OPERATIONAL EXECELLENCE IN INVENTORY REDUCTION IN ABB PROCESS AUTOMATION

Description
Inventories constitute most significant part of assets of large majority of the companies in India. Inventory a double edged sword is usually an asset of an organization, if not used properly it will become liability. It is therefore absolutely very important to manage inventories efficiently and effectively in order to overcome unnecessary investment.

OPERATIONAL EXECELLENCE IN INVENTORY REDUCTION IN ABB PROCESS AUTOMATION

Rahul Das Batch-17, XIME - PGDM

A Project Report on

“OPERATIONAL EXCELLENCE IN INVENTORY REDUCTION IN ABB PROCESS AUTOMATION”
A Project Report submitted in partial fulfilment of the requirement for the award of POST GRADUATE DIPLOMA IN MANAGEMENT (PGDM)

Submitted by:

Rahul Das PGDM, Roll No. 118, 17th Batch (2011-2013) Submitted on 02th July, 2012

Under the guidance of Mr. Ravindra N Project guide (Assistant Vice-president, Head-System Integration, ABB PA) Mr. Natesha MS Project guide (Assistant Vice-president, Head-Operational Excellence, ABB PA)

XIME
Xavier Institute of Management & Entrepreneurship Electronics City-Phase II, Bangalore-560100

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ACKNOWLEDGEMENT
The 8 weeks of internship at ABB PA has been an enriching experience in terms of learning and application of theory into practice. The real time experience that I have received is something which cannot be emulated in a class room scenario and will be highly helpful for my professional growth. It has been a fruitful, exciting and value adding exercise for me. It bears the imprint of many people, and I wish to express my sincere gratitude towards all those who made it possible. First, I would like to express my earnest appreciation and gratitude towards our President, Professor J. Philip, President, XIME for conceptualizing the summer training program. I would like to thank our former Dean, Mr. S.D.Tyagaraj and Current Dean Dr.Regi Mathew for implementing the concept of summer training program. I am grateful to Mr. V.V. Ravikumar and Mr.D.Subramaniam, my project guides, for guiding me during my internship. I would like to express my gratitude towards Mr. Ravindra N, Assistant VP, Head-System Integration, ABB PA and Mr. Natesha MS Assistant Vice-president, Head-Operational Excellence, ABB PA, for lending me tremendous support and encouragement during these 2 months of SIP and for giving me an opportunity to work with the organization and guiding me throughout my Project work. My sincere thanks to Mr. Mayuranath PS, Mr.Ajit Bairannavar and Mr.Venkatesh BS for their guidance and patience while tending to my doubts and resolving my queries.

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EXECUTIVE SUMMARY
The report documents the project undertaken during the 8 -week period from May 07, 2012 to June 30, 2012 in the Process Automation (PA) department, ABB Peenya, Bangalore. During this period, I studied the inventory practices followed in ABB PA and provided a blue-print using which the inventory pile-up can be reduced alongwith meeting the project demand for the various divisions involved under ABB PA. Inventories constitute most significant part of assets of large majority of the companies in India. Inventory a double edged sword is usually an asset of an organization, if not used properly it will become liability. It is therefore absolutely very important to manage inventories efficiently and effectively in order to overcome unnecessary investment. OBJECTIVE The objective of the project was two-fold. The foremost objective was to show tangible and measurable inventory reduction. The second was to do provide a policy for inventory control in order to meet project demand. Primary Objective ? ? ? To understand the inventory policy of ABB PA. To study ABC analysis and ageing schedule and implement the same for ABB PA inventory management. To show reduction in terms of inventory size and cost for the months of July, Aug and Sep?12. Secondary Objective ? ? ? To analyse the inventory data and find out if there exist any specific inventory types. To implement non-parametric hypothesis tests in order to find out if there exist patterns in inventory orders for specific items. To provide a policy for inventory control which meets the project demands as well as prevents inventory pile-up.

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RESULTS ? ? ? ? ? ? ? ? The inventory categorization using ABC analysis showed the contribution of various classes of inventory A, B, C to the overall share of inventory stock. The segregated inventory under ABC also showed the number and cost of items below and above the 180 day period. Based on the ABC analysis, 3BSE and 3BYN items were found to contribute the most to the RM Inventory stock. This was across all cost categories – A, B and C. The inventory reduction was shown using the inventory matching approach and the results were documented. An Inventory reduction of 6% was shown for PA Metals for the months of July, Aug and Sep?12. An Inventory reduction of 4% was shown for PA Minerals for the months of July, Aug and Sep?12. The analysis of Project Bill of Materials showed that a 3BSE items were being ordered more frequently than others for projects. At the same time, 3BYN items are not getting ordered for projects although they contribute a significant share to the overall inventory stock. CONCLUSION Businesses are increasingly using Inventory control as a strategic device in order to obtain a competitive advantage over their rivals. Having such a system in place enables firms not only to meet customer demand but also reduce their inventory input costs. Inventory reduction enables businesses to cut down on material costs and maintenance and at the same time remain price-competitive and generate increased profits. From ABB PA?s perspective, the inventory reduction process carried out will lead to decrease in inventory pile-up without hampering the demand from projects. The outflow of excess inventory would increase due to more demand items getting liquidated from unrestricted stock. As a result, the management will be able to track the incoming inventory with respect to the project demand. The study enabled the learning of valuable expertise in the domain of project business and its inventory ordering process. The learning and experience of working in ABB will go a long way in aiding me to use the same while working in a project business.

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RECOMMENDATIONS In order to meet current project demand and reduce inventory at the same time, the following recommendations were put forth to fulfill the objective. ? ? ? ? Check the status of current ordering for Projects. If not ordered, then match from unrestricted RM stock and only order for the items which are not in stock. In order to simplify the inventory matching process, the BOM has to be made available to the inventory managers of respective divisions. The divisions should identify the list of materials they don?t need. This list should be circulated within the entire PA department. The project BOMs should be checked matched against the unrestricted stock of all divisions. As a result, if a project from PAME isn?t able to match all its items from PAME free stock, the same can be checked against the free stock of all other divisions. ? As the software development is being done using demo S/W, the licensed S/W should be brought JIT and reduce inventory holding costs.

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1. INTRODUCTION
The term inventory refers to assets, which will be sold in future in the normal course of business operations. The assets, which the firm stores as inventory in anticipation of need, are raw materials, work-in-progress/process, and finished goods. Every enterprise needs inventory for smooth running of its activities. It serves as a link between the production and distribution process. Inventories cost account for nearly 55 percent of the cost of production, as it is clear from an analysis of financial statements of large number of private and public sector organizations. So, it is essential to establish suitable procedures for proper control of materials from the time of purchase order placed with supplier until they have been consumed properly and accounted for. Objective of inventory management The main objectives of inventory management are operational and financial. The operational object means availability of materials and spares in suffici ent quantities for undisturbed flow of production. The financial objective means investments in inventories should not remain idle and minimum working capital should be locked in it. The other objectives are: ? ? ? ? ? ? ? ? ? To ensure continues supply of inventories to the production. To avoid over stocking and under stocking. To maintain optimum level of investment in inventories. To keep material cost under control, to keep low cost of production. To eliminate duplication in ordering or replacing stocks. To minimize losses through, deterioration, pilferage, wastage and damages. Designing structures for good inventory management. Perpetual inventory control of materials. To facilitate data for short and long term planning and control of inventory.

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1.1. BACKGROUND In a large firm the inventory department is responsible for diverting inventory to where the work is to be done and issue instructions for the same. In a project business, this function gets modified and the inventory has to be tracked for each and every project under execution. This requires setting of definite time schedules so that necessary materials are ordered and delivered to the project concerned in proper time not too long before han d neither lest it should interfere with other work nor after they are required as this result in idle time. ABB Process Automation (PA) is pre-dominantly a project business alongside product manufacturing which make up for the total revenue share. The ABB PA division has several Business Units (BUs). They are as follows ? ? ? ? ? ? ? ? ? ? Oil, Gas and Petrochemicals (OGP) Minerals (MI) Metals (ME) Control Technologies(CT) Marine and Crane Applications (MA) Pulp and Paper (PP) Turbo Chargers (TC) Measurement Products (MP) Life Sciences Services

For each BU, there are separate functions for project, service and product businesses. The inventory ordering is based on the project configuration for each of the project deliverables. Based on the project configuration, the Bill of Materials is developed by the engineering team and the order is placed to the supplier. A monthly detailed list of RM inventory is maintained for each of the BUs of ABB PA. This data is presented in the form of inventory ageing and co st. Based on the last date of a RM inventory item coming in stock, it is categorized as per the following ageing periods ? ? ? ? ? < 31 days 31-60 days 61-90 days 91-180 days 181-360 days Summer Internship Report Page 8

? ?

361-720 days > 720 days

1.2. CURRENT SCENARIO As per the current inventory policy of ABB PA, the inventory level should be 7-8% of Annual Sales. The current inventory procurement and reservation practice followed in ABB PA across all BUs is mentioned as follows – ? ? ? ? ? ? Once a project is finalized by the sales team, the task of project configuration layouts are forwarded to the engineering team. The engineering team prepares the layouts and subsequently comes up with the Bill of Materials (BOM) and Bill of Quantity (BOQ) for the project. The inventory at a given point in time can be in either of the followin g states – E, O, Q and blank. For the inventory to be in E and O state would mean, it is with the vendor at present but has been reserved for the project. For the inventory to be in Q state would mean, it is reserved for the project and is currently present in the stock. For the inventory to be in Blank state would mean, it hasn?t been reserved for any project and it lies unrestricted in the stock. The current inventory holding of ABB PA is 705 Million (INR). Of which, the share of RM inventory holding for PA Metals, Minerals and Pulp & Paper is 57%. The major contributors to this pile-up of inventory are –

? ? ? ? ? ?

Ordering of inventory at one-shot – The practice of ordering inventory for a project which will would have multiple releases. Staggered project releases – A project release might be happening under small releases spread across the financial year.

