Case Analysis On Merloni Elettrodomestici Spa Economics Essay

Merloni Elettrodomestici SpA is an Italian company based in Fabriano and is one of Europe’s biggest makers of home appliances. In February 2005, Merloni Elettrodomestici was renamed Indesit Company, Indesit being the best known of the Group’s brands outside Italy. The Company was also operating under its “historic” brand, Ariston, and the regional brands Hotpoint, Scholtes and Stinol.

During the perio from 1984 to 1986, Merloni undertook a number of initiatives to improve efficiency in inventory control and logistics. One such initiative was the transit point experiment where in the Milano region, regional distribution centre was eliminated in favour of tranit points which held zero inventory.

The following is an analysis of the Transit Point Experiment conducted by Merloni:

Cost saving in terms of infrastructure cost of regional warehouses and their maintenance.

The company would require lesser amounts of overall inventory to be maintained.

As regional warehouses would be closed there would be lesser labor requirements.

Transit Point methodology works similar to JIT where-in the required amount of goods are shipped at the required time.

Since it works more on the Pull from the customer and due to elimination of regional warehouses, the effect of bullwhip should be lower.

Because the regional warehouses will be eliminated, the capacity storage of the central warehouses should be expanded to meet the requirements of the extra Cycle inventory. This would come as an additional cost to the company.

Intensive planning of daily shipment should be done. It is not only required to calculate the exact amount of goods to be shipped but also the arrangement of the goods (to eliminate time in loading/unloading activities).

Because of this intensive planning more skilled administration staff would be required.

If the customer does not order wihin 3pm, the delivery of the product would happen only after the second day. This can lower customer satisfaction.

Since no inventory is maintained in near-by locations (as all goods come from central warehouse) if there is any excess demand or out of stock condition (for retailers), the goods will have to be fetched from central warehouse which would take a lot of time. This can lead to loss of goodwill with retailers especially those serving the rural markets.

Demand variability is not easily supported by employing Transit Point methodology. If there is an urgent demand for goods in excess of truckload capacity then it can lead to huge additional cost.

Another important point which is not mentioned in the case is the importance of the transportation medium. If any of the vehicles breaks-down it could lead to huge delays and pile up of demand. Merloni needs to keep some extra vehicle for a backup. It also needs to maintain the vehicles in good condition. The cost of this has not been accounted for. Since the experiment was carried out only in Milano a relatively smaller numbers of trucks (1 Trailer truck and 3 Small trucks) were required.

If the Transit Point methodology is applied through-out Italy, Merloni will need to build up infrastructure and teams to coordinate the the movement of trucks and their transactions.

In Merloni, it is the responsibility of the warehouse manager to manage and develop the customer relationship. If the warehouses are eliminated Merloni would still need additional office space for the warehouse managers who also act as Customer Relationship Managers.

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Another important question is where would Merloni keep the spare parts required for its service personnel. If these too are kept at the central warehouse it could lead to delay thus have a negative impact on the quality of service.

The Merloni experiment was conducted when the weather was good. If the weather is bad near the central warehouse but alright in other areas where there is demand, then it can lead to delays. The cost of such delays would be large as Merloni would have to use extra vehicles to ensure the earliest delivery of all the goods once the weather becomes good.

Quantitative Analysis:

Now we shall look at a quantitative analysis of cost incurred by the company before and after using Transit Point methodology. The case is for region of Roma (information as per exhibit 10). (Ax) would represent cost incurred by using Pre-Transit Point methodology and (Bx) would denote cost incurred by using Transit Point methodology.

Calculate the Average Volume/Month at the Regional Distribution Centre (RDC) in Roma.

Assuming 20 working days in a month.

Average daily demand served from regional warehouse = 154.8 pieces

Average Volume/Month = Average daily demand x No of working days

= 154.8 x 20

= 3096 pieces.

Operating Cost at RDC

From exhibit 10 of the case it can be seen that the operating cost at Roma is 3605 Lire/Piece/Month

Average inventory levels at RDC = 1200 pieces (from Exhibit 8a)

Total Operating Cost/Month at Roma RDC = Operating Cost/Piece/Month x Avg Inventory

= 3605 x 1200

= 4326000 Lire

Therefore, Operating Cost per piece sold = Total operating cost / No of pieces sold

= 4326000 / 3096

= 1397.28 Lire – (A1)

As per the case, by using Transit Point methodology the Operating Cost has reduced to 20%.

Therefore, New Operating Cost per piece sold = 20 % of original Total Operating Cost

= 0.20 x 1397.28

= 279.45 Lire -(B1)

Inventory Cost at RDC

From exhibit 10 of the case it can be seen that the inventory cost at Roma is 1035 Lire/Piece/Month.

