A PROJECT REPORT ONImprovement of Shipping Productivity

Summer Internship Project at Hindustan Coca Cola India Private Limited

Symbiosis Institute of Operations Management

By

Elizebeth Thadathil (36)Surajit Goswami (87)

Project Guide: Mr. R.G. Datir

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CERTIFICATE

This is to certify that Elizebeth Thadathil & Surajit Goswami have successfully completed their project on “IMPROVEMENT OF SHIPPING PRODUCTIVITY” at Hindustan Coca Cola Beverages Pvt Ltd. They worked under the guidance of Mr. R.G. DATIR from 26.03.2012 to 26.05.2012.

During this period we found them to be extremely resourceful and hardworking. The suggestions being furnished and their report will be of great help to the company.

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Acknowledgement

First and foremost we would like to thank our Project guide Mr. R.G. Datir (Shipping Manager) for his constant encouragement and guidance during the course of our Project, without whose help and constant inspiration this project would not have been a success. His way of explaining practical aspects of the project were phenomenal.

At the same time we are grateful to Mr. Abhay Joshi (Plant manager) to have facilitated the essentials required for the project.

We would also like to thank Dr. Vandana Sonwaney (Director – SIOM) and Mr. P. N. Parameshwaran (Placements head-SIOM) Mr. Venkatesh V.G and Mr. Bibhuti Tripathi who helped us get this opportunity and came up with valuable inputs extremely necessary for the completion of this project

Last but not the least we would like to express my sincere gratitude to

Hindustan Coca cola India Pvt Ltd, Nasik, for giving us this wonderful

opportunity to work with them for our summer project and their noble

guidance would take us a long way in my careers.

Elizebeth Thadathil and Surajit Goswami

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Table of Contents

1. EXECUTIVE SUMMARY............................................................................6

2. COMPANYBACKGROUND…………………………………………………………..8

3. NEED FOR THE PROJECT………………………………………………………....17

4. DEFINITION AND SCOPE OF PROJECT…………………………………........18

5. THE FLOW OF PROCESS……………………………………………………........19

6. CONCEPTUAL REVIEW OF PROJECT…………………………………… ........22

7. DATA COLLECTION FORMATS…………………………………………….........24

8. ANALYSIS AND FINDINGS…………………………………………………........26

9. IMPROVEMENT OF UNLOADING PRODUCTIVITY………………………....44

10. BIBLIOGRAPHY………………………………………………………………......49

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TABLE OF ILLUSTRATIONS/CHARTS

Item Label Page

Fig 1 Coca Cola India History 9

Fig 2 Process flowchart of RGB production line 19

Fig 3 Process flowchart of Shipping Process 20

Fig4 Transporterwise breakup 26

Fig 5 Gate Intime 27

Fig 6 Fish Bone Analysis 27

Fig 7 Time taken to unload 100 cases 28

Fig 8 Unloading process timewise distribution 29

Fig9 Loading time 30

Fig 10 Loading process timewise distribution 31

Fig 11 Pareto Analysis 32

Fig 12 Distribution Process Map 33

Fig 13 Components Of Turnaround time 34

Table-1 Waiting Time under FCFS System 36

Table-2 Waiting Time under SPT System 38

Table-3 Casual Labour Productivity 44

Table-4 Mathari Labour Productivity 45

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1. EXECUTIVE SUMMARY

Project Title: “Improvement of Shipping Productivity”

Logistics is the management of the flow of goods and services between the point of origin and the point of consumption in order to meet the requirements of customers. Logistics involves the integration of information, transportation, inventory, warehousing, material handling, and packaging, and often security. Logistics is a channel of the supply chain which adds the value of time and place utility. Today the complexity of production logistics can be modeled, analyzed, visualized and optimized by plant simulation software. Distribution system is the heart of the entire business of coca cola. Hence,

The project focuses on

- Mapping the process of shipping and determining the wastages involved.

- Finding out ways to streamline the distribution system internally.

- Finding out ways to reduce the truck turnaround time.(inbound processes only)

- Finding out ways to improve loading and unloading productivity.

MAJOR FINDINGS

DATA BASED APPROACH

MTS and RAJ are the major carries for the company. 9 % of temporary trucks are incorporated to satisfy the time loss

occurred due to long truck turn-around time. 5 hrs 11 min is the average time spent by any truck inside the

HCCBPL, Nasik. 9.56 Min is the time taken to unload 100 cases. 12.34 Min is the time taken to load 100 cases Major non value adding activities are:

o Wait (Before unloading): 232 Min

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o Wait (Before loading): 120 Mino Motion in transit : 115 Min

QUALITATIVE FINDINGS

Skilled labour or well trained labour not present for loading and unloading areas.

There is inability to draw in the same labour force consistently for the same kind of operation

Improper coordination and control in the shipping area.

Dedicated planning person not available.

RECOMMENDATIONS

Changing the truck entry system from First Come First Serve(FCFS) to Shortest Processing Time(SPT)

Introduction of new material handling equipments. More Number of Mathari workers should be employed in Unloading Labor and equipment utilization (eg. truck has to wait for a forklift or

driver). Change in methodology adopted in unloading should be done. Pallets should be available in time to avoid duplication of activities.

Faster removal of the unloaded material from near the truck to avoid congestion.

 Improving Order placement and order registration process Value Stream mapping of picking, loading and dispatch processes

revealed the stress areas which is unloading area and the transit time delay.

Induction of a dedicated person for the planning of shipping activities.

Ensuring that the order delivery to shipping reaches on time. Sales needs to coordinate with customers for the same.( When forecast is available on time and the actual orders can be judged or ascertained from before then the delay and confusion arising out of it can be definitely eradicated.)

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2. COMPANY BACKGROUND:

Coca-Cola is the world’s leading manufacturer, marketer and distributor of non-alcoholic beverages, concentrates and syrups. Along with Coca-Cola, the world’s best known brand, the company markets four of the world’s top five soft drink brands. Throughout the world, no other brand is as recognizable as Coca Cola. With operations in 110 countries, a diverse workforce comprised of 98 different nationalities, communicating in more than 100 different languages, The Coca Cola company is part of the fabric of life in each of the communities they serve throughout the world. It operates as a local business partner, enhancing the workplace, preserving the environment and strengthening the community.

Coca Cola is the most popular and best selling soft drink in history, as well as the best known product in the world. Coca-Cola was invented in May 1886 by Dr. John S. Pemberton in Atlanta, Georgia when he concocted caramel-coloured syrup in a three-legged brass kettle in his backyard. He first distributed the product by carrying it in a jug down the street to Jacob’s Pharmacy and customers bought the drink for five cents at the soda fountain. Carbonated water was teamed with the new syrup, whether by accident or otherwise, producing a drink that was proclaimed “delicious and refreshing”, a theme that continues to echo today wherever Coca- Cola is enjoyed.

The name Coca-Cola was suggested by Dr. Pemberton’s book keeper, Frank Robinson. He suggested that “the two Cs would look well in advertising.” The first newspaper ad for Coca-Cola soon appeared in The Atlanta Journal, inviting thirsty citizens to try “the new and popular soda fountain drink.” Hand-painted oil cloth signs reading “Coca-Cola” appeared on store awnings, with the suggestions “Drink” added to inform passersby that the new beverage was for soda fountain refreshment.

