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  • 1. Winters Project Report On Establishing Dynamic Inventory Norms for High Gross Margin Products At Submitted in partial fulfilment of Post Graduate Diploma in Industrial Engineering Under the able guidance of: Submitted by: Mr. V.G.S. Mani Manoj Sharma Director Logistics, Whirlpool Roll no.- 36 & PGDIE-35 Prof. Narayanan N. Professor, NITIE, Mumbai National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 0
  • 2. CERTIFICATE This is to certify that the Project Work titled “Establishing Dynamic Inventory Norms for High Gross Margin Products” has been successfully completed at Whirlpool of India Ltd. (Faridabad Plant) by Manoj Sharma under my guidance, in partial fulfillment of the Post Graduate Diploma in Industrial Engineering at National Institute of Industrial Engineering (NITIE), Mumbai. Prof. Narayanan N., NITIE, Mumbai. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 1
  • 3. ACKNOWLEDGEMENTS “The only constant thing in this world is change. The world hates it, but yet it is the only thing that leads to progress”. Hence, I take this opportunity to extend my sincere thanks to NITIE, Mumbai and Whirlpool of India Ltd. for offering a unique platform to earn exposure and garner knowledge in the field of Logistics management, inventories assessment and demand planning. I am thankful to my project guide, Mr. V.G.S. Mani, for his kind support, guidance and encouragement he has extended to me throughout the project. I would like to thank Mr. Deepak Bhatnagar for his vital inputs and able guidance provided during the project. I also thank the entire logistics team at Faridabad for there unconditional support and co-operation throughout the project, in spite of their hectic activities and work schedules. I am also thankful to the people in the plant for their direct and indirect inputs towards this project. I am also thankful to my college guide Prof. Narayanan N., who has been guiding me throughout my project. Working through this project has been indeed a very enriching experience. MANOJ SHARMA PGDIE 35th batch, NITIE, MUMBAI National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 2
  • 4. TABLE OF CONTENTS CERTIFICATE ................................................................................………………………………….2 ACKNOWLEDGEMENTS ..............................................................................................................2 EXECUTIVE SUMMARY ...............................................................................................................4 COMPANY PROFILE ....................................................................................................................5 WHIRLPOOL CORPORATION ........................................................................................................5 WHIRLPOOL OF INDIA LIMITED ....................................................................................................6 INTRODUCTION…………………………………………………………………………………………...7 NEED OF THE PROJECT……………………………………………………………………………….10 OBJECTIVE ……………………………………………………………………………………………….11 PROBLEM STATEMENT………………………………………………………………………………...12 CURRENT PRACTICE…………………………………………………………………………………...13 INVENTORY POLICY DECISION .............................................................................................14 CONTINUOUS REVIEW POLICY……………………………………………………………………….15 PERIODIC REVIEW POLICY……………………………………………………………………………16 HYBRID APPROACH POLICY……………………………………………………………………….....17 REPLENISHMENT PLANNING…………………………………………………………………………18 BASIS OF MODEL FORMULATION……………………………………………………………………18 ASSUMPTIONS AND CONSTRAINTS………………………………………………………………...22 REPLENISHMENT MODEL FRAMEWORK…………………………………………………………...23 REPLENISHMENT MODEL……………………………………………………………………………..23 MODEL BEHAVIOR………………………………………………………………………………………25 MODEL FUNCTIONING………………………………………………………………………………….26 TERMINOLOGIES USED IN MODEL…………………………………………………………………..27 CALCULATION............................................................................................................................28 EXCEL FORMULAS………………………………………………………………………….30 HOW TO USE THE MODEL ........................................................................................................31 SIMULATION TEST ....................................................................................................................39 CONCLUSION…………………………………………………………………………………………….43 FUTURE SCOPE…………………………………………………………………………......................44 BIBLIOGRAPHY…………………………………………………………………………………………..45 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 3
  • 5. Executive Summary We all know the Supply Chain buzzword of all time quot;right product at right place at right time,quot; but how much we are living up to it. Today, goods are made anticipating the customers' demands. quot;Anticipation,quot; are always anticipations, they may be right, may be wrong. If they are less than actual value, we will lead into loss of sales, and if it is more, it will pile up the inventory, which is the root cause of all the evils in factories. Increase in inventory level might lead to obsolesce of the product due to aging and blocking of the working capital and thus reducing cash flow and bottom line. Inventory might serve as a cover to increase service level, but the cost ultimately will increase the price of the product. At present one of the challenges the company is facing is the cut throat competition. To survive in this competitive market the company needs to improve the availability of products at the bottom of the supply chain to 100%. Among all other products high gross margin products are in top priority for the think tanks of the company. High Gross Margin (HGM) products are such products which have gross profit margin between15% to 25%. The project is aimed at establishing the stock norms and defining appropriate safety stocks, reorder point, maximum stock level of products at branch and regional level that will ensure the service level for HGM products to be 95% or above. Defining the stock norms for a product is essential to ensure its availability at the storage locations. Generally the planning tools used for this purpose define it at an aggregate level which results in erroneous allocation of SKUs. In this project we have tried to consider the fact that demand history of each product at a particular location might not be correctly reflected at aggregate level and should be analyzed separately for each SKU and storage location. Here we have designed a replenishment model to generate dynamic stock norms on a runtime basis. The replenishment model is capable of prompting triggers to the user as to when the order should be placed and how much quantity must be ordered. The replenishment model works on the basic principal of hybrid approach inventory policy where the inventory position is monitored continuously and an order of (S-IP) is generated when IP reaches the ROP level. The inventory policy used in the model exploits the advantage of both continuous and periodic review approach. The model has been tailored to fit the company’s requirement, hence three ROP levels has been maintained at branch levels to fulfill the skewed demand of the month while regional stock are maintained using only one ROP for the month. The model has the ability to run on the real time in synchronization with the calendar date and update the stock norms automatically both during the month and across the months. Before going for the pilot run of the model it has been tested using simulation through MS Excel. If the model is implemented and stock norms dictated by it are followed properly this can result in to huge savings by reducing IBT and improving customer service levels. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 4