Varied Lead Time of arrival of inventory items on the shop floor.
Long Project Cycle

Execution Delays Clearance from Customer

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ABB PA RM INVENTORY Metals, Minerals, Pulp & Paper (June'12)
2,35,39,174 44,33,806 3,57,10,911 7,42,10,014 7,85,17,522

PAME-INDS PAME-INDO

PAME -INCV
PAMI-INHK 14,10,35,917 PAMI-INDP PAPP-INDR PAPP-INDQ PAPP-INHW 7,96,685

4,38,62,513

Figure 1.1 – ABB PA RM Inventory Stock – Metals, Minerals and Pulp & Paper (as of June?12) PA DIVISIONS PAME-INDS PAME-INDO PAME -INCV PAMI-INHK PAMI-INDP PAPP-INDR PAPP-INDQ PAPP-INHW TOTAL June?12) The following observations can be made from the RM inventory figures mentioned above. ? As inferred from Figure 1.1 and Table 1.1, the BUs PAME, PAMI contribute a share of 57% to the current RM inventory stock. INVENTORY VALUE 7,85,17,522 14,10,35,917 7,96,685 4,38,62,513 7,42,10,014 3,57,10,911 2,35,39,174 44,33,806 40,21,06,541

Table 1.1 – Cost Break of ABB PA Inventory – Metals, Minerals and Pulp & Paper (as of

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?

As a result, in order to achieve any significant inventory reduction the unrestricted RM stock of PAME, PAMI should be targeted for liquidation.

As a result of the above observations, the scope of the project is entailed to PA Metals, Minerals and Pulp & Paper. Since the type of items is same across all BUs of ABB PA, the opportunity for tangible inventory reduction is possible. 1.3. OBJECTIVES Companies today must be fast and nimble enough to react quickly to changes in customer demand and do it with little inventory. Gone are the days when manufacturers could stockpile large quantities of raw materials, load up the shop floor with wo rk-in-process and pack warehouses with finished goods. In some cases, inventory is so bloated that a high percentage of it will become obsolete before it is sold. Worse, too much inventory is a certain indicator of more serious and costly business process and systems problems that can be rooted very deeply across the organization. Keeping in concern the ABB PA division, there has been a gradual stock-pile of RM inventory during the past quarter (4 th quarter, FY?11). A major concern is the tracking of materials which have been lying in stock for greater than 180 days. This number is significant for PAME (Metals) and PAMI (Minerals). While taking care of demand needs, the current stock has to be brought down to internally set optimum levels. In lieu of this, the objectives of the project are outlined as follows – Primary Objective ? ? ? To understand the inventory policy of ABB PA. To study ABC analysis and ageing schedule and implement the same for ABB PA inventory management. To show reduction in terms of inventory size and cost for the months of July, Aug and Sep?12. Secondary Objective ? ? ? To analyse the inventory data and find out if there exist any specific inventory types. To implement non-parametric hypothesis tests in order to find out if there exist patterns in inventory orders for specific items. To provide a policy for inventory control which meets the project demands as well as prevents inventory pile-up. Summer Internship Report Page 11

Scope of the Study ? ? This study is to find the facts and opinions of inventory management and contr ol at ABB plant. In accordance with the present trends it aims mainly at finding out the inventory reduction from the current stock and suggests a path -way for the same. Limitation of the Study ? ? ? ? ? Time was restricted to only 8 weeks of project work in the organization. The information, which was needed, could not be made public by the organization. The study are related to ABB ltd Bangalore only The finding and suggestion cannot be generalized. The study covered a wide concept hence wide collection and coverage of information was not easily possible.

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2. METHODOLOGY
With an aim to achieve inventory reduction, the ABB PA inventory ordering process was studied. The primary inventory data was obtained from the ABB PA dept . Interactions were carried out with Point of Contacts in the ABB PA to further understand the inventory procurement and ordering process. Based on the interaction with inventory managers of Metals, Minerals and Pulp& Paper divisions, the following methodology was adopted. ? ? ? ? ? ? The inventory data of May?12 was categorized using ABC analysis at both value and ageing levels. The bill of materials (BOM) for July, Aug and Sep?12 projects were taken and the lists were matched against the current inventory held. The matched inventory was diverted from the existing stock to meet the project demand. Only the residual non-matched inventory was to be ordered. The reduction from matched inventory was measured and compared against the current inventory. Non-parametric hypothesis tests were performed on the ABC data and project BOM to identify demand patterns for specific items. 2.1 ABC ANALYSIS One of the most important considerations for inventory control is the value of consumption of inventory items in a year. ABC analysis is a Pareto representation of the inventory items in terms of value consumption. It is explained as follows – ? ? ? ? Only a small number of inventory items consume a very large share of inventory consumption during the year. A little larger number of inventory items covers a moderate share of annual inventory consumption. A very large number of items just cover a very small share of annual inventory consumption. It has been observed that in an industrial unit only 10% of items have 70% of the annual inventory consumption. These items deserve highest attention and are classified as „A? items.

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? ?

Similarly 20% of the items covering 20 % of the inventory investment are „B? class items. Balance 70% of the inventory items are termed as „C? class items.

In order to analyse the inventory levels of 3 ABB PA divisions Metals, Minerals and Pulp & Paper and provide a categorization for the same, the ABC labelling technique was used. The following procedure was used during ABC analysis – ? The May?12 inventory data was filtered under the following time periods in which the inventory was held - < 31 days. 31-60 days, 61-90 days, 91-180 days, 181-360 days, 361-720 days and > 720 days. ? ? ? Using a split of 180 days, the inventory data for the 3 divisions was segmented. As a result, there were two general time period tabs - < 180 days and > 180 days. Under each tab, ABC analysis based on the cost of the inventory held was carried out. The high rupee volume items were tagged as „A?. These items contribute to app. 70% of the total inventory cost and will be app. 5% of the total inventory materials being held. This is applicable to inventory data clubbed under two tabs - < 180 days and > 180 days. ? The medium rupee volume items were tagged as „B?. These items contribute to app. 25% of the total inventory cost and will be app. 10% of the total inventory materials being held. ? The low rupee volume items were tagged as „C?. These items contribute to app. 5% of the total inventory cost and will be app. 85% of the total inventory materia ls being held.

The results of ABC analysis for the 3 divisions are presented in the Results section. 2.2 INVENTORY REDUCTION Once the May?12 inventory items were categorized using ABC analysis, the next step was Inventory Reduction. In order to achieve this objective, an Inventory Reduction Plan was prepared. This is mentioned as follows – ? ?

Identify inventory items and use for current projects as per need and demand for each item. Route the excess inventory lying in one ABB PA division to other divisions based on project demand for that particular division and all other divisions . Summer Internship Report Page 14

? ? ? ?

Sell the excess non-patented inventory to current vendors based on current value. Divert excess inventory to ABB PA product division. This again has to be checked with Product division demand and allowed inventory levels for that division. Scrap off non-patented items based on obsolescence For repeat customers, identify inventory to be used for them and divert to them. Again this has to be checked with the sales and marketing te am for data on repeat business.

?

Use Newsvendor Model or EOQ to determine optimal inventory levels. For this the demand data as historical data for past projects would be needed. In case of EOQ modelling, Estimates of various cost elements such as costs, set-up costs need to be figured out. holding costs, ordering

? ?

Determine policy on safety stock (if not already present) and Levels of safety stock with a view to eliminate excess stock. Periodic Review of the inventory levels for all 6 divisions and val idate if inventory data is real time/near real-time and reliable. This can cut unnecessary stock build up.

Due to time constraints, the inventory reduction was carried using the first approach outlined in the plan. The project BOMs were matched with the current unrestricted stock present in each division and the matched items were liquidated. Only the non matched residual items were sent for ordering.

The Inventory reduction process was done on the unrestricted free stock of Metals (PAME) and Minerals (PAMI). ? ? ? ?

The BOM of an unordered project was taken and matched with the unrestricted free stock of that division. The two item array were copied to an EXCEL 2007 worksheet and matched. The following matching command was used in EXCEL 2007 to arrive at the r esults. IF(ISERROR(MATCH(A x ,$B$ x :$B$ y ,0)),"", A x ) where A x = the BOM element in the xth row of A column of worksheet, and $B$ x :$B$ y = the total array of unrestricted Inventory items of a division

?

Using this logic, a corresponding element in the BOM would be searched against the whole unrestricted items array. If a match was found, the logic would return TRUE.

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?

Once the matched items were identified, the total reduction for an inventory item was calculated keeping the mind the Unit price of Inventory and th e no. of units demanded for that item.

The inventory reduction was done for PA Metals and Minerals for the month of July, Aug and Sep?12 based on the project BOMs for which ordering had not been done. The results of inventory reduction are presented in the Results Section.

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3. FINDINGS
With the objective of performing ABC analysis and inventory reduction, the following results were obtained.

3.1 FINDINGS OF ABC ANALYSIS FOR METALS (PAME), MINERALS (PAMI) AND PULP & PAPER (PAPP) Based on the ABC analysis carried out on the May?12 inventory data, the following results were obtained. ? PAME May’12 Inventory – The Metals division has the largest inventory share among all 6 PA divisions. 1. PAME-INDO – With a total inventory value of 108 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 70 million (INR). For ABC classification, refer to Figure 1.2 and 1.3. (b) > 180 days – The combined value of inventory was 38 million (INR). For ABC classification, refer to Figure 1.2 and 1.3.

PAME-INDO INVENTORY VALUE
2,68,41,761.13 8,00,00,000.00 7,00,00,000.00 6,00,00,000.00 5,00,00,000.00 4,00,00,000.00 3,00,00,000.00 2,00,00,000.00 1,00,00,000.00 0.00 A B 1,68,96,643.78 35,70,273.05 C 5,02,23,125.20 19,04,949.68 90,59,446.32 > 180 DAYS

< 180 DAYS

Class of Items
Figure 1.2 – PAME-INDO Inventory Value (As of May?12).

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PAME-INDO INVENTORY ITEMS
219

450 400 350 300 250 200 150 100 50 0 A B C 32 57 101

115
> 180 DAYS < 180 DAYS 214

Class of Items
Figure 1.3 – PAME-INDO Inventory Items (As of May?12) 2. PAME-INDS – With a total inventory value of 75 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 47 million (INR). For ABC classification, refer to Figure 1.4 and 1.5. (b) > 180 days – The combined value of inventory was 28 million (INR). For ABC classification, refer to Figure 1.4 and 1.5.

PAME-INDS INVENTORY VALUE
6,00,00,000.00 5,00,00,000.00 1,97,22,130.76

> 180 DAYS
< 180 DAYS

4,00,00,000.00
3,00,00,000.00 2,00,00,000.00 1,00,00,000.00 0.00 A B C 3,33,22,743.69 1,15,68,342.59 24,04,472.69 67,47,809.91 14,08,134.20

Class of Items
Figure 1.4 – PAME-INDS Inventory Value (As of May?12). Summer Internship Report Page 18

PAME-INDS INVENTORY ITEMS

450
400 350 300 250 200 150 100 50 0 A B C 50 45 177 92 161 236 > 180 DAYS < 180 DAYS

Class of Items
Figure 1.5 – PAME-INDS Inventory Items (As of May?12). 3. PAME-INCV – With a total inventory value of 0.8 million (INR), the ABC categorization revealed the following(a) < 180 days – There were no inventory items ageing below 180 days. (b) > 180 days – The combined value of inventory was 0.8 million (INR). For ABC classification, refer to Figure 1.6 and 1.7.