Total Inventory Cost / Month = Invetory Cost/Piece/Month x Avg Inventory

= 1035 x 1200

= 1242000 Lire

Inventory cost per piece sold = Total inventory cost / No of pieces sold

= 1242000 / 3096

= 401.16 Lire. – (A2)

Using the Transit Point methodology, zero inventory is maintained.

Therefore, Inventory cost per piece sold = 0 Lire – (B2)

Short Haul Transportation Cost

The short haul transportation cost is the cost of transporting goods from regional warehouse or transit point to retailers. This cost would be common for both pre and during Transit Point methodoly usage period

Short Haul Transportation cost = 4300 Lire/Piece – (A3),(B3)

Long Haul Transportation Cost

is the cost of transporting goods from the central warehouse to the regional warehouse or transit point.

During the pre Transit Point period goods were transported from the central warehouse to the regional warehouses using trailer trucks.

Total number of pieces to be shipped per month = 3096 pieces

Capacity of one trailer truck = 120 pieces

Therefore, Number of trailer trucks required = Total quantity / Capacity of trailer truck

= 3096 / 120

= 25.8 trucks

Distance between Roma and Fabriano = 165 Km approx. (source: http://www.distance-calculator.co.uk/distance-from-fabriano-to-rome.htm)

From Exhibit 11, Cost of using a trailer truck for transport upto 165 Km = 0.36 Million Lire

Therefore, Total transporation cost = Cost/Truck x No of trailer trucks

= 360000 x 25.8

= 9288000 Lire

Transportation cost per piece sold = Total transportation cost / No of pieces sold

= 9288000 / 3096

= 3000 Lire – (A4)

In Transit Point methodology both trailer truck and smaller trucks can be used depending upon the lot size.

Since the average daily demand is 154.8 pieces, a minimum of one trailer truck will have to be used every day.

i.e. Total volume of goods carried by trailer trucks/month = No of trailer truck in a month x Volume carried by 1 trailer truck

= 20 x 120

= 2400 pieces

The remaining amount would be carried by smaller trucks.

Volume to be carried by smaller trucks = 3096 -2400

= 696 pieces.

Therefore, No of smaller trucks required per month = Volume carried by smaller trucks / Capacity of smaller truck

= 696 / 45

= 16 trucks

This means that in addition to trailer truck a smaller truck also needs to be done for 4 days in every week.

From Exhibit 11, Cost of using a smaller truck for transport upto 165 Km = 0.2 Million Lire

Total transportation cost = (Cost / Trailer truck x No of trailer trucks) + (Cost / Small truck x No of smaller trucks)

= (360000 x 20) + (200000 x 16)

= 10400000 Lire

Transportation cost per piece sold = Total transportation cost / No of pieces sold

= 10400000 / 3096

= 3359.17 Lire -(B4)

Inventory cost at central warehouse

Because the regional warehouses are going to be removed, some amounts of inventory will be moved to the central warehouse.

Total inventory level at all 17 regional warehouses = 14330 pieces

Assuming 50% of this is Cycle Stock and the remaining Safety Stock, the Cycle Stock (= 7165) will be moved to the central warehouse.

Average Safety stock = 7165 / 17

= 421 pieces.

Safety stock required at central warehouse as per Risk Pooling = 421 x √17

= 1735 pieces.

Therefore, additional stock required at central warehouse = Safety stock + Cycle stock

= 1735 + 7165

= 8900 pieces.

Assuming inventory cost as those prevailing in Roma, the extra inventory cost at central warehouse = 8900 x 1035

= 9211500 Lire

Additional inventory cost/month/piece sold = 9211500/(20*3096)

= 148.76 Lire -(B5)

Therefore, Total Cost incurred by the company before deploying Transit Point methodology

= (A1) + (A2) + (A3) + (A4)

= 1397.28 + 401.16 + 4300 + 3000

= 9098.44 Lire

Total Cost incurred by the company by deploying Transit Point methodology

= (B1) + (B2) + (B3) + (B4) + (B5)

= 279.45 + 0 + 4300 + 3359.17 + 148.76

= 8087.38 Lire

Therefore by using Transit Point methodology, Merloni has saved 1011.06 Lire.

Now taking this Transit point experiment to India, we can make the following observations

Geography – The geography of India is different from Italy. India is equally wide in North- South and East – West directions. The approximate width is ~3500Kms. This is very high compared to Italy. The towns and cities are farther apart compared to Italy. For a product like home appliances (refrigerator , washing machine , dish washer etc) the market is still in towns and cities in India. The road conditions are also not that good. This means the transportation time between cities will be more compared to Italy. Another point to consider is the demand in a town; this may not be enough to meet a truck load of products. Company will have to find a way to store the excess products which is not being supplied. See exhibit1 for details.

