Complexity Management. Lars Hvam Technical University of Denmark

Transcription

1 Complexity Management Lars Hvam Technical University of Denmark

2 Background Why Complexity Management? Recent studies show The main concern of 1,500 CEOs: Increasing complexity is the biggest threat 94% of 1,400 CEOs state that managing complexity is important to the success of their company 70% of 1,400 CEOs consider complexity one of the biggest challenges the company faces Between 3-5% EBIT is lost due to increasing complexity that is not managed properly IBM (2010) KPMG (2011) Kahn (2012) What drives complexity? No of customer requirements No of markets No of product variants No of modules and components No of production sites No of sub-suppliers No of DCs No of planning levels No of changes of ownership 2

3 Scania: Best-in-class profitability Top management focus on product architecture and control of the product assortment SCANIA s present product architecture is from 1980 High productivity High level of introduction of new product technology High level of automization in the factories 3

4 Scania: Modular Product System Source: Scania 4

5 Lessons learned from world class companies in complexity management Focus on product portfolio management, e.g. IKEA has product codes for each warehouse. The whole business is optimized for this amount of product codes Focus on KPI-structures that support control of complexity, e.g. Volvo has KPI s on both vertical and horizontal level, that focus on overall reduction of components (reduced to 1/3 over a few years) Nilfisk Advance currently works on reduction of # of customers and products, brands, private labels, platforms, product variants (all continously) LK has reduced complexity in the IHC-products by introducing 7 packages with std. functionalities (has lead to increased sales and reduced cost in the entire supply chain) Lego has implemented differentiated cost models to predict the cost of complexity 5

6 Complexity management 1. In most manufacturing companies 5-10 % of the products and 5-10 % of the customers count for percent of both revenue and gross margin 2. The remaining % of products and customers are not adding any significant revenue to the company, but they create a substantial amount of complexity and costs 3. Not all products and customers contribute with positive gross margins 4. In many manufacturing companies new product variants are added every year without removing the same amount of products - leading to more product variants and increasing complexity and costs 5. In many companies e.g. product managers do not have any sufficient information on product and complexity costs on detailed level 6. Complexity in the product line leads to complexity and inefficiency in all business processes sales, product development, production, supply, logistics, distribution, after sales etc. 7. The true costs of complexity in e.g. production and distribution are not visible or documented. 8. We need a structured approach to analyse, reduce and control the complexity in the product assortment and business processes 6

7 Example: ABC analysis shows how a few variants make up the majority of revenue The least selling product variants that make up only 5% of total revenue account for 59% of total product variants Product variants Revenue C 5% Variants Idle 1496 B 15% A 30% C 59,1% 1351 variants 3714 variants Active* 2218 A+ 50% B 24,3% A 12,4% A+ 4,2% *Active defined as products sold during the last 12 months ABC analysis lists product variants by highest revenue and denotes those making up the first 50% revenue A+, the next 30% A, the next 15% B and the final 5% C 7

8 Example: Components and sub assemblies for C-products fill up inventories in production and create production flow congestion 120% 100% 80% 60% 40% 20% 0% Max 100% 120% 100% 80% 60% 40% 20% Average capacity Avg. 91% utilization 0% Inventory holdings Stock capacity C 21% B 25% A 27% A+ 28% Avg. 89% Stock capacity C 43% B 26% A 18% A+ 13% Max 100% Max 100% 120% 100% 80% 60% 40% 20% 0% Avg. 91% Stock capacity C 25% B 17% A 26% A+ 33% Inventories become a production bottleneck as 100% utilization is reached. Available inventory significantly reduced by slow moving C- variants Average inventory time Inventory time [days] 35,0 30,0 25,0 20,0 15,0 10,0 5,0 0,0 Weighted average: 10 days 6,7 C variants have on average 4 times more inventory time 8,7 16,4 30,4 A+ A B C Inventory time [days] Weighted average: 3,3 days 1,24 C variants have on average 20 times more than A 2,35 5,57 25,91 A+ A B C Inventory time [days] 12,0 10,0 8,0 6,0 4,0 2,0 0,0 Weighted average: 1,4 days 0,9 C variants have on average 10 times more inventory time 1,3 2,6 10,8 A+ A B C Large differences in inventory cost as C-variants have longer inventory time 8

