To create a Inventory Management System

A Master’s Project Proposal


Inventory Management Application for Small Retail Stores In India

By
Soubhagya Nayak

Submitted to the faculty of the graduate school of the
Rochester Institute of Technology
Department of Information Technology

Committee Chairperson: Dr. Deborah Gears
Committee Member: Prof. Rayno Niemi






Table Of Contents


1. Abstract 3
2. Introduction 4
3. Problem Statement 5
4. Literature Review 8
5.Proposal 12
6. Methodology 14
6.1 Analysis & Design: 14
6.2 Development: 16
6.3 Testing &Deployment: 19
7. Deliverables 19
8. Schedule 20
9. References: 21











1. Abstract

Small retail stores known as ‘Kirana’ stores in India are facing stiff competition from organized retail chains and supermarkets. To compete with these organized retail stores and maximize their profit, the Kirana stores need to manage their inventory efficiently. Inventory management plays a crucial role in the success of a Business. A well-organized inventory management system can add significant value to a business as organizations invest 45% to 90% on inventory. Every organization irrespective of its size manages and controls inventory for customer satisfaction and revenue generation; the same principle applies to the small retail stores in India. These small retail stores need a forecasting and inventory management technique by using an inventory management application that will help them to visualize the future requirement and the movement of the stock. This proposed application will help them to minimize inventory cost and stock out situations; so, they can maximize profit and retain customers by fulfilling customer demand and providing customer satisfaction.





2. Introduction

Retail Industry in India is growing rapidly. According to Business Monitor International (BMI) the retail sales in India will grow from $395.96 billion in 2011 to $785.12 billion by 2015 [Venkatesh, 2011,para.3]. To this day, the major players in the Indian retail industry are the small retail stores across the country, the equivalent of Mom-and-Pop stores in the USA. The Indian version of these Mom-and-Pop stores is called the ‘Kirana’ stores (or ‘Dokans’ in some regions) and the size of the store varies from one to ten employees. These stores are owned by single family or households. Kirana stores are mostly located in rural and semi urban areas, and towns in India. These small retail stores contribute 98% of total retail sales in India [Agarwal, 2009, para. 3].
However, since last few years when India had opened its doors to Foreign Direct Investment (FDI) in retail industry, these small retail stores (Kirana stores) have been facing a major threat from Indian Supermarket chains such as Big Bazaar, Subhikhya, Reliance World as well as global retail giants like Wal-Mart. These Organized supermarkets use state of the art technology to manage their whole supply chain and hence, they never face the over stocking and under-stocking problem. To stay in business, the small stores need an information system to manage and cut the inventory cost by reducing the unwanted inventory. To keep the exact amount of stock, they need a forecasting system as well as accurate information about the products. By visualizing the future and getting accurate and on time information, they can take wise decision and minimize the cost associated with the inventory and hence maximize the profit.
3. Problem Statement