PAME-INCV INVENTORY VALUE

6,00,000.00 5,00,000.00 4,00,000.00 3,00,000.00 2,00,000.00 1,00,000.00 0.00 A B C 2,00,206.69 5,46,735.66 > 180 DAYS

49,742.18

Class of Items
Figure 1.6 – PAME-INCV Inventory Value (As of May?12).

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PAME-INCV INVENTORY ITEMS

10
8 6 4 2 0 A B C 8 9

7

> 180 DAYS

Class of Items
Figure 1.7 – PAME-INCV Inventory Items (As of May?12). ? PAMI May’12 Inventory – The Minerals division had the 2 nd largest inventory share among all 3 divisions for which ABC analysis was conducted. 1. PAMI-INHK – With a total inventory value of 28 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 21 million (INR). For ABC classification, refer to Figure 1.8 and 1.9. (b) > 180 days – The combined value of inventory was 7.2 million (INR). For ABC classification, refer to Figure 1.8 and 1.9.

PAMI-INHK INVENTORY VALUE
> 180 DAYS < 180 DAYS 1,46,71,292.42

2,00,00,000.00 1,50,00,000.00 1,00,00,000.00 50,00,000.00 0.00 A B C 3,69,143.25 51,13,772.06 17,40,605.36 10,98,824.53

53,28,667.43

Class of Items
Figure 1.8 – PAMI-INHK Inventory Value (As of May?12). Summer Internship Report Page 20

PAMI-INHK INVENTORY ITEMS

200 150 82 100 50 0 A B C 40 9 21 32 73 > 180 DAYS < 180 DAYS

Class of Items
Figure 1.9 – PAMI-INHK Inventory Items (As of May?12). 2. PAMI-INDP – With a total inventory value of 80 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 56 million (INR). For ABC classification, refer to Figure 1.10 and 1.11. (b) > 180 days – The combined value of inventory was 24 million (INR). For ABC classification, refer to Figure 1.10 and 1.11.

PAMI-INDP INVENTORY VALUE
6,00,00,000.00

5,00,00,000.00
4,00,00,000.00 3,00,00,000.00 2,00,00,000.00 1,00,00,000.00 0.00

1,69,85,689.27

> 180 DAYS 3,91,02,342.59 59,04,986.61 1,44,53,713.03 28,77,890.60 12,06,311.01 C < 180 DAYS

A

B

Class of Items
Figure 1.10 – PAMI-INDP Inventory Value (As of May?12).

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PAMI-INDP INVENTORY ITEMS

400 300 200 100 0 A 39 16 B 99 39 C 184 > 180 DAYS < 180 DAYS 117

Class of Items
Figure 1.11 – PAMI-INDP Inventory Items (As of May?12). ? PAMI May’12 Inventory – The Minerals division had the 2 nd largest inventory share among all 3 divisions for which ABC analysis was conducted. 1. PAPP-INHW – With a total inventory value of 3.8 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 3.7 million (INR). For ABC classification, refer to Figure 1.12 and 1.13. (b) > 180 days – The combined value of inventory was 0.1 million (INR). For ABC classification, refer to Figure 1.12 and 1.13.

PAPP-INHW INVENTORY VALUE
30,00,000.00 25,00,000.00 20,00,000.00 15,00,000.00 10,00,000.00 5,00,000.00 0.00 A B C 9,82,675.05 2,16,521.19 25,47,567.15 9,436 95,095.62

31,068
> 180 DAYS

< 180 DAYS

Class of Items
Figure 1.12 – PAPP-INHW Inventory Value (As of May?12). Summer Internship Report Page 22

PAPP-INHW INVENTORY ITEMS
30
25 20 15 10 5 0 A B C 6 7 11 6 24 > 180 DAYS < 180 DAYS

6

Class of Items
Figure 1.13 – PAPP-INHW Inventory Items (As of May?12). 2. PAPP-INDQ – With a total inventory value of 29 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 22 million (INR). For ABC classification, refer to Figure 1.14 and 1.15. (b) > 180 days – The combined value of inventory was 7 million (INR). For ABC classification, refer to Figure 1.14 and 1.15.

PAPP-INDQ INVENTORY VALUE
2,50,00,000.00 2,00,00,000.00 1,50,00,000.00 17,63,502.17 1,00,00,000.00 50,00,000.00 0.00 A B 1,55,94,965.15 55,77,587.24 11,45,232.34 C 3,66,704.69 > 180 DAYS < 180 DAYS 48,86,846.17

Class of Items
Figure 1.14 – PAPP-INDQ Inventory Value (As of May?12).

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PAPP-INDQ INVENTORY ITEMS
160 140 120 100 80 60 40 20 0 A B C 23 26 43 37 96 > 180 DAYS < 180 DAYS 61

Class of Items
Figure 1.15 – PAPP-INDQ Inventory Items (As of May?12). 3. PAPP-INDR – With a total inventory value of 32 million (INR), the ABC categorization revealed the following(a) < 180 days – The combined value of inventory was 29 million (INR). For ABC classification, refer to Figure 1.16 and 1.17. (b) > 180 days – The combined value of inventory was 3 million (INR). For ABC classification, refer to Figure 1.16 and 1.17.

PAPP-INDR INVENTORY VALUE
2,50,00,000.00

2,00,00,000.00
1,50,00,000.00

48,86,846.17

17,63,502.17 1,00,00,000.00 50,00,000.00 0.00 A B 1,55,94,965.15 55,77,587.24 11,45,232.34 C 3,66,704.69

> 180 DAYS < 180 DAYS

Class of Items
Figure 1.16 – PAPP-INDR Inventory Value (As of May?12).

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PAPP-INDR INVENTORY ITEMS
250 200 150 100 50 0 A B C 17 25 53 134 64 > 180 DAYS < 180 DAYS 81

Class of Items
Figure 1.17 – PAPP-INDR Inventory Items (As of May?12).

3.2 FINDINGS OF INVENTORY REDUCTION FOR METALS (PAME), MINERALS (PAMI)

The process of Inventory Reduction as described in the Methodology Section was implemented based on the BOMs of 4 Metals and 4 Minerals Projects, the ordering for which were not done and was slated for release during July, Aug, Sep?12. The list of the projects mentioned is as follows – ? Metals i) RSP – 23 panels ii) RSP – 91 panels iii) BSP iv) Globe Radio ?

Minerals i) ACC Wadi ii) JayaJyothi Cement iii) Prism Cement iv) JK Paper – PM1 and PM3 QCS v) TATA Noamundi

The inventory reduction achieved by matching the project BOMs with the unrestricted stock of both Metals and Minerals is shown as part of Appendix.

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The PAME inventory reduction process lead to the following results – ? ? ? A total of 79 inventory items were matched from the 4 project BOMs. The total inventory liquidation was calculated to be 14 million (INR). As of 1 St June?12, the overall PAME inventory was 219 million (INR). A total reduction of 6% was obtained from the process.

PAME INVENTORY REDUCTION-ITEMS MATCHED
40 35 BOM Items

30
25 20 15 10 5 0 RSP-23 RSP-91 BSP GLOBE RADIO

Figure 1.18 – PAME Inventory Reduction – No. of Items Matched

PAME INVENTORY REDUCTION - VALUE (INR)
70,00,000.00 60,00,000.00 50,00,000.00

40,00,000.00
30,00,000.00 20,00,000.00 10,00,000.00 0.00

RSP-23

RSP-91

BSP

GLOBE RADIO

Figure 1.19 – PAME Inventory Reduction – Value of Items Matched

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The PAMI inventory reduction process lead to the following results – ? ? ? A total of 73 inventory items were matched from the 5 project BOMs. The total inventory liquidation was calculated to be 5 million (INR). As of 1 St June?12, the overall PAME inventory was 118 million (INR). A total reduction of 4% was obtained from the process.

PAMI INVENTORY REDUCTION - ITEMS MATCHED

30 20 BOM items 10 Matched Items

0
JayaJyothi Cement Prism Cement JK Paper PM1 QCS JK Paper PM3 QCS ACC Wadi TATA Noamundi

. Figure 1.20 – PAMI Inventory Reduction – No. of Items Matched

PAMI INVENTORY REDUCTION - VALUE (INR)
35,00,000.00 30,00,000.00 25,00,000.00 20,00,000.00 15,00,000.00 10,00,000.00 5,00,000.00 0.00 JayaJyothi Cement Prism Cement JK Paper PM1 QCS JK Paper PM3 QCS ACC Wadi TATA Noamundi

.

Figure 1.21 – PAMI Inventory Reduction – Value of Items Matched

The overall reduction from both PAME and PAMI inventory stock can be further increased by routing greater inventory from the curr ent stock for future projects and less ordering of complete project BOMs. A detailed of future project BOMs is thereby required to achieve the same.

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4. DATA ANALYSIS
The results obtained from the ABC technique and inventory reduction process were subjected to hypothesis testing to identify patterns in inventory items and ordering preferences.

4.1 ANALYSIS OF ABC DATA

The ABC data was analysed to identify patterns of most commonly occurring items . The goal was to find the out the contribution of common ite ms to the inventory stock. This would later help towards greater inventory reduction as these common items would be tracked. An analysis of the Inventory data for both Metals and Mineral s showed the following results. ? Metals – i) PAME-INDO (1) The share of 3BSE items is 37 million (INR). (2) The share of 3BYN items is 51 million (INR). (3) The share of Other items is 20 million (INR). ii) PAME-INDS (1) The share of 3BSE items is 28 million (INR). (2) The share of 3BYN items is 16 million (INR). (3) The share of Other items is 30 million (INR).

PAME-INDO TYPE OF INVENTORY
4,00,00,000 3,50,00,000 3,00,00,000 2,50,00,000 < 180 days > 180 days

2,00,00,000
1,50,00,000 1,00,00,000 50,00,000 0 3BSE 3BYN Others

.

Figure 1.22 – PAME-INDO Type of Inventory Items Summer Internship Report Page 28

PAME-INDS TYPE OF INVENTORY

2,50,00,000 2,00,00,000 1,50,00,000 < 180 days > 180 days

1,00,00,000
50,00,000 0 3BSE 3BYN Others

.