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By the year 1886, sales of Coca-Cola averaged nine drinks per day. The first year, Dr. Pemberton sold 25 gallons of syrup, shipped in bright red wooden kegs. Red has been a distinctive colour associated with the soft drink ever since. For his efforts, Dr. Pemberton grossed $50 and spent $73.96 on advertising. Dr. Pemberton never realized the potential of the beverage he created. He gradually sold portions of his business to various partners and, just prior to his death in 1888, sold his remaining interest in Coca-Cola to Asa G. Candler, an entrepreneur from Atlanta. By the year 1891, Mr. Candler proceeded to buy additional rights and acquire complete ownership and control of the Coca-Cola business. Within four years, his merchandising flair had helped expand consumption of Coca-Cola to every state and territory after which he liquidated his pharmaceutical business and focused his full attention on the soft drink. With his brother, John S. Candler, John Pemberton’s former partner Frank Robinson and two other associates, Mr. Candler formed a Georgia corporation named the Coca-Cola Company. The trademark “Coca- Cola,” used in the marketplace since 1886, was registered in the United States Patent Office on January 31, 1893.

The business continued to grow, and in 1894, the first syrup manufacturing plant outside Atlanta was opened in Dallas, Texas. Others were opened in Chicago, Illinois, and Los Angeles, California, the following year. In 1895, three years after The Coca-Cola Company’s incorporation, Mr. Candler announced in his annual report to share owners that “Coca-Cola is now drunk in every state and territory in the United States.”

As demand for Coca-Cola increased, the Company quickly outgrew its facilities. A new building erected in 1898 was the first headquarters building devoted exclusively to the production of syrup and the management of the business. In the year 1919, the Coca-Cola Company was sold to a group of investors for $25 million. Robert W. Woodruff became the President of the Company in the year 1923 and his more than sixty years of leadership took the business to unsurpassed heights of commercial success, making Coca-Cola one of the most recognized and valued brands around the world. Our Quality Policy - “To ensure customer delight, we commit to quality in our thoughts, deeds and actions by continually improving our processes…Every time.”

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Fig-1:Coca Cola India HistoryCoca-Cola was the leading soft drink brand in India until 1977, when it left rather than reveal itsformula to the Government and reduce its equity stake as required under the Foreign regulationAct (FERA) which governed the operations of foreign companies in India. Coca-Cola re-entered the Indian market on 26th October 1993 after a gap of 16 years, with its launch in Agra. An agreement with the Parle Group gave the Company instant ownership of the top soft drink brands of the nation. With access to 53 of Parle’s plants and a well set bottling network, an excellent base for rapid introduction of the Company’s International brands was formed. The Coca-Cola Company acquired soft drink brands like Thums Up, Goldspot, Limca, Maaza, which were floated by Parle, as these products had achieved a strong consumer base and formed a strong brand image in Indian market during the re-entry of Coca-Cola in 1993. Thus these products became a part of range of products of the Coca-Cola Company.

Coca-Cola in India is the country’s leading beverage Company with an unmatched portfolio of beverages. The Company manufactures and markets leading beverage brands like Coca-Cola, Thums Up, Fanta, Fanta Apple, Limca, Sprite, Maaza, Minute Maid, Burn, Kinley and Georgia range of tea coffee, Nestea and Fanta Fun Taste.

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1950,New Delhi.First Bottling Plant

1958,First Concentrate plant in India

1973, 22 bottling plants in 13 states.

1978, Withdraws from India

1993, Re-enters India

The Coca-Cola Company has invested nearly USD 1.1 billion in its operations in India since its re-entry back into India in 1992. The Coca-Cola system in India directly employs over 25,000 people including those on contract. The system has created indirect employment for more than 1,50,000 people in related industries through its vast procurement, supply and distribution system.

In India, the Coca-Cola system comprises of a wholly owned subsidiary of The Coca-Cola Company namely Coca-Cola India Pvt Ltd which manufactures and sells concentrate and beverage bases and powdered beverage mixes, a Company-owned bottling entity, namely, Hindustan Coca-Cola Beverages Pvt Ltd; thirteen authorized bottling partners of The Coca-Cola Company, who are authorized to prepare, package, sell and distribute beverages under certain specified trademarks of The Coca-Cola Company; and an extensive distribution system comprising of our customers, distributors and retailers. Coca-Cola India Private Limited sells concentrate and beverage bases to authorized bottlers who are authorized to use these to produce our portfolio of beverages. These authorized bottlers independently develop local markets and distribute beverages to grocers, small retailers, supermarkets, restaurants and numerous other businesses. In turn, these customers make our beverages available to consumers across India. The Coca-Cola System in India has more than 1 million retailers and our business has a multiplier effect on employment and earning opportunities. Coca-Cola in India is the largest domestic buyer of sugar and one of the top buyers of mango pulp. The Coca-Cola System in India business also positively impacts industries like Glass, Plastics, Resin Manufacturers, Sugar, Automobiles, White Goods Manufacturers, Banking etc.

“Think local, act local”, is the mantra that Coca-Cola follows, with punch lines like “Life ho to aisi” for Urban India and “Thanda matlab Coca-Cola” for Rural India. This resulted in a 37% growth rate in rural India visa-vie 24% growth seen in urban India. Between 2001 and 2003, the per capita consumption of cold drinks doubled due to the launch of the new packaging of 200 ml Returnable Glass Bottles (RGBs) which were made available at a price of Rs. 5/- per bottle. This new market accounted for over 80% of India’s new Coca-Cola drinkers. At Coca-Cola, they have a long standing belief that everyone who touches their business should benefit, thereby inducing them to uphold these values, enabling the Company to achieve success, recognition and loyalty worldwide. Coca-Cola posted a strong growth of 20% in India in sales in volume terms during the first quarter of 2012.

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Hindustan Coca-Cola Beverages Pvt Ltd, Nashik Plant

HCCBPL Nashik is one of the oldest and the smallest bottling plants owned by Coca-Cola India Pvt Ltd. This bottling unit was taken over from Parle. It has one RGB bottling line and one Kinley water line. RGB line works 24X7 during season which is for 4 months, 6 months in 2 shifts and 2 months in 1 shift. So the total working hours in a year = 5200 Hrs. The annual production of the plant is 20,00,000 crates. Kinley water line works 24X7 throughout the year. Recently they setup a warehouse/distribution centre in Nashik.

HCCBPL Nashik RGB line can bottle 12 SKUs. They are

Thums Up 200ml Thums Up 300ml Sprite 200ml Sprite 300ml Fanta 200ml Fanta 300ml Coca-Cola 200ml Coca-Cola 300ml Limca 200ml Limca 300ml Kinley Soda 200ml Kinley Soda 300ml

33% of the annual production is for Thums Up, another 33% is for Sprite and the remaining 34% is distributed among Fanta, Kinley Soda, Coca-Cola and Limca SKUs. Maaza and Minute Maid, which are also available in RGBs are not manufactured at Nashik plant.

