BARIA PLANNING SOLUTIONS GROUP 8BARIA PLANNING SOLUTIONS CASE ANALYSIS GROUP - 830 September 2014

Team MemberS: C Gaurav Gokul Ram K Niranjan J K Bhagyaraj ShailendEr KUMAR Usha B

1. What are the organizational and operational issues that underlie the problems facing Baria Planning Solutions (BPS)?BARIA PLANNING SOLUTIONS GROUP 8

Problems faced by BPS solutions: BPS project win rate for new sales, renewal, and pilot projects has reduced by 2%, 6.25% and 3% respectively when compared to the projected win rates. This negative impact on the performance of the company is been attributed to the delays incurred by the sales support group of the company. The underlying reasons behind this problem could be grouped in to organizational and operational as below. 1. Organizational issues:1.1 Sales team and proposal deadlines:BPS provides procurement optimization solutions for four different industry sectors Energy, Government, Manufacturing and Retail. This has made the organization to divide the proposal support team of the sales support division into above four industry specific units. The sales people were not able to get the timely assistance from this proposal support team and because of this the sales people were failing to meet the proposal deadlines. This made the customers to switch to other competitors. This will have a negative impact on the organization revenue for the next 2 years.1.2 Cross training difficulty: The sales support team has four functions Data analysis, Data engineering, Proposal support and Pricing. The man power from the Data analysis and data engineering team of the support group cannot be trained to work in the proposal support team. This difficulty in cross training has created difficulties in sharing the work load with in the sales support group. Experts were needed to work for the sales support group; this has also made it impossible for other units (data analysis, data engineering, pricing) to share its work load incase if the number of requests are very high.1.3 Absence of synergy across the sales and sales support:Head of sales has repeatedly emphasized the need to shift the sales support division to a more industry centric structure. These inconveniences had led to lack of synergy between sales and sales support team2. Operational Issues:Variable input of works from various sectors in different time periods.The projects arise randomly in different quarters. It is not possible to predict the number of projects in advance. This has led to inter arrival variability among the projects (Refer table). This table as calculated shows the standard deviation in the arrival of projects for various functions. This shows that there is an inter arrival variabilityThe order processing times for various processes are different. This has led to uneven Order wait time (Refer table in hrs.) for various processes. This calculated table tells that the waiting time for Proposal support is very high whereas for pricing is too low. So processes are not stream lined.BARIA PLANNING SOLUTIONS GROUP 8

2. Suppose you walk into BPS one day, and find that the average work in process of sales requests is 100. What would be your estimate of the turn-around time (Manufacturing Lead Time) for a new Sales Support request in Retail Sector, based on the information given in the case?As per status quo, the total inventory (no. of requests under process) at any instant in the system is 15.82. The breakup of inventory per activity alongwith its total process time (Request process time+ Wait time) is :ActivityRequest Process Time (in hr)Request wait time (hr)Total time spent in process (hr)No. of Requests in processNo. of Requests in Queue (Ii)Total Inventory (Requests in process and waiting)

Full Year 2010

1-Data Engineering2.454.126.57 0.76 1.272.03

2-Data Analysis2.574.607.17 0.79 1.422.22

3-Proposal Support

Energy Sector10.5818.9729.55 0.81 1.452.27

Government Sector10.5818.1328.71 0.63 1.071.70

Manufacturing Sector6.3517.6023.95 0.76 2.112.87

Retail and Other Sectors15.8756.8572.73 0.85 3.043.88

4 - Pricing2.190.592.78 0.68 0.180.86

TOTAL 15.82Extrapolating the inventory from 15.82 requests to 100 requests in the same proportion as per the above table, we get the parameters for different activities as below:ActivityNo. of Requests in processNo. of Requests in Queue (Ii)Total Inventory (Requests in process and waiting)

Full Year 2010

1-Data Engineering4.798.0512.83

2-Data Analysis5.028.9814.00

3-Proposal Support

Energy Sector5.139.1914.32

Government Sector3.956.7710.72

Manufacturing Sector4.8113.3318.14

Retail and Other Sectors5.3619.1924.55

4 Pricing4.281.165.43

TOTAL 33.33 66.67 100.0

From the above tables we can see that at this stage the time taken to come out of the system by the first request in data engineering stage through the channel for retail sector = 2.45 (data engg. process time) + 7.17 (total data analysis time) + 72.73 (retail proposal total time) + 2.78 (total time for pricing process) = 82.68 hour. After this, every following request will come out of the system after every 72.73 hr. (the time required for the retail proposal is the largest, making it the bottleneck activity).Total time required for 12.83 requests in data engineering stage to get processed and come out of the system= 82.68+11.83*72.73 = 943 hr.So, the total lead time for a new sales support request in the retail sector is 943+72.73 = 1015.73 hr.