  • 6. Company Profile Vision and Mission Our pervasive vision, “Every Home, everywhere, with pride, passion and performance”, rests on the pillars of innovation, operational excellence, customer-centric approach and diversified talent. These are embedded within our business goals, strategy, processes and work culture. Be it our products that are the result of innovation and operational excellence to meet every need of our consumers or the people behind these products that come from a wide spectrum of backgrounds, everything we do features a distinct Whirlpool way Introduction to Whirlpool Corporation Whirlpool Corporation is a global manufacturer and marketer of major home appliances, with annual sales of more than $13 billion (for year 2005), 68,000 employees and nearly 50 manufacturing and technology centers around the globe. The company manufactures in 13 countries and markets products in approximately 170 countries under 11 major brand names such as Whirlpool, KitchenAid, Roper, Estate, Bauknecht, Laden and Ignis. Whirlpool originated from a company called Upton Machine Company in 1911 near Lake Michigan in St. Joseph, Michigan, which was setup by the Upton brothers. Their first ever product was the electric, motor- driven wringer washer. The Upton Machine Company was the exclusive supplier to a then retail giant Sears, Roebuck and Co. for the washer’s line which used to be sold under the name “Allen”. Today, Whirlpool Corporation is the largest North American supplier of major appliances to Sears under the Kenmore brand. In 1929, the Upton Machine Company merged with the Nineteen Hundred Washer Company of Binghamton, New York. Shortly after the Second World War, Nineteen Hundred made the first ever top loading washing machine, which was followed by a highly successful Whirlpool brand automatic washer launched in 1948. Following the success of its automatic washer, the company was rechristened as Whirlpool Corporation. This was the beginning of the Whirlpool of today. Post 1956, Whirlpool Corporation has grown both in terms of technology and markets. The company has expanded its facilities worldwide. In today’s era of globalization, the company has also realized the need to shift manufacturing to low cost and high quality countries like India and China. These countries are looked upon not just as potential markets but also as low cost centers, which helps reduce the purchase value for Whirlpool Global. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 5
  • 7. Introduction to Whirlpool of India Limited Whirlpool Corporation is a global manufacturer and marketer of major home appliances, with annual sales of more than $19 billion (for year 2005), 80,000 employees and nearly 60 manufacturing and technology centers around the globe. Whirlpool of India’s net sales for the period April 2005 -March 2006 stood at Rs. 1274 crores with an operating profit of Rs. 14.57 crores. Company witnessed a growth of 25% (Approx.) in net sales over the same period last year. The company manufactures in 13 countries and markets products in approximately 170 countries under 11 major brand names such as Whirlpool, Maytag KitchenAid, Roper, Estate, Bauknecht, Laden and Ignis. The parent company is headquartered at Benton Harbor, Michigan, USA with a global presence in over 170 countries and manufacturing operation in 13 countries with 11 major brand names such as Whirlpool, KitchenAid, Roper, Estate, Bauknecht, Laden and Ignis. The company boasts of resources and capabilities beyond achievable feat of any other in the industry. Whirlpool initiated its international expansion in 1958 by entering Brazil. However, it emerged as truly global leader in the1980's. This encouraging trend brought the company to India in the late 1980s. It forayed into the market under a joint venture with TVS group and established the first Whirlpool manufacturing facility in Pondicherry. Soon Whirlpool acquired Kelvinator India Limited in 1995 and marked an entry into Indian refrigerator market as well. The same year also saw acquisition of major share in TVS joint venture and later in 1996, Kelvinator and TVS acquisitions were merged to create Indian home appliance leader of the future, Whirlpool India. This expanded the company's portfolio in the Indian subcontinent to washing machines, refrigerator, microwave ovens and air conditioners. Today, Whirlpool is the most recognized brand in home appliances in India and holds a market share of over 25%. The company owns three state-of-the-art manufacturing facilities at Faridabad, Pondicherry and Pune. Each of these manufacturing set-ups features an infrastructure that is witness of Whirlpool's commitment to consumer interests and advanced technology. In the year ending in March 2006, the annual turnover of the company for its Indian enterprise was Rs.1, 375 Crores. According to IMRB surveys Whirlpool enjoys the status of the single largest refrigerator and second largest washing machine brand in India. The company's brand and image speaks of its commitment to the homemaker from every aspect of its functioning. It has derived its functioning principles out of an undaunted partnership with the homemakers and thus a slogan of “You and whirlpool, the world's best homemaker” dots its promotional campaigns. The products are engineered to suit the requirements of ‘smart, confident and in-control' homemaker who knows what she wants. The product range is designed in a way that it employs unique technology and offers consumer relevant solutions. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 6
  • 8. Introduction Many real-world supply chains have complex network structures, which consist of multiple layers of production and distribution facilities. To cope with uncertainties in demand and supply, these supply chains often have many millions of Rupees of capital tied up in inventories. One important question, of course, is how to best manage inventories in complex multistage supply chains, so as to meet customer expectations with minimum system wide inventory holding cost. One way to answer this question is to characterize the optimal inventory policies. In practice, many companies employ simple heuristic policies, such as the installation base-stock policies, to control inventory at each facility. In an installation policy, each facility only needs the inputs from immediate upstream and downstream facilities, and makes ordering decisions based on its local order and inventory status. An important and challenging question for the company is how to optimally coordinate the installation policies at all facilities, so as to minimize system wide inventory holding cost while meeting the end customers’ service requirements. In other words, given that each facility is managed autonomously by an inventory policy, how can a central planner determine the policy parameters for all facilities in the best possible way? In this project, we attempt to address this question for a class of HGM products. The project is focused on establishing optimum safety stocks at all stock points, for which a replenishment model has been designed to continuously monitor the inventory position and trigger the ROP as soon as the inventory goes below a determined level. The replenishment model assume practical demand pattern and has been tested by using simulation with the help of MS Excel. Before going in to details let us first understand what are HGM products and their importance to the company. High gross margin products Products having gross profit margin of greater than 15% are declared as the high grass margin products. The percentage contribution of these products from the total product sales is given below: Category Total % of Total Direct Cool 790929 58.00% Frost Free 214334 15.72% Washing Machines 288102 21.13% Air Conditioners 24043 1.76% Micro Wave Ovens 39339 2.88% Front Loading Terminals 2023 0.15% SUMO 4809 0.35% Grand Total 1363579 100.00% Table 1. Annual Sales 2006 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 7
  • 9. Annual Sales 2006 DC NF DC 58% 16% NF WSR AC M WO FLT SUMO WSR SUM O 21% 0.35% FLT AC MWO 0.15% 1.76% 2.88% Figure 1. Categorywise Product Sale year 2006 Category Total % of Total HGM HGM % DC 790929 58.00% 278788 35.25% NF 214334 15.72% 64304 30.00% WSR 288102 21.13% 104370 36.23% Total 1293365 94.9% 447462 34.60% Table 2. HGM Products Vs Total 790929 800000 700000 600000 500000 288102 400000 214334 300000 278788 200000 104370 100000 64304 0 Total WSR DC NF HGM DC NF WSR Figure 2. HGM Products Vs Total National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 8