Figure 1.23 – PAME-INDS Type of Inventory Items ? Minerals – i) PAMI-INDP (1) The share of 3BSE items is 51 million (INR). (2) The share of 3BYN items is 24 million (INR). (3) The share of Other items is 4 million (INR). ii) PAMI-INHK (1) The share of 3BSE items is 11 million (INR). (2) The share of 3BYN items is 9.8 million (INR). (3) The share of Other items is 6.7 million (INR).

PAMI-INDP TYPE OF INVENTORY

4,00,00,000 3,50,00,000 3,00,00,000 2,50,00,000 2,00,00,000 1,50,00,000 1,00,00,000 50,00,000 0 3BSE 3BYN Others

< 180 days > 180 days

.

Figure 1.24 – PAMI-INDP Type of Inventory Items

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PAMI-INHK TYPE OF INVENTORY
1,00,00,000 90,00,000 80,00,000 70,00,000 60,00,000 50,00,000 40,00,000 30,00,000 20,00,000 10,00,000 0 3BSE 3BYN Others

< 180 days > 180 days

Figure 1.25 – PAMI-INDP Type of Inventory Items

4.2 ANALYSIS OF INVENTORY REDUCTION DATA

The analysis of the project BOMs for Metals and Minerals shows that no. of 3 BSE items are ordered the most. A detailed look at the ordering of 3BSE items for the 4 Metals and 5 Minerals projects shows the following – ? Metals – Out of total 103 items present in BOM for all 4 projects, 92 items were of 3BSE type. The total worth of Matched 3BSE items is 11 million (INR). As mentioned previously, the total worth of PAME 3BSE items is 65 million (INR). Due to the liquidation of matched items, the total reduction from 3BSE items is 16%.

PAME BOM - TOTAL 3BSE ITEMS
40 30 20 10 0 RSP-23 RSP-91 BSP GLOBE RADIO 15 35 22 3BSE Items

20

Figure 1.26 – PAME – Total 3BSE Items Present in Project BOMs

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PAME BOM - TOTAL VALUE OF MATCHED BSE ITEMS (INR)
58,59,567.92 60,00,000.00 50,00,000.00 40,00,000.00 30,00,000.00 20,00,000.00 10,00,000.00 0.00 RSP-23 RSP-91 BSP GLOBE RADIO 10,22,759.44 3,19,013.07 Value 41,55,692.04

Figure 1.27 – PAME – Total Value of Matched 3BSE Items Present in Project BOMs ? Minerals – Out of total 94 items present in BOM for all 5 projects, 84 items were of 3BSE type. The total worth of Matched 3BSE items is 5 million (INR). As mentioned previously, the total worth of PAMI 3BSE items is 62 million (INR). Due to the liquidation of matched items, the total reduction from 3BSE items is 8%.

PAMI BOM - TOTAL 3BSE ITEMS
3BSE Items 30 25 20 15 10 5 0 JayaJyothi Cement Prism Cement JK Paper PM1 QCS JK Paper PM3 QCS ACC Wadi TATA Noamundi

Figure 1.28 – PAMI – Total 3BSE Items Present in Project BOMs

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PAMI BOM - TOTAL VALUE OF MATCHED BSE ITEMS (INR)
35,00,000.00 30,00,000.00 25,00,000.00 20,00,000.00 15,00,000.00 10,00,000.00 5,00,000.00 0.00 JayaJyothi Cement Prism Cement JK Paper PM1 QCS JK Paper PM3 QCS ACC Wadi TATA Noamundi Value

Figure 1.29 – PAMI – Total Value of Matched 3BSE Items Present in Project BOMs ?

A look at the project BOMs for both PAME and PAMI also shows that a total of 101 million (INR) worth of 3BYN items are present in the current stock of two divisions. Any future liquidations plans will have to take into concern the holding of 3BYN items and the routing of these items for future projects.

4.3 ANALYSIS USING NON-PARAMETRIC TESTING ON ABB PA ME, MI, PP RM INVENTORY STOCK

For any liquidation to occur in the future, the RM inventory under stock for the period > 180 days will be have to be targeted first. As identified in the above section, 3BSE and 3BYN are the major types of inventory present in RM stock for ABB PAME, PAMI.

Any future inventory reduction plan will have to take into consideration all the 3 Inv entory types of 3BSE, 3BYN and others for liquidation. Since the first priority during inventory liquidation will be given to items which are in stock for the period > 180 days. As a result, it is important to know the distribution of such inventory types under the time period slots (< 180 days and > 180 days) – whether the proportion of 3BSE, 3BYN and other items is same for the period > 180 days. Based on the future project BOMs and distribution pattern of the above mentioned inventory types, the inventory liquidation plan can be structured to ensure that project demand is sufficed using particular inventory needed.

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In this regard, it is important to find out if the proportion of 3BSE, 3BYN and other items across both PAME and PAMI is same for the period >180 days. This is can be achieved using Non-parametric hypothesis testing. The objective is to determine whether or not there is a relationship between the proportion of items under stock for the period > 180 days and the type of inventory – 3BSE, 3BYN and others.

4.3.1 Hypothesis testing using Chi-Square Statistic

The relationship between two categorical variables can be examined by putting the variable data as a part of a 2-way table. Central to the idea of Hypothesis testing is the formulation of the Null Hypothesis. The null hypothesis in a two-way table is that there is no association between the row variable and the column variable and the alternative hypothesis is that there is an association.

To test the null hypothesis a statistic is computed that compares the entire set of observed counts with the set of expected counts. This statistic is called the chi -squared statistic and is given by:

?2 = ? (O-E)2/E
E where O = observed cell count & E = expected cell count To test the null hypothesis the observed cell counts are compared with the expected cell counts calculated under the assumption that the null hypothesis is true. If the null hypothesis were true the row (or column) percentages would all be the same. Row total × Column Total Expected cell count = N where, N = Overall Total

For the ABB PAME PAMI RM Inventory data, the chi-square test was performed using IBM SPSS 20. In order to perform the Chi-Square Test in SPSS 20, the data has to be in the following format. TYPE 3BSE 3BSE 3BYN 3BYN PERIOD 0 1 0 1 QUANTITY As under each ABB PA MA, MI Sub -Function As under each ABB PA MA, MI Sub-Function As under each ABB PA MA, MI Sub -Function As under each ABB PA MA, MI Sub -Function Page 33

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OTHERS OTHERS

0 1

As under each ABB PA MA, MI Sub -Function As under each ABB PA MA, MI Sub-Function

Where 0 stands for inventory under stock for period >180 days and 1 stands for inventory under stock for period 180 days. Alternate Hypothesis-H 1 : There is difference in the proportion of PAME-INDO Inventory Types in stock for > 180 days. The Chi-Square Test is performed with 95% confidence. The results of the Chi-square test are mentioned as follows –

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Case Processing Summary Cases Valid N PERIOD 733 * TYPE Percent 100.0% Missing N 0 Percent 0.0% Total N 733 Percent 100.0%

Table 1.3 – PAME-INDO Chi-Square Test- Case Processing Summary

PERIOD * TYPE Cross-tabulation TYPE 3BSE PERIOD 0 Count Expected Count % within TYPE Count Expected Count % within TYPE Count Expected Count % within TYPE 59 61.1 45.70% 70 67.9 54.30% 129 129 100.00% 3BYN 185 210.2 41.70% 259 233.8 58.30% 444 444 100.00% OTHERS 103 75.7 64.40% 57 84.3 35.60% 160 160 100.00% 347 347 47.30% 386 386 52.70% 733 733 100.00% Total

1

Total

Table 1.4 – PAME-INDO Chi-Square Test- Period*Type Cross-Tabulation
Chi-Square Tests Value Chi- 24.491a 24.668 733 df Asymp. Sig. (2-sided)

Pearson Square

2 2

.000 .000

Likelihood Ratio N of Valid Cases

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 61.07. Table 1.5 – PAME-INDO Chi-Square Test- Results

As observed from Table 1.5 Pearson Chi-Square test value = 24.491. The obtained p-value = 0.000. This is significant as it is less than 0.05. Thus, the null hypothesis is false and hence it is proved that, there is difference in the proportion of PAME-INDO Inventory Types in
stock for > 180 days.

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4.3.1.2 Hypothesis Testing Using PAME-INDS RM Inventory Data (June’12) The RM Inventory Break-up for PAME-INDO is represented in the following table. TYPE 3BSE 3BSE 3BYN 3BYN OTHERS PERIOD 0 1 0 1 0 QUANTITY 84 97 100 204 130

OTHERS 1 146 Table 1.6- PAME-INDS RM Inventory Break-Up (3BSE, 3BYN and Others) Null Hypothesis- H o : There is no difference in the proportion of PAME-INDS Inventory Types in stock for > 180 days. Alternate Hypothesis-H 1 : There is difference in the proportion of PAME-INDS Inventory Types in stock for > 180 days. The Chi-Square Test is performed with 95% confidence. The results of the Chi-square test are mentioned as follows Case Processing Summary Cases Valid PERIOD * TYPE N 761 Percent 100.0% Missing N 0 Percent 0.0% Total N 761 Percent 100.0%

Table 1.7 – PAME-INDS Chi-Square Test- Case Processing Summary
PERIOD * TYPE Cross-Tabulation TYPE PERIOD 0 3BSE Count 84 Expected Count 74.7 % within TYPE 1 46.4% Count 97 Expected Count 106.3 % within TYPE Total 53.6% 3BYN 100 125.4 32.9% 204 178.6 67.1% 304 304.0 100.0% Total OTHERS 130 314 113.9 314.0 47.1% 146 162.1 52.9% 276 276.0 100.0% 41.3% 447 447.0 58.7% 761 761.0 100.0% Page 36

Count 181 Expected Count 181.0 % within TYPE 100.0%

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Table 1.8 – PAME-INDS Chi-Square Test- Period*Type Cross-Tabulation
Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square Likelihood Ratio N of Valid Cases

14.643a 14.806 761

2 2

.001 .001

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 74.68. Table 1.9 – PAME-INDS Chi-Square Test- Results

As observed from Table 1.5 Pearson Chi-Square test value = 14.463. The obtained p-value = 0.001. This is significant as it is less than 0.05. Thus, the null hypothesis is false and hence it is proved that, there is difference in the proportion of PAME-INDS Inventory Types in stock
for > 180 days. 4.3.1.3 Hypothesis Testing Using PAMI-INDP RM Inventory Data (June’12) The RM Inventory Break-up for PAMI-INDP is represented in the following table. TYPE 3BSE 3BSE 3BYN 3BYN OTHERS PERIOD 0 1 0 1 0 QUANTITY 59 48 94 248 19