Small retail stores are facing strong competition from organized retail stores and supermarkets due to the FDI in retail industry in India. These stores have limited working capital to run the business. In order to survive the competition and retain the customers, they need to utilize the capital available to them in an efficient manner. These stores invest more on products than any other resources to run the business. Kirana stores have very limited capital to invest in the business, so they should invest it wisely and get the most return on investment (ROI). Inventory consumes most of their investments and they maintain a wide variety of products, so it is important for them to get accurate information about the products. To get all the required information to manage their inventory, Kirana stores rely on their intuition or follow a cumbersome method by writing on paper/notebook. Lack of inventory records leads to mismanagement of stock and adds to administrative costs (“Tech Boost For Kirana,” n.d., Para.5). The most important thing for them is to minimize the cost associated with the inventory, so that they can maximize the profit. To stay in business, these stores need to cut down their inventory cost and boost their sales. The Kirana stores need to be organized enough to maintain the needed inventory at the time of requirement and thus reducing the unwanted stock.
The Kirana stores keep stocks of hundreds of different product; these products include Fast Moving Consumer Goods (FMCG), grocery items, food items, school stationary and beverages and to name a few. They manage inventory completely based on their intuition and past experience, hence most of the time they face stock out situation and overstocking problem. The stock out situation creates a lot of customer dissatisfaction and forces them to prefer the organized retail stores. Without proper information regarding the inventory movement and trend, they keep more stocks than that are required. Due to the over stocking of the products, they loose a considerable amount of profit because of the product expiration. They often carry over a huge seasonal inventory from one season to the next due to overstocking as they don’t have information or resources to forecast demand. Hence, the capital invested on those products now becomes a liability till the next season and ultimately is affecting the business. In most cases the seasonal products expire or sometimes the demand shifts towards another product in the next season, hence the Kirana Stores unable to sell these products and incur a huge loss.
Another problem these Kirana stores face is inability to distinguish between slow moving and fast moving goods. The Store owners normally look back to the last month’s sale to estimate the quantity of new products to be purchased. Most of the time, the sale made in the previous month can never be used to predict with a fair degree of accuracy the sale for the following month(s), so it results either overstocking or out-of-stock situations. When a store keeps a product for a long time, it results in customer dissatisfaction due to the buying behavior of the customers in India. A lot of buyers in India look for the fresh product even if it is a non-edible, non-perishable product; they always look for the manufacturing date of the product before buying. If the product is a few months old, the store loses its goodwill and hence customers move to a different store or prefer organized retail stores where they can find fresh inventory all the time. Instead of throwing out the expired products, if the Kirana stores know in advance that some products are nearing expiry or sitting on an isle for a long time (in the case of non-perishable goods), they can organize promotional events or offer discounts on the products. In this way small businesses can retain customers as well as get some ROI instead of complete loss.
The store owners don’t get a periodic overview of the inventory movement, they don’t get information about the product by product category and brand, hence it hinders their decision making ability.
