  • 10. The management of inventory systems is a crucial part of supply chain management. In order to reduce costs and to improve efficiency an integrated approach is needed. For instance, decreasing inventory levels and thereby lowering inventory costs induces more frequent shipments of smaller sizes, which in general increases transportation unit costs. We believe that careful coordination of shipment and replenishment policies can lead to substantial cost savings. In this project we propose a model to allocate safety stocks in a multi item multi location distribution network. We consider a stock-based shipment policy which means that orders for a destination are shipped when inventory position of the product drops below the reorder point level. The required amount to be shipped depends on the inventory position, the maximum stock level and the replenishment lead-time of the supplied stock points or branch. We model this interaction considering the replenishment lead-time as constant. We consider a divergent network of warehouses keeping stock of different items. Inventories are controlled using an (s, S) installation stock policy, which means a replenishment order is placed at the moment where the local inventory position, which is defined as the physical stock plus the stock on order or the material in transit minus backorders, is equal or smaller than the reorder level ROP. Customer demand is stochastic, only observed at the lowest echelon, and modeled as a compound renewal process which means that inter-arrival time of orders as Well as demand sizes are modeled as deterministic. Demand which cannot be satisfied is backordered. We assume the batch sizes Q to be calculated by the standard EOQ formula. Measures of product availability like Target service levels, the fill rate and ESC i.e. expected shortage per cycle are used to avoid stock out. Hybrid inventory review policy has been modified taking in to consideration the skewed billing pattern of the company. The monthly demand is thus divided in to three phases of 10 days each and average percentage of monthly sale is assigned to each phase. The percentage of each phase can be altered as per the expected billing pattern which is more or less constant for every month. Thus depending on which phase of the month we are running in the ROP level would be decided while the safety stock will remain at a constant level throughout the month. This indicates that for each month we will have three ROP levels and one safety stock. While at the regional level we will be having only one ROP and SS for the month. Billing Pattern Series1 70% 70% 60% 50% % Billed 40% 20% 30% 10% 20% 10% 0% 1-10 10-20 20-31 Days and Phases Figure 3. Monthly billing pattern National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 9
  • 11. Need of the project Profit margin on goods and services are shrinking day by day due to the inevitable competitive business environment. The scenario at whirlpool of India limited is not much different. In such a situation products which are still yielding high profit margins are of greater importance to the company. In each of the three major product categories (DC, FF, WM) High Gross margin products contributes around 35% to the annual sales of the total category. The company simply cannot afford to lose even a fractional percentage of sales in these products. Hence company wanted to be very particular in terms of availability of these products and maintaining specified customer service level at each branch and regional level. The project requirement is evident from the following reasons There was no minimum or maximum stock bound defined for the stocks at any branch. ROP was not maintained at any location which caused frequent stock-outs. There were no measures or KPIs to check the availability of products (like CSL or Fill rate etc). No standards were defined as to when to place order and how much to order. No replenishment model was in place to monitor the inventory position of HGM products. Increased number of IBT (inter branch transfers) due to inappropriate allocation. Apart from the basic requirements of solving the above mentioned problems the company is planning to establish regional warehouse to increase the product availability and refrain from the opportunity cost lost due to stock-out. The company is interested in knowing the minimum and maximum amount of decoupling inventory that should be placed at the regional warehouse. To reduce the above listed inefficiencies and eradicate these problems following factors are essential: • The inventory policy to be followed. • Calculate Optimum safety stocks at branch and regional levels. • Define the maximum stock levels, ROP levels at branch level. • Following the stock norms at all stock points. • Design a replenishment model through which the inventory position at all stock points can be tracked and necessary reordering steps could be taken. • Maintain the customer service level required to meet the desired ESC constraint. Only defining these terms would not be sufficient until we follow the service level requirement and control the lead time variation. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 10
  • 12. Objective The objective of the project is: To optimize inventory levels while maintaining or increasing service levels. To increase product availability. To determine the optimum safety stock, ROP and maximum stock limit for HGM product at branches. To design a replenishment model that account for the demand skew ness during the month. To come up with regional inventory levels of high gross margin products. To limit ESC to the minimum possible level. To increase customer service level to 95% and above if possible. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 11
  • 13. Problem statement Since forecasted demand in general differs from actual demand safety stock is hold to be able to satisfy customer demand. One major flaw in the system was that the safety stock SS and ROP for the products at the stock-point level was not worked out. The company was loosing some sale due to unavailability of the right amount of SKU at the right place. One of the major aims of inventory management is to balance service requirements with the costs related to inventory availability. Therefore, it is important to know how to allocate optimum safety stocks among different stock-points, respectively which reorder levels ROP the stock-points should use. In this project we provide a model to compute these reorder levels in a multi- echelon multi-item distribution network under a continuous review stock based inventory policy such that prescribed service requirements can be met. Some Seasonal cycles like summers are common to all the regions i.e. global seasonality while some are specific to the region only like regional festivals and new year surge in demand are local seasonality. As we all know that India is a country of huge geographical and religious diversity and so does their buying pattern. Even in the lean season some regions show sudden surge in demand due to some regional or local festivals. It has normally happened that company encountered stock-out conditions due to such sudden increase in demand. Another problem that is encountered is the inappropriate allocation of stocks at certain storage locations due to aggregate forecast. The aggregate forecast of the product is drilled down to SKU level many a times it is different from the actual demand in a particular location. HGM products are categorized as separate class of product due to their huge contribution to the profit margin of the company but still there is no separate planning for managing inventory of these SKUs and assuring their availability to the end customer. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 12