3. What alternatives are available for dealing with the problems in the Sales Support group? How did you evaluate the alternatives? What actions should Christy Connor propose to Brandon Ali?BARIA PLANNING SOLUTIONS GROUP 8

Status Quo :In the status quo, we analyzed the various factors like Request process time, avg request inter arrival time, capacity per year, through put, capacity utilization, request wait time, total process time etc. (Refer Annexure 1). Then we realized the bottle neck was created in the waiting time of each request to be processed. This was almost around 24.82 hours (Wt avg of 18.97, 18.13, 17.60, 56.85). Hence the various alternatives were evaluated based on Request Wait time primarily. The various alternatives that we have got are :

1. Cross Training (Dropping industry focused approach for sale support team)2. Fully Industry Centric organization3. Reorganizing proposal support by type of sales

Option 1: Cross TrainingIn this option, all the employees in proposal support are cross trained so that any employee can be given any project and the entire team becomes a consolidated one. The biggest advantage of this option is the substantial decrease in the request wait time, which was almost 6.44 hours in the proposal support stage (Refer Annexure 2). The cost incurred to do the cross training will be $6058 approx (=(105000/52)*3)Limitations: Team members need to remain in each role for 3 months at the least to stay effective USP of BPS by offering individual expertise in every industry is lost. Team members become generalists from being experts in one particular sector.Option 2: Industry Centric Organization The various functions of the sales flow including data engineering and data analysis get divided on basis of the industry so that the sense of ownership will increase inside a single industry. Dedicated resources are expected to focus on the priority issues and expedite them. But from the Annexure 3 we can see that the request wait time for each industry while drawing sales proposals will still remain high at 18.97 hours for Energy sector and it reaches a maximum of 56 hours for the retail sector. Significant reduction is seen in the request wait time for the Data engineering, analysis and pricing phases.Limitations: Significant increase in manpower is required adding to costs It does not address the constraint in the organization which is faster sales proposalOption 3: Reorganizing proposal support by type of sales This option explores the opportunity of dividing the proposal team based on the kind of project they receive as shown in the figure. This mandates that the team has to be cross trained across industries and akin to option 1 the industry expertise gets diluted here too. We will have to hire more people for the new sales team as their workload becomes 169% (refer annexure 4). This option does result in significant reduction in the request wait time as shown in the annexure 4. Limitations: USP of BPS by offering individual expertise in every industry is lost. Team members become generalists from being experts in one particular sector. Need to mandatorily hire manpower for new sales team.Proposed Solution:Based on the 3 options listed above, we are proposing that BPS should go for Option 1 of Cross training the sales support team and dissolve the industry focus structure as it results in the maximum reduction of the request wait time. The cost of cross training is also less. Even though the firm might lose out on the USP of individual expertise in each industry, the revenues and turnaround time for each project will reduce. This will result in the firm remaining competitive. One major constraint that the firm needs to watch out for is that since people working in each industry of the proposal team hail from different firms, the team dynamics need to be cultivated with care. Further Steps Hire 3 more heads (refer annexure 2) for further reduction in the request wait time from 6.44 hours to 3.61 hours. Streamline the solution selling process by training and coming up with generic frameworks for faster turnaround time. It will also reduce the standard deviation for processing requestsBARIA PLANNING SOLUTIONS GROUP 8

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ANNEXURE 1BARIA PLANNING SOLUTIONS GROUP 8

ANNEXURE 2BARIA PLANNING SOLUTIONS GROUP 8

ANNEXURE 3BARIA PLANNING SOLUTIONS GROUP 8

ANNEXURE 4BARIA PLANNING SOLUTIONS GROUP 8