Strategic Aspects

Mission

Our Roadmap starts with our mission, which is enduring. It declares our purpose as a company and serves as the standard against which we weigh our actions and decisions.

• To refresh the world.• To inspire moments of optimism and happiness.• To create value and make a difference.

Vision

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Our vision serves as the framework for our Roadmap and guides every aspect of our business by describing what we need to accomplish in order to continue achieving sustainable, quality growth.

• People - Be a great place to work where people are inspired to be the best they can be.

• Portfolio - Bring to the world a portfolio of quality beverage brands that anticipate and satisfy people's desires and needs.

• Partners - Nurture a winning network of customers and suppliers, together we create mutual, enduring value.

• Planet - Be a responsible citizen that makes a difference by helping build and support sustainable communities.

• Profit - Maximize long-term return to shareowners while being mindful of our overall responsibilities.

• Productivity - Be a highly effective, lean and fast-moving organization.

Our Winning Culture

Our Winning Culture defines the attitudes and behaviours that will be required of us to make our 2020 Vision a reality.

Live Our Values

Our values serve as a compass for our actions and describe how we behave in the world.

• Leadership - The courage to shape a better future• Collaboration - Leverage collective genius• Integrity - Be real• Accountability - If it is to be, it's up to me• Passion - Committed in heart and mind• Diversity - As inclusive as our brands• Quality - What we do, we do well

Focus On The Market

• Focus on needs of our consumers, customers and franchise partners• Get out into the market and listen, observe and learn• Possess a world view• Focus on execution in the marketplace every day• Be insatiably curious

Work Smart

• Act with urgency• Remain responsive to change

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• Have the courage to change course when needed• Remain constructively discontent• Work efficiently

Act Like Owners

• Be accountable for our actions and inactions• Steward system assets and focus on building value• Reward our people for taking risks and finding better ways to solve

problems• Learn from our outcomes - what worked and what didn’t

Be the Brand

• Inspire creativity, passion, optimism and fun

Coca-Cola’s six strategic priorities

Accelerate carbonated soft-drinks growth led by coca cola - Coca Cola leads with their strengths. Carbonated soft drinks remain their most profitable business and Coca Cola is the most popular brand in the world. This strategy paves the way for growth.

Selectively broaden our family of beverage brands to drive profitable growth - Enormous opportunity exists in categories such as juice and juice drinks, bottled water, teas, energy drinks, coffee and more.

Grow system profitability and capability together with our bottling partners - Coca Cola is a company of relationships, and one of our most important relationships is the one we share with our bottling partners. In 2003, those relationships became more profitable and productive.

Serve customers with creativity and consistency to generate growth across all channels - We will continually strive to increase growth for the customer’s businesses, helping create a context for the company's growth.

Direct investments to highest-potential areas across markets - Coca Cola tailor their business approach to the individual marketplace based on its stage of development. In this way, we direct our investments in a way that makes the most business sense.

Drive efficiency and cost-effectiveness everywhere - By leveraging technology, creating alignment across business units and achieving economies of scale, we are able to operate with more efficiency.

Functional Aspects

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Every organization is made up of different departments, each of these departments help Coca Cola achieve their objectives. As Coca Cola is a large multinational company, the amounts of departments are huge. Each country has its own Head Office and departments. Coca Cola is geographically split into five geographic operating segments, also known as strategic business units (SBU's). The five SBU's are North America, Africa, Asia, Europe, Eurasia and Middle East and finally Latin America. There are 6 functional departments within Coca Cola, these are

Marketing Finance Packaging Sales Research and Development Administration

Marketing

The Coca Cola marketing department at the Atlanta Headquarters develops core strategies for company brands to ensure that all communication is consistent in every market. With this cohesive effort, the Coca-Cola system maximizes its resources for market leadership and profitable growth. The marketing departments are responsible for marketing the products and advertising the products and promoting the products. If all these departments perform their duty firmly then the objectives of The Coca-Cola Company will be met.

Finance

The finance department of the Coca Cola Company is responsible for financial record keeping. This involves keeping records of money received and paid out. The financial records will be used to produce the annual reports for the shareholders so that they can see the company performance. The Finance department is also responsible for the management accounts of the business like marketing etc. The Coca-Cola Company finance department is also responsible for making budget of the company and for each department like marketing department or research and development department. They will also be involved in the planning process like taking over or any major decision.

Packaging

The packaging department of The Coca-Cola Department is responsible for the packaging of the products. They have to make the packaging attractive so that that product meets the eyes of the consumers. Bringing

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new products package is their responsibility. It works with the companies bottling partners to produce an attractive combination.

Sales

The sales department of the Coca Cola Company is to coordinate the selling program. They also have to make the distribution methods, etc. Also, decide how much to sell and how much to store in the warehouse and to choose the transporting method which is the most cost efficient and the quickest way.

Research and Development

This department has their budget given by the finance department and their responsibility is to investigate new products. They work closely with marketing by looking at marketing research findings. They have to bring new products in the market for the change because the consumer cannot stick with the same old products. If necessary then they also have to improve the quality of the products. The Coca-Cola Company research department has done a lot of research and recently they have launched many new products like Diet coke with lemon, Fanta Tropical, Minute maids, Fanta raspberry, Fanta blue berry etc.

Administration

This department is essential for keeping the business going. They act as a help support of the company, it is not the central purpose the business but every business organization would need this department. Most businesses rely on administration to be organized. They deal with enquiries, give messages produce documents and give information to any customer. The complaints that this department will get would be transferred to the research and development department to make the product better or fix the problem that the consumer is having. These departments are the most important department of The Coca-Cola Company because they helps the company to meets the objectives of The Coca-Cola Company i.e. surviving, customer satisfaction and make more profits. As I said that the help desk department satisfies the customer by providing the information they needs and taking the complaints and passing to the research and development departments who improves the products.

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3. NEED FOR THE PROJECT

The Shipping Activities focus on ensuring that the retail outlets get the right quantity and quality of relevant SKU’s from the complete range of products manufactured by the company in a timely and cost effective manner. It is the critical link between the standardized manufacturing and local market dynamics.

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Therefore the main objective is to optimize fill rates in retail outlets at optimum costs, maximum quality and desired delivery standards.

The firm employs a fleet of trucks from the market through an intermediary, the Transporter. The transporter manages the entire fleet according to the delivery requirements. Though there is a strict focus on revenue growth, there are hidden inefficiencies that creep in.

The Project aims at identifying those and coming up with quantitative models and qualitative recommendations to fill the gaps and improve the

efficiency of the process. We focused on the transportation metric ,Truck turnaround time. This is calculated by measuring the average time elapsed between a truck's arrival at the facility and its departure. It indicates the efficiency in space utilization, receiving processes, and shipping processes.  It also directly affects the freight carrier profits on the business.

The firm manages its business with the set of trucks at prices as in the agreement with the transporter. But during the peak season ,the requirement of trucks go up than the forecasted estimates and the firm is compelled to employ trucks from the market at sky rocketed prices, the spot rates. This is an additional cost to the firm which could be avoided to a great extent by reducing the non-value adding components of the Truck’s TAT.