Infrastructure – Another option we can consider is to have a transit point method for big cities like Mumbai, New Delhi, and Bangalore etc. We can have a transit point set up in outskirts of city and we can have small trucks to distribute units to retailers. This will help to free up or reduce the storage space in ware house in each city. But this again will depend on where you have the center ware house located and will be applicable only if ware house is in a day’s drive from the city. Also we can try this in states like Kerala where the towns are closer by. But even though this frees up inventory storage space, company may still have to have a small space to store items which don’t get distributed or collected the same day. As given in Merloni case we will not be able to leave products in alley or plan to keep in sales office as space is a big constraint.

As in Merloni case we may not be able to reuse the storage space for an exhibition house in the case of India, as the storage location is located in outskirts of city in most places. The market segment for home appliances is the people who stay in the city limits and will be reluctant to travel so much for buying a home appliance.

Transportation – The fuel price costs and spare parts costs will contribute to the transportation cost and will drive it higher. This in turn will result in a higher transportation cost per unit and will eat into the margins. This will be significant in case of a transit point experiment since the delivery is made per day. Another concern is the quality of service – timely delivery and state of goods delivered. The time of delivery is very critical in the case of a transit point plan. The delivery to the hub should reach on time to ensure the timely delivery of goods to retailers. With the poor condition of roads and lack of service/repair support along the way, there is a significant risk associated with timely delivery. If a truck breaks down, it is definitely going to add half a day delay to the delivery.

Inventory – As explained in Infrastructure section, the transit point plan will help to reduce inventory held in big cities and move the same to central ware house location. This again may help company to close down its own Storage location in cities and use private/public warehouse option for the storage of minimal inventory in cities.

Customer Service – With the transit point plan, the timely delivery of goods in big cities will improve. This will make the retailers in this area happy. But if we try to implement this pan India, it will result in poor delivery times and dissatisfaction. The reasons for this are given above.

Labor & Cost – There may not be any significant reduction in labor expenses, as company may have to get new systems in place to support the transit point plan for big cities. This will kind of compensate for the reduction in storage space cost we are getting in cities. Again we will also need people to take care of the transit point plan execution in big cities.

Recommendation

Based on the above analysis, below given are the recommendations to implement a transit point plan in India.

Implement this plan only in big cities like Bangalore, New Delhi, Mumbai, Chennai ,Kolkata, Kerala etc. We can try out this experiment first in a big city like Mumbai and get learning’s from there to improve the system and then fan it out to other cities.

Another option to make this work is to have multiple large ware houses spread across the country so that every major city is located at a distance of 300-400Kms from the ware house. From this point we can try to run trucks to cities and do a delivery of products to retailers in the city and nearby areas in a 12Hr time frame.

For e.g. We can collect distribution data from Tier II big cities like Ahmadabad , Bhopal , Allahabad ,etc. over a period of time and see if it any of the cities have enough demand to meet a truck load. We can also include the close by towns for this calculation. Based on this we can run a transit point distribution around those cities. See exhibit 2.

The transit point plan will work well for a manufacturing line where the demand is more fixed than a home appliance store. This will work well as the factory have a well defined schedule on what products will be running in which Assy lines and for how long. In the case of a home appliance store the demand is driven high by a number of local factors like local holidays, local festivals, bonus pay out etc. For a diverse country like India these factors vary widely. A local festival is more limited to a local city or a town and may not be even applicable for the entire state. This makes it more difficult to forecast and plan.

On a big picture the transit point plan in this format cannot be implemented pan India as it will result in delayed deliveries and low satisfaction levels. The poor infrastructure, widely spread cities, diverse culture and quality of transportation service – all act as variables and makes forecast difficult and can cause a failure in the plan.

Exhibit 1

Let us consider a case where the central ware house is located in Bhopal. The approximate distance to close by cities like is as given below.

1. Bhopal – Ahmadabad – 500Kms

2. Bhopal – Allehabad – 500kms

3. Bhopal – New Delhi – 700kms

4. Bhopal – Patna – 700kms

In Indian road conditions we cannot expect a truck to cover more than 400Kms during night and to add to it there will be delays in check posts , for having dinner , traffic blocks etc. More issues will be there in rainy season & winter. Hence it is clear that with a central ware house in Bhopal we will not be able to make a delivery to these cities on time.

Exhibit 2

We can have ware house in Ahmadabad and use it to meet the demands of close by cities like Vadodara, Surat, and Gandhi Nagar etc.

1. Ahmadabad to Vadodara – 100Kms

2. Ahmadabad to Surat – 300kms

3. Ahmadabad to Gandhi Nagar – 70kms

4. Ahmadabad to Udaipur – 250kms.

But before implementing this we need to do an ROI calculation to see whether this is viable or not.