Wind Power: Growing Pains and Solutions PMC White Paper

Transcription

1 Wind Pwer: Grwing Pains and Slutins PMC White Paper Michigan Avenue Dearbrn, Michigan

2 Wind Pwer: Grwing Pains and Slutins SUMMARY Envirnmental cncerns, glbal energy demand frecasts, and lng-term price trends fr fssil fuels are driving grwth in alternative energy surces in the early years f the 21 st century. Wind pwer is an imprtant cmpnent in the slutin t the glbal energy challenge. The glbal wind pwer market has grwn dramatically in the last decade. In the last tw years, wind pwer accunted fr ver a quarter f all new electricity generating capacity in the United States. The rapid grwth in wind pwer has created pprtunities fr material suppliers, cmpnent manufacturers, turbine assemblers, and installatin cntractrs t expand their businesses. Sme f this grwth is due t gvernment tax plicies that may r may nt be sustainable. Mrever, technlgy differentiatin cannt sustain cmpetitive advantage in the lng run. Therefre, wind pwer winners will utilize peratinal excellence t reduce csts, increase reliability, and becme truly cmpetitive with ther pwer generating alternatives. Cncurrent with the grwth in wind pwer, the dmestic aut industry is experiencing a dramatic and likely permanent cntractin. Adam Smith s invisible hand is re-allcating human capital and intellectual prperty t ther sectrs f the ecnmy. Sme f this talent and knwledge will be applied t reduce csts and increase quality acrss the wind pwer value chain. This paper utlines the autmtive peratins engineering methds that will be used. INDUSTRY GROWING PAINS Blade, generatr, gear bx, and twer manufacturers are all under pressure t increase quality and reduce csts while ramping up vlumes. Material suppliers and installatin cntractrs are als under similar pressures. Here are sme examples f industry-wide manufacturing challenges: Imprve reliability while simultaneusly increasing perfrmance and incrprating advanced materials Imprve integratin f prduct design / engineering and manufacturing / installatin Develp lean assembly and installatin prcesses t reduce csts, imprve prductin prcess cntrls, increase thrughput, and reduce lead times Develp a glbal supply base capable f manufacturing increasingly sphisticated cmpnents and sub-assemblies Remve supply base bttlenecks while simultaneusly reducing the cst f purchased parts Imprve transprtatin and supply chain lgistics while simultaneusly increasing the size f blades and twers OPERATIONS ENGINEERING SOLUTIONS Autmtive is perhaps the mst cmpetitive cnsumer durable gds industry n the planet. Cmpanies must cmpete nt nly n prduct, but als n prcess. When these hard lessns are taken ut int ther sectrs f the ecnmy, the value prpsitin is very gd. Five autmtive peratins engineering tls and methds are directly applicable t wind industry grwing pains: Page 2

3 Wind Pwer: Grwing Pains and Slutins Thery-f-Cnstraints (systemic prblem slving) Industrial Engineering / Lean Thinking (basic blcking and tackling t reduce the seven wastes) Value Stream Mapping (visualizing the seven wastes) Quality Systems (statistics and rbust prcesses) Manufacturing / Business Prcess Simulatins (predicting future peratinal and financial results) Thery f Cnstraints Thery-f-Cnstraints is a prven prcess t slve business prblems. T-O-C views an rganizatin as a chain f dependent activities r functins all wrking twards a gal. The cnstraint is the weakest link in the chain the link that mst severely limits the rganizatin s ability t achieve the gal. In business, the gal is usually t make mre mney bth nw and in the future. Eliyahu Gldratt utlined the fllwing five step prcess t imprve rganizatinal perfrmance in his first bk n Thery-f-Cnstraints, The Gal: Step 0: Define the system. In this cntext, the system includes bth the gal and the activities and functins that deliver the gal: Wh and what cntributes t prductin and cash flw? Step 1: Identify the system s cnstraint. Finding the cnstraint in a large, cmplex rganizatin can be a challenge. A simple rule f thumb: If a link in the chain is blcked then the cnstraint is dwnstream. If a link is starved then the cnstraint is upstream. Step 2: Decide hw t explit the cnstraint. Hw can we get the mst ut f the cnstraint: Apprve vertime? Reduce setup times? Imprve scheduling? Increase in-cming inspectin? Step 3: Subrdinate everything else t the decisins made in Step 2. What can nn-cnstraints d t ensure that the cnstraint is as prductive as pssible: Crss-train peple? Imprve quality? Perfrm extra inspectins? Take lunch and breaks at different times? Step 4: Elevate the system s cnstraint. Add capacity if and nly if the cnstraint s perfrmance has been truly maximized. Step 5: If a cnstraint is brken in Step 4, g back t Step 1. Repeat the prcess n the next cnstraint until the rganizatin s gal has been met. If the gal is pen-ended (e.g., make mre mney), then this prcess never ends. Industrial Engineering / Lean Thinking Once the cnstraint is identified, industrial engineering and lean thinking can be applied t increase thrughput at the cnstraint and reduce perating csts at nn-cnstraints. Industrial engineering is the discipline f utilizing inputs in the mst efficient way pssible t achieve planned utputs. Lean thinking is a management philsphy fcused n the reductin f the seven wastes: (1) Over-prductin (making mre than custmer demand) (2) Mtin (human r machine) Page 3

