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Part 2 of a Finished Goods Supply Chain Case Study: Moving Products Between Warehouses and Depots

The “Great Indian Bazaar” – the rapidly growing and evolving Indian retail market – presents special and daunting distribution challenges for fast-moving consumer goods (FMCG) products. For a typical producer, about 100 stock keeping units (SKUs) flow through 40 to 50 depots to 2,000 to 3,000 distributors to more than 1 million outlets varying in size from the tiniest corner shop to large supermarkets.

Depending upon the product and the company’s manufacturing strategy, production could be sourced from a handful to as many as 50-plus plants spread out throughout the country. Inevitably, not all plants produce all the products. At the end of the supply chain, every organization attempts to minimize shortages and stock outs.

The improvement effort in this case study was aimed at increasing the availability of finished goods stocks at the distributors by using Total Quality Management (TQM) principles such as just-in-time, value stream mapping, employee involvement, eliminating non-value-added stages and initiating a demand pull supply in the flow. Part 1 described the transformation of the link at the end of the supply chain – from depot to distributor. Part 2 describes the model proposed for the upstream links – between the warehouses at the factories (S&F) and the depots.

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.

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The logic for dispatching was as follows:

  • Large-volume SKUs could easily make full truckloads and were dispatched directly to the depot.
  • For smaller volume SKUs, a full truckload could only be achieved by batching a large number of SKUs for a number of depots. Such “mixed” loads were received from several factories for different SKUs.
  • A full truckload was prepared at the mother depot for each depot by combining the small 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

Figure 1: Flow of Stock in Western Region

Step 3: Generate Countermeasure Ideas

The team’s first countermeasure idea was to eliminate non-value-added stages of the process. Essentially, the existing system operated under the following pretenses:

  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 was being produced.
  4. Smaller SKUs going to all depots, and all SKUs to the smaller depots, were sent to an intermediate stock point called a mother depot.
  5. Whenever a truckload was assembled for any depot, the mother depot dispatched the goods to that depot – the aim being to minimize transport cost.
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TQM suggested the following system:

  1. Map the average flows between each production center and its customer depot for the SKUs produced at that factory.
  2. Estimate the frequency of dispatch for a direct truck depending upon truck sizes available 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 be replenished 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 color were equated to zero.

Table 1: Direct Flows for Wester Region

 Depot
FactoryABCDEFGHIJKLMTotal
1    
2        
3              
4     
5   
6154            
7198    10       
885            
9 
10105    
11          
12 
13 
144619 3220112134311416 14 
159959171717102732415971711244 
1622820           
1795           
18 
19        9     
20389221254740291238748207796097 
21   180  70   62 43 
224391302410  2323 60163618 
2312742 6320363392328672281613 
2473         36  
25 9   
263262331012528348499164654431646 
Total15167421873743081474065904059152625762756703
Total15257462113893231694195994179452845942936914
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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

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 in transport savings
  2. Eliminating unloading, loading and reloading and storage at mother depot
  3. Reducing stock in mother depot and transit
  4. Reducing product breakage in handling

The team’s 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 truckload was 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.

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Table 2: Existing Dispatch Loading System vs. Proposed System

Dispatch Plan (existing system)
Depot12345TotalO StockC Stock
SKU 16006001,2002,0914,4914,491
Other SKUs      5,2145,214
Dispatch Plan (demand pull)
Demand12345TotalO StockC Stock
SKU 16937804352,8524,7604,491 
SKU 21621,034906811,968226 
SKU 325638890 
SKU 4593883377844,206 
SKU 559468662692 
Total856251,9351,5073,9388,2629,7051,443

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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