4. Literature Review

Kirana stores dominate Indian retail market. According to Tech Boost for Kirana “The un-organized sector constitutes over 95% of retail business, which includes Kirana stores, wholesalers and distributors.” (n.d., Para.2). However, organized retail stores are gaining popularity in India nowadays. The organized retail stores provide the best customer satisfaction experience by providing quality products throughout the year. They never experience under stocking and overstocking issues, thanks to proper management of the inventory they carry by using sophisticated computer applications that forecast and manage the inventory requirements of the business. On the other hand, Kirana stores rely on the traditional pen and paper method to store Inventory related information. According to Tech Boost for Kirana, “Most processes are manual without any IT support to keep a tab of inventory, forecast demand or connect with other wholesalers and vendors. This limits their capacity to capture and store information to proactively increase their offerings in sync with the needs of the consumer.” (n.d., para.3). Getting valuable information from this traditional system is never possible as it is difficult to store these records and get information out of that data stored in paper.
Organized retail stores focus more on inventory management as inventory plays a major role in business’ success. Control of inventory, which typically represents 45% to 90% of all expenses for business, is needed to ensure that the business has the right goods on hand to avoid stock-outs, to prevent shrinkage (spoilage/theft), and to provide proper accounting (Score,2002, para.1). Inventory management is the first and the most important thing for retail business. Inventory investment directly affects profit and cash flow. The management of inventory for a company that sells products is crucial to the success of the company” (Peavler, para.1).
An effective Inventory Management system needs a forecasting technique to plan and manage resources. Forecasting sales can help the business to visualize the future requirement of the products and hence help the business to stock the ideal quantity of product at the right time. According to Hoshmand "Forecasting is an integral part of the planning and control system, and organizations need a forecasting procedure that allows them to predict the future effectively and in a timely fashion" (2009,p.2).
Big organizations use sophisticated analytics and business intelligence application to get accurate and on-time information about their products and sales. Due to the complexity and cost associated with these applications, it is not possible for small retail stores to use these applications. However, there are several statistical models that can be used to create an application for the small retail stores. Holts Exponential smoothing method is one of the most popular methods for forecasting time series data.
Holt’s exponential smoothing method is a very popular method for forecasting due to the simplicity of the model compared to the ARIMA model. It requires fewer inputs from users and gives better results compared to other similar forecasting models. The extension of Holt’s model is known as Holt-Winters model. Holt-Winters methods allow us to deal with univariate time series, which contain both trend and seasonally factors. Their popularity is due to their simple model formulation and good forecasting results (Gardner, 1985). Due to its simplicity, most business uses this technique to forecast demand. Most of the big database vendors like Oracle and Microsoft apply Holts-Winter method for forecasting. Many companies use the Holt-Winters (HW) method to produce short-term demand forecasts when their sales data contain a trend and a seasonal pattern. Fifty years old this year, the method is popular because it is simple, has low data-storage requirements, and is easily automated. It also has the advantage of being able to adapt to changes in trends and seasonal patterns in sales when they occur (Goodwin, 2010).
Economic Order Quantity (EOQ) is the second most important factor in inventory management. EOQ is defined as the quantity that should be ordered in one order to reduce the inventory cost. According to Muller max “why should we care about the financial aspect of inventory? Because Inventory is money” (Muller, 2002). So, it is important to consider the inventory cost and by determining the EOQ, it can be minimized. Another advantage of the EOQ model is that it provides specific numbers particular to the business regarding how much inventory to hold, when to re-order it and how many items to order. This smoothes out the re-stocking process and results in better customer service as inventory is available when needed (Hosfeld, n.d.,Para.3).
It is also important to calculate re-order level. Re-order level determines when an order should be placed based on the current demand and lead-time to avoid stock out situation. Another inventory control mechanism is to determine slow moving and fast moving products. By distinguishing products based on the sales frequency, business can keep optimized, ideal quantities of stock depending on whether or not the goods are fast moving. Seasonality is a major factor in retail business. So, the inventory management system should deal with seasonality of certain goods enabling businesses to store exact quantity for the current season. Seasonal products if not sold in a particular season will consume a lot of working capital. There is also a risk of damage to these products when stored for long durations. Most of the time these products become obsolete as the trend shifts rapidly towards new product.












5.Proposal

I propose to develop an inventory management solution for the small retail stores in India that can forecast sales, manage inventory and provide periodic overview of the movement of goods in the stores. Business can generate periodic inventory reports of based on the product movements, product category, brand and seasonality, hence can distinguish slow moving and fast moving products by category. This will help the small store stock the optimum amount of products based on the demand.
It will help the Kirana stores by forecasting the seasonal demand for products, so that the store owners can keep accurate inventory for these very specific products. By storing accurate stock, the business can reduces the inventory carrying cost.
This application will forecast sales for seasonal and non-seasonal products, determine slow moving and fast moving products based on sales. It will generate periodical reports about the products that are going to be expired soon based on the expiration date. The application will forewarn the business to sell those products that entered first in to the inventory based on First In First Out method (FIFO) by determining the product purchase date. Hence, it will enhance the customer satisfaction by providing new/fresh products, and it also ensures that older units do not get stacked away and proper rotation of stored goods does happen. This application will help the business to determine EOQ to reduce the inventory cost based on the EOQ formula. To avoid stock out situations, it will help the business to determine re-order level based on mathematical formulas and product demands. It will also give the business a periodical view of the movement of inventory by generating reports based on the sale. Business can compare the sales of a particular product based on different brands, so that they can focus more on the brands that have more sales. This application will help in determining the net worth of stock in inventory that will help the business to understand the capital investment in the business. This application will help the business to avoid the stock out and overstocking problems. These functionalities of this application will be obtained through inventory control models and statistical and mathematical formulas. In future, this module can be integrated with the finance module to help generating profit loss account, balance sheet, cost sheet etc. It can also be integrated with sales and purchase module to get automated sales and purchase data through barcoding technology and also provide valuable information regarding the sale and purchase of products.