  • 14. Current practices The points given below will provide brief overview of the current practices at whirlpool: Yearly forecast is released at the start of each year which is then revised on a quarterly basis to account for the changes in the demand pattern or in anticipation of demand due to new product introduction or any promotional activity. Based on these quarterly forecast numbers the central logistics department arrives at monthly sales plan which is then drilled down to branch level and conveyed to the branches till 25th of every month for any corrections required. After making necessary changes at the model/SKU level the branches should revert back to the central logistics department till 28th of each month, where the aggregation of the plans is done and final requirement for the month is conveyed to production department and to the respective branches. The inventory requirement is taken care of by the central logistics department with an objective to meet the target closing inventory levels for each month at branches. This target closing inventory is basically the buffer the company uses to counter any uncertainties in demand and fluctuation in lead times. The closing inventory for the current month is calculated by using the formula Target Closing inventory = Actual Closing inventory (current Month) + % of next month sale plan The percentage of next month’s sales plan included varies from 10% to 50% depending on the proximity to the peak season. This formula is really helpful in protecting the stock-out at an aggregate level but when it comes to the inventory of a particular model/SKU at a specific branch, the formula doesn’t work effectively. For some models we stock at let say at location A in excess, while the same model is out of stock at location B at the same time. This is evident from the fact that company is incurring high cost in making IBT s (inter branch transfers) from one branch to other. Over Rs.36000000 Annually is invested in IBTs. This wrong allocation also results in delayed replenishment due to extended lead times. Thus it is very important to ensure that right material reaches the right location at the right time. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 13
  • 15. Inventory Policy decision For a single inventory location that serves a number of downstream nodes in the supply chain, several stochastic inventory policies can be applied. The size of the safety directly depends on the type of the inventory policy that is in effect. Here we are following continuous review inventory policy. The underlying conception for a single-stage inventory policy is as follows. An inventory node is supplied from a quot;sourcequot; which fulfills orders for the considered product after a certain replenishment lead time. If the source is a production segment or rather production stage of the same company, then the replenishment lead time is a function of the flow time of a production order and depends on numerous factors, the utilization of the production stage being one of them. If the source is another inventory node of the company, then the order is a demand observed by this inventory node and the replenishment lead time depends on the inventory available on hand as well as on the time required for material handling and transportation processes. If the source is an external supplier, then the replenishment lead time is equal to the customer order waiting time provided by the supplier, plus an additional time required for material handling and transportation. In all mentioned cases it is clear that the replenishment lead time may be subject to random variations. Inventory policies differ in two aspects, namely the mechanism used to trigger replenishment orders and the decision rule that specifies the determination of the order size. The specific inventory policies are defined through the combination of the decision variables s (reorder point), r (review interval, order cycle), q (order quantity) and S (order level) as follows: Continuous review policy Periodic review policy Hybrid approach policy A replenishment policy consist of decisions regarding when to reorder and how much to reorder. These decisions determine the cycle and safety inventories along with fill rate and the CSL. There are several forms of replenishment policies. We restrict attention to three instances listed above. There are pros and cons of each type of policy, by this exercise we will be able to know the best fit policy for whirlpool of India limited. Further we will do certain customization if required. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 14
  • 16. Continuous review policy (s, Q) Figure 4. Schematic diagram of continuous Review Policy Under the (s, Q) policy, the point in time at which replenishment orders are triggered, depends on the size of the reorder point s, whereas the order quantity q is constant over time. In the ideal (textbook) form of the (s, Q) policy, the inventory position is continuously monitored. The inventory position is the sum of the inventory on hand plus the inventory on order minus the outstanding backorders (backlog). The inventory management system (or the inventory manager) acts according to the following decision rule: If at a review instant the inventory position has reached the reorder point s (from above), and then launches a replenishment order of size q. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 15
  • 17. Periodic review (r, S) policy Figure 5. Schematic diagram of Periodic Review Policy If an (r, S) inventory policy is in effect, the points in time at which replenishment orders are released are determined through the review interval r. The inventory management system proceeds according to the following decision rule: In constant intervals of r periods launch a replenishment order that raises the inventory position to the target order level S. Obviously, the ( r, S ) policy is an inventory policy with periodic review. The order size at a time of a review depends on the demands and the development of the inventory observed in the preceding periods. If r=1, then this policy is called base-stock policy. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 16
  • 18. Hybrid Approach (s, S) policy Figure 6. Schematic diagram of Hybrid Approach Policy Under an (s, S) inventory policy, the points in time when an order is triggered are determined in the same way as with the (s, Q) policy, i.e. through the reorder point s. However, the order quantity is now, similar to the (r,S) policy, a function of the inventory development over time. In the literature this policy is sometimes characterized with the help of a third parameter which specifies the length of the review interval r. In this notation the policy is called (r, s, S) policy.22 In the case of r = 0, continuous review is in effect. If demands arrive unit-sized, then the (r = 0, s, S) policy is identical to the (s, Q) policy with continuous review. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 17
