Location Analysis And Planning Of Selected Companies

Introduction

An imperative situation looked by organizations while building up their base is the issue of area. For organizations, it is essential to choose a reasonable area to guarantee long haul aggressiveness. Organizations select the most fitting area via doing an area examination. Location analysis can be characterized as the basic leadership strategy used for distinguishing the most appropriate area inside a set or without a pre-built up set for a particular reason. Area investigation includes analyzing the area necessities of businesses and firms against their current site conditions. An area is chosen by considering different factors, for example, benefits, affect on ventures, incomes and activities, production network factors. The major steps involved in location analysis are

1. Decide on the criteria
2. Identify the important factors
3. Develop location alternatives
4. Evaluation of alternatives
5. Selecting the most suitable location.

The factors, which determine the selection of a location, are

1. Regional factors: Regional factors include the location of raw materials, labour situation of a region, location of markets, climate conditions and the tax structure of the region.
2. Community considerations: Community considerations include the quality of life within the community, attitude of the people in the community, Environmental regulations, taxes etc.
3. Site related factors: Land issues, ease of transportation, legal barricades and environmental regulations come under site related factors.
4. Multiple plant strategies: Multiple plant strategies can be further categorised into

• Product plant strategy: Under product plant strategy, companies operate separate plants for each product.
• Process plant strategy: Here the plants are selected based on the process used to manufacture products. Each plant will work on a certain type of process.
• Market plant strategy: Plants are constructed depending upon the markets to be served.

Using the location analysis, location planning of four different companies was done which are:

1. Vodafone Idea Ltd
2. Godrej India
3. Costco Retail
4. IBM India Pvt Ltd.

Vodafone Idea Ltd

Vodafone Idea Ltd is a merger between telecom monsters Vodafone and Idea Cellular Pvt Ltd. It was shaped to recapture the piece of the overall industry lost by the passage of Jio. The organization needed to do area examination to decrease the quantity of outlets with the end goal to diminish the expense of activity i. e. there were stores of both Idea and Vodafone inside a separation of 220m, 1km, 100m and so on. So they needed to decrease the quantity of stores and place them in a suitable area. The area considered was Pune. Factor rating strategy was utilized to choose the area. Normal weight time, store space, organization possessed/contracted, store perceivability, separation, area and leave cost were given settled weights. Based on the analysis, the conclusions were recorded in Table 2. Idea store in Pune Satara road was selected as the ideal location for the new Vodafone-Idea store as it had the highest score of 64%. Godrej and Boyce Godrej and Boyce had five different warehouses for its business in the northern part of India. These five distribution centers had around 160 or more individuals. The states secured were Jammu and Kashmir, Himachal Pradesh, Punjab, Haryana and Uttarakhand. With the end goal to decrease the operational cost the organization was wanting to build up a solitary distribution center for its north Indian activities. The organization required a solitary distribution center of 1, 50, 00, 000 square feet that can take into account the entire of north India. By doing as such organization could lessen their transportation cost and could likewise tackle the availability issues. The proposed locations were Nahar logistics Park, Punjab Government Warehouse, Chandigarh near Manufacturing Plant, Kharar, Dhir Warehouse and Mahindra Warehouse. The factor rating strategy was embraced to choose the reasonable area for stockroom. The accompanying components were considered for choosing the distribution center, which are lease and expenses, work, network, exchange association, separation and Government awards. Certain weights were doled out to them to carry out the factor rating analysis. Based on the analysis it was found that Dhir warehouse scored the highest score of 79. 48% and it was selected as the suitable location for warehouse.

Costco Retail

Costco Wholesale Corporation, famously known as Costco, is an American MNC, which works a chain of membership-only warehouse clubs. Costco was the second biggest retailer on the planet after Walmart, in 2015. It is additionally the world's biggest retailer of choice and prime beef, organic foods, rotisserie chicken and wine. Its worldwide headquarters are situated in Issaquah, Washington but its first warehouse was opened in Seattle in the year 1983. As of August 23rd, 2018, Costco had a sum of 759 distribution centers; with 527 clubs in United States, 100 in Canada, and critical number in Mexico, UK, Japan, South Korea, Taiwan, and so on. In the United States, Costco's principle rivals working enrollment distribution centers are Sam's Club and BJ's Wholesale Club. Costco was the principal organization to develop from zero to $3 billion in deals in less than six years and in 2017, Costco had 90. 3 million individuals. Costco works six distribution center areas in the seven-county metro region of the Twin Cities, recognized as Minneapolis and St. Paul, Minnesota, USA. The primary target of this examination is to discover a reasonable area for its development in this district. This should be possible by gathering the information about these six distribution centers and their clients. In light of this information, investigation would be performed to check whether some kind of example rose in these zones. Geographic Information Systems (GIS) and demographic data were used to visually display the different store locations as well as to plan a tour of the prospective areas to access availability, land-use, and zoning to determine the best area in the seven-county metro area for Costco to locate their next warehouse store. Information extricated and analyzed from U. S evaluation information included populace numbers, family unit estimate, families, financial status, business status, training level, and nearness to real streets. The information were utilized to distinguish the socioeconomics of Costco clients. Organization data and existing webpage locations of Costco, Sam's Club, and Walmart areas were gotten from the individual organization's sites. The data about significant streets, metro region limits, and the state limit were acquired from the Minnesota Department of Natural Resources. Demographic criteria study involved:

• Family households >= 2
• Associate degree or higher >= 45%
• Median household income>=$55, 000
• Median family income>=$65, 000
• Population aged 25-64 >= 52%
• Population aged 16 or older in the workforce >= 75%

In view of the criteria said above, census tracts were recognized that contained demographic information coordinating every one of the six of the criteria. A polygon layer representing census tract regions that coordinated the recognized criteria and was assigned for further examination. A one, three, and five-mile various ring support was made around each store; the three to five mile range representing the region of accommodation and the point of destination for a purchaser to movement. Thinking about the recognized census tracts containing the six criteria of a Costco shopper and the most extreme five-mile buffer ring, territories to consider as possibility for another Costco warehouse area were distinguished and included Eagan in Dakota County and Woodbury situated in Washington County. Utilizing ground investigation, two practical areas in Eagan and three in Woodbury were discovered appropriate. On the off chance that two areas are attractive, the two urban communities would have the capacity to suit a distribution center without cover rivalry.

IBM Data Center Planning

International Business Machines Corporation (IBM) is an American multinational IT company started in 1911 and headquartered in Armonk, New York. It operates in over 170 countries. IBM is also a major research organization, holding the record for most U. S. patents generated by a business (as of 2018) for 25 consecutive years. Nicknamed Big Blue, IBM is one of the 30 organizations incorporated into the Dow Jones Industrial Average and one of the world's biggest managers, with (starting at 2017) more than 380, 000 workers. Known as "IBMers", IBM representatives have been granted five Nobel Prizes, six Turing Awards, ten National Medals of Technology and five National Medals of Science. IBM has an extensive and assorted arrangement of items and administrations. As of 2016, these offerings fall into the categories of cloud computing, cognitive computing, commerce, data and analytics, Internet of Things (IoT), IT infrastructure, mobile, and security. Research has likewise been a vital piece of IBM since its establishing years. Now, organizations need robust data center infrastructure to meet the demands of an always-on world. Data center services from IBM can help build scalable and resilient data centers to sustain growth, reduce risk and transition seamlessly to a modern data center that is optimized for cloud. We further discuss how IBM finds its locations for its respective data centers. The need for a new data center in India arose when the demand of enterprises to store data locally increased. IBM understood that a new data center would assist them in data handling laws that were being introduced in various countries. Most SMEs are primarily based in India and they needed access to local data, less ping time and responsive web sites. Other sectors such as telecom and banking required data to be stored locally for less ping time with their existing data centers. Various locations were suggested like Indore, Delhi, Mumbai, Bangalore, Chennai and Hyderabad based on the suggestions given below

• Uninterrupted Power
• Proximity to Water for emergency Cooling
• Labour
• Connectivity
• Setup Cost
• Local Regulations
• Ping Time to other DCs
• Political Stability
• Proximity to data cabling.

These suggestions held a priority as displayed in the graph. Based on these criteria and the respective weights assigned to each, the analysis was performed which is shown in Table 5. As we can see from the above tabulation, that Mumbai had received the highest score. This data center enabled IBM to bid for sensitive government and defense contracts. The first data center was setup in Mumbai and the second proposed is in Chennai area as part of smart cities and in a bid to gain more in the $2 trillion market by 2025.

Conclusion

As can be seen from the above four examples of products and services, location analysis involves studying and evaluating a large number of factors, always taking individual customer requirements into account. It enables us to achieve the best position and location to achieve our strategic goals. We should always remember that the overall objective of site analysis is always to select the optimal location in terms of feasibility, economy, and future sustainability. This study can be further developed to improve upon site selection location research tools for successful site selection of outlets. In order to make informed site location and location decisions, a more efficient approach of on-site assessment and evaluation of customers and markets can be undertaken. This would help in a more accurate analysis of the market, sites and trade areas and also help in uncovering potential, in-fill opportunities and eliminating overlaps in existing markets. A more detailed business data, market surveys, demographic data, and household consumer data can be used to uncover new marketing opportunities and identify profitable consumers. By analyzing and combining the data layers formed in this study and benchmarking the current sales performance of the stores, we can also find an optimal store location and manage the network more efficiently.