[This post was originally published on 3 April 2017.]
Displayed across the top of my book is the phrase “Largest Tank Battle in History.” Apparently some people dispute that.
What they put forth as the largest tank battle in history is the Battle of Brody in 23-30 June 1941. This battle occurred right at the start of the German invasion of the Soviet Union and consisted of two German corps attacking five Soviet corps in what is now Ukraine. This rather confused affair pitted between 750 to 1,000 German tanks against 3,500 to 5,000 Soviet tanks. Only 3,000 Soviet tanks made it to the battlefield according to Glantz (see video at 16:00). The German won with losses of around a 100 to 200 tanks. Sources vary on this, and I have not taken the time to sort this out (so many battles, so little time). So, total tanks involved are from 3,750 to up to 6,000, with the lower figure appearing to be more correct.
Now, is this really a larger tank battle than the Battle of Kursk? My book covers only the southern part of the German attack that started on 4 July and ended 17 July. This offensive involved five German corps (including three Panzer corps consisting of nine panzer and panzer grenadier divisions) and they faced seven Soviet Armies (including two tank armies and a total of ten tank and mechanized corps).
My tank counts for the southern attack staring 4 July 1943 was 1,707 German tanks (1,709 depending if you count the two Panthers that caught fire on the move up there). The Soviets at 4 July in the all formations that would eventually get involved has 2,775 tanks with 1,664 tanks in the Voronezh Front at the start of the battle. Our count of total committed tanks is slightly higher, 1,749 German and 2,978 Soviet. This includes tanks that were added during the two weeks of battle and mysterious adjustments to strength figures that we cannot otherwise explain. This is 4,482 or 4,727 tanks. So depending on which Battle of Brody figures being used, and whether all the Soviet tanks were indeed ready-for-action and committed to the battle, then the Battle of Brody might be larger than the attack in the southern part of the Kursk salient. On the other hand, it probably is not.
But, this was just one part of the Battle of Kursk. To the north was the German attack from the Orel salient that was about two-thirds the size of the attack in the south. It consisted of the Ninth Army with five corps and six German panzer divisions. This offensive fizzled at the Battle of Ponyiri on 12 July.
The third part to the Battle of Kursk began on 12 July the Western and Bryansk Fronts launched an offensive on the north side of the Orel salient. A Soviet Front is equivalent to an army group and this attack initially consisted of five armies and included four Soviet tank corps. This was a major attack that added additional forces as it developed and went on until 23 August.
The final part of the Battle of Kursk was the counter-offensive in the south by Voronezh, Southwestern and Steppe Fronts that started on 3 August, took Kharkov and continued until 23 August. The Soviet forces involved here were larger than the forces involved in the original defensive effort, with the Voronezh Front now consisting of eight armies, the Steppe Front consisting of three armies, and there being one army contributed by the Southwestern Front to this attack.
The losses in these battles were certainly more significant for the Germans than at the Battle of Brody. For example, in the southern offensive by our count the Germans lost 1,536 tanks destroyed, damaged or broken down. The Soviets lost 2,471 tanks destroyed, damaged or broken down. This compares to 100-200 German tanks lost at Brody and the Soviet tank losses are even more nebulous, but the figure of 2,648 has been thrown out there.
So, total tanks involved in the German offensive in the south were 4,482 or 4,727 and this was just one of four parts of the Battle of Kursk. Losses were higher than for Brody (and much higher for the Germans). Obviously, the Battle of Kursk was a larger tank battle than the Battle of Brody.
What some people are comparing the Battle of Brody to is the Battle of Prokhorovka. This was a one- to five-day event during the German offensive in the south that included the German SS Panzer Corps and in some people’s reckoning, all of the III Panzer Corps and the 11th Panzer Division from the XLVIII Panzer Corps. So, the Battle of Brody may well be a larger tank battle than the Battle of Prokhorovka, but it was not a larger tank battle than the Battle of Kursk. I guess it depends all in how you define the battles.
“…war alone cannot account for the vast number of Afghan migrants or the great distance they are travelling.”
“Globally, up until 1960, the ratio of refuges to fatalities in conflict zones was below 5:1.”
“…in 2015 there was an almost unprecedented 50 as asylum applicants for every civilian killed.”
“Whereas in 1979 over 90% of the Afghan refugees travelled less than 500 km and cross one border, now more than 90% travel over 5,000 km to seek asylum…”
“There are now 1.3 million internally displaced Afghans, with the total increasing by 400,000 a year.”
“The pull of economic opportunity plays a large part in the decision to migrate.”
“In 2015, the population of Afghanistan was 32 million.”
“…it is nonetheless obliged to import enough wheat to feed 10 million people…”
“…Afghanistan’s population will pass 40 million in ten years.”
“the natural growth rate of 2.3% a year added 700,000 to the Afghan population in 2015.”
“Unless there is a dramatic improvement in the economy and security in that time, 16 million will depend on food aid…”
Zentralbild, II. Weltkrieg 19139-45 Der von der faschistischen deutschen Wehrmacht während des Krieges entwickelte neue Panzerkampfwagen Typ “Panther”. UBz: die Verladung neuer “Panther”-Panzerkampfwagen zum Transport an die Front (1943).
The Voice of America (VOA) interviewed me about Kursk and the current Russian Army for some articles they were working on. The interviewer, Alex Grigoryev, was a journalist in Russia before he immigrated to the United States. The first interview, on Kursk, is on video here, with me speaking in English with Russian subtitles: https://www.golos-ameriki.ru/a/ag-kursk-battle-book-of-cristopher-lawrance/4384650.html
A few things I would change, but I don’t think I completely embarrassed myself.
Consistent Scoring of Weapons and Aggregation of Forces: The Cornerstone of Dupuy’s Quantitative Analysis of Historical Land Battles by
James G. Taylor, PhD,
Dept. of Operations Research, Naval Postgraduate School
Introduction
Col. Trevor N. Dupuy was an American original, especially as regards the quantitative study of warfare. As with many prophets, he was not entirely appreciated in his own land, particularly its Military Operations Research (OR) community. However, after becoming rather familiar with the details of his mathematical modeling of ground combat based on historical data, I became aware of the basic scientific soundness of his approach. Unfortunately, his documentation of methodology was not always accepted by others, many of whom appeared to confuse lack of mathematical sophistication in his documentation with lack of scientific validity of his basic methodology.
The purpose of this brief paper is to review the salient points of Dupuy’s methodology from a system’s perspective, i.e., to view his methodology as a system, functioning as an organic whole to capture the essence of past combat experience (with an eye towards extrapolation into the future). The advantage of this perspective is that it immediately leads one to the conclusion that if one wants to use some functional relationship derived from Dupuy’s work, then one should use his methodologies for scoring weapons, aggregating forces, and adjusting for operational circumstances; since this consistency is the only guarantee of being able to reproduce historical results and to project them into the future.
Implications (of this system’s perspective on Dupuy’s work) for current DOD models will be discussed. In particular, the Military OR community has developed quantitative methods for imputing values to weapon systems based on their attrition capability against opposing forces and force interactions.[1] One such approach is the so-called antipotential-potential method[2] used in TACWAR[3] to score weapons. However, one should not expect such scores to provide valid casualty estimates when combined with historically derived functional relationships such as the so-called ATLAS casualty-rate curves[4] used in TACWAR, because a different “yard-stick” (i.e. measuring system for estimating the relative combat potential of opposing forces) was used to develop such a curve.
Overview of Dupuy’s Approach
This section briefly outlines the salient features of Dupuy’s approach to the quantitative analysis and modeling of ground combat as embodied in his Tactical Numerical Deterministic Model (TNDM) and its predecessor the Quantified Judgment Model (QJM). The interested reader can find details in Dupuy [1979] (see also Dupuy [1985][5], [1987], [1990]). Here we will view Dupuy’s methodology from a system approach, which seeks to discern its various components and their interactions and to view these components as an organic whole. Essentially Dupuy’s approach involves the development of functional relationships from historical combat data (see Fig. 1) and then using these functional relationships to model future combat (see Fig, 2).
At the heart of Dupuy’s method is the investigation of historical battles and comparing the relationship of inputs (as quantified by relative combat power, denoted as Pa/Pd for that of the attacker relative to that of the defender in Fig. l)(e.g. see Dupuy [1979, pp. 59-64]) to outputs (as quantified by extent of mission accomplishment, casualty effectiveness, and territorial effectiveness; see Fig. 2) (e.g. see Dupuy [1979, pp. 47-50]), The salient point is that within this scheme, the main input[6] (i.e. relative combat power) to a historical battle is a derived quantity. It is computed from formulas that involve three essential aspects: (1) the scoring of weapons (e.g, see Dupuy [1979, Chapter 2 and also Appendix A]), (2) aggregation methodology for a force (e.g. see Dupuy [1979, pp. 43-46 and 202-203]), and (3) situational-adjustment methodology for determining the relative combat power of opposing forces (e.g. see Dupuy [1979, pp. 46-47 and 203-204]). In the force-aggregation step the effects on weapons of Dupuy’s environmental variables and one operational variable (air superiority) are considered[7], while in the situation-adjustment step the effects on forces of his behavioral variables[8] (aggregated into a single factor called the relative combat effectiveness value (CEV)) and also the other operational variables are considered (Dupuy [1987, pp. 86-89])
Figure 1.
Moreover, any functional relationships developed by Dupuy depend (unless shown otherwise) on his computational system for derived quantities, namely OLls, force strengths, and relative combat power. Thus, Dupuy’s results depend in an essential manner on his overall computational system described immediately above. Consequently, any such functional relationship (e.g. casualty-rate curve) directly or indirectly derivative from Dupuy‘s work should still use his computational methodology for determination of independent-variable values.
Fig l also reveals another important aspect of Dupuy’s work, the development of reliable data on historical battles, Military judgment plays an essential role in this development of such historical data for a variety of reasons. Dupuy was essentially the only source of new secondary historical data developed from primary sources (see McQuie [1970] for further details). These primary sources are well known to be both incomplete and inconsistent, so that military judgment must be used to fill in the many gaps and reconcile observed inconsistencies. Moreover, military judgment also generates the working hypotheses for model development (e.g. identification of significant variables).
At the heart of Dupuy’s quantitative investigation of historical battles and subsequent model development is his own weapons-scoring methodology, which slowly evolved out of study efforts by the Historical Evaluation Research Organization (HERO) and its successor organizations (cf. HERO [1967] and compare with Dupuy [1979]). Early HERO [1967, pp. 7-8] work revealed that what one would today call weapons scores developed by other organizations were so poorly documented that HERO had to create its own methodology for developing the relative lethality of weapons, which eventually evolved into Dupuy’s Operational Lethality Indices (OLIs). Dupuy realized that his method was arbitrary (as indeed is its counterpart, called the operational definition, in formal scientific work), but felt that this would be ameliorated if the weapons-scoring methodology be consistently applied to historical battles. Unfortunately, this point is not clearly stated in Dupuy’s formal writings, although it was clearly (and compellingly) made by him in numerous briefings that this author heard over the years.
Figure 2.
In other words, from a system’s perspective, the functional relationships developed by Colonel Dupuy are part of his analysis system that includes this weapons-scoring methodology consistently applied (see Fig. l again). The derived functional relationships do not stand alone (unless further empirical analysis shows them to hold for any weapons-scoring methodology), but function in concert with computational procedures. Another essential part of this system is Dupuy‘s aggregation methodology, which combines numbers, environmental circumstances, and weapons scores to compute the strength (S) of a military force. A key innovation by Colonel Dupuy [1979, pp. 202- 203] was to use a nonlinear (more precisely, a piecewise-linear) model for certain elements of force strength. This innovation precluded the occurrence of military absurdities such as air firepower being fully substitutable for ground firepower, antitank weapons being fully effective when armor targets are lacking, etc‘ The final part of this computational system is Dupuy’s situational-adjustment methodology, which combines the effects of operational circumstances with force strengths to determine relative combat power, e.g. Pa/Pd.
To recapitulate, the determination of an Operational Lethality Index (OLI) for a weapon involves the combination of weapon lethality, quantified in terms of a Theoretical Lethality Index (TLI) (e.g. see Dupuy [1987, p. 84]), and troop dispersion[9] (e.g. see Dupuy [1987, pp. 84- 85]). Weapons scores (i.e. the OLIs) are then combined with numbers (own side and enemy) and combat- environment factors to yield force strength. Six[10] different categories of weapons are aggregated, with nonlinear (i.e. piecewise-linear) models being used for the following three categories of weapons: antitank, air defense, and air firepower (i.e. c1ose—air support). Operational, e.g. mobility, posture, surprise, etc. (Dupuy [1987, p. 87]), and behavioral variables (quantified as a relative combat effectiveness value (CEV)) are then applied to force strength to determine a side’s combat-power potential.
Requirement for Consistent Scoring of Weapons, Force Aggregation, and Situational Adjustment for Operational Circumstances
The salient point to be gleaned from Fig.1 and 2 is that the same (or at least consistent) weapons—scoring, aggregation, and situational—adjustment methodologies be used for both developing functional relationships and then playing them to model future combat. The corresponding computational methods function as a system (organic whole) for determining relative combat power, e.g. Pa/Pd. For the development of functional relationships from historical data, a force ratio (relative combat power of the two opposing sides, e.g. attacker’s combat power divided by that of the defender, Pa/Pd is computed (i.e. it is a derived quantity) as the independent variable, with observed combat outcome being the dependent variable. Thus, as discussed above, this force ratio depends on the methodologies for scoring weapons, aggregating force strengths, and adjusting a force’s combat power for the operational circumstances of the engagement. It is a priori not clear that different scoring, aggregation, and situational-adjustment methodologies will lead to similar derived values. If such different computational procedures were to be used, these derived values should be recomputed and the corresponding functional relationships rederived and replotted.
However, users of the Tactical Numerical Deterministic Model (TNDM) (or for that matter, its predecessor, the Quantified Judgment Model (QJM)) need not worry about this point because it was apparently meticulously observed by Colonel Dupuy in all his work. However, portions of his work have found their way into a surprisingly large number of DOD models (usually not explicitly acknowledged), but the context and range of validity of historical results have been largely ignored by others. The need for recalibration of the historical data and corresponding functional relationships has not been considered in applying Dupuy’s results for some important current DOD models.
Implications for Current DOD Models
A number of important current DOD models (namely, TACWAR and JICM discussed below) make use of some of Dupuy’s historical results without recalibrating functional relationships such as loss rates and rates of advance as a function of some force ratio (e.g. Pa/Pd). As discussed above, it is not clear that such a procedure will capture the essence of past combat experience. Moreover, in calculating losses, Dupuy first determines personnel losses (expressed as a percent loss of personnel strength, i.e., number of combatants on a side) and then calculates equipment losses as a function of this casualty rate (e.g., see Dupuy [1971, pp. 219-223], also [1990, Chapters 5 through 7][11]). These latter functional relationships are apparently not observed in the models discussed below. In fact, only Dupuy (going back to Dupuy [1979][12] takes personnel losses to depend on a force ratio and other pertinent variables, with materiel losses being taken as derivative from this casualty rate.
For example, TACWAR determines personnel losses[13] by computing a force ratio and then consulting an appropriate casualty-rate curve (referred to as empirical data), much in the same fashion as ATLAS did[14]. However, such a force ratio is computed using a linear model with weapon values determined by the so-called antipotential-potential method[15]. Unfortunately, this procedure may not be consistent with how the empirical data (i.e. the casualty-rate curves) was developed. Further research is required to demonstrate that valid casualty estimates are obtained when different weapon scoring, aggregation, and situational-adjustment methodologies are used to develop casualty-rate curves from historical data and to use them to assess losses in aggregated combat models. Furthermore, TACWAR does not use Dupuy’s model for equipment losses (see above), although it does purport, as just noted above, to use “historical data” (e.g., see Kerlin et al. [1975, p. 22]) to compute personnel losses as a function (among other things) of a force ratio (given by a linear relationship), involving close air support values in a way never used by Dupuy. Although their force-ratio determination methodology does have logical and mathematical merit, it is not the way that the historical data was developed.
Moreover, RAND (Allen [1992]) has more recently developed what is called the situational force scoring (SFS) methodology for calculating force ratios in large-scale, aggregated-force combat situations to determine loss and movement rates. Here, SFS refers essentially to a force- aggregation and situation-adjustment methodology, which has many conceptual elements in common with Dupuy‘s methodology (except, most notably, extensive testing against historical data, especially documentation of such efforts). This SFS was originally developed for RSAS[16] and is today used in JICM[17]. It also apparently uses a weapon-scoring system developed at RAND[18]. It purports (no documentation given [citation of unpublished work]) to be consistent with historical data (including the ATLAS casualty-rate curves) (Allen [1992, p.41]), but again no consideration is given to recalibration of historical results for different weapon scoring, force-aggregation, and situational-adjustment methodologies. SFS emphasizes adjusting force strengths according to operational circumstances (the “situation”) of the engagement (including surprise), with many innovative ideas (but in some major ways has little connection with previous work of others[19]). The resulting model contains many more details than historical combat data would support. It also is methodology that differs in many essential ways from that used previously by any investigator. In particular, it is doubtful that it develops force ratios in a manner consistent with Dupuy’s work.
Final Comments
Use of (sophisticated) mathematics for modeling past historical combat (and extrapolating it into the future for planning purposes) is no reason for ignoring Dupuy’s work. One would think that the current Military OR community would try to understand Dupuy’s work before trying to improve and extend it. In particular, Colonel Dupuy’s various computational procedures (including constants) must be considered as an organic whole (i.e. a system) supporting the development of functional relationships. If one ignores this computational system and simply tries to use some isolated aspect, the result may be interesting and even logically sound, but it probably lacks any scientific validity.
REFERENCES
P. Allen, “Situational Force Scoring: Accounting for Combined Arms Effects in Aggregate Combat Models,” N-3423-NA, The RAND Corporation, Santa Monica, CA, 1992.
L. B. Anderson, “A Briefing on Anti-Potential Potential (The Eigen-value Method for Computing Weapon Values), WP-2, Project 23-31, Institute for Defense Analyses, Arlington, VA, March 1974.
B. W. Bennett, et al, “RSAS 4.6 Summary,” N-3534-NA, The RAND Corporation, Santa Monica, CA, 1992.
B. W. Bennett, A. M. Bullock, D. B. Fox, C. M. Jones, J. Schrader, R. Weissler, and B. A. Wilson, “JICM 1.0 Summary,” MR-383-NA, The RAND Corporation, Santa Monica, CA, 1994.
P. K. Davis and J. A. Winnefeld, “The RAND Strategic Assessment Center: An Overview and Interim Conclusions About Utility and Development Options,” R-2945-DNA, The RAND Corporation, Santa Monica, CA, March 1983.
T.N, Dupuy, Numbers. Predictions and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles, The Bobbs-Merrill Company, Indianapolis/New York, 1979,
T.N. Dupuy, Numbers Predictions and War, Revised Edition, HERO Books, Fairfax, VA 1985.
T.N. Dupuy, Understanding War: History and Theory of Combat, Paragon House Publishers, New York, 1987.
T.N. Dupuy, Attrition: Forecasting Battle Casualties and Equipment Losses in Modem War, HERO Books, Fairfax, VA, 1990.
General Research Corporation (GRC), “A Hierarchy of Combat Analysis Models,” McLean, VA, January 1973.
Historical Evaluation and Research Organization (HERO), “