. At the end of the supply chain, every

organization attempts to minimize product shortages and stock outs. The effort described here

is aimed at improvements in availability of finished goods stocks.

The project in this case study used Total Quality Management (TQM) concepts such as just-

in-time, value stream mapping, eliminating non-value-added steps and initiating demand pull

supply in the flow in order to help transform the supply chain. The effort used these

principles within the structure of TQMs Seven Steps of Problem Solving (similar to

DMAIC), shown below:

Step 1: Define problem

Step 2: Find root causes

Step 3: Generate countermeasure ideasStep 4: Test model

Step 5: Check results

Step 6: Implement in regular operation

Step 7: Prepare QI story

Step 8: Repetitive practice of SOPs

The model was developed in the Western region of the company. The supply chain was

divided in to three loops 1) depot to distributor, 2) factory to depot and 3) production.

This case study is in three parts, each part dealing with one loop; the depot to distributor

process is covered here in Part 1.

Step 1: Defining the Problem

To begin improving the depot to distributor process, a cross-functional team was assembled

(sales, operations, planning, materials, IT) from the corporate and regional offices. After

completing a two-day quality mindset training program, the team began the project.

They started by defining the availability problem. In TQM, a problem is defined as the

desired state minus the current status. In this case, the desired state is 100 percent availability,

or all SKUs present at all distributors (DI) on all days (A100).

For stock outs (or unavailability), the team suggested using this metric: 1 SKU stocked out for

1 day at 1 DI = 1 DISKUDay. Likewise, 2 SKUs stocked out for 2 days at 2 DIs = 2*2*2


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DISKUDays. Therefore, the percentage of stock outs = the number of DISKUDays in a

month*100/A100. For perfect availability, DISKUDays must = 0.

Data for the daily stock outs at each retail outlet was unavailable. Therefore, the team could

not determine the current status. However, to create some quantification of the problem, a

crude value stream map for material flow was compiled for end -of-month status across theregion (Figure 1).

Figure 1: Current-state of Material Flow

Total stock: 39 + approximately 14 (i.e., more than 50 a day)

Key: Triangle = stock (days) points, square = stock (days), S&F = factories

The problems exposed were:

Heavy supply push to meet end-of-month sales targets Overstocking at DIs

The operating team confirmed this analysis.

Part 1 of the project introduced the concept of demand pull from depot to DI. The metricselected was reduction in weekly skew in sales during the month. The team decided to pilot

the demand pull process in nine distributors that get products from one depot in the region. A

careful measurement of supply to distributors (primary sales) and supply from distributors to

retail outlets (secondary sales) was made. The results are shown in Table 1.

Table 1: Sales Measurements

WeekIdeal %

Sales

Secondary Sales

%

Primary Sales

%

1 23 17 62 23 25 12

3 23 21 24

4 23 23 27

The goal of the project was to reduce the primary sales skew (supply push) from 30 percent

to 10 percent in the last 3 days of the month.

Step 2: Find the Root Causes

To help find the root causes of the problem, the team constructed value stream maps of the

current state (Figures 2 and 3).


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Figure 2: Current StateSales System

Figure 3: Current StateProduction Planning and Supply

The root causes of the problem were apparent:

1. Information of actual sales was not available until a week after the month ended, andit was therefore of little use in planning supply.

2. Production planning was based upon a forecast, because the system had to meet whatthey believed the demand would be 50 days later

Step 3: Generate Countermeasure Ideas


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The countermeasure was clearly a demand-pull system, which would include the following

features:

Quick sales information flow from DIs Supply to demandreplenish stock Agreement on maximum stock levels (norms) for each DI for each SKU Shortest possible replenishment cycle to minimize maximum stock levels while

ensuring minimum stock outs

The team also made a future-state map to incorporate these changes (Figure 4). Each stage

gives its demand (Kanban) to its supplier, who supplies as per demand.

Figure 4: Future State of Material Flow

Key: K = Kanban (i.e., demand), S = Supply

While in principle this setup is very simple, getting it to work in practice involves very

fundamental changes in mindsets across the supply chain (including 500 DIs, sales, logistics

and factory management).

The first step in breaking the mindsets was a series of meetings to help the team internalize

the concepts through specially designed training sessions and four computer games, followed

by a pilot.