Reduction in TAT would lead to the existing trucks being able to make more trippages thereby cutting down on the number of vehicles required and the corresponding cost.

The second leg of the project arises from the need to improve the unloading productivity. The trucks usually adopt a two way route, which means that the truck reaches the facility with the empty RGBs(Returnable Glass Bottles) which gets unloaded before they can load the various SKU’s for the respective destination. The data suggests that the unloading productivity is nearly half of the loading productivity. The faster the truck unloads, the earlier it would be ready for loading. The peak season being a crucial time in maintaining the fill rates at the retail outlets, the need to improve the productivity in unloading becomes important.

4. DEFINATION AND SCOPE OF PROJECT

This will come under the Define phase of the project.

Project Objectives

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1. Understanding the processes involved in the logistics operation at HCCBPL,Nasik.2. Identifying the major contributors of the relatively higher Truck TurnAround Time and reduce it.3. Increase the Unloading productivity by increasing the vehicle and manpower utilization.

Project KPIs

1. Reduction in Average Waiting Time per truck = Waiting time as per the current system – Waiting time as per the proposed system.

2. Percentage improvement in the Unloading Productivity = Productivity as per the proposed solution – Productivity at present.

In Scope

1. Unloading Bay, the Loading Bay and the Storage Area2. Shipping Department at HCCBPL Nashik Plant.

Out of Scope

The D56 Depot/Warehouse outside the facility.

Net Benefits

1. Cost Savings2. Improvement in Productivity.3. Better Customer Service and thereby revenue growth.4. Better asset utilization both in terms of vehicles and auxiliary

equipments.

5. PROCESS FLOWCHART

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Figure 2 – Process flowchart of RGB production line

5.1 Process Flowchart

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Figure 3 – Process flowchart of Shipping Process

Project Roadmap

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The complete project was divided into five phases namely, Phase I - Define

A detailed study of the logistics activities at the facility. A clear understanding of the problems and inefficiencies associated.

Phase II - Measure Determining the data requirements, the critical data points and the

method of collection. Prepare data collection templates for the unloading and the loading

activities. Data collection and Organization of the data.

Phase III - Analyze Quantitative Analysis Identification of major contributors of the inefficiencies involved.

Phase IV - Improve Quantitative Model Qualitative Recommendations Experimentation with the proposed solutions.

Phase V – Control• To be implemented only after successful completion of the Improve

phase.

6. CONCEPTUAL REVIEW OF PROJECT

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The entire project includes the following major steps.

1. Identifying Management Dilemma: Triggers need for decision and symptom of actual problem. As per the discussion with the team it was found out that tracking the hidden inefficiencies in the shipping area was of extreme importance.

2. Define Management Question: Concerns with generating & evaluating solutions, choice of objectives e.g. what should be done, which one etc. After study of the process few problem areas were identified and a plan was made to track them.

3. Resource allocation & budget: For tracking the inefficiencies investments were not made substantially.

4. Research Proposal: In a meeting the problem was defined and actions to be taken to track were suggested and agreed upon finally.

5. Data Collection: Primary data Vs Secondary Data. A mix of primary and secondary data was used for the project. Data points were defined and a period of 15 days of data collection was agreed upon to ensure that it would be able to absorb all the spikes in the timings and will be consistent enough to work upon and come to conclusions.

6. Data are edited to ensure consistency & locate omissions. Reduces recording errors. Data coding reduces responses to more manageable system for storage & processing

7. Data Analysis & Interpretation: Managers need information not data. Develops summaries, looks for patterns by application of statistical tools, relationship among variables are explored.

8. Reporting Results: Necessary to report and transmit the findings & recommendations to managers for the intended purpose of decision making. Report should be developed from the client’s perspective.

Areas under study includes:1. Unloading Bay

The unloading bay receives the empty RGBs from the distributors. During the peak season(March-may), unloading happens 24X7. A maximum of two trucks can be unloaded simultaneously mainly due to unavailability of space. A supervisor is in charge of the unloading bay and notes down the number of crates unloaded from truck in terms of the number of ultra and full depth crates unloaded. An incentive of 4paise/crate is awarded to the unloading personnel compared to the Loading personnel since the process is slower due to the sorting and segregation of the bottles SKU wise into the respective pallets. If the number of unsorted bottles in crate is more than 4, then these crates are sent to the sorting bay for proper sorting. Palletization happens at the unloading bay.

2. Loading Bay

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The activity involves loading of the various SKUs onto those trucks lined up for dispatch to their destinations. The trucks are designated by the transporter according to their availability. The good practice of having the storage location close to the Loading area is followed here. The Loading is done SKU wise with 2 personnel per truck. The vehicle leaves the bay after the invoicing is done. Sometimes when additional products have to be added on from the warehouse, the invoicing is done there.

3. Storage Area within the Plant

The fulls are stored within the plant in accordance with the Quality policy. The shelf life of the products tend to reduce with higher temperature (direct sunlight) and time. A practice of First Expiry First Out is followed here. The products are stored SKU wise for ease of removal at the time of loading.

4. Shipping Department

The Order Allocation process for the trucks is done after the clearance is done in term of fund transfer and other processes. After the clearance, the loading commences and comes to an end with the final invoicing. The Loading and Unloading checklists are maintained. The warehousing function is solely managed by the shipping department.

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7. DATA COLLECTION FORMATS

Two different templates have been used for data collection at the Unloading and loading bays respectively. This was part of the primary data collection process.

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Apart from the primary data, secondary data has also been used for the analysis.It includes entry and exit timings of the trucks at the gates. Also the transporter, distributor and destination from the ERA (Empty Receipt Advice).

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8 ANALYSIS AND FINDINGS

Tools used for Analysis:1. Fish Bone Analysis2. Pareto Chart3. Process Mapping4. Ms Excel (Macros, Solver)5. Minitab

Data was collected for a period of 16 days at all the 4 points and relevant filtering was done to come up with refined data. Later on analysis of the same was done.

Some of the findings were

39% (241)

37% (227)

15% (93)

9% (59)

Transporter wise break-up

MTS RAJ SIMRAN OTHERS

FIG 4: Transporter wise breakup

The above figure shows the distribution of the transporters used over a period of 14 days. It is observed that MTS and RAJ are the two most favored transporters in the plant.

Around 9% of trucks are sourced from external sources wherein there is a possibility of incurring extra costs owing to the SPOT RATES charged by the suppliers.

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00:00 AM-00:59AM

01:00 Am-01:59 Am

02:00 AM-02:59 AM

03:00 Am-03:59 AM

04:00 AM-04:59 AM

05:00 AM-05:59 AM

06:00 AM-06:59 AM

09:00 AM-09:59 AM

10:00 AM-10:59 AM

11:00 AM-11:59 AM

12:00 NOON-12:59 PM

01:00 PM-01:59 PM

02:00 PM-02:59 PM

03:00 PM-03:59 PM

04:00 PM-04:59 PM

05:00 PM-05:59 PM

06:00 PM-06:59 PM

07:00 PM-07:59 PM

08:00 PM-08:59 PM

09:00 PM-09:59 PM

10:00 PM-10:59 PM

11:00 PM-11:59 PM

12 1114 12

5 3 2

1710 11

17

26 2621

3428

20 1914

510 12

Gate IN timewise distribution

Time slot (Hourly basis)

Num

ber

of t

ruck

s

FIG 5: Gate in timewise distribution

Attempts could be made to distribute the load uniformly across the day. There are periods of congestion and slack in a day.