OTHERS 1 26 Table 1.10- PAMI-INDP RM Inventory Break-Up (3BSE, 3BYN and Others) Null Hypothesis- H o : There is no difference in the proportion of PAMI-INDP Inventory Types in stock for > 180 days. Alternate Hypothesis-H 1 : There is difference in the proportion of PAMI-INDP Inventory Types in stock for > 180 days. The Chi-Square Test is performed with 95% confidence. Th e results of the Chi-square test are mentioned as follows -

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Case Processing Summary Cases Valid N PERIOD 494 * TYPE Percent 100.0% Missing N 0 Percent 0.0% Total N 494 Percent 100.0%

Table 1.11 – PAMI-INDP Chi-Square Test- Case Processing Summary
PERIOD * TYPE Cross-Tabulation TYPE PERIOD 0 Count Expected Count % within TYPE 1 Count Expected Count % within TYPE Total Count Expected Count % within TYPE 3BSE 59 37.3 55.1% 48 69.7 44.9% 107 107.0 100.0% 3BYN 94 119.1 27.5% 248 222.9 72.5% 342 342.0 100.0% OTHERS 19 15.7 42.2% 26 29.3 57.8% 45 45.0 100.0% Total 172 172.0 34.8% 322 322.0 65.2% 494 494.0 100.0%

Table 1.12 – PAMI-INDP Chi-Square Test- Period*Type Cross-Tabulation
Chi-Square Tests Value 28.861a 27.855 494 df Asymp. Sig. (2-sided)

Pearson Chi-Square Likelihood Ratio N of Valid Cases

2 2

.001 .001

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 15.67. Table 1.13 – PAMI-INDP Chi-Square Test- Results

As observed from Table 1.5 Pearson Chi-Square test value = 28.861. The obtained p-value = 0.001. This is significant as it is less than 0.05. Thus, the null hypothesis is false and hence it is proved that, there is difference in the proportion of PAMI-INDP Inventory Types in stock
for > 180 days. Summer Internship Report Page 38

4.3.1.4 Hypothesis Testing Using PAMI-INHK RM Inventory Data (June’12) The RM Inventory Break-up for PAMI-INDP is represented in the following table. TYPE 3BSE 3BSE 3BYN 3BYN OTHERS PERIOD 0 1 0 1 0 QUANTITY 39 21 54 42 38

OTHERS 1 63 Table 1.14- PAMI-INDP RM Inventory Break-Up (3BSE, 3BYN and Others) Null Hypothesis- H o : There is no difference in the proportion of PAMI-INHK Inventory Types in stock for > 180 days. Alternate Hypothesis-H 1 : There is difference in the proportion of PAMI-INHK Inventory Types in stock for > 180 days. The Chi-Square Test is performed with 95% confidence. The re sults of the Chi-square test are mentioned as follows Case Processing Summary Cases Valid PERIOD * TYPE N 257 Percent 100.0% Missing N 0 Percent 0.0% Total N 257 Percent 100.0%

Table 1.15 – PAMI-INDP Chi-Square Test- Case Processing Summary
PERIOD * TYPE Cross-Tabulation TYPE PERIOD 0 Count Expected Count % within TYPE Count Expected Count % within TYPE Count Expected Count % within TYPE 3BSE 39 30.6 65.0% 21 29.4 35.0% 60 60.0 100.0% 3BYN 54 48.9 56.3% 42 47.1 43.8% 96 96.0 100.0% Total OTHERS 38 131 51.5 131.0 37.6% 63 49.5 62.4% 101 101.0 100.0% 51.0% 126 126.0 49.0% 257 257.0 100.0%

1

Total

Table 1.16 – PAMI-INHK Chi-Square Test- Period*Type Cross-Tabulation
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Chi-Square Tests Value 12.996a 13.144 257 df Asymp. Sig. (2-sided)

Pearson Chi-Square Likelihood Ratio N of Valid Cases

2 2

.002 .001

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 29.42. Table 1.17 – PAMI-INHK Chi-Square Test- Results

As observed from Table 1.5 Pearson Chi-Square test value = 12.996. The obtained p-value = 0.002. This is significant as it is less than 0.05. Thus, the null hypothesis is false and hence it is proved that, there is difference in the proportion of PAMI-INHK Inventory Types in stock
for > 180 days. Using the Chi-Square Test Statistic results, it can be concluded that there is difference in proportion of the 3 RM inventory types - 3BSE, 3BYN and others lying in stock for period > 180 days. The inventory storage is a flow concept – there is regular in-flow and out-flow of inventory based on project demand. Because of this, it isn?t possible to have a same proportion of inventory types for both time period slots < 180 days and > 180 days. For Inventory type like 3BYN which is significant in size and amount, the liquidation will be possible based on future project demand. Before Ordering of 3BYN items for any current project is done, the status of it should be checked in the Unrestricted Free RM stock.

4.4 INVENTORY ANALYSIS OF ABB PA ME, MI, PP RM INVENTORY STOCK USING ECONOMIC ORDER QUANTITY (EOQ) MODEL

Economic order quantity is the order quantity that minimizes total inventory holding costs and ordering costs. The intention is to determine the optimal number of units so that the total costs can be minimized for the purchase, delivery and storage of the product. The required parameters to the solution are the total demand for the year, the purchase cost for each item, the fixed cost to place the order and the storage cost for each item per year.
The underlying assumptions of the EOQ model are as follows – ? ? The ordering cost is constant. The rate of demand is known, and spread evenly throughout the year. Summer Internship Report Page 40

? ? ? ? ? ? ? ? ?

The lead time is fixed. The purchase price of the item is constant i.e. no discount is available The replenishment is made instantaneously; the whole batch is delivered at once. Only one product is involved.

The variables involved in EOQ computation are as follows – Order Quantity (Q) Optimal Order Quantity (Q*) Annual Demand Quantity (D) Cost of Ordering (S) ? Cost of shipping and Handling Holding Cost per unit (H) ? Includes Storage, Insurance and Salary costs

4.4.1 Using EOQ Model to determine Inventory Estimate for ABB PAME

The computation for inventory for ABB PAME has been done keeping in mind the following considerations – ? ? ? ? ? ? ? ? ? The monthly demand is variable. The forecasted sales revenue has been taken as demand for computing the inventory estimate per quarter. The lead times are variable. The ordering cost has been taken as the cost of purchasing the order. This has been computed as the salary of the purchasing department. The Holding cost per unit has been calculated using labour wages in the storage, energy costs and Insurance for the storage facilities. The labour wage has been calculated using the data from Labour Ministry – Wages for Engineering labour, Karnataka. The demand forecast in terms of sales has been used as the Demand variable. The Demand per quarter has been taken as the average demand for each of the quarters. The Inventory Units for the rest of the year have been fo recasted based on April, May and June ?12 Inventory data, using the EXCEL 2007 FORECAST function. The optimal inventory estimate has been calculated in terms of Indian Rupees and for each quarter.

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4.4.2 Calculation of the Holding Cost per unit The ordering cost for ABB PAME has been calculated using the following procedure – ? ? ? ? The cost of Energy, Insurance and labour wages have been taken into consideration while calculating the holding cost of inventory. The annual cost of energy, Insurance was brought down to per quarter contribution. The labour cost was calculated using pay-scales for Grade I, Grade II and Grade III labourers. As mentioned in the previous section, the no. of inventory units was calculated for each month using the monthly data for April, May and June?12 and subsequently using the EXCEL FORECAST function.

Using the EXCEL 2007 FORECAST function, the inventory units for months July 2012 March 2013 have been calculated using the inventory data for April, May and June 2012. Month April May June July August September October November December January February Inventory Forecast 2012-13 (Thousand INR) 1,24,672* 90,715* 46,595* 74,596 38,154 13,693 45,401 13,888 88,599 1,31,566 1,16,702

March 57,843 Table 1.18: Inventory forecast for ABB PAME for the year 2012-13 (TINR)
*

Actual inventory nos. for the month of April, May and June?12.

Based on the quarterly holding costs and the inventory units forecasted above, the quarterly holding costs per unit are mentioned as follows – ? ? ? ?

Q1 Holding Cost per Unit = Rs. 11 Q2 Holding Cost per Unit = Rs. 22 Q3 Holding Cost per Unit = Rs. 19 Q4 Holding Cost per Unit = Rs. 09 Summer Internship Report Page 42

Refer to APPENDIX 7.3 for detailed calculation of Holding Cost per unit per quarter.

4.4.3 Calculation of the Ordering Cost

As mentioned above, the ordering cost has been taken as the cost incurred while paying salary to ABB PAME purchasing department. In order to calculate the total cost of the purchasing department, it is important to know the department structure. The hierarchy of the ABB PAME purchasing department is shown below.

President Purchasing ABB PA

Associate President ABB PAME

Purchasing Manager ABB PAME

Contract Administrator ABB PAME

Inventory Supervisor ABB PAME

Figure 1.30: ABB PAME Purchasing Department Structure

The salaries of the posts of ABB PA President (Purchasing), ABB PAME Associate Vice President (Purchasing), ABB PAME Purchasing Manager, ABB PAME Contract

Administrator and ABB PAME Inventory Supervisor have been computed a fter consultation with ABB PA HR department. Kindly note that the figures used are not exact and have been changed to protect company confidentiality. The annual ordering cost is the sum total of the annual salary of the personnel involved in the ABB PAME purchasing department. The amount obtained has been brought to per Summer Internship Report Page 43

quarter cost. Based on the Hierarchy structure and the salaries for the personnel involved, the Ordering Cost per quarter = Rs.38,75,125

Refer to APPENDIX 7.4 for detailed calculation of Ordering Cost per quarter.

4.4.4 Calculation of the Quarterly Inventory Estimate Using EOQ model

The following variables have been taken into consideration while calculating the Inventory estimate. Demand per Quarter = D (in rupees terms) Holding Cost per Unit per Quarter = H Ordering Cost per Quarter = S Optimal Inventory = Q * (in rupees terms) The optimal inventory using EOQ model is given by -

The inventory estimate calculated using the values obtained above is shown below . Quarters (2012-13) Q1 Q2 Q3 Q4

D (INR) 34,16,34,744 52,98,96,000 50,01,07,667 28,03,39,952

H (INR) 11 22 19 9

S (INR) 38,75,125 38,75,125 38,75,125 38,75,125

Q (INR) 1,55,14,670 1,36,62,867 1,42,82,782 1,55,37,427

Table 1.19: Inventory estimate (2012 -13) using EOQ model Refer to APPENDIX 7.5 for detailed calculation of Inventory Estimate using EOQ model.