Step 4: Test the ideas

The team began the pilot at nine DIs and one depot. The steps followed were:

1. Identify depot and DIs2. Train personnel3. Develop an IT program to keep track of stock at the DIs and calculate the demand

based upon stock norms and available stock

4. Design system in detail:-Calculate average sales per day of each DI over the last 3 months for each SKU-Determine feasible truck sizes for delivery and maximum stock replenishment


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frequency feasible

-Agree on stock norms to be maintained at DIs (these were at least 50 percent lower

than the averages measured)

-Most importantly, develop the formats and IT system to generate demand for each

dispatch easily on the computer

-Develop a pull tracker where the daily stock level compared to the agreed norm isplotted. If the tracker shows stock going above stock norm, action can be taken to stop

the supply push.

-Bring stocks down to normsmuch more difficult than one would imagine, as

company results were based on primary sales and this one-time correction amounts to

a loss of sales.

Keeping in mind that disbelief that the concepts would work was widespread, intense

discussions were required at each stage of the process. It took six months to simply get the

pilot started.

Compared to a stock norm of 4,800 cartons, the stocks were at 20,000 at the end of July.Savage de-stocking in August followed by tight monitoring and troubleshooting over six

months gradually made the system work.

December to March saw stock levels averaging at 70 percent of the norm, with some minor

topping up still happening at the end of the month.

Step 5: Check the Result

Table 2 shows the change after the pilot project.

Table 2: Sales Measurements After Pilot

Secondary Sales

% Post-

improvement

Primary Sales %

Post-

improvement

Primary Sales %

Pre-improvement

Week

120.6 20.9 6

Week

225.1 28.9 12

Week3

23.4 24.0 24

Week

423.3 27.7 27

Days

29 to

30

7.6 8.5 30

The skew had virtually been eliminatedprimary sales were less than 1 percentage point

different than secondary sales in the last 3 days of the month. Simultaneously, the variations

in day-to-day sales reduced.


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Supply was now clearly operating in the demand-pull mode. What was particularly

heartening was the response from the distributorsStock levels are down and supply is

even and as per demand.

Steps 6 and 7: Implement Across System and Document Results

After the pilot, the system was implemented across the regions 12 depots and 200 major DIs

in the next six months. Numerous local implementation problems were doggedly overcome.

A quality improvement story was prepared and presented to management. Based on this

project, the organization eventually decided to extend this model countrywide. A model

(covered inParts 2and3of this case study) for a demand-pull system in the upstream links of

the supply chain also was developed.

Steps 1 and 2: Define the Problem and Find Root Causes

Drawing upon the current-state value stream map of material flow from Part 1 of the case

study, the team realized that movement from factory warehouses to distributors occurred

through two channels: 1) from S&F directly to the depot or 2) from S&F to the depot via a

mother depot.

The logic for dispatching was as follows:

Large-volume SKUs could easily make full truckloads and were dispatched directly tothe depot.

For smaller volume SKUs, a full truckload could only be achieved by batching a largenumber of SKUs for a number of depots. Such mixed loads were received fromseveral factories for different SKUs.

A full truckload was prepared at the mother depot for each depot by combining thesmall SKUs from a number of factories to each distributor.

Data indicated that 30 percent of the stock moved in the Western region went through the

mother depot. The flow is indicated in Figure 1.

Figure 1: Flow of Stock in Western Region


The teams first countermeasure idea was to eliminate non-value-added stages of the process.

Essentially, the existing system operated under the following pretenses:
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1. Factories produce a mix of SKUs, some high volume and other low volume.2. Minimizing changeovers with long runs of each SKU within the monthly planning

cycle was the norm.

3. Larger-volume SKUs could go to the larger depots in trucks whenever the SKU wasbeing produced.

4. Smaller SKUs going to all depots, and all SKUs to the smaller depots, were sent to anintermediate stock point called a mother depot.5. Whenever a truckload was assembled for any depot, the mother depot dispatched the

goods to that depotthe aim being to minimize transport cost.

TQM suggested the following system:

1. Map the average flows between each production center and its customer depot for theSKUs produced at that factory.

2. Estimate the frequency of dispatch for a direct truck depending upon truck sizesavailable and the total volume.

3. Use smaller trucks where possible to maximize replenishment frequency.4. Supply to demand.5. Eliminate the use of mother depot (non-value-adding stage) as much as possible,

using it only as last resort for very small dispatch flows between a supply point and its

customer.

Using these principles, the team prepared a blueprint based upon past data. They agreed that a

dispatch frequency of at least once a week was adequate given the stock of 10-plus days at

the depots. The mapping of direct flows for the Western region is shown in Table 1.