4 Wind Pwer: Grwing Pains and Slutins (3) Waiting (human r machine) (4) Cnveyance (mvement frm ne lcatin t anther) (5) Over-prcessing (making features nt valued by the custmer) (6) Inventry (raw materials r finished gds) (7) Crrectin (scrap and rewrk) The principles f lean manufacturing started with Henry Frd and were refined int what is knwn tday as the Tyta Prductin System (TPS). When a delegatin frm Tyta visited the United States after Wrld War II, they cncluded that mass prductin was nt suitable in pst-war Japan. They were, hwever, inspired by a supermarket s simple but elegant prcess fr re-stcking shelves. TPS is a prcess-driven, lng-term philsphy f cntinuus imprvement and waste reductin. It is based n a pull system t avid ver-prductin and minimize inventries, a culture f getting quality right the first time, standardized wrk, and visual cntrl. Many csts are assigned when a prduct is designed. Cmpanies are nw applying lean thinking t reduce waste in prduct develpment: standardized parts, mdular cmpnents, design review checklists, etc. Bth manufacturers and service prviders incur significant wastes as material mves int, thrugh, and ut f their peratins. Applying lean thinking up frnt t packaging design, cntainer density, internal material flws, and external lgistics can reduce ttal csts. Quality Systems Autmtive quality and reliability has imprved dramatically in the last thirty years. Dr. W. Edwards Deming intrduced statistical prcess cntrl t Japan after Wrld War II. These principles were incrprated int lean thinking and the Tyta Prductin System. Mre recently, Six-Sigma has cntinued t refine the applicatin f statistical methds t imprving quality. ISO 9000 is a cmprehensive standard that can be used t assess the rbustness f a cmpany s quality system and perating practices. With just ten questins frm the standard, a far-reaching quality audit can be perfrmed: (1) Management Respnsibility: What is the quality plicy? (2) Custmer Satisfactin: Hw is custmer satisfactin measured and tracked? (3) Cntract Review: Hw are all custmer requirements verified befre rder acceptance? (4) Quality Planning: Hw is the quality f new prducts and/r new services ensured? (5) Purchasing: Hw is the quality f purchased prducts and/r services ensured? (6) Prcess Cntrl: Hw are prductin, inspectin, and maintenance activities cntrlled? Page 4