  • 19. Replenishment planning The most important part of inventory optimization process is to decide the when to order and how much to reorder. These in turn depends upon the replenishment policy followed by the company. Effective replenishment planning can result in increased sales, reduced labor costs, and reduced order cycle time by keeping stores and distribution centers appropriately and efficiently stocked. Replenishment Planner can automate the task of examining millions of SKU-location combinations to determine the right replenishments to maintain high service levels with efficient, profitable inventory investments. Replenishment orders take into consideration forecasts, safety stock, available inventory, and ordering policies. Several ordering policies can be chosen, ranging from simple min-max order point comparisons to dynamic order point/order up to level and time-supply calculations, depending on the nature of the item (whether it is slow or fast moving, or a basic or seasonal item). Basis of model formulation Optimizing Safety Stock levels by calculating the magical balance of minimal inventory while meeting variable customer demand is sometimes described as the Holy Grail of inventory management, many companies look at their own demand fluctuations and assume that there is not enough consistency to predict future variability. They then fall back on the trial and error best guess weeks supply method or the over simplified 1/2 lead time usage method to manage their safety stock. Unfortunately, these methods prove to be less than effective in determining optimal inventory levels for many operations. If our goal is to reduce inventory levels while maintaining or increasing service levels we will need to investigate more complex calculations. One of the most widely accepted methods of calculating safety stock uses the statistical model of Standard Deviations of a Normal Distribution of numbers to determine probability. This statistical tool has proven to be very effective in determining optimal safety stock levels in a variety of environments. The basis for this calculation is standardized; however, its successful implementation generally requires customization of the formula and inputs to meet the specific characteristics of our operation. Understanding the statistical theory behind the formula is necessary in correctly adapting it to meet our needs. Normal distribution: Term used in statistical analysis to describe a distribution of numbers in which the probability of an occurrence, if graphed, would follow the form of a bell shaped curve. This is the most popular distribution model for determining probability and has been found to work well in predicting demand variability based upon historical data. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 18
  • 20. Standard deviation: Used to describe the spread of the distribution of numbers. Standard deviation is calculated by the following steps: • Determine the mean (average) of a set of numbers. • Determine the difference of each number and the mean • Square each difference • Calculate the average of the squares • Calculate the square root of the average. We can also use Excel function STDEVPA to calculate standard deviation. In safety stock calculations, the forecast quantity is often used instead of the mean in determining standard deviation. Lead time: Highly accurate lead times are essential in the safety stock/reorder point calculation. Lead time is the amount of time from the point at which we determine the need to order to the point at which the inventory is on hand and available for use. It should include supplier or manufacturing lead time, time to initiate the purchase order or work order including approval steps, time to notify the supplier, and the time to process through receiving and any inspection operations. Lead-time demand: Forecasted demand during the lead-time period. For example, if our forecasted demand is 3 units per day and our lead time is 12 days our lead time demand would be 36 units. Forecast: Consistent forecasts are also an essential part of the safety stock calculation. If we don't use a formal forecast, we can use average demand instead. The forecast is usually revised every month. The model is also capable of updating its SS ROP and Max. Inventory levels every month in synchronization with the calendar date. Forecast period: The period of time over which a forecast is based. The forecast period used in the safety stock calculation may differ from our formal forecast periods. For example, we may have a formal forecast period of four weeks while the forecast period we use for the safety stock calculation may be one week. In our case we have considered the forecast period of one year as per the company standards. And the standard deviation for each SKU at a particular location is specifically calculated by the model. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 19
  • 21. Demand history: A history of demand broken down into forecast periods. The amount of history needed depends on the nature of our business. Businesses with a lot of slower moving items will need to use more demand history to get an accurate model of the demand. Generally the more history the better as long as sales pattern remains the same. The demand history should include at least one complete seasonal cycle thus last years data is considered for analysis. Reorder point. Inventory level which initiates an order. • Reorder Point = Demand During Lead Time(DDLT) + Safety Stock(SS) Service level: Desired service level expressed as a percentage. Our target is to maintain it equal to or above 95%. Service factor: Factor used as a multiplier with the Standard Deviation to calculate a specific quantity to meet the specified service level. I have included a service factor table below or we can use Excel function NORMSINV to convert service level percentage to service factor. Service Level Service Factor Service Level Service Factor 50.00% 0.00 90.00% 1.28 55.00% 0.13 91.00% 1.34 60.00% 0.25 92.00% 1.41 65.00% 0.39 93.00% 1.48 70.00% 0.52 94.00% 1.55 75.00% 0.67 95.00% 1.64 80.00% 0.84 96.00% 1.75 81.00% 0.88 97.00% 1.88 82.00% 0.92 98.00% 2.05 83.00% 0.95 99.00% 2.33 84.00% 0.99 99.50% 2.58 85.00% 1.04 99.60% 2.65 86.00% 1.08 99.70% 2.75 87.00% 1.13 99.80% 2.88 88.00% 1.17 99.90% 3.09 89.00% 1.23 99.99% 3.72 Table 3. Service Level and Corresponding Service factor National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 20
  • 22. Role of safety inventory in the supply chain Safety inventory is the inventory carried for the purpose of satisfying demand that exceeds the amount forecasted for a given period. Safety inventory is carried because demand forecasted are uncertain and a product shortage may result if actual demand exceeds the forecast demand. Demand forecast are unlikely to be completely accurate. Given forecast errors, actual demand over a period may be higher or lower than the forecasted. To protect the loss of sale due to uncertainty in demand one needs to have buffer in stock, this buffer is known as safety inventory. Trade-off between availability and holding cost A trade-off that must be considered when planning safety inventory, on one hand raising inventory increases product availability and thus the margin captured from customer purchase. On the other hand raising level of safety inventory increases inventory holding costs. This issue is particularly significant in industries where demand is very volatile. Carrying excessive inventory can help counter demand volatility but can really hurt if new products come up on the market and demand for the product in inventory dries up. Importance of product availability The level of product availability is measured using the cycle service level or the fill rate, which are metrics for the amount of customer demand satisfied from the available inventory. The level of product availability is an important component of any supply chain’s responsiveness. A supply chain can use high level of product availability to improve its responsiveness and attract customers. This increases revenues of the company by increasing the sales through high product availability. However a high level of product availability requires large inventories and large inventories tend to raise cost for the organization. Therefore the project is focused at achieving a balance between the level of availability and the cost of inventory. This optimal level of product availability is one that maximizes supply chain profitability. Whether the optimal level of availability is high or low depends on where the company believes they can maximize profits. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 21
  • 23. Assumptions and Constraints Constraints: • The Expected shortage per cycle ESC should not exceed 2 units. • The Cycle Service level should be above 95%. • The regional warehouse will run at single stock norm at least for a month. Assumptions: • Demand is normally distributed over the planning horizons. • Monthly billing pattern is assumed skewed in nature. • Demand during each of the period is independent and normally distributed. • Production process is flexible enough to accommodate and fulfill the demand of branches and • Internal Suppliers are able to replenish the quantity ordered under the specific replenishment lead times. • Database of all the HGM products are maintained in the required format. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 22