6. Methodology
6.1 Analysis & Design:
In this phase the requirements will be analyzed and the design of the application will be created. Data and object analysis will be conducted. UML and ER diagrams will be created and eventually developed further with the development of the application. Below are the two context diagrams that depict the data flow. The V-0 is the high level view of the system and the V-1 is drilled down view of the system. In these diagrams the rectangular object represents external entity those communicate with the system. Process performs task and activities by communicating with the other objects in data flow diagram (DFD). The data stores are represents the objects that store the data. The single arrow mark shows the information flow between the objects.
Below are the graphical representation of Entity, Process and Data Stores that are being used in the data flow diagrams.
Process Data Store Entity




Context Diagram V-0
Figure-1





Context Diagram V-1

Figure-2

6.2 Development:
The Inventory Management application will be a Windows based application developed using C# (Object Oriented Programing language) as the front end and MySQL database as the backend to store the data. The application would be based on a stand-alone system. Using the UI, users can enter data and view the reports. For the scope of this project, the sales and purchasing data required for inventory management will be entered manually through an interface. These sales and purchase data will be collected from the purchase order and from the sales record. In future scope, this application will be enhanced to get data from the Point of sales system and purchasing module by reading the barcode.
Time series analysis model will be used to analyze the data and exponential smoothing technique will be used for forecasting. For other inventory related calculations, the mathematical/inventory models will be used. Mentioned below, are the models and formulas that will be used for this inventory management application.
6.2.1 Holt’s Exponential smoothing model (Hoshmand, 2010, Chapter 5)


A_t=αY_t+(1-α)(A_(t-1)+T_(t-1)) (Eq-1)
T_t=β(A_t-A_(t-1) )+(1-β)T_(t-1) (Eq-2)
Y ̂_(t+x)=A_t+xT_t (Eq-3)
A_t = Smoothed value
α= Smoothing constant (0< α<1)
β = Smoothing Constant (0< β<1)
T_t=Trend estimate
x = Periods to be forecast into future
Y ̂_(t+x) =Forecast for x periods into the future


6.2.2 Winters’ seasonal Exponential Smoothing model (Hoshmand, 2010, Chapter 5)

Winters seasonal exponential smoothing is an extension of Holt’s formula. It adds another smooth factor known as seasonality and commonly known as Holt-winters model.
A_t=α Y_t/I_(t-1) +(1-α)(A_(t-1)+T_(t-1)) (Eq-1)
T_t=β(A_t-A_(t-1) )+(1-β)T_(t-1) (Eq-2)
I_t= γ Y_t/A_t +(1-γ) I_(t-L) (Eq-3)
Y ̂_(t+x)=(A_t+xT_t)I_(t-L+x) (Eq-4)

A_t = Smoothed value
α= Smoothing constant (0< α<1)
β = Smoothing Constant (0< β<1)
γ = Smoothing constant for seasonality (0< γ <1)
I_t = Seasonal estimate measured as an index
T_t=Trend estimate
x = Periods to be forecast into future
Y ̂_(t+x) =Forecast for x periods into the future



6.2.3 Economic Order Quantity (EOQ):
According to Bozarth (2011) EOQ can be calculated using the following formula.
EOQ = √((2×A×Cp)/Ch)
A = Demand for the year
Cp = Cost to place a single order
Ch = Cost to hold one unit inventory for a year

6.2.4 Reorder-level:
ROL = Maximum usage × Lead Time
Lead time = Time between placing an order and get the supply
6.3 Testing &Deployment:
In this phase the application will be tested using the simulated data and the results will be monitored. Test cases will be prepared to test the application. Once the application is tested successfully, it will be considered for deployment.
7. Deliverables
Design diagrams
Implementation code
Instruction to use the application
Final reports explaining design, code implementation, result and conclusion
Presentation reviewing the project