5 Wind Pwer: Grwing Pains and Slutins (7) Inspectin and Test Status: Hw are defective materials and parts identified and segregated? (8) Crrective and Preventive Actin: Hw are quality prblems identified, crrected, and prevented? (9) Handling, Strage, Packaging, Preservatin, and Delivery: Hw are prducts prtected? (10) Training: Hw are training needs assessed and delivered? Value Stream Mapping Value Stream Mapping (VSM) is a methd t illustrate the seven wastes and t identify their surces. A VSM is a cmprehensive view f all the actins, value-added and nn-value added, required t bring a prduct r service t a custmer. A VSM includes prduct flws as well as infrmatin flws. In additin t illustrating prcess lgic, a VSM rganizes key data fr each prcess step: cycle times, change-ver times, lt sizes, uptimes, scrap rates, inventry levels, inventry delays, transprt times, shipping frequencies, etc. A VSM is a gd first step in thinking systemically. Taking a value stream perspective ensures wrking n the big picture and therefre helps t avid lcal ptimizatin. VSM s are equally valid fr manufacturing, service, and administrative prcesses. Once a current state VSM is cmpleted, it prvides managers and emplyees an effective tl t find cnstraints and discuss alternative actins t reduce waste. Sftware prgrams have been develped t facilitate the develpment f value stream maps. While nice t have, they are nt essential. VSM s n brwn paper cvered with sticky ntes are just as valid. Manufacturing and Business Prcess Simulatin While a VSM is a gd first step in thinking systemically, the methd has ne significant limitatin: it is a static snapsht, nt a mving picture. A simulatin prvides a dynamic view f the value stream as well as the ability t run what-if experiments t predict future peratinal and financial results. As such, simulatin is a very pwerful and versatile tl t maximize investment efficiency and mitigate risk in bth manufacturing and business prcesses. Discrete event simulatins are built by cnnecting mdeling elements (machines, cnveyrs, buffers, parts, peple, etc.) in the prcess flw lgic. Next the perfrmance f each element is described with variables such as cycle times, dwntimes, changever times, cnveyr min/max/flats, buffer sizes, shift hurs, etc. By happy cincidence, mst f the data required t build a discrete event simulatin has already been rganized n the Value Stream Map Uncertainty in any perfrmance variable can be captured by fitting a prbability distributin arund a mean value. By using a different randm number stream fr each prbability distributin, the events in the mdel are independent f each ther just as in the real wrld. At the end f a run, the simulatin sftware cllates the results and generates reprts. What-if experiments are easily perfrmed by making changes t the input data set, re-running the mdel, and then cmparing the results. Many manufacturing and business prcesses share resurces in cmplex ways. In such cases, finite capacity simulatins can create multi-prduct, multi-prcess prductin schedules fr imprved custmer service, reduced inventries, and better utilizatin f resurces. Finite capacity simulatins are equally Page 5

6 Wind Pwer: Grwing Pains and Slutins applicable t manufacturing and service rganizatins and can be interfaced with shp-flr and human resurce systems. Finite capacity simulatins have similar dynamic what-if capabilities. What if a shipment f parts is running late? What if a machine is dwn fr the day? Fr ver tw decades, discrete event simulatins and finite capacity simulatins have prven equally adept in finding cnstraints and testing strategies t break them per the Gldratt five-step prcess. CONCLUSIONS A cmbinatin f these five autmtive peratins engineering tls can address the grwth pains being experienced by the wind pwer industry. The systemic perspectives prvided by Thery-f-Cnstraints and value stream mapping will ensure that functinal interfaces and hand-ffs are cnsidered. Lean thinking and quality systems will squeeze ut waste and imprve custmer satisfactin. Simulatins will find bttlenecks and reduce the risk and uncertainty arund capacity investments. First adaptrs f these peratins engineering tls in the wind pwer industry will achieve dminant market psitins, greater ecnmies f scale, and superir financial returns. ABOUT THE AUTHOR Steve Beeler is a Directr at Prductin Mdeling Crpratin (PMC), a full-service peratins engineering and management cnsulting firm based in Dearbrn, Michigan. Steve s Special Situatin s practice targets middle-market manufacturing and service cmpanies. Steve came t PMC frm the Frd Mtr Cmpany. During his twenty years at Frd, Steve guided Nrth American assembly and stamping plants thrugh the ISO 9001 registratin prcess, develped a ttal plant simulatin prcess, and assembled crss-functinal / multi-natinal teams t mdel enterprise prfitability. Steve hlds a BSME frm Massachusetts Institute f Technlgy, an MBA frm Indiana University, and is a Prfessinal Engineer. Steve is a member f the Sciety f Autmtive Engineers, the Turnarund Management Assciatin, and the Assciatin fr Crprate Grwth. Steve can be reached at (313) x1141 r sbeeler@pmcrp.cm Page 6