  • 24. Replenishment Model framework: As we are following hybrid approach to the inventory management we need to continuously monitor the inventory position. At the same time we should be informed enough of the reorder point, safety stocks and maximum stock levels at each of the stocking location. Since these variables will vary depending upon the product and the storage location we are looking. These requirements encouraged us to build a replenishment model that can help us in tracking inventory positions and abiding the stock norms at storage locations. Replenishment Model Figure 7. Pictorial view of Replenishment Model Note: The screenshot of the model shown above depicts only Regional stocks. The branch stocks corresponding to the regions can be viewed just by unhiding the columns in the model sheet. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 23
  • 25. As it is clear from the above screenshot there are three major fields in the model namely 1. Adjustable field: This field consists of six variables but the cells those are in white color only are the adjustable ones. First component is average daily demand D during the first 10 days of the month. It means we can adjust the percentage sales expected in the first 10 days of the month in the picture above this has been set to 10% for central region. This adjustment we can do at regional level. The second element is average demand during next 10 days of the month (day 10 to day 20). The cell in white color corresponding to this row can also adjust in the same way. The percentage sale in the last 10 days of the month (day 20 to 30) will be automatically entered. User need not bother about. Another component in this field is SL (service level) that can be adjusted to reduce the expected shortage per cycle ESC. As soon as the ESC goes above 2 units the user will be prompted by changing the specific ESC cell to pink. We need to increase the service level to reduce the ESC. It is worth noting that we can adjust the monthly sale percentage only at regional level. 2. Inputs field: Under this field we have only three variables and all the three component rows are mend to be changed by the user as per the inventory status of the respective branch or region. As we know that this is the only input field in the model which has to be monitored and updated on a continuous basis. 3. Outputs field: this is the most important field and thus its variables like SS, ROP, IP, Max. Stock, ESC. This field would be the triggering field of the model, which will notify the user as to what step need to be taken to maintain optimum stocks at storage locations to minimize ESC and increase the availability of product at these locations. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 24
  • 26. Replenishment Model capabilities Replenishment Model is designed to reduce overstock and out-of-stock conditions, leading to increased sales and lower inventory levels by: • Enabling dynamic model stocks based on forecasted demand rather than static models that cannot reflect demand variability over time • Providing a constraint-based approach to modeling hard supply chain constraints (such as item availability, shipping, receiving and location calendars, ordering frequency, item affectivity dates, lot sizes, etc.) as well as soft constraints (such as handling and storage capacity at distribution centers and stores, and vendor minimums). • The system can generate plans that can be achieved because they are in sync with the retailer’s real supply chain capabilities and capacity • Providing forward looking, time-phased demand and replenishment plans so the organization understands its inventory commitment at any point in time and can communicate with vendors • Providing advanced techniques for replenishing high gross margin SKUs. • Allocating appropriate inventory while increasing customer service levels Model Behavior The model has been further customized to balance the skewed billing pattern observed by the company. The model is capable of generating the optimum regional inventory levels where the billing pattern during is assumed as linear. The table given billow will show the variables and their frequency of assuming different values in a specified period of time as per the storage location. Variable Period Branches Regions ROP Month 3 1 SS Month 1 1 Max. Stock Month 1 1 Table 4. Frequency of assuming values The model has been formulated to help managers predict optimal safety stock and ROP levels of different SKUs at specific branch and regional levels along with maintaining desired service level and ESC. The model is easy to use and provides essential outputs for effective inventory management. The model is a customized version of continuous review policy. It differs from the basic policy in a way that it exploits the concept of dynamic ROP during the month to negate the skewed billing pattern at branch level while at National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 25
  • 27. regional level there is a single value of ROP for the month. Since demand for the products are highly seasonal we can’t maintain a single ROP throughout the year. Hence the regional warehouse smoothen the weekly demand fluctuation by supplying the additional amount from its buffer stock. The regional warehouse automatically changes ROP, SS and Max Stock levels each month. The replenishment model take last one year’s demand data as the input to the model and works out the corresponding month’s safety stock, ROP, maximum stock at branch and regional level that needs to be maintained. Model is capable of changing the ROP level automatically in synchronization with calendar day of the month. As the ROP and SS depend on the monthly demand and its deviation from mean demand for the year, these limits would change depending on the input to the model. The user only needs to fill either the past one year’s data or the annual forecast data of the corresponding SKU/model and the current inventory position data like (OH,GIT,PQ) the model will automatically calculate the inventory position IP at branch and regional level. Model Functioning It is necessary to understand the functioning of the model. Basically there are three main indicators that will help the user in determining whether he is running at the correct inventory level or not. The three indicators and the color prompt assigned to each of them are given below: Indicator Row Color Indication ROP ORANGE ROP reached Replenishment order should be placed OQ BLUE Number of units to be ordered SS RED Immediate attention is required (Stock-out condition) Max. Stock YELLOW Overstock condition Table 5. Color indications used in the replenishment model If the IP is greater than the maximum stock limit the maximum stock cell for corresponding branch will show YELLOW color. Indicating that the branch is running over stock and need not be supplied anymore till the ROP level is reached. In the same way as the inventory position at the branch or regional level decrease below its ROP level it prompts the user by showing ROP of respective location in RED color. As the inventory policy followed in the model is (SS, T) each time the ROP is reached the order quantity will show the quantity (T-IP) to be ordered. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 26
  • 28. Terminologies Used in the Model The following is a list of the variables and the terminology used in model: Dm Demand Per Month D Avg. Demand Per Day σ Stand. Dev of Demand Q Order Quantity LT Transit Lead Time in Days SL Desired Service level DDLT Demand During Lead Time OH On hand Inventory GIT Goods in transit σL Stand Dev. Of Demand during Lead time Nm No. of Trips/orders per month SS Safety Stock ROP Reorder Point CSL Cycle Service Level ESC Expected Shortage Per Cycle Fr Fill Rate PQ Pending quantity IP Inventory position OQ Order quantity S Max. Stock level Table 6. Symbols used in the replenishment model National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 27
