Presentation Transcript

1. Product Portfolio Management at HPA Case Study in Information Management

   ISM 158: Business Information Strategy April 13, 2010
2. Outline The benefits and challenges of product variety Analytics for variety management Implementation and impact at HP 2
3. Product variety at HP today Over 2,000 laser printers Over 20,000 enterprise server & storage SKUs Over 8,000,000 possible desktop & notebook PC configurations
4. Why offer product variety? Expand market reach – offer something for everyone Many geographies Many customer types (consumer, small-to-medium business, enterprise) Many industries (healthcare, technology, energy, government…) Be a “one stop shop” – offer comprehensive solutions Increase brand visibility Win marketshare
5. Challenges of product variety Product design costs • Forecast inaccuracies Sales & marketing costs • Inventory-driven costs Administrative costs • Obsolescence costs Sales Productivity costs Company Suppliers Inventory-driven costs Availability / stockouts Delivery time predictability Order cycle time Confusion Customers
6. Challenges of variety:illustration of inventory driven costs Two similar laptop models: The two laptop models have independent, identically distributed random demand D1, D2in each week. Variance of D1, D2is 2. “Safety stock” inventory of each product is typically k where k is a constant related to the desired service level. Total safety stock: 2k Pool into a single laptop model: Assume no loss in demand (total random demand for single product is D=D1+D2.) Variance of D=D1+D2is 22. If we apply the same service level objective, then required safety stock for the pooled product is (2)k. By pooling demand from two independent products with equal volumes, the required safety stock and associated inventory-driven costs is reduced by (2- 2)/2 = 29%.
7. Variance of D1+D2 Var(D1+D2) = E[(D1+D2 – E(D1+D2))2] = E[(D1+D2 – 2)2] where  = E[D1] =E[D2] = E[((D1 – ) + (D2– ))2] = E[(D1– )2]+ E[(D2– )2] + 2E[(D1– )(D2 – )] = Var[D1]+ Var[D2] + 2Cov[D1,D2] Since D1,D2 are independent, then: Var(D1+D2) = Var[D1]+ Var[D2] = 22
8. The organizational divide Supply Chain Marketing Marketing Better forecasting Precise buffer stocks Less inventory Lower cost Shorter order cycle Reliable deliveries More platforms More skus More features More market share More choices Happier customers
9. Product Variety Management Lifecycle After products have been launched, use sales data to maximize value from the existing portfolio Post-launch Variety Management Pre-launch Variety Management Before bringing a product to market, estimate its Return On Investment (ROI) Explicitly consider the costs of variety in this ROI analysis
10. Outline The benefits and challenges of product variety Analytics for variety management Implementation and impact at HP
11. Post-launch variety management Use order history to understand products’ relative importance Evaluate unimportant products for discontinuance Improve operational focus on key products. For example: Divert limited resources toward forecasting & managing key products Allocate inventory budget toward key products to improve availability How to evaluate products relative importance from order history? Rank by revenue Rank by units shipped ….