The mapping was done in three stages:

1. All volume flows for a month between each factory (left hand column) and depot(horizontal top row) were mapped.

2. The squares colored yellow showed the volumes that with normal size trucks could bereplenished once per week at least. The squares in blue showed volumes that could be

replenished directly at least once per week using smaller trucks. The squares that were

neither blue nor yellow had to flow through mother depot.

3. To compute the volume that could be supplied directly, the squares with no colorwere equated to zero.

Table 1: Direct Flows for Wester Region

Depot

Factory A B C D E F G H I J K L M Total

1 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0

3

4 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0

6 15 4


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7 19 8 10

8 8 5

9 0 0 0 0 0 0 0 0 0 0 0 0 0

10 10 5 0 0 0 0 0 0 0 0

11 0 0 0 0

12 0 0 0 0 0 0 0 0 0 0 0 0 0

13 0 0 0 0 0 0 0 0 0 0 0 0 0

14 46 19 32 20 11 21 34 31 14 16 14

15 99 59 17 17 17 10 27 324 15 97 17 112 44

16 22 8 20

17 9 5 0

18 0 0 0 0 0 0 0 0 0 0 0 0 0

19 920 389 221 25 47 40 29 123 87 48 207 79 60 97

21 180 70 62 43

22 439 130 24 10 23 23 60 16 36 18

23 127 42 63 203 63 39 23 286 72 28 16 13

24 7 3 36

25 0 0 0 0 0 9 0 0 0 0

26 326 233 101 25 28 34 84 99 16 465 44 316 46

Total 1516 742 187 374 308 147 406 590 405 915 262 576 275 6703

Total 1525 746 211 389 323 169 419 599 417 945 284 594 293 6914

Of the total volume supplied to all depots (6,914 tons in a month), as much as 6,703 tons (95

percent) could be supplied directly. Currently, about 30 percent went through mother depots.

The configuration could therefore be run as shown in Figure 2.

Figure 2: Improved Flow of Stock in Western Region

The system in Figure 2 would result in an additional 25 percent stock moving with several

cost-saving advantages:

1. Combining transport from S&F to depot in one stage instead of two, resulting intransport savings

2. Eliminating unloading, loading and reloading and storage at mother depot3. Reducing stock in mother depot and transit


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4. Reducing product breakage in handlingThe teams second countermeasure idea involved mixing dispatches with all available SKUs,

big and small. Existing dispatch practice was to dispatch full truckloads of large volume

SKUs. The smaller SKUs were collected and dispatched to a mother depot. There, small

SKUs from all factories for each depot were collected and dispatched when a full truckloadwas available. This inevitably led to large lead times, indeterminate arrival times at the

distributors and resultant stock outs.

The countermeasure proposed was to dispatch (and produce) to the demand mix in each

truck. Software was developed so that the S&F could download the gap between stock norm

and opening stock for each SKU daily, and calculate the requirement. Dispatch to each depot

would be affected as per a predetermined minimum frequency (Table 1). Stock norms were

determined based upon replenishment frequency and downstream demand fluctuations, and

stored in the database.

Step 4: Test the Idea

It is not possible to test mixed dispatches, even on a pilot scale, unless production cycles and

mix also are converted to short production cycles, each producing the mix demanded. The

team did conduct a conceptual test to compare dispatch loading using the existing system and

the proposed system. Table 2 shows an example of the difference.

Table 2: Existing Dispatch Loading System vs. Proposed System

Dispatch Plan (existing system)

Depot 1 2 3 4 5 Total O Stock C Stock

SKU 1 600 0 600 1,200 2,091 4,491 4,491 0

Other SKUs 5,214 5,214

Dispatch Plan (demand pull)

Demand 1 2 3 4 5 Total O Stock C Stock

SKU 1 693 0 780 435 2,852 4,760 4,491

SKU 2 162 0 1,034 90 681 1,968 226

SKU 3 0 25 63 0 0 88 90

SKU 4 0 0 59 388 337 784 4,206

SKU 5 0 0 0 594 68 662 692

Total 856 25 1,935 1,507 3,938 8,262 9,705 1,443

The demand-pull dispatch system was clearly superior, as it would get a mix of large and

small SKUs directly to the depot at a much higher frequency and regularity.

To implement Steps 4 through 6 (test the idea, check the result and standardize in operation),

production scheduling also needed to be changed to the demand pull system. This major

change will be described in Part 3 of this case, which also will describe the results achieved

in the model.