9 Example: Small-batch C-products leads to decreased machine utilization Average * utilization 120% 100% 80% 60% 40% 20% 0% Avg. 84% Changeover time 120% 100% 80% 60% 40% 20% 0% Avg. 88% Changeover time 120% 100% 80% 60% 40% 20% 0% Avg. 59% Changeover time Unused production potential despite being behind schedule Changeover time C 20% B 24% A 28% A+ 28% C 32% B 27% A 27% A+ 13% C 27% B 18% A 27% A+ 28% Production time lost to many changeovers Average unit changeover time Changeover time sec Average changeover cost: 4 sec 1,2 C variants have on average 40 times more setup time 21,2 33,2 47,4 A+ A B C Changeover time sec Average changeover cost: 21,2 sec 8,4 C variants have on average 9 times more setup time 15,7 27,8 79,4 A+ A B C Changeover time sec C variants have on average 3,5 times more setup time Average changeover cost: 69,8 sec 48,8 53,3 75,7 224,3 A+ A B C Large differences in production cost as C-variants have longer unit changeover time *Utilization calculated from standard process 9time

10 Example: Production machinery utilization is highly dependent on batch sizes when setup times are relative high Output per run and batch size correlation* Number of plates per run Plates per run vs. average batch size Average batch size Throughput cores per run Throughput per run vs. average batch size - 10,0 20,0 30,0 Average batch size Machines have higher output the higher the batch sizes Difference between high and low output Batch size 20 -> 40 (+100%) Output 220 -> 320 (+45%) Batch size 10 -> 20 (+100%) Output 125 -> 200 (+60%) Output increases are significant when increasing batch sizes 10 *Run defined as one machine running one shift

11 Reduction of complexity costs due to change in customization point Point of customization determines complexity costs in production and distribution. E.g. Complete production and packing in factory in China and distribution in Europe is changed to configuring and packing of products and assessories in Europe. Reduced costs at 2-3 % of EBIT mainly due to reduced stock. 11

12 Reduced number of components leads to reduced sourcing costs Overview of complexity cost drivers & Supplier Complexity Cost Model Component volume Component specification level Example of component group Motor asm. RPM Flow Pressure Durability 800 RPM 1000 RPM 200L/H 300L/H 500L/H 100bar 120bar 140bar 500 hours hours hours Supplier A Variant 1 x x x x Variant 2 x x x x Variant 3 x x x x Supplier B Variant X x x x x /piece x? How can we affect supplier complexity cost factors with varying volumes and specification levels? volume Characterization Performance parameters 12

13 Reduce complexity 13

14 Example: Order Contribution Ratio (GR) 60,00% 40,00% Actual CR 20,00% 0,00% -20,00% -40,00% -60,00% Orders 14

15 7 case studies: All with significant variations in CR 15

16 Complexity management How to identify, quantify and reduce complexity initial grouping and profitability analysis

17 Goals and steps Goals To analyze possible complexity costs in order to understand the true product costs Understand complexity costs and obtain the true product costs Trimming/pruning the product program and customers Long term initiatives for product and process improvements Boost EBIT margin! Steps 1. Definition of product and customer categories focus on finished goods level and/ or modules and components 2. ABC-analysis. Initial identification of potential products to eliminate and goal setting 3. Analysis of complexity costs and set of complexity adjusted costs 4. Calculation of savings and potential 5. Evaluate the product range based on true complexity costs and other relevant factors. Suggested products to eliminate/ substitute. Short term and long term initiatives. 17

18 1. Scope of the analysis PFMP: modeling of product/customer view Define product and customer categories Overall goal setting Identify data sources for case period (e.g. covering full 12 months of 2012) Unit sales per product Unit sales per customer Cost/Gross margin per product Cost/Gross margin per customer. Determine scope of the analysis 18

19 2. Define product and customer categories A B ,01 0, Contribution Margin KEUR (log) C 0,1 0,01 0,001 Net Sales KEUR (log) The task is to find Small Loosers in C Sacrificial Sinners in B Silent Killers in A Pricelist Non-pricelist A B C Total * NS and CM1 from Business Warehouse 19

