In　a　very　recent　paper　by　Bal　et　al.　(Bal,　H.,　OrkcCu,　H.　H.,　&　Celebioglu,　S.　(2008).　A　new　method　based　on　the　dispersion　of　weights　in　data　envelopment　analysis.　Computers　&　Industrial　Engineering,　54(3),　502512),　a　data　envelopment　analysis　(DEA)　model　which　incorporates　the　coefficients　of　variations　(CVs)　of　input-output　weights　was　proposed　to　improve　the　discrimination　power　of　IDEA　and　balance　input-output　weights.　This　note　points　out　that　the　input　and　output　weights　in　DEA　are　of　different　dimensions　and　units.　The　weights　with　different　dimensions　and　units　cannot　be　simply　added　together　and　averaged.　In　other　words,　the　DEA　model　with　the　inclusion　of　CVs　of　input-output　weights,　which　was　referred　to　as　CVDEA　model　for　short,　makes　no　sense　if　input　and　output　data　are　not　normalized　to　eliminate　their　dimensions　and　units.　This　note　also　illustrates　the　facts　that　the　CVDEA　model　can　cause　significant　efficiency　changes　when　a　scale　transformation　is　performed　for　an　input　or　output　and　may　produce　multiple　local　optimal　solutions　due　to　its　nonlinearity,　leading　to　totally　different　assessment　conclusions.　These　facts　reveal　that　the　CVDEA　model　suffers　from　serious　drawbacks　and　its　applications　for　efficiency　assessment　should　be　very　cautious.　(C)　2008　Elsevier　Ltd.　All　rights　reserved.