Fish Bone Diagram

Fig-6: Fish Bone Diagram

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MACHINE

INFRASTRUCTURE

METHODS

MAN

High Queue Time

Absconding Drivers Labour Issues

Communication Gap

Forklift unavailability

Vehicle breakdown within the

facilitySpace related issues at

the unloading bayAdvanced

techniques of palletization

Truck Allocation

Lack of Information

Sharing

UNLOADING AREA FINDINGS

Time take to unload 100 cases is 9.89 minutes.(Average)

No of occassions above agv time

No of occassions below agv time

179228

Time taken to unload 100 cases

FIG 7: TIME TAKEN TO UNLOAD 100 CASES

In a day using calculations we can find that around 2625 cases extra can be unloaded . The inefficiencies reflect themselves in the form of worker utilization and effect on additional time taken for the process.

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00:00 AM-00:59AM

01:00 Am-01:59 Am

02:00 AM-02:59 AM

03:00 Am-03:59 AM

04:00 AM-04:59 AM

05:00 AM-05:59 AM

08:00 AM-08:59 AM

09:00 AM-09:59 AM

10:00 AM-10:59 AM

11:00 AM-11:59 AM

12:00 NOON-12:59 PM

01:00 PM-01:59 PM

02:00 PM-02:59 PM

03:00 PM-03:59 PM

04:00 PM-04:59 PM

05:00 PM-05:59 PM

06:00 PM-06:59 PM

07:00 PM-07:59 PM

08:00 PM-08:59 PM

09:00 PM-09:59 PM

10:00 PM-10:59 PM

11:00 PM-11:59 PM

1216

1921

13

51

4

24

13

2118

24

39

2826

28

17

28

1618

16

Unloading process time wise distribution

Time slot (hourly basis)

Num

ber

of t

ruck

s

FIG8: Unloading process time wise distribution

From this pivot table again we can observe the uneven distribution of load at times when the efficiency of workforce can be maximum owing to the favorable conditions of temperature and climate.

30

LOADING AREA FINDINGS

112 227

Time taken to load 100 cases

FIG 9: Loading time

Average loading time for 100 cases is 12.52 mins. Out of the data collected 112 occasions took more than average time for the loading.

90 minutes is the average waiting time before loading starts. In this duration an additional 545 cases can easily be loaded.

31

01:00 Am-01:59 Am

02:00 AM-02:59 AM

03:00 Am-03:59 AM

04:00 AM-04:59 AM

05:00 AM-05:59 AM

09:00 AM-09:59 AM

10:00 AM-10:59 AM

11:00 AM-11:59 AM

12:00 NOON-12:59 PM

01:00 PM-01:59 PM

02:00 PM-02:59 PM

03:00 PM-03:59 PM

04:00 PM-04:59 PM

05:00 PM-05:59 PM

06:00 PM-06:59 PM

07:00 PM-07:59 PM

08:00 PM-08:59 PM

09:00 PM-09:59 PM

10:00 PM-10:59 PM

11:00 PM-11:59 PM

5 5

1 1 1 2

10 11

1921

17

23

31

25

29

37

25

17

33

25

Loading process time wise distribution

Time slot (Hourly basis)

Num

ber

of t

ruck

s

FIG 10: LOADING PROCESS TIMEWISE DISTRIBUTION

The lean period can be observed from the graph here. If the sales order can be anticipated then the loading of the trucks can be done during the time which is not utilized well (which is approximately a 9 hour slot on an average)

The loading process gets delayed due to the absence of dedicated and consistent casual workforce and not arranging of the material to be loaded before the arrival of order.

The delay in arrival of pick list is also a reason though the delay time is not great here but definitely substantial.

32

Pareto Chart

FIG-11: PARETO CHART

Notations:

1. Queue Time.

2. (Waiting Time before unloading + Documentation before departure).

3. Loading Time.

4. Waiting Time before Loading.

5. Unloading Time.

Queue Time, a non-value adding activity contributes to around 63.4% of the total turn around time. Hence we identified this as the bottleneck activity.

33

Process Mapping

FIG-11: DISTRIBUTION PROCESS MAP

34

Q u eu in g acco rd in g to th e

to ken n u m b er

A rriva l at th e U n lo ad in g B ay.

U n lo ad in g P ro cess

O rd er C learan ce fo r Lo ad in g. Lo ad in g p ro cess.

In vo ice & F in a l D o cu m en tati o n .

D ep artu re

Components of the Truck Turn Around Time

S.No

Components Average Time Spent(mins)

1. Queue Time( Outside the plant)

344 (5 hrs 44 mins)

2. Queue Time ( Inside the plant )

232( 3 hrs 52 mins)

3. Waiting Time before Unloading

10

4. Unloading 65 (1 hr 5 mins)

5. Waiting Time before Loading

190 (3 hrs 10 mins)

6. Loading Time 57

7. Time for Final Documentation

15

8. Departure 5

FIG-13: COMPONENTS OF TURNAROUND TIME

The component wise distribution shows that the major contributors of the Truck’s TurnAround Time are:

1. Queue Time2. Waiting before Loading3. Unloading Time

Queue Time, a non-value adding activity contributes to around 63.4% of it. Hence we identified this as the bottleneck activity.

CURRENT SYSTEM

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Currently the trucks follow a First Come First Serve system wherein the truck that arrives first reports at the gate. The security personnel allots him the token number, his number in the queue and the truck arrives at the unloading bay accordingly.

The problems with kind of a system are many. In some cases, trucks carrying relatively lesser number of crates have to wait for hours together for the unloading process and then followed by the Loading process. At other times, the drivers anticipating a long queue time, go missing after the token number is taken, this causes the movement of the truck to be stalled for a long time. Such causes cumulatively result in wastage of time which otherwise could have resulted in the trucks making more trippages.

The current facility allows four trucks to be in the queue within the facility. The unloading bay can process two trucks at a given time. This is majorly due to the space constraint at the location.

PROPOSED SYSTEM

To deal with the menace of trucks waiting in a long queue and being unavailable for loading and dispatch, we proposed a system which is a combination of the First Come First Serve system and the Shortest Processing Time system. According to which the trucks that arrive at the facility is grouped in teams of four, and the one with the lowest number of crates is the first in the queue for unloading. It effectively means that the trucks are arranged in groups of four on the basis for arrival at the facility and then based on the shortest processing time within their group of four.

After these four trucks complete the unloading process, the truck having the lowest number of crates in the next group of four arrives at the unloading bay.

Now this system comes with huge benefits. The trucks with lesser number of crates get unloaded faster which means that they are available for loading sooner. The faster the unloading process, the earlier the trucks would be available for loading.