12. Limitations of simple product rankings Ignores interdependencies among products
13. Order coverage A customer order is covered by a product portfolio if all of its products are included in the portfolio Order, revenue or margin coverage of a portfolio is the number, revenue or margin of historical orders that can be completely fulfilled from the portfolio covered order non-covered order A product portfolio
14. Designing a product portfolio to maximize coverage Problem statement: Given a portfolio size n, find the portfolio of n products that maximizes revenue coverage relative to a given set of recent orders
15. A diversion: a brief introduction to linear programming Maximize ct x Subject to: Ax b x 0 Solution technique: the Simplex Method (George Dantzig, 1947) Decision variables x = x1 x2 xn Linear objective functionc t x ct = (c1, c2, …, cn) is an n-vector of objective coefficients Linearconstraints Ax b, x 0 A = is an m x n matrix of constraint coefficients a11a12 … a1n a21a22 … a2n … am1am2 … amn b = b1 b2 bm is an m-vector of resources
16. A diversion: integerlinear programming Maximize ct x Subject to: Ax b Solution technique: Branch-and-Bound and variations Integer-valued Decision variables x = x1 x2 xn Linear objective functionc t x ct = (c1, c2, …, cn) is an n-vector of objective coefficients xi0,1,2,…., i=1,…,n Linearconstraints Ax b A = is an m x n matrix of constraint coefficients a11a12 … a1n a21a22 … a2n … am1am2 … amn b = b1 b2 bm is an m-vector of resources
17. Designing a product portfolio to maximize coverage Problem statement: Given a portfolio size n, find the portfolio of n products that maximizes revenue coverage relative to a given set of recent orders An integer programming formulation: Objective function Notation xp=1 if product p is included yo=1 if order o is covered Ro revenue of order o Maximize oRoyo Subject to: yoxpfor each (o,p) where product p is in order o p xpn xp,yo0,1 Decision variables Constraints
18. Revenue Coverage Optimization Tool(RCO) Rank products according to their importance to revenue coverage RCO ranking corresponds to efficient frontier of revenue coverage and portfolio size Use RCO ranking to identify: Core Portfolio Extended Portfolio Possible candidates for discontinuance % of revenue covered # of products
19. Evolution of RCO formulation Integer ProgramIP(n) IP(n): Find product set of size n that maximizes total revenue of orders covered. Notation: xp=1 if product p is included yo=1 if order o is covered Ro revenue of order o Maximize oRoyo Subject to: yoxpif product p is in order o p xpn xp,yo0,1
20. Evolution of RCO formulation Integer ProgramIP(n) Lagrangian Relaxation LR() IP(n): Find product set of size n that maximizes total revenue of orders covered. LR(): Maximize revenue of covered orders minus lambda times portfolio size. Maximize oRoyo- (pxp) Subject to: yoxpif p is in order o 0 xp, yo 1 “Selection problem” Maximize oRoyo Subject to: yoxpif product p is in order o p xpn xp,yo0,1
21. Evolution of RCO formulation Integer ProgramIP(n) Lagrangian Relaxation LR() Parametric Bipartite Max Flow Problem LR(): Maximize revenue of covered orders minus lambda times portfolio size. Maximize oRoyo- (pxp) Subject to: yoxpif p is in order o 0 xp, yo 1 “Selection problem” Min s-t cut is an optimal solution to selection problem (Balinsky 1970) Max flow  min cut (Ford-Fulkerson) orders products min cut   R1   s t .. .  .. .  Rn 
22. Performance evolution Prior algorithm for bipartiteparametric max flow Integer ProgramIP(n) Lagrangian Relaxation LR() HPLabs SPMF arc balancing HPLabsSPMF vertex balancing CPLEX CPLEX C++ C++ C++ days +memory limitations hours+memory limitations 20 minutes for many values 2 minutes for all  10 seconds for all  Computation times on Personal Systems Group’s typical worldwide 3 month order data
23. Comparison to traditional ranking RCO Revenue impact Maximum order revenue Units shipped Revenue generated
24. Outline The benefits and challenges of product variety Analytics for variety management Implementation and impact at HP 24
25. Product discontinuance decisions Take aim at products in the tail of the ranking These products don’t generate much revenue of their own, nor do they enable sales of other high-revenue products This analysis enabled fact-based discussions between marketing and sales organizations It led to discontinuance of over 3000 products since 2004 % of revenue covered # of products
26. The Recommended Offering program Define Recommended Offering: the top ranked products covering 80% of revenue Shift inventory investment to Recommended Offering products Offer customers quick delivery time on orders that are completely within the Recommended Offering Significantly improved order cycle time & competitiveness % of revenue covered # of products
27. Summary of business impact Over $500M in savings and $180M in ongoing annual savings Significant order fulfillment improvements Thousands of SKUs eliminated Analytics Marketing Supply Chain Fact-based discussions Data-driven decisions Power of analytics Our customers are the real OR winners!
28. Takeaways The benefits and challenges of product variety Perspectives of different organizations within a firm on product variety Metrics to understand product importance from order history How effective use of analytics can bridge the organizational divide and bring about operational efficiencies and competitive advantage
29. Thank you