The inventory obtained is the average for each quarter because the average demand has been used to compute the same. The total inventory in rupees terms required for each quarter is mentioned below. ? ? ? ? Q1 inventory estimate = Rs. 4,65,44,009 Q2 inventory estimate = Rs. 4,09,88,601 Q3 inventory estimate = Rs. 4,28,48,346 Q4 inventory estimate = Rs. 4,66,12,281

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A comparison with the Inventory Holding for the month of June?12 will show that the inventory estimate for Q1 Apr-June?12 is less than the ABB PAME RM inventory stock for the month of June?12. Q1 Inventory estimate = Rs. 4,65,44,009 ABB PAME June?12 RM Inventory Stock = Rs. 21,95,53,439 Inventory Reduction achieved = Rs. 17,30,09,430 % Inventory Reduction Achieved = 78.8% Therefore, it be concluded that inventory reduction can be achieved by mapping inventory needs with demand. Inventory estimation as a tool for inventory reduction can be used on a quarterly basis to measure inventory needs to fulfill project -wise demand orders.

4.5

DEMAND

FORECASTING

USING

CORRELATION

AND

REGRESSION

ANALYSIS

The relationship between demand and Inventory is further explored in this section. The objective is to establish a linear relationship between the two variables demand and inventory. Based on the findings and sales force data, a demand forecasting model can be created using Regression analysis. The strength of the relationship between the dependent variable, demand with the independent variables inventory and sales force will be based on the results of the regression analysis.

4.5.1 Correlation and Dependence

As observed from the EQO modelling and the inventory estimate obtained, there is a relationship between the demand forecast and inventory estimate. The dependence between demand forecast and inventory estimate can be established using a statistical tool. Using Correlation analysis, it can be found out if the two variables share a casual relationship. Using Pearson Correlation Coefficient, the degree of linearity between the two variables can be found out. Demand 34,16,34,744 52,98,96,000 50,01,07,667 28,03,39,952 Inventory Estimate 1,55,14,670 1,36,62,867 1,42,82,782 1,55,37,427

Table 1.20: The demand v/s Inventory estimate 2012 -13 Summer Internship Report Page 45

Using the above data table as reference, the Pearson Correlation Coefficient has been calculated using IBM SPSS 20. The result has been shown below. Correlations Demand Demand Pearson Correlation Sig. (2-tailed) N Inventory Estimate Pearson Correlation Sig. (2-tailed) N 4 -.966 .034 4 4
*

Quantity -.966* .034 4 1

1

*. Correlation is significant at the 0.05 level (2 -tailed) Table 1.21: Demand v/s Inventory Estimate Pearson Correlation Coefficient

As observed from the correlation analysis, the Pearson Correlation Coefficient = -.966. This shows that there exists a strong negative linear relationship between Demand and Inventory. This can be explained from the fact that the inventory estimate for a p articular quarter is based on the demand for that quarter and will be used to satisfy the demand.

4.5.2 Regression Analysis Using Demand as the Dependent Variable

The focus of regression analysis is on the relationship between dependent variable and one or more independent variables. The objective of performing a regression analysis between demand, inventory and sales force is to determine the variance of the depend ent variabledemand when any of the independent variables -inventory or sales force are changed. The variables for the regression analysis have been chosen due to the following reasons – ? As observed from above demand and inventory are strongly anti-correlated. The demand sufficed for a particular project is dependent on the amount of inventory required for the construction of the panels. As a result, demand becomes the dependent variable and inventory is the independent variable (x 1 ). ? In a project business, the size of the sales force is paramount to determining future demand as it is the job of the pre-sales team to pursue clients. As a result, greater demand is strongly correlated to a strong and responsive sales force. As a result, demand becomes the dependent variable and sales force is the independent variable (x 2 ). Summer Internship Report Page 46

The demand, inventory and sales force data is presented as follows – Demand 34,16,34,744 52,98,96,000 50,01,07,667 Inventory (x1) 1,55,14,670 1,36,62,867 1,42,82,782 Sales Force (x2) 34,16,347 52,98,960 58,32,695

28,03,39,952 1,55,37,427 30,92,789 Table 1.22: Demand, Inventory and sales force forecast for 2012 -13 The regression analysis is performed using IBM SPSS 20. The results are mentioned below. Model Summaryb Model 1 R .987a R Square .975 Adjusted R Std. Error of the Square Estimate .925 33241067.107

a. Predictors: (Constant), Sales Force, Inventory b. Dependent Variable: Demand Table 1.23: Regression Analysis – Summary Output Coefficientsa Unstandardized Coefficients Model 1 (Constant) Inventory Sales Force B Std. Error 1,25,49,02,203 83,91,54,311 -70 42 48 33 Standardized Coefficients Beta -.537 .475 t 1.495 -1.457 1.290 Sig. .375 .383 .420

a. Dependent Variable: Demand Table 1.24: Regression Analysis – Coefficients Residuals Statisticsa Predicted Value Residual Std. Predicted Value Std. Residual Minimum 30,22,62,496 -2,19,22,544 -0.93 Maximum 52,64,01,312 2,40,86,132 0.95 Mean 41,29,94,591 0 0.00 Std. Deviation 11,94,97,754 1,91,91,739 1.00 N 4.00 4.00 4.00

-0.66

0.72

0.00

0.58

4.00

a. Dependent Variable: Demand Table 1.25: Regression Analysis – Residual Statistics

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Figure 1.31: Estimated Means – The effect of Inventory on Demand

Figure 1.32: Estimated Means – The effect of Sales Force on Demand

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Based on the results of the Regression analysis, the regression equation for the variables demand, inventory and sales force can be expressed as follows – Y = 1,25,49,02,203 – 70X1 + 42X2 Y = Demand, X 1 = Inventory Estimate and X 2 = Sales Force In order to test the validity of the model, the inventory estimate and sales force figures are used as inputs in the regression equation. The results obtained are as follows. Quarter Q1 Q2 Q3 Q4 Table 1.26: Demand Forecasted Demand Forecasted Using regression

34,16,34,744 31,23,61,928 52,98,96,000 52,10,57,843 50,01,07,667 50,00,80,639 28,03,39,952 29,71,79,457 Demand Comparison – Original Forecasted demand v/s Demand Forecasted

using regression

60,00,00,000 50,00,00,000 40,00,00,000 30,00,00,000

Demand Forecasted Demand Forecasted Using regression

20,00,00,000
10,00,00,000 0 Q1 Q2 Q3 Q4

Figure 1.33: Demand Comparison – Original Forecasted Demand v/s Forecasted Demand using regression As observed from the model summary, the strength of the model R 2 =0.975. Based on the findings of the regression analysis and the comparison performed above, it can be concluded that the above working mode l can be used as a tool to estimate the per quarter demand by ABB PAME. However, there are few limit ations to the analysis which will be taken up in the later section.

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4.6 LIMITATIONS OF THE ANALYSIS

The following are the limitations of the analysis? The inventory reduction analysis was done with the available project BOMs. A larger number of BOMs would?ve shown a better ordering pattern of inventory items for both PAME and PAMI projects. ? The inventory estimate is based on the demand forecasted. A change in demand due to sudden influx or reduction in projects will affect the demand and thereby the inventory estimate. ? ? ? ? The Holding cost per unit per quarter has been computed as the sum of energy, labour and insurance costs. Other factors like rent have not taken into consideration. The salary of clerks and peons working in the Purchasing department has not been taken into account while calculating the Ordering cost. The use of EOQ model to estimate the inventory has been used because it can provide a estimate which would be smooth throughout a period of time. Inventory estimation can be done using a dynamic lot order model. However, such models haven?t been taken up for analysis because the order quantity is an important in such models. However, for a project business like ABB PA, the order quantity varies from project to project and thus can?t be used. ? ? The Reorder level hasn?t been calculated using the EOQ model. This again is attributed to the nature of the project business. The inventory ordering is not based on the demand of each BU; it is based on the demand of each project being undertaken in each BU. As a result, the inventory ordering is one-time for a project and there is no scope to reorder such inventory again. ? Similarly the Safety Stock hasn?t been calculated using the EOQ model as its calculation is based on the number of times inventory is reord ered during a year. However, projects in ABB PA BUs follow a thumb -rule of keeping 1% of the total inventory ordered for each project as safety stock. ? ? This is not based on any calculation but on historical data and domain expertise of the project managers. In a project business, the product quote plays an important part in securing client deals. As a result, the sales revenue is proportional to the product quote. This makes the product price, the independent variable and demand in terms of sales revenue the dependent variable. However, this independent variable couldn?t be included in the regression analysis due to scarcity of data for the same. Summer Internship Report Page 50

5. INVENTORY CONTROL & TRACKING POLICY
Based on the analysis performed and the subsequent findings, an Inventory control and tracking policy was developed. It included recommendations for inventory control and reduction and steps to be followed for physical tracking of inventory. 5.1 Recommendations ? ? ? ? Check the status of current ordering for Projects. If not ordered, then match from unrestricted RM stock and only order for the items which are not in stock. In order to simplify the inventory matching process, the BOM has to be made available to the inventory managers of respective divisions. The divisions should identify the list of materials they don?t need. This list should be circulated within the entire PA department. The project BOMs should be checked matched against the unrestricted stock of all divisions. As a result, if a project from PAME isn?t able to match all its items from PAME free stock, the same can be checked against the free stock of all other divisions. ? An online inventory removal request form has to be developed in order to route inventory from the unrestricted RM free stock. The form will include the following fields i) ABB PA BU code ii) Project Code iii) Details of the Inventory requested – Material Code, Description, Last Purchase date, Current cost based on depreciation value. iv) Reason why the inventory is being requested. v) Signatures of the Project Manager and BU- Inventory Supervisor. ? ? The online request form can be linked to the ERP MM module to enable proper tracking of inventory routing requests and subsequent inventory reduction. As the software development is being done using demo S/W, the licensed S/W should be brought JIT and reduce inventory holding costs.