  • 29. Calculation Inventory management is about two things: not running out, and not having too much. The organization’s aim to not run out, along with uncertainties in demand and supplier lead times are why we have inventory in the first place. Essentially, inventory is a reserve system to prevent a stock out. However, as important as it is to prevent such a stock out, we also don’t want to hold onto too much inventory because of holding costs. So how do we balance the two? And What is the right amount that should be ordered in each replenishment? More importantly, when should we re-order in order to prevent a stock out? The answer to this can be determined by obtaining and applying the following information about the inventory we wish to manage. Safety inventory is carried for the purpose of satisfying demand that exceeds the amount forecasted for the period. Safety Stock = Z*SQRT (LT*σD^2 + D^2*σL^2) Where LT = Average Lead Time σD = Standard Deviation of Demand D = Avg. Demand σL = Standard Deviation of Lead Time Standard deviation of lead time: It is very important to track how long shipments take from suppliers. Assuming we have tracked the data, excel can very easily help we determine our standard deviation. In excel, go to the toolbar and click on Insert, then click on Function, and choose STDEV and click ok. Then, enter in as much lead time data we have and presto, we have our standard deviation. Here in our model we have assumed that the lead time and their variation are correlated and can be assumed as constants after consulting with the subject matter experts. Thus the deviation of lead time used in the model is given below. Lead time range Standard deviation of lead time 1-4 days 1 5-7 days 2 8 and above 3 Table 7. Lead time Deviation limits National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 28
  • 30. Re-order point=LT*D + Z*SQRT (LT*σD^2 + D^2*σL^2) Where In this formula, the first term LT*D= Demand during lead time The second term {Z*SQRT (LT*σD^2 + D^2*σL^2)} is the term that allows for the safety stock. In other words, the second term is the optimal safety stock level. It is not simple to gather all the data that is needed for the calculations. For a product with multiple parts, each part needs to have its own re-order point calculations and its own safety stock calculation. This can all become very confusing if proper computer modeling is not employed. Although as mentioned excel earlier, excel is probably not sufficient for our company’s software needs. If we have not already done so, it is very important to look into an integrated software package for these calculations and many others. But the model designed during the project is a customized one which can serve whirlpool’s purpose. There is a scope of further refinement in the model after few pilot runs. IP = (On Hand) + (On-Order) – (Back Orders) Policy: When Inventory Position is less than or equal to the Reorder Point, R, order (S-IP) units. Where S is the Maximum inventory level Fill rate: It measures the proportion of customer demand that is satisfied from available inventory. Fr = 1-ESC/Q Where Fr is the Fill rate ESC is the Expected Shortage per replenishment cycle and Q is the Economic order quantity ESC: is the average units of demand that are not satisfied from inventory in stock per replenishment cycle ESC = (-SS) {1-NORMSDIST(SS/σL) } + σL { NORMSDIST(SS/ σL) } National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 29
  • 31. Excel Formula Sheet Copied to Description Cell No. Formula Copied To Row Remarks Column Standard Deviation C17 STDEVPA(C5:C16) C17 to AF17 Calender Day of where I33 is the G33 DAY(I33) Month date It is automatically Date I33 TODAY() updated daily IF(MONTH($B5)=MONTH($B$46),C May-07 C34 C34 to AF34 C34 to C45 5,0) May-07 C46 SUM(C34:C45) C46 to AF46 Average Daily C20 C5/30 C20 to AF20 C20 to C31 Demand S.D. of Daily Standard Deviation C32 STDEVPA(C20:C31) C32 to AF32 Demand It is close to the IF($G$33<=10,C48,IF($G$33<=20, Applied Demand C47 C47 to AF47 practical Demand C49,C50)) Observed C48 to G48, I48 to L48, Demand During 1st 10 C48 C46*$H$48/10 N48 to R48,T48 to X48,Z48 Days to AD48,AF48 C49 to G49, I49 to L49, Demand During 2nd C49 C46*$H$49/10 N49 to R49,T49 to X49,Z49 10 Days to AD49,AF49 C50 to G50, I50 to L50, Demand During 3rd 10 C50 C46*$H$50/10 N50 to R50,T50 to X50,Z50 Days to AD50,AF50 Standard Deviation C51 C32 C51 to AF51 Lead Time C52 Constant C52 to AF52 Standard Deviation of C53 IF(C52<=4,1,IF(C52<=7,2,3)) C53 to AF53 Assumed LT Service Level C54 Adjustable Variables C54 to AF54 Max. Stock C60 ROUND(C68+C62+C50*C52,0) C60 to AF60 Inventory Position C61 C56+C57-C58 C61 to AF61 Economic Order SQRT(2*SUM(C5:C16)*200/(4241*0 C62 C62 to AF62 Quantity .045)) Order Quantity C63 IF(C61<=C69,C60-C61,0) C63 to AF63 IF($G$33+C52<10,C48*C52,IF($G$ C65 to F65, I65 to K65, Demand during LT C65 33+C52<20,C49*C52,IF($G$33+C5 N65 to Q65,T65 to 2<=31,C50*C52,C48*C52))) W65,Z65 to AC65 G65,L65,R65,X65,AD65,AF For Regional Demand during LT G65 G50*G52 65 Stocks S.D. of Demand during C66 SQRT(C52*C51^2+C49^2*C53^2) C66 to AF66 LT Number of trips/month C67 C46/C62 C67 to AF67 Safety Stock C68 NORMSINV(C54)*C66 C68 to AF68 Reorder Point C69 C65+C68 C69 to AF69 Cycle Service level C70 NORMDIST(C65+C68,C65,C66,1) C70 to AF70 (-C68)*(1- Expected Shortage C71 NORMDIST(C68/C66,0,1,1))+C66*( C71 to AF71 Per Cycle NORMDIST(C68/C66,0,1,0)) Fill Rate C72 (C62-C71)/C62 C72 to AF72 Table 8. Excel Formula Sheet National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 30
  • 32. How to use the model The model is designed on the basis of continuous review inventory policy. According to this policy the inventory position should be reviewed continuously and an order should be generated as soon as the ROP is reached. The quantity to be ordered is defined by the OQ (order quantity) and it is equal to the OQ=T (Target inventory)-IP (Inventory position at ROP). The model is easy to use and provides valuable outputs for the company. The step by step procedure of using the model is explained below: First of all the user should be clear enough of his requirement. Use of the model is demonstrated here with the help of an example: Step 1: Select the excel file named “Replenishment Model” from the PROJECT folder Figure 8. Replenishment Model Step1 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 31
  • 33. Step 2: Select the sheet named after the product category for which you want to know the stock norms (like Direct Cool, Frost Free or Washing Machine).These sheets are Tab colored in green color to differentiate them from the database sheets. Figure 9. Replenishment Model Step 2 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 32
  • 34. Step 3: Right click on the “Material Group” cell (A4) and choose edit query, one select workbook window will open click ok. Figure 10. Replenishment Model Step 3 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 33
  • 35. Step 4: A Microsoft query wizard will open go to file menu and select “open” a window with title open query will open. Figure 11. Replenishment Model Step 4 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 34
  • 36. Step 5: From open Query window Select the category whose status you want to know about (in our case suppose it’s DC) Figure 12. Replenishment Model Step 5 • As soon as we select the category and click “open” in the same window all the models/SKUs corresponding to that category will open. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 35
  • 37. Step 6: Suppose we want to select “Dc Total” and click “open” one years data for that SKU will be shown in the Microsoft query wizard itself. Figure 13. Replenishment Model Step 6 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 36
  • 38. Step 7: Again go to the file menu and select “Return data to Microsoft office excel” and we will be automatically redirected to the excel file where we have raised the query. Figure 14. Replenishment Model Step 7 National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 37
