Date post: | 11-Jan-2017 |
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Technology |
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Analytics for Optimizing the
Enterprise
Dr. P GopalakrishnanChief Mentor
BigInfoLabs
Analytics for Enterprise OptimizationP. Gopalakrishnan, PhD
Technology ConsultantMentor, BigInfo Labs
Former VP of Technology, IBM Corp. Armonk, NY
Enterprise Processes• Large enterprises have many complex processes
R&D Marketing Sales
Manufacturing Fulfilment Purchasing
H&R
R&DR&D
MarketingMarketing
SalesSales
ManufacturingManufacturing
FulfilmentFulfillment
PurchasingProcurement
H&RHR
FinanceFinance
FinanceOperations
OperationsOperations
Optimization
• Analytic methods have been widely applied in areas that focus on questions external to the enterprise– Customer analytics, buying behavior, demographics, segmentation– Market insights, market position
• Significant efficiency improvements (and improved financial results) possible from applying analytics inside the enterprise
• But it needs …– Leadership and culture – commitment to making decisions based on
data and analytics– Ability to bridge from insights to execution– “Numbers need to become language”
A few examples
• Two areas – Sales– R&D
• Sales– Territory alignment– Quota and incentives– Sales productivity– Customer assignment
• R&D– Innovation and intellectual property strategy– Talent management
• Decisions based on experience and gut feel• Customer
targeting• Pricing• Productivity
The good old (?) days
Analytics can support many sales decisions
Sales process
Customer targeting
Incentive compensation
Geographic deployment
HiringPerformance management
Goal settingSales force size and
structure
Zoltners & Sinha, Kellog School of Management
Example: Territory assignment
Client profitability
Sales force performance
Sales person to client assignment
• Individual productivity• Areas of expertise• Served channels
• Industry• Historical revenue• Forecasts of revenue• Coverage segments
• Maximize sales potential
• Balance workload• Minimize disruption• Minimize travel
Ref: Baier et. Al, IBM Journal of R&D, Nov/Dec 2012
Example: Incentives
Source: Zoltners & Sinha, Kellog School of Management
achievement
Incentive payout
Payout appears to be capped for high performers
Analysis of incentive payout shows high performers are not being paid adequately. This could lead to dissatisfaction and attrition of high performers.
R&D Process
Concept Research Development
Development
Manufacturing
Manufacturing Market
Analytics can help• Identify technology threats and opportunities• Monitor competition and market throughout the R&D lifecycle• Protect intellectual property, assess risks• Manage talent – recruiting and retention• Investment decisions• Schedule risks and costs
Example: Monitoring trends
• An R&D leader is interested in gathering insights on emerging technology trends.
• Sources of information may be scientific literature, news, social media activity
• Diversity of portfolio often makes it difficult to analyze • Big data and unstructured analytics can help
Example: Monitoring Emerging Trends
Example: Intellectual Property Management
• Questions across a variety of concern areas– Competition– Patentability– Freedom to operate– Risk of infringing someone else’s IP– Monitoring potential infringement
• IP as a potential revenue source
Example: IP Management
Open IssuesSome tough analytics problems arise in the management of R&D
• What is the RoI of R&D investment? If R&D budget is increased / decreased what is the impact on revenue?– Example: Ratio of revenue increase to prior year R&D investment
• What is the best use of R&D investment across a range of projects?
• What is the optimal allocation of resources to R&D projects, balancing skills, recruiting, retention, cost
• What is the risk that the product will not be ready on time?
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