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tep 1: Define the Problem

The company supplied about 70 SKUs to more than 500 distributors through 13 depots from

24 factories in the Western region. After discussing the situation, the company decided to

develop the market-mix-led demand-pull model for one product line with 13 SKUs produced

from two factories through the 13 depots to 200 distributors. The metric for the projectinvolved product availability, with availability being defined as the every SKU being

available every day at every stock point. The unit used to measure it was defined as a data

point(DP). A DP was defined as follows:

1 SKU available on 1 day at 1 stock point = 1 DP 2 SKUs available on 1 day at 1 stock point = 2 DPs 2 SKUs available on 2 days at 2 stock points = 8 DPs

Therefore, for the test population,

% availability = % DPs / (maximum DPs during period)

A stock point could be a depot or distributor. With the depot to distributor system already

following the demand-pull model, the metric was applied to depot availability. The current

state showed a 10 percent stock out of DPs. In phase one, the team agreed to try to reduce this

by 50 percent to 5 percent stock out of DPS.

Step 2: Root Cause Analysis

The prevalent planning system was a forecast-based supply push system. The team completed

a map of the forecasting system, shown in Figure 1.

Figure 1: Information MappingArriving at Sales Forecast


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Typically, the sales system added up the actual sales once per month to get an accurate count.

The expected demand from the sales teams estimates spread out through the field, planned

marketing promotions and monthly trends also were considered when issuing the sales

forecast for the next month.

There were two problems:

1. This forecast suffered from the typical bullwhip effectshortage/excess of stockswere magnified at each stage and led to wild swings in demandwhich only

increased the amplitude of the bullwhip wave.

2. The sales forecast for any month was available one week after the month had begumand the real production plan only emerged nine days after the month had begun. This

delay produced two serious distorting effects. First, for the first 10 days of the month,

production followed the demand of pattern the previous months days 1-10. Second,

the supply chain had only 20 days to react to changes in monthly demand patterns.

Mismatches between demand and supply were bound to be the outcome of the currentprocess.


The team drew the solutions from just-in-time principles. They believed a dramatic reduction

in the sales-to-planning cycle could be achieved by implementing demand-pull production.

They reduced the number of stages needed to generate the plan by:

Aligning specific factories to specific depots for specific SKUs

Monitoring stock levels at its customer depots for each factory daily Replenishing to agreed maximum stock levels at agreed frequencies depending upon

downward supply

Conducting fortnightly production planning based upon market aggregated supply todistributors during the past fortnight adjusted for stock levels at factory, depot and

distributors

Introducing special promotions and production requirements for each factory, to becommunicated by central planning

Step 4: Test the Idea

Key personnel in the factory, planning and logistics departments were trained in the demand-pull model. One product line with multiple SKUs produced in two factories and supplied to

13 depots was selected for the trial. Formats and operating principles for combinations of

production mix to suit varying sales requirements were developed to set the stage for the trial.

Modeling was done for four fortnights by generating the demand pull locally and comparing

it to the demand that would have been produced with the monthly supply-push planning

system. The employees discovered that the mix required was different, but it would only

prove beneficial and not result in a supply disaster.

Step 5: Check the Result

The steady improvement in stock out DPs at the distributors and depots are captured in Table

1.


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Table 1: Changes in DPs from July to March

Month DPs Depot % Depot DPs Distributor % Stock Out

July 2821 10.5

Sept 2730 5.5Oct 2821 3.5

Nov 2730 1.5

Dec 2821 0.1 43,400 1.74

Jan 2821 1.9 43,400 2.28

Feb 2548 1.3 39,200 1.04

Mar 2821 1.0 47,740 0.46

The improvements resulted in a 90 percent reduction on stock out days at the depot and 67

percent at the distributor. Also, 0.46 percent DP stock outs at distributors meant a total of 230stock out days spread over 200 distributors and 31 days (i.e., stock outs must at best be just

one or two SKUs stocked out at one point for one or two days, implying virtually zero stock

out at the retailers). Likewise, 2,821 depot stock out points over 30 days and 13 depots means

very short stock-outs that were made up before loss in sales at the retailer could occur.

After experiencing three months of testing, the operating team, satisfied with the results and

having had a long exposure to TQM, decided to roll out the model on its own.

Just-in-time is a powerful method for improving delivery in a supply chain while reducing

stocks, operating pressures and costs. Effective implementation involves cutting cycle times

of both Information and materials by converting batch process to flow processes, removingnon-value-added activities and establishing decentralized supplier/customer relationships

with central control for special circumstances.

case study finished goods supply chain

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