This means quicker the dispatch, the delivery to the customer and most importantly higher the trips made by the truck. The higher the number of trips made by a vehicle, on an annual basis lower will be the number of trucks employed from the market. Long queue time result in the movement of trucks being stalled, thereby the need to employ additional trucks from the market to meet the current requirement. This is a cost to the company since during the peak season, the scenario becomes critical.

36

The trucks have to employed at market spot rates which are much higher than the usual negotiated rates.

A thorough quantitative analysis has been done for the current and the above proposed system.

Quantitative Analysis

Tool Used: Microsoft Excel (Macros)

Unloading Bay-1(FCFS)

Unloading Bay-2(FCFS)

No. of Crates handled

Truck Load

Processing Time

Waiting Time

Total Time

Truck Load

Processing Time

Waiting Time

Total Time

0 220 22 0 22 350 35 0 35550 550 55 22 77 0 0 35 35

1169 0 0 77 77 1169 116.9 35 151.9268 268 26.8 77 103.8 0 0 151.9 151.9

1093 1093 109.3 103.8 213.1 0 0 151.9 151.9973 0 0 213.1 213.1 973 97.3 151.9 249.2334 334 33.4 213.1 246.5 0 0 249.2 249.2269 269 26.9 246.5 273.4 0 0 249.2 249.2425 0 0 273.4 273.4 425 42.5 249.2 291.7

1153 1153 115.3 273.4 388.7 0 0 291.7 291.7281 0 0 388.7 388.7 281 28.1 291.7 319.8887 0 0 388.7 388.7 887 88.7 319.8 408.5

1002 1002 100.2 388.7 488.9 0 0 408.5 408.5961 0 0 488.9 488.9 961 96.1 408.5 504.6299 299 29.9 488.9 518.8 0 0 504.6 504.6417 0 0 518.8 518.8 417 41.7 504.6 546.3572 572 57.2 518.8 576 0 0 546.3 546.3

1089 0 0 576 576 1089 108.9 546.3 655.2369 369 36.9 576 612.9 0 0 655.2 655.2480 480 48 612.9 660.9 0 0 655.2 655.2158 0 0 660.9 660.9 158 15.8 655.2 671208 208 20.8 660.9 681.7 0 0 671 671385 0 0 681.7 681.7 385 38.5 671 709.5

1126 1126 112.6 681.7 794.3 0 0 709.5 709.5609 0 0 794.3 794.3 609 60.9 709.5 770.4

1059 0 0 794.3 794.3 1059 105.9 770.4 876.3798 798 79.8 794.3 874.1 0 0 876.3 876.3

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635 635 63.5 874.1 937.6 0 0 876.3 876.3493 0 0 937.6 937.6 493 49.3 876.3 925.6805 0 0 937.6 937.6 805 80.5 925.6 1006.1572 572 57.2 937.6 994.8 0 0 1006.1 1006.1420 420 42 994.8 1036.8 0 0 1006.1 1006.1611 0 0 1036.8 1036.8 611 61.1 1006.1 1067.2

1150 1150 115 1036.8 1151.8 0 0 1067.2 1067.2184 0 0 1151.8 1151.8 184 18.4 1067.2 1085.6

1058 0 0 1151.8 1151.8 1058 105.8 1085.6 1191.4757 757 75.7 1151.8 1227.5 0 0 1191.4 1191.4891 0 0 1227.5 1227.5 891 89.1 1191.4 1280.5882 882 88.2 1227.5 1315.7 0 0 1280.5 1280.5969 0 0 1315.7 1315.7 969 96.9 1280.5 1377.4

1097 1097 109.7 1315.7 1425.4 0 0 1377.4 1377.4710 0 0 1425.4 1425.4 710 71 1377.4 1448.4473 473 47.3 1425.4 1472.7 0 0 1448.4 1448.4918 0 0 1472.7 1472.7 918 91.8 1448.4 1540.2

1116 1116 111.6 1472.7 1584.3 0 0 1540.2 1540.2881 0 0 1584.3 1584.3 881 88.1 1540.2 1628.3455 455 45.5 1584.3 1629.8 0 0 1628.3 1628.3625 0 0 1629.8 1629.8 625 62.5 1628.3 1690.8534 534 53.4 1629.8 1683.2 0 0 1690.8 1690.8

Table-1:Waiting Time under FCFS System

Calculations:

Total Waiting Time= 38048.6 minutes.

Average Waiting Time= 576.502 minutes (9 hrs 36mins)

Unloading Bay-1(SPT)

Unloading Bay-2(SPT)

No. of Crates handled

Truck Load

Procesing Time

Waiting Time

Total Time

Truck Load Procesing

TimeWaiting

TimeTotal Time

0 220 22 0 22 350 35 0 35268 268 26.8 22 48.8 0 0 35 35550 0 0 48.8 48.8 550 55 35 90

1093 1093 109.3 48.8 158.1 0 0 90 901169 0 0 158.1 158.1 1169 116.9 90 206.9269 269 26.9 158.1 185 0 0 206.9 206.9334 334 33.4 185 218.4 0 0 206.9 206.9425 0 0 218.4 218.4 425 42.5 206.9 249.4973 973 97.3 218.4 315.7 0 0 249.4 249.4281 0 0 315.7 315.7 281 28.1 249.4 277.5

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887 0 0 315.7 315.7 887 88.7 277.5 366.21002 1002 100.2 315.7 415.9 0 0 366.2 366.21153 0 0 415.9 415.9 1153 115.3 366.2 481.5299 299 29.9 415.9 445.8 0 0 481.5 481.5417 417 41.7 445.8 487.5 0 0 481.5 481.5572 0 0 487.5 487.5 572 57.2 481.5 538.7961 961 96.1 487.5 583.6 0 0 538.7 538.7158 0 0 583.6 583.6 158 15.8 538.7 554.5369 0 0 583.6 583.6 369 36.9 554.5 591.4480 480 48 583.6 631.6 0 0 591.4 591.4

1089 0 0 631.6 631.6 1089 108.9 591.4 700.3208 208 20.8 631.6 652.4 0 0 700.3 700.3385 385 38.5 652.4 690.9 0 0 700.3 700.3609 609 60.9 690.9 751.8 0 0 700.3 700.3

1126 0 0 751.8 751.8 1126 112.6 700.3 812.9493 493 49.3 751.8 801.1 0 0 812.9 812.9635 635 63.5 801.1 864.6 0 0 812.9 812.9798 0 0 864.6 864.6 798 79.8 812.9 892.7

1059 1059 105.9 864.6 970.5 0 0 892.7 892.7420 0 0 970.5 970.5 420 42 892.7 934.7572 0 0 970.5 970.5 572 57.2 934.7 991.9611 611 61.1 970.5 1031.6 0 0 991.9 991.9805 0 0 1031.6 1031.6 805 80.5 991.9 1072.4184 184 18.4 1031.6 1050 0 0 1072.4 1072.4757 757 75.7 1050 1125.7 0 0 1072.4 1072.4