  • 39. Step 8: Here we will see all the branch and regions in the columns and rows will show three fields namely Adjustable, Input and Output field. Figure 15. Replenishment Model Step 8 • We need to fill the current inventory data like OH(on Hand), GIT (goods in transit) and, PQ(pending quantity) only in the input field • As soon as this above step gets completed the output of the model is ready to inform us that whether stocks at each of the regions/branches are adequate enough or there is a need to place National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 38
  • 40. an order or the present stock is exceeding the maximum stock limit. • Under the Adjustable field we can adjust/change only those cells that are represented by white color. • The output field is not made for user manipulation. • If the stock position at any branch or region inventory position falls below the ROP level corresponding to that location the ROP field will change to “red color” representing that ROP has reached and order of size OQ(order quantity) whose color is changed to blue has to be ordered to the immediate supplier. • Similarly if the inventory position at any location exceeds the maximum stock limit defined for that SKU/model the “Max. Stock” cell corresponding to that location will change its color to yellow showing that the maximum stock limit has been exceeded For each new HGM stock keeping unit the user has to repeat the same exercise. Simulation Test Given that demand may not be perfectly normal and may be seasonal, it’s a good idea to test and adjust inventory policies using a computer simulation before they are actually implemented. The simulation should use a demand pattern that truly reflects actual demand including any lumpiness as well as seasonality. The inventory policies obtained using the replenishment model made during the project can then be tested and adjusted if needed to obtain the desired service level. Surprisingly powerful simulations can be built using MS Excel. Identifying problems in a simulation can save a lot of time and money compared to facing these problems once the replenishment model is implemented and inventory policy is in place The simulation test in our case has been conducted for the “Premier Steel M3” SKU. The simulation run is conducted assuming the worst possible demand pattern during the month; this will ensure that if the model works successfully under this kind of demand behavior it is expected to work smoothly with other combinations of billing pattern too. Demand for two months was taken and 10%, 20%, 70% billing in the three phases of the month has been considered. The order placed by all the branches has been assumed as demand at the regional warehouse on the same dates. The results of the simulation run is shown in the next page National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 39
  • 41. Test Results Premier Steel M3 Delhi demand 600 500 S t o c k le v e l 400 300 SS 200 ROP IP 100 Max Stock 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Days Figure 16. Simulation Result for Delhi Branch Premier Steel M3 Lucknow 400 350 300 S to c k le v e l 250 200 SS 150 ROP 100 IP 50 Max Stock 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Days Figure 17. Simulation Result for Lucknow Branch National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 40
  • 42. Premier Steel M3 Ghaziabad 350 300 250 S t o c k le v e l 200 SS 150 ROP 100 IP 50 Max Stock 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Days Figure 18. Simulation Result for Ghaziabad Branch Premier Steel M3 Indore 250 200 S to c k le v e l 150 SS 100 ROP 50 IP Max Stock 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Days Figure 19. Simulation Result for Indore Branch National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 41
  • 43. Premier SteeM3 Central Region 1400 1200 SS 1000 ROP S t o c k le v e l 800 IP 600 Max Stock 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Days Figure 20. Simulation Result for Central Region The result shows that Replenishment model has cleared the simulation test and the regional warehouse is successfully able to cater the demand of the corresponding branches. It is assumed that this model will behave in the same manner for all other regions and products. We can now implement this model to replenish the storage location. This test provided us the confidence that the stock norms calculated by the model are realistic and will work successfully in the practical environment. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 42
  • 44. Conclusion It is believed that this Replenishment model sets out a viable reference for stock management for all classes of High gross margin products in the companies. The model is applicable to other products categories along with HGM products with slight modification. A key benefit of the methodology is that it allows the management to understand the drivers of inventory levels. This alone will improve stock management. As the model is designed in MS Excel it is easy to edit and can be refined further to incorporate any changes, if considered essential by the management. The model provides a robust base for appropriate availability of stocks at all storage locations. Project will help the management in reviewing the inventory position at all storage locations for a product at a time. Fortunately, initial implementation of the model is less time consuming and easy due to the amount of data which must be collected and analyzed is not huge. The appropriate stock level depends on a number of factors and this will change from period to period. However, the model can be used to calculate a SS, ROP, S, CSL and fill rate for a particular period. The model is capable of providing realistic stock norms under practical situations. Successful simulation test suggests that the replenishment model is ready to be implemented. Some other factors are also worth considering: It is quite clear that variability in demand will be a key determinant of inventory levels. Thus, by reducing variability, it will be possible to reduce inventory. Variability can be reduced by improved forecasting and through consolidation of stock holding since then the peaks and valleys of demand will tend to cancel each other out. This must be considered in future inventory management strategies and can be a key determinant of future space planning. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 43
  • 45. Future Scope The replenishment model is working fine right now but there is always a difference between theory and the practical situation. The model can be refined and repaired as per the demand of the situation after the implementation. The replenishment model can be easily extended to other products too just by providing access to the respective database in the required format. The model is not sufficient for all the software needs of the company related to the replenishment. Nonetheless it provides an initial framework through which the appropriate stock levels can be maintained at the branch and regional level to reduce the ESC to as low as two units per cycle by improving the service levels. The forecasting accuracy can be improved further to reduce the uncertainty in demand which in turn will reduce the inventory levels. Lead time variability should be controlled to further lower the stock levels. The model needs some more computer programming that could link the database to the model and manual interventions can be reduced further. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 44
  • 46. Bibliography Books References 1. Arnold J. R. T. & Chapman S., INTRODUCTION TO MATERIALS MANAGEMENT, 5th edition, Pearson Education. 2. Sunil Chopra & Peter Meindl, SUPPLY CHAIN MANAGEMENT, 2nd edition, Pearson Education 3. Ballou, Ronald H., Business Logistics/Supply Chain Management, 5th edition, Prentice Hall Internet References 1. 2. 3. National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai 45