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5.2 Physical Inventory Tracking ? Conducting the Inventory ? The actual physical count should be made by teams of two individuals each. i) One individual will handle and count the material. The second individual will record the item number and physical count on Physical Count Sheets. ii) Invalid item locations shall be corrected on the Physical Count Sheets at the same time the count is recorded. iii) Items “found” on the shelve during the inventory that are not listed on the Physical Count Sheets are to be added to a blank Physical Count Sheet and shown as a discrepancy. ? Auditing the Inventory ? The BU Purchasing Manager will audit the count sheets, by comparing the Physical Count Sheets to the “Before Inventory” Site Stock Item List. i) In case of any discrepancies, the recounting will be done another team. ii) Verified discrepancies will be highlighted and the adjustments will be made to match the physical quantity. ? Adjustments ? Adjustments to the system (computer) quantity will be made based on the actual physical count. i) Once the adjustments are made and recorded, the “After Inventory” Item List Report can been produced. ? After Completion of the Physical Count, the BU Purchasing Manager will have to provide account for all individual item value discrepancies (both positive and negative) of greater than 5%. i) This will be accomplished by comparing the “Before Inventory” Site Stock Item List and “After Inventory” Site Stock Item List. ii) A report will have to be prepared as a result of this comparison. iii) The report will have the material code of the discrepant item(s), the rupee value of the discrepancy (either negative or positive) and an explanation of the apparent discrepancy. iv) The Report after approval from the BU Associate President-Purchasing will become part of the permanent inventory record.

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6. CONCLUSION
The 8 weeks summer project in ABB Process Automation has been a very enriching experience that has taught me a lot in every aspect. Every part of project has been special in terms of exposing me to the different aspects of inventory management in ABB PA. Working in a corporate setup with very experienced employees by itself was big learning experience and I believe it has given me the necessary impetus to foray into the world of inventory management as a career.

If given an opportunity again, I would love to be associated with this organization again.

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7. APPENDIX

7.1 Company Profile

Asea Brown Boveri (ABB) is a leader in power and automation technologies that enable utility and industry customers to improve performance while lowering environmental impact. The ABB Group of companies operates in around 100 countries and employs about 130,000 people. ABB operations in India include 14 manufacturing facilities with over 8000 employees. Customers are served through an extensive countrywide presence with more than 23 marketing offices, 8 service centers, 3 logistics warehouses and a network of over 550 channel partners. The ABB Group is increasingly leveraging the Indian operations for projects, products, services, engineering and R&D. Key highlights about ABB India are ? ? ? The Company was incorporated on 24.12.1949 as The Hindustan Electric Company Limited. On 24.09.1965, the Company?s name was changed to Hindustan Brown Boveri Limited (HBB). Pursuant to the Scheme of Amalgamation of Asea Limited with HBB with effect from 1st January 1989, the name was further changed to Asea Brown Boveri Limited, with effect from 13.10.1989. ? Effective 16.04.2003, the name was further changed to ABB Limited.

ABB?s power generation business was globally transferred into the new 50 -50 JV with Alstom in 1999. In India the power generation business has been demerged and transferred to ABB Alstom Power India Ltd. with effect from 1st April 1999. In consideration of the transfer of the power business, each shareholder of ABB has been allotted one share in ABB Alstom Power India Ltd. for every share held in the company.

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7.2 EOQ - The Total Cost Function

The single-item EOQ formula finds the minimum point of the following cost function: Total Cost = purchase cost + ordering cost + holding cost ? ? ? Purchase cost: This is the variable cost of goods: purchase unit price × annual demand quantity. This is P×D Ordering cost: This is the cost of placing orders: each order has a fixed cost S, and we need to order D/Q times per year. This is S × D/Q Holding cost: the average quantity in stock (between fully replenished and empty) is Q/2, so this cost is H × Q/2

To determine the minimum point of the total cost curve, partially differentiate the total cost with respect to Q (assume all other variables are constant) and set to 0:

Solving for Q gives Q* (the optimal order quantity):

Therefore:

.

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Figure 1.33: Economic Order Quantity

7.3 Calculation of Holding Cost per Unit per Quarter

Number if Grade I labourers = 4 Number of Grade II labourers = 8 Number of Grade III labourers = 12 Per day wage of Grade I labourer = Rs.117 Per day wage of Grade II labourer = Rs.115 Per day wage of Grade III labourer = Rs.113 Number of Days worked in a quarter = 100 Total Labour wages per quarter = (4* Rs.117+8* Rs.115+12* Rs.113)*100 = Rs. 27440 Total Holding Cost per Quarter = (Annual Energy Expenses + Annual Insurance)/4 + Total Labour wages per quarter

Annual Energy Expense Annual Insurance Expense Quarterly Labour Wages Total Holding Cost per Quarter Table 1.27: Total Holding Cost Per Quarter Summer Internship Report

16,60,000.00 18,17,000.00 18,17,000.00 9,37,850.00 Page 56

Month April May June July August September October November December January February

Inventory Forecast 2012-13 1,24,672 90,715 46,595 74,596 38,154 13,693 45,401 13,888 88,599 1,31,566 1,16,702

March 57,843 Table 1.28: Inventory forecast for 2012-13 based on April, May and June? 12 Inventory Data.

Quarter Q1 Q2 Q3 Q4

Average Inventory 87,327 42,147 49,296 1,02,037

Holding Cost 9,37,850 9,37,850 9,37,850 9,37,850

Holding Cost/Unit 11 22 19 9

Table 1.29: Holding cost per unit per quarter

7.4 Calculation of Ordering Cost

As mentioned in the 4.4.2.2, the salary of the personnel in the purchasing department was taken as the annual purchasing cost.

1) Annual Salary of ABB PA President (Purchasing) = Rs. 6000000 2) Annual Salary of ABB PAME Associate-President (Purchasing) = Rs. 5225000 3) Annual Salary of ABB PAME Purchasing Manager = Rs. 1715500 4) Annual Salary of ABB PAME Contract Administrator = Rs. 1600000 5) Annual Salary of ABB PAME Inventory Supervisor = Rs. 960000

Total Ordering Cost per Quarter = {(1) + (2) + (3) + (4)}/4 = Rs. 3875125 Summer Internship Report Page 57

7.5 Calculation of Inventory Estimate Using EOQ model As per the EOQ formula, the optimal inventory quantity to be ordered is given by –

The Inventory estimate for each of quarters of 2012-13 is shown below – Demand (D) Holding Cost per Unit (H) Ordering Cost (S) 38,75,125 38,75,125 38,75,125 38,75,125 Optimal Inventory (Q*) 1,55,14,670 1,36,62,867 1,42,82,782 1,55,37,427

34,16,34,744 11 52,98,96,000 22 50,01,07,667 19 28,03,39,952 9 Table 1.30: EOQ calculation

7.6 Inventory Reduction Results – Matching with Project BOMs

7.6.1 Globe Radio SL. No. Material Quantity Stock Stock for Type available Globe for Globe Unit Price Total Units Diverted Total Price

PAME
1 2 3 4 5 6 7 8 3BSE018172R1 3BSC630197R1 3BSE030220R1 3BSE018135R1 3BSE008546R1 3BSE008534R1 3BSE008536R1 3BSC950089R3 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 Q 15.00 26.00 17.00 2.00 1.00 100.00 51.00 45.00 31.00 6,466.12 608.46 32,667.15 77,025.00 28,005.26 18,682.59 1,713.70 1,543.65 2,416.79 2.00 2.00 2.00 2.00 1.00 1.00 2.00 2.00 2.00 12,932.23 1,216.91 65,334.31 1,54,050.00 28,005.26 18,682.59 3,427.40 3,087.30 4,833.58

PAPP_INDR
9 10 11 12 3BSE008516R1 3BSE008508R1 3BSE008510R1 3BSE013230R1 2.00 2.00 2.00 8.00 4.00 23.00 14.00 1,940.00 12,517.95 4,119.15 4,907.26 1,135.98 2.00 2.00 2.00 8.00 25,035.91 8,238.30 9,814.51 9,087.86 3,43,746.16

Table 1.31: Globe Radio – Reduction through Inventory Match Summer Internship Report Page 58

7.6.2 BSP SL. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 Material Quantity for BSP 13.00 4.00 60.00 34.00 46.00 450.00 190.00 7.00 720.00 7.00 33.00 33.00 33.00 Q Stock Type Stock available for BSP Unit Price Total Units Diverted Total Price

3BSE030220R1 3BSE042238R1 3BSE022366R1 3BSE008516R1 3BSE038415R1 3BSE008508R1 3BSE008510R1 3BSE013228R1 3BSE013230R1 3BSE013234R1 3BSE008536R1 3BSE013208R1 3BSE022464R1

Q

Q 14 15 16 17 3BSC950107R1 3BSC950107R2 3BSC950107R3 3BSE042250R1 20.00 15.00 10.00 1.00

Q Q

17.00 32,667.15 1.00 2,08,347.34 61.00 11,579.40 74.00 9,451.60 7.00 17,140.85 79.00 14,624.77 908.00 4,162.48 347.00 4,854.26 10.00 28,506.24 1,472.00 1,743.80 114.00 2,517.48 45.00 1,543.65 66.00 4,706.07 17.00 1,844.81 12.00 1,982.82 4.00 2,333.69 27.00 638.09 6.00 888.17 2.00 1,934.31 10.00 12,616.89

2.00 65,334.31 1.00 2,08,347.34 60.00 6,94,763.85 34.00 3,21,354.38 3.00 51,422.54 43.00 6,28,865.22 450.00 18,73,117.90 190.00 9,22,308.60 7.00 1,99,543.69 720.00 12,55,533.45 7.00 17,622.36 33.00 50,940.37 33.00 1,55,300.43 17.00 31,361.83 12.00 23,793.81 4.00 9,334.76 20.00 12,761.86 6.00 5,329.04 2.00 3,868.62 1.00 12,616.89 65,43,521.22

Table 1.32: BSP – Reduction through Inventory Match 7.6.3 RSP – 23 Panels SL. No. Material Quantity for RSP23 2.00 4.00 Q 3 4 5 6 3BSC630197R1 3BSE030220R1 3BSE018103R1 3BSE041882R1 2.00 6.00 2.00 16.00 Q Stock Type Stock Unit Price Total available Units for RSPDiverted 23 1.00 2,87,431.21 1.00 3.00 2.00 26.00 6.00 7.00 278.00 6,457.47 5,940.43 608.46 26,750.72 18,031.61 14,631.81 3.00 1.00 2.00 6.00 2.00 13.00 Total Price

1 2

3BSE018160R1 3BSE018172R1

2,87,431.21 19,372.40 5,940.43 1,216.91 1,60,504.32 36,063.21 1,90,213.47 Page 59

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7 8 9 10 11 12 13 14

3BSE022462R1 3BSE008516R1 3BSE038415R1 3BSE008508R1 3BSE008510R1 3BSE013230R1 3BDZ000398R1 3BSE064644R1

8.00 4.00 4.00 36.00 26.00 70.00 2.00 2.00

Q

3.00 74.00 74.00

11,539.00 5,398.25 9,697.80 14,858.06 4,162.48 4,014.39 4,866.53 1,244.81 1,746.52 24,378.79 3,942.76