1058 0 0 1125.7 1125.7 1058 105.8 1072.4 1178.21150 1150 115 1125.7 1240.7 0 0 1178.2 1178.2882 0 0 1240.7 1240.7 882 88.2 1178.2 1266.4891 891 89.1 1240.7 1329.8 0 0 1266.4 1266.4969 0 0 1329.8 1329.8 969 96.9 1266.4 1363.3

1097 1097 109.7 1329.8 1439.5 0 0 1363.3 1363.3473 0 0 1439.5 1439.5 473 47.3 1363.3 1410.6710 0 0 1439.5 1439.5 710 71 1410.6 1481.6918 918 91.8 1439.5 1531.3 0 0 1481.6 1481.6

1116 0 0 1531.3 1531.3 1116 111.6 1481.6 1593.2455 455 45.5 1531.3 1576.8 0 0 1593.2 1593.2534 534 53.4 1576.8 1630.2 0 0 1593.2 1593.2625 0 0 1630.2 1630.2 625 62.5 1593.2 1655.7881 881 88.1 1630.2 1718.3 0 0 1655.7 1655.7

Table-2: Waiting Time Under SPT System

Calculations:

The Total Waiting Time is 37288.6 minutes

The Average Waiting time is 560.99 minutes (9 hrs 20 minutes)

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Thereby, a reduction of 16 minutes in the waiting time per truck with the current set of data.

Steps for the Current System:

1. The primary data collected was tabulated with the table containing Arrival time of the truck, the number of crates handled, their processing and waiting times.

2. The data was organized in terms of the arrival time of the trucks.3. The average processing time for 100 crates was estimated and used

for further calculations.4. The trucks were allotted to the two slots at the unloading bay as per

the availability.5. The waiting time for each truck and the cumulative waiting time are

calculated.6. Steps (4) & (5) are executed using the program in macros (MS

Excel).7. The total waiting time and the average waiting time is calculated.

Steps: Proposed System

1. The primary data collected was tabulated with the table containing Arrival time of the truck, the number of crates handled, their processing and waiting times.

2. The data was divided in groups of four and within them arranged in the ascending order of the number of crates handled.

3. The trucks were allotted to the two slots at the unloading bay as per the availability at the bay.

4. The waiting time for each truck and the cumulative waiting time are calculated.

5. Steps (4) & (5) are executed using the program in macros (MS Excel).

6. The total waiting time and the average waiting time is calculated.7. The difference in waiting times for the current system and the

proposed system is calculated.8. Step (7) is repeated by generating random number of crates and re-

executing the program.

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9. After around 100 such executions, the average reduction in waiting time was found to be 14.365 minutes per truck.

The Current Requirement:

Type of Vehicle

Time in mins

Standard Loadability

Estimated Annual Trippages

Required No. of Vehicles

LCV 525600(365*24*60)

350 2024 92

HCV 525600(365*24*60)

600 3542 236

On an annual basis, the estimated number of trippages to be made is :

For LCV’s = 2024

For HCV’s = 3542.

This is by considering the standard loadibility for LCVs and HCVs as 350 and 600 respectively.

Thereby the annual requirement for vehicles to be employed from the market is:

LCV’s = 92

HCV’s = 236

41

The Proposed System and the corresponding Requirement

With the proposed system, the waiting time per truck reduces by 14.365 minutes. This means that the trucks are on the run and would be able to make more trippages. As the trucks would be able to make more trippages, the requirement of the number of trucks to be employed would reduce.

Type of Vehicle

Time in mins

Average Reduction in Queue Time

Annual Trippages that can be made

No. of Vehicles Required

Reduction in the number of vehicles (Annual)

LCV 525600

14.365 2142.518 87 5

HCV 525600

14.365 3921.635 214 22

The number of trippages that can be made is

For LCV’s =2142

For HCV’s = 3921

Thereby the new annual requirement of vehicles is as follows

LCV’s = 87, a reduction of 5 vehicles

HCV’s = 214, a reduction of 22 vehicles

This can be huge in terms of savings for the company.

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QUALITATIVE RECOMMENDATIONS:

1. Accurate information sharing between the distributor and the shipping department regarding the arrival of trucks at the facility. Thereby a slotting system can be made to make the arrival of trucks evenly spread during the day.Benefits: a. At least a few hours of truck’s waiting time before unloading can

be avoided.b. Workload would be evenly spread at the site, which means

avoiding peak hours.

Implementation :

a. Communicate rules for early and late arrival in terms of penalties or incentives. Enough flexibility should be given to long haul transport. The planning can be basically kept at 70 %.

b. Implementing an IT based slotting system would avoid the manual administrative work and total avoidance of human errors.

2. The current layout of the unloading bay can be enhanced by levelling up the heightened space behind the bay. This would provide the space for unloading one more truck i.e. three trucks at one go. The pallets around the truck need to be arranged in an organized manner to enable smooth traffic flow especially for the forklifts.

Benefits :

a. Enables better space and forklift utilization. This will effectively lead to a faster unloading process. Faster unloading process points to an early dispatch of fulls to the customer.

b. Avoidance of delays before the next unloading due to cramming up of the space. This can be due to pallets lying idle, the forklift not being able to access them or arrangement of the pallets and crates in a disorderly fashion.

Best Practice:

The unloading bay should be located near to the gate whereas the loading bay should be located near to the production facility. This is adequately followed at the facility.

43

3. Distributors could be given incentives for ordering Full Truck Loads (FTLs) or at least co-operating for the milk run function.

Benefit: This would result in an increase in vehicle utilization. It could further lead to a reduction in the number of trips/orders.

4. Checklists and Standard operating procedures need to be followed for the loading and unloading activities.Benefit: The process would become less error prone and can also help in terms of time saving. For example, if the invoicing is done while the loading takes place, the time that it takes for documentation after the loading process can be avoided. This means faster the truck exits from the facility. It calls for two advantages one, that of the truck reaching its destination early, the other that of the truck moving out of the facility and thereby making space for the next truck to begin with the loading process.

5. Ready availability of auxiliary equipments like forklifts and pallets. Especially during the peak season, a forklift breakdown would prove costly. Hence proper maintenance should be done as well as agreement should be reached with the forklift providers in case of an emergency.Benefit: This would lead to an improvement in productivity both in case of the loading and unloading activities.

6. A high end solution to think of would be the option of Automated Truck Loading Systems. With a financing option of leasing, the investment can be recovered in around 3years. Implementation hassles include changes in infrastructure.Benefits: The loading time is just 3 minutes.

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9. IMPROVEMENT OF UNLOADING PRODUCTIVITY

Unloading is the bottleneck activity and consumes more time because the empties have to be sorted SKU wise. This is now critical since unless the truck gets unloaded it cannot proceed for loading and thereby dispatch. The process being comparatively slower not much can be done on that front without substantial investment in infrastructure. Thus the efficiency of the Unloading personnel becomes important.

The Unloading and Loading activity are done by personnel employed from the Mathadi union and as Casual Labour. The Mathadi workers are employed on a permanent basis whereas the Casual Labour employment is based on the requirement. The payment for their services is based on the piece rate wage system.