8. Schedule



Proposal Submission 18th January 2012
Analysis & Design 1 week
Development & Testing 3 weeks
Project Report 1 Week
Project Defense (Tentative) March 15th







9. References:

Muller, M. (2002). Essentials of Inventory Management. Saranac Lake, NY: AMACOM Books. Retrieved from
http://site.ebrary.com.ezproxy.rit.edu/lib/rit/docDetail.action?docID=10120185

David, V. J. (1996). Basics of Inventory Management: From Warehouse to Distribution Center. Menlo Park, CA: Course Technology Crisp. Retrieved from
http://site.ebrary.com.ezproxy.rit.edu/lib/rit/docDetail.action?docID=10060430

Hoshmand, A. R. (2010). Business Forecasting: A Practical Approach. Florence, KY: Routledge. Retrieved from
http://site.ebrary.com.ezproxy.rit.edu/lib/rit/docDetail.action?docID=10358627

Hyndman, Koehler, R., Ord A., Keith (2008). Forecasting with Exponential Smoothing: The State Space Approach. Berlin/Heidelberg, DEU: Springer. Retrieved from
http://site.ebrary.com.ezproxy.rit.edu/lib/rit/docDetail.action?docID=10239339

Venkatesh, G.(2011). Retail technology: not yet checked out. The Express Computer. Retrieved from
http://www.expresscomputeronline.com/20110930/coverstory01.shtml

Agarwal, A.(2009). Retail Trade in India – Small Store Format is still the King. The Digital Inspiration. Retrieved from
http://www.labnol.org/india/retail-trade-india/6634/

Tech Boost for Kirana.(n.d.). The Franchise Plus. Retrieved from
http://www.franchise-plus.com/features.asp?others_id=22

Rosemary Peavler.(n.d.) Inventory Investment and Maximizing Your Profit. Retrieved from
http://bizfinance.about.com/od/inventory/a/Inventory_Investment.htm

Score (2002).Inventory Control. Retrieved from
http://www.ct-clic.com/newsletters/customer-files/inventory0602.pdf

Paul Goodwin (2010). The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong. Retrieved from
http://forecasters.org/pdfs/foresight/free/Issue19_goodwin.pdf

Sarita Hosfeld.(n.d.) The Advantages & Disadvantages of Economic Order Quantity (EOQ). The chron.com. Retrieved from
http://smallbusiness.chron.com/advantages-disadvantages-economic-order-quantity-eoq-35025.html

Gardner, Jr, E.S (1985). Exponential smoothing: the state of the art, Journal of Forecasting, 4, 1-28 .

Atkinson, C. (2005). What to do about seasonality. The inventory management review. Retrieved from
http://www.inventorymanagementreview.org/2005/06/what_to_do_abou.html

14 Thompson, J.(2011). India’s irony: Mom-and-pop shops control destiny of global brands. The Mobile Commerce Daily. Retrieved from
http://www.mobilecommercedaily.com/2011/09/22/india’s-irony-mom-and-pop-shops-control-destiny-of-global-brands

Bernadette, T. (2011). New small business rule: Sacrifice inventory, save the store. The USA Today. Retrieved from
http://www.usatoday.com/money/smallbusiness/story/2011-12-03/cnbc-inventory/51592374/1

Hurlbut, T. (2005). Low Hanging Fruit, Sludge and Everything Else: Strategies for turning dead inventory into cash. The Inc. Retrieved from
http://www.inc.com/resources/retail/articles/200505/deadinventory.html

Kalekar, P.S. (2004). Time series Forecasting-using Holt-Winters Exponential Smoothing. Retrieved from
http://www.it.iitb.ac.in/~praj/acads/seminar/04329008_ExponentialSmoothing.pdf
Bozarth, C. (2011). Economic Order Quantity (EOQ) Model: Inventory Management Models: A Tutorial. Retrieved from
http://scm.ncsu.edu/scm-articles/article/economic-order-quantity-eoq-model-inventory-management-models-a-tutorial