3.00 8.00 4.00 4.00 36.00 5.00 21.00 8.00 62.00 2.00 2.00

34,617.01 43,185.98 38,791.21 59,432.24 1,49,849.43 20,071.97 1,02,197.22 9,958.48 1,08,284.44 48,757.59 7,885.51 13,23,773.03

Q Q

4.00 908.00 5.00 342.00 8.00 1,464.00 5.00 2.00

Q Q Q

Q

Table 1.33: RSP-23 Panels – Reduction through Inventory Match 7.6.4 RSP – 91 Panels SL. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Material Quantity RSP-91 1.00 1.00 1.00 2.00 1.00 4.00 2.00 2.00 1.00 6.00 3.00 8.00 15.00 82.00 41.00 12.00 Stock Type Stock available RSP-91 6.00 3.00 1.00 17.00 4.00 16.00 2.00 1.00 1.00 15.00 26.00 7.00 17.00 3.00 278.00 74.00 4.00 3.00 15.00 Unit Price Total Units Diverted 1.00 1.00 1.00 2.00 1.00 4.00 2.00 1.00 1.00 6.00 3.00 7.00 15.00 3.00 79.00 41.00 4.00 3.00 5.00 Total Price

3BSE061234R1 3BSE061236R2 3BSE061248R1 3BSE061255R1 3BSE046743R1 3BSE047697R1 3BSE064644R1 3BSE018160R1 3BSE050199R1 3BSE018172R1 3BSC630197R1 3BSE018103R1 3BSE030220R1 3BSE041882R1 3BSE022462R1 3BSE038415R1

Q

Q

Q Q

1,34,866.28 1,06,804.22 1,92,345.05 1,03,234.46 0.46 0.47 3,942.76 2,87,431.21 4,53,413.18 6,466.12 608.46 18,031.61 32,667.15 11,539.00 14,631.81 5,398.25 14,858.06 20,184.56 15,120.59

1,34,866.28 1,06,804.22 1,92,345.05 2,06,468.92 0.46 1.88 7,885.51 2,87,431.21 4,53,413.18 38,796.70 1,825.37 1,26,221.24 4,90,007.29 34,617.01 11,55,912.64 2,21,328.15 59,432.24 60,553.68 75,602.93 Page 60

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17 18 19 20 21 22

3BSE008516R1 3BSE008508R1 3BSE008510R1 3BSE013230R1 3BDZ000398R1 3BSE061216R5 10

21.00 265.00 90.00 387.00 15.00

Q Q

Q 1.00

74.00 908.00 347.00 8.00 1,464.00 5.00 20.00 3.00

9,697.80 4,162.48 4,854.26 1,244.81 1,746.52 24,378.79 24,701.94 42,519.02

21.00 265.00 90.00 8.00 379.00 5.00 15.00 1.00

2,03,653.87 11,03,058.32 4,36,883.02 9,958.48 6,61,932.28 1,21,893.97 3,70,529.05 42,519.02 66,03,941.96

Table 1.34: RSP-91 Panels – Reduction through Inventory Match

7.6.5 ACC Wadi SL. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Quantity for ACC Wadi 3BSE061234R1 2.00 3BSE061236R2 2.00 3BSE061245R1 1.00 3BSE061255R1 4.00 3BSE061342R1 1.00 3BSE061235R1 1.00 3BSE018161R1 3.00 3BSE018172R1 3.00 3BSC630197R1 1.00 3BSE030220R1 4.00 3BSE022366R1 37.00 3BSE008516R1 20.00 3BSE040662R1 22.00 3BSE038415R1 9.00 3BSE008512R1 225.00 3BSE008514R1 67.00 3BSE013230R1 47.00 3BSE013231R1 287.00 3BSE008508R1 5.00 3BSE008510R1 5.00 Material Stock Type Stock available Unit Price Total Units Diverted 2.00 2.00 1.00 4.00 1.00 1.00 3.00 3.00 1.00 4.00 37.00 20.00 22.00 9.00 225.00 67.00 47.00 287.00 5.00 5.00 Total Price

5.00 90,867.80 15.00 97,913.80 3.00 57,655.80 13.00 87,888.70 2.00 62,683.93 9.00 10,929.39 3.00 1,55,176.69 35.00 4,838.90 26.00 1,802.38 66.00 19,742.39 215.00 6,353.41 664.00 6,811.40 376.00 8,644.12 156.00 10,006.91 1,412.00 2,065.97 15.00 2,524.27 2,204.00 1,060.97 1,387.00 983.03 955.00 2,722.88 92.00 3,609.12

1,81,735.60 1,95,827.59 57,655.80 3,51,554.79 62,683.93 10,929.39 4,65,530.08 14,516.69 1,802.38 78,969.55 2,35,076.22 1,36,228.02 1,90,170.67 90,062.16 4,64,842.31 1,69,125.96 49,865.75 2,82,129.05 13,614.40 18,045.61 30,70,365.92

Table 1.35: ACC Wadi – Reduction through Inventory Match

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7.6.6 JayaJyothi Cement SL. No. 1 2 3 4 Material Quantity for JayaJyothi 3.00 3.00 2.00 2.00 3.00 5.00 5.00 Stock Type Stock available 6.00 35.00 19.00 66.00 4.00 955.00 92.00 Unit Price Total Units Diverted 3.00 3.00 2.00 2.00 3.00 5.00 5.00 Total Price

3BSE018161R1 3BSE018172R1 3BSE018103R1 3BSE030220R1

1,79,769.52 4,838.90 16,460.72 19,742.39 1,414.77 2,722.88 3,609.12

5,39,308.55 14,516.69 32,921.44 39,484.78 4,244.32 13,614.40 18,045.61 6,62,135.78

5 3BSC950192R1 6 3BSE008508R1 7 3BSE008510R1

Table 1.36: JayaJyothi Cement – Reduction through Inventory Match

7.6.7 Prism Cement SL. No. Quantity for Prism 1.00 5.00 1.00 1.00 2.00 Stock Type Stock available Total Units Diverted 1.00 5.00 1.00 1.00 2.00

Material

Unit Price

Total Price 1,49,525.29 24,194.48 1,802.38 1,414.77 7,205.03 1,84,141.95

1 3BSE050198R1 2 3BSE018172R1 3 3BSC630197R1 4 3BSC950192R1 5 3BSC950263R1

13.00 1,49,525.29 35.00 26.00 4.00 2.00 4,838.90 1,802.38 1,414.77 3,602.52

Table 1.37: Prism Cement – Reduction through Inventory Match

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7.6.8 JK Paper PM1 QCS SL. No. 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Material Quantity Stock Stock Unit Price Total Total Price for ACC Type available Units Wadi Diverted 2.00 3.00 1,55,176.69 2.00 3,10,353.39 1.00 26.00 1,802.38 1.00 1,802.38 1.00 19.00 16,460.72 1.00 16,460.72 1.00 66.00 19,742.39 1.00 19,742.39 6.00 664.00 6,811.40 6.00 40,868.41 5.00 156.00 9,755.42 5.00 48,777.09 1.00 955.00 2,722.88 1.00 2,722.88 1.00 92.00 3,609.12 1.00 3,609.12 17.00 2,204.00 1,060.97 17.00 18,036.55 1.00 5.00 90,867.80 1.00 90,867.80 1.00 9.00 10,929.39 1.00 10,929.39 1.00 1.00 1.00 1.00 1.00 4.00 35.00 47.00 6.00 27.00 4,588.08 4,838.90 4,754.59 2,210.77 571.94 1.00 1.00 1.00 1.00 1.00 4,588.08 4,838.90 4,754.59 2,210.77 571.94 5,81,134.38

3BSE018161R1 3BSC630197R1 3BSE018103R1 3BSE030220R1 3BSE008516R1 3BSE038415R1 3BSE008508R1 3BSE008510R1 3BSE013230R1 3BSE061234R1 3BSE061235R1 3BSE061110R510 3BSE018172R1 3BSE013208R1 3BSE022464R1 3BSC950107R1

Table 1.38: JK Paper PM1 QCS – Reduction through Inventory Match 7.6.9 JK Paper PM3 QCS SL. No. 1 1 2 3 4 5 6 7 8 9 10 11 Material Quantity for JK Paper 1.00 1.00 1.00 1.00 6.00 4.00 1.00 1.00 16.00 1.00 1.00 1.00 Stock Type Stock available Unit Price Total Units Diverted 3.00 1.00 1.00 1.00 6.00 4.00 1.00 1.00 16.00 1.00 1.00 1.00 Total Price

3BSE018161R1 3BSC630197R1 3BSE018103R1 3BSE030220R1 3BSE008516R1 3BSE038415R1 3BSE008508R1 3BSE008510R1 3BSE013230R1 3BSE061234R1 3BSE061235R1 3BSE061110R510

3.00 7,788.16 26.00 12,500.00 19.00 506.94 66.00 19,742.39 664.00 6,811.40 156.00 9,755.42 955.00 2,722.88 92.00 1,802.38 2,204.00 1,060.97 5.00 9,755.42 9.00 10,929.39 4.00 3,639.55

23,364.47 12,500.00 506.94 19,742.39 40,868.41 39,021.67 2,722.88 1,802.38 16,975.57 9,755.42 10,929.39 3,639.55 1,81,829.06

Table 1.39: JK Paper PM3 QCS – Reduction through Inventory Match

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7.6.10 TATA Noamundi SL. No. 1 2 3 4 5 6 7 8 9 10 11 12 Material Quantity Stock Stock for TATA Type available Noamundi 1.00 1.00 1.00 1.00 1.00 1.00 2.00 1.00 1.00 6.00 4.00 6.00 Unit Price Total Total Price Units Diverted 1.00 1,49,525.29 1.00 16,460.72 1.00 19,742.39 1.00 35,845.07 1.00 20,824.16 1.00 6,148.18 2.00 13,622.80 1.00 8,644.12 1.00 10,006.91 6.00 16,337.28 4.00 14,436.49 6.00 6,365.84 3,17,959.24

3BSE050198R1 3BSE018103R1 3BSE030220R1 3BSE048845R1 3BSE031155R1 3BSE022462R1 3BSE008516R1 3BSE040662R1 3BSE038415R1 3BSE008508R1 3BSE008510R1 3BSE013230R1

13.00 1,49,525.29 19.00 16,460.72 66.00 19,742.39 3.00 35,845.07 10.00 20,824.16 1.00 6,148.18 664.00 6,811.40 376.00 8,644.12 156.00 10,006.91 955.00 2,722.88 92.00 3,609.12 2,204.00 1,060.97

Table 1.40: TATA Noamundi – Reduction through Inventory Match

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