After extensive observation and data collection, it was observed that the skill or efficiency level of the Mathadi workers exceed that of those workers employed as casual labour.

Data Collection:

Casual Labour Productivity

Date Loading (Crates)

Unloading (Crates)

Number of CLs

in Loading

Number of CLs in

Unloading

Amount

Earned

Loading Productivity (day wise)

Unloading Productivity (day wise)

01-Apr 10410 8527 8 11 4250 1301.25 775.1802-Apr 7715 9923 4 10 4066 1928.75 992.3003-Apr 5700 8136 3 9 3255 1900.00 904.0004-Apr 550 2765 1 5 792 550.00 553.0005-Apr 4400 9932 3 11 3420 1466.67 902.9106-Apr 7440 6505 4 7 3179 1860.00 929.2907-Apr 0 7055 0 13 1834 0.00 542.6908-Apr 10200 13764 7 14 5534 1457.14 983.1409-Apr 0 13798 0 18 3587 0.00 766.5610-Apr 7095 9482 6 16 3885 1182.50 592.6311-Apr 8005 13683 4 14 5091 2001.25 977.3612-Apr 4405 10483 3 10 3607 1468.33 1048.3013-Apr 2875 11603 3 9 3536 958.33 1289.2214-Apr 0 3845 0 6 864 0.00 640.8315-Apr 930 12235 1 9 3283 930.00 1359.4416-Apr 10775 10074 7 13 4775 1539.29 774.9217-Apr 8185 8012 4 9 3670 2046.25 890.22

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18-Apr 5930 9222 3 14 3584 1976.67 658.7119-Apr 3809 14471 3 17 4524 1269.67 851.2420-Apr 6120 8679 3 10 3481 2040.00 867.9021-Apr 2560 8227 1 6 2597 2560.00 1371.1722-Apr 15856 10816 12 10 5984 1321.33 1081.6023-Apr 5465 14774 3 15 4934 1821.67 984.9324-Apr 6370 7640 5 9 3261 1274.00 848.8925-Apr 4883 8357 3 11 3149 1627.67 759.7326-Apr 5097 8979 5 10 3354 1019.40 897.9027-Apr 2950 9490 3 12 3058 983.33 790.8328-Apr 7832 7588 6 3 3486 1305.33 2529.3329-Apr 11120 6132 6 8 3819 1853.33 766.5030-Apr 6163 9932 3 11 3761 2054.33 902.91

Table-3: Casual Labour Productivity

Mathadi Productivity

Date Loading (Crates)

Unloading (Crates)

Number of CLs

in Loading

Number of CLs in

Unloading

Amount

Earned

Loading Productivity (day wise)

Unloading Productivity (day wise)

01-Apr 5582 0 2 0 1116 2791.00 0.0002-Apr 13780 3789 5 2 3741 2756.00 1894.5003-Apr 17477 1548 7 1 3898 2496.71 1548.0004-Apr 18500 2351 7 1 4311 2642.86 2351.0005-Apr 12647 837 6 1 2747 2107.83 837.0006-Apr 18322 400 7 0 3768 2617.43 0.0007-Apr 11225 0 3 0 2245 3741.67 0.0008-Apr 4495 0 3 0 899 1498.33 0.0009-Apr 16085 2985 6 2 3952 2680.83 1492.5010-Apr 14905 3060 5 1 3707 2981.00 3060.0011-Apr 14170 0 7 0 2834 2024.29 0.0012-Apr 12267 3452 6 2 3351 2044.50 1726.0013-Apr 12688 3135 5 3 3284 2537.60 1045.0014-Apr 8065 0 2 0 1613 4032.50 0.0015-Apr 8570 0 4 0 1714 2142.50 0.0016-Apr 10310 3719 4 3 3029 2577.50 1239.6717-Apr 12225 3186 4 2 3206 3056.25 1593.0018-Apr 13373 3160 5 1 3396 2674.60 3160.0019-Apr 12340 1501 5 1 2768 2468.00 1501.0020-Apr 15195 1005 6 1 3240 2532.50 1005.0021-Apr 3834 955 2 0 973 1917.00 0.0022-Apr 10127 0 3 0 2025 3375.67 0.00

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23-Apr 14122 0 7 0 2824 2017.43 0.0024-Apr 12056 1575 5 1 2726 2411.20 1575.0025-Apr 9389 1025 6 1 2083 1564.83 1025.0026-Apr 9336 0 6 0 2867 1556.00 0.0027-Apr 16102 0 7 0 3220 2300.29 0.0028-Apr 4290 0 2 0 858 2145.00 0.0029-Apr 8940 0 4 0 1788 2235.00 0.0030-Apr 14370 4429 5 2 3926 2874.00 2214.50

Table-4:Mathari Labour Productivity

Summarized, It shows that the Productivity for the Unloading and Loading activities are as follows:

S.No.

Type Activity Productivity

1. Casual Labour Unloading 887

2. Casual Labour Loading 1516

3. Mathadi Workers Unloading 1684

4. Mathadi Workers Loading 2430

The unloading process being tedious, it is incentivised by an additional 4 paise than the loading piece rate wages. But inspite of this the proportion of workers involved in the two activities is far from equal.

The data for the month of April is:

47

S.No. Type Activity Number of Man-days

As a % of the type

As a % of Total Workers

48

1. Casual Labour

Unloading 320 73.73 52.89

2. Casual Labour

Loading 114 26.27 18.84

3. Mathadi Workers

Unloading

25 14.62 4.13

4. Mathadi Workers

Loading 146 85.38 24.13

This points out that only 14% of the Mathadi workers engage themselves in the Unloading activity whereas their efficiency is 1.8 times that of the casual labour.

On the Casual Labour side, 73% of them are involved in the unloading activity.

Because of the high productivity of the Mathadi workers, they tend to earn more at the Loading site and hence the mass migration of them towards the Loading activity.

On an average a manday at the loading site earns them Rs. 303 whereas at the unloading site earns them only Rs. 213.

Analysis:

Tool Used for Optimization: Microsoft Excel (Solver)

The Current Scenario:

Type Loading Unloading Total

49

CLs 114 320 434

Mathadi 146 25 171

Productivity 2029.246 945.58 1411.352

This is with majority of the Mathadi workers at the Loading site and the Casual Labour group at the Unloading site.

Hence for optimization , i.e. to obtain the best possible productivity, the ideal distribution of Mathadi and Casual Labour workers.

Proposed Solution:

Type Loading Unloading Total

CLs 114 320 434

Mathadi 129 42 171

Productivity 2020 1018.2 1411.5

Interpretation:

To pull the Mathadi workers towards unloading, the unloading activity needs to be further incentivised. But the increase in the productivity would be substantial.

The proposed solution would increase the unloading productivity by 7.75 %.This would require shifting of 17 mathadi workers to the unloading site.

The additional cost to the company would be Rs. 1724 because the Loading productivity would go down by a thin margin.

The company will also incur the extra cost of incentivising the Unloading activity further.

10. BIBLIOGRAPHY/REFERENCES

1. www.cocacola.com

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2. www.wikipaedia.com

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