20 2. Product adjustment and goal setting SKU removal categories Types of SKUs in scope Profitability/ margin Typical revenue per SKU Portion that should be removed Characteristics of SKUs that should be removed Cleaning the attic SKUs for which there is essentially no activity Products which have not been sold in the last 12 moth All All Removing the small losers Very unprofitable C area Small revenue C area Nearly all 80% All except those: - with a promising future - early in lifecycle Removing the sacrificial sinners Unprofitable B area Medium revenue B area Many 50% Cannot become profitable by removal of other SKU High revenue substitutability Removing the silent killers Profitable A area High revenue A area Some 5% Very high revenue substitutability * Focusing the portfolio on what customers value what s left is a much more concentrated and powerful offering to your customers Wilson and Perumal, Waging war on complexity costs, Mc Graw Hill

21 3. Focus on variable costs Insufficient cost models for capturing true costs of production and distribution Calculation models focus on variable costs Overhead costs are seen as fixed and not allocated to products and activities The cost distribution is asymmetric Decisions on product variants, produtction equipment, process improvements etc. are mainly based on calculations of variable costs leads to sub optomization True costs on products, production and distribution are not known Material costs Overhead costs Wages 21

22 3. Complexity analysis Analysis of cost structure Cost structure analysis Cost distribution in the value chain 100% Net Revenue Identification of top complexity cost areas? Sales costs? Engineering costs? Production costs?? Distribution costs? Production costs Distribution costs Sales costs % % % R&D/Eng. costs Corporate costs EBIT % % % 22

23 3. Analyse complexity cost Set complexity adjusted costs Analyse the overall cost structure Analyse cost structure of the possible complexity costs estimate possible savings Set complexity adjusted costs 23

24 3. Complexity cost factors example Calculate potential savings from each of the identified complexity cost drivers Example: change overs in production A items: 2 % of total cost B items: 5 % of total cost C items: 12 % of total cost Example: Stock costs Stock costs A fast B medium C slow Avge days in stock days 2,87 8,96 104,82 Stock cost EUR/item 2,9 8,7 92,6 24

25 3. Complexity analysis Identification and analysis of complexity cost factors Identification of top 3-5 complexity cost factors, e.g.: Engineering costs Costs of setup and changeovers in production Cost of poor quality ERP-costs Cost of inventory Parts/components Finished goods Cost of freight Cost of purchase and warehouse order handling Costs in sales and ordering Costs in sourcing and purchasing Low volumes Analysis of complexity cost factors Sales and ordering overview Production line overview 25

26 Adjusted Contribution Ratio (CR) gives a more true picture of the products profitability 26

27 3. Contribution margins and contribution ratio s adjusted with complexity factors 27

28 4. Calculate savings and potential example Develop pruning scenarios Light vs. heavy pruning Full vs. substitution Calculate potential savings from each of the identified complexity cost drivers 1 ABC Avg. order sizes Avg. cost pr. order Sales price 1.000EUR Order cost pr. item Adjustment of cont. ratio (CR) pr. product A items EUR 9,00 EUR -0,90% 1 Activity metrics based: Ex.: Admin. Cost in sales: Costs pr. order line (incl. a relation between ABCproducts and avg. order sizes) B items EUR 15,00 EUR -1,50% C items EUR 22,50 EUR -2,25% 2 Variant sensitivity/dependency of cost centers: Ex.: Product Maintenance being ~80% dependent on the number of components (pruning scenario dependent) 2 Org./ department Product Maintenance FTEs Variant sensitivity % Comp. Reduction % Cost reduction % 10 80% 30% 24% 28

29 4. Realisation of possible savings Quantified savings may be real money or funny money. An example is stock keeping costs including insurance, bank interests, house rents, handling and scrap. Real money: Bank interests and insurance are real money, a saving which is immediately capitalised. Funny money: Rent and handling are funny money step wise capitalisation. 1 % less space in stock give no savings, but e.g. 30 % reduction of space may lead to a reduction of rent or man power. Requires an effort to capitalise a saving. Reduction of number of components leads to time reduction in e.g. maintenance of product data or time reduction when purchasing components. Here you need to specify how the saved hours may be capitalised by e.g. reduction of man power, expand the revenue e.g % or by using the freed resources for e.g. develioping the cooperation with vendors, or developing new products. Savings are specified as real money og funny money. Within funny money for any given saving we also need to specify how the freed capacity may be reduced or used for other purposes. 29

30 4. Savings and potential example The case company has a turnover at approx. 2,7 bio euro, and has removed 30 % of item no s in part of their product assortment corresponding to 13 % of their turnover Cost elements Savings Pilot project - share of revenue Scrapped products Inventory Waste Updating database 108KEUR 802KEUR 546KEUR 6KEUR Other OPC Os 87% 13% -SC GBI 6% * Marketing material? SKU creations? Over specification? Logistics/delivery? Other segments 7% -SC GBI = 6% of total revenue -SC GBI ~ 6% of total savings -SC GBI savings = KEUR Total savings 1.462kEUR 30 Total savings 1462KEUR / 6% = KEUR Approx 1 % EBIT

31 4. Quantification/indication of potential savings Quantification of potential short term savings from Elimination of specification ranges and customers Elimination of module variants and components, e.g.: Inventory levels Engineering hours Rationalization of supply Indication of potential mid/long term savings from, e.g: More well defined solution space Standardization of modules and components Customization process improvements Motivation for elimination Focus the portfolio on what customers really value Concentrate our scarce resources Increase process performance Drive more revenue per product and make them more cost competitive in today s market. Expose gaps in the portfolio. Removing poor SKUs makes room for new ones EUR? 31

32 32 5. Evaluate the product range based on true complexity costs and other relevant factors Identify products to be adjusted based on the true complexity costs and other relevant criterias Make decisions on collected data, not feelings Drivers for product substitution/elimination: Focus the portfolio on what customers really value Concentrate our scarce resources Increase process performance Drive more revenue per product and make them more cost competitive in today s market. Expose gaps in the portfolio. Removing poor SKUs makes room for new ones Product category Percent Percent A 25% 69% B 31% 32% C 42% 3% Gross margin k USD (log) Gross margin k USD (log)

33 5. Considering soft product properties not only CM1 Product substitutability Linked revenue The life cycle position Other strategic aspects 33

34 5. Complexity cost reduction initiatives Preparation of solution catalogue Evaluation of the product range Evaluation of ranges/solutions/customer groups and other options Definition of components/modules to fix Definition of future complexity reduction initiatives Regain control of offerings Scoping and redesign/respec initiatives Customer order decoupling points 34 Short term (3-6 Months) Eliminate/substitute variants Re-price Redesign/re-spec Reconfigure variant creation (modify solution space) Mid/long term Re-evaluate customization strategy Identify profitable product platform and architecture Identify/reconfigure production setup

35 5. Theoretical fixing scenarios Quantification of scenario impact and potential 8 scenarios for each product brand are evaluated, based on 2 x 2 x 2 base scenarios which employ basic fixing measures, covering high/low level of product pruning, with full/ no product substitution, high/low level of positive price adjustment Decisions on which L6s to prune, which L6s are susbstitution targets and how much the prices are adjusted are made for each brand separately, recognizing the different challenges faced by each brand The scenarios yield new overall net revenues, contribution ratios and contribution margins for each brand. The total scenario impact is calculated and summarized for combined consumer HPW portfolio Costs that are dependent on the number of L6 variants which are subtracted in the scenarios where applicable (e.g. white collar FTEs in Suzhou and sales companies, L4 component inventory in Suzhou and L6 inventory in EDC, FTEs in Suzhou and sales companies) Net revenue is lost if no L6s are substituted C. margin and c. ratio is increased due to price adjustment and shift of sales volume to higher margin L6 Low level scenarios yield 5% increase in portfolio profitability High level scenarios yield up to 10,5% increase 35

36 Project plan Time schedule 1. Product program analysis 2. ABC-analysis 1 Potential products to fix 3. Complexity analysis Workshop 1 Complexity adjusted costs 4. Quantification of potential savings 5. Complexity cost reduction initiatives Workshop 1 Potential savings 1 List of products to eliminate & other cost reduction initiatives April May June 2016 August 1 Deliverables 36

37 Resources - example Activity Define scope/ product families to include. Define product categories Internal hours ABC analysis. Check and validation of data Analyse complexity costs set complexity adjusted costs Identify initiatives for complexity reduction Calculate savings and potential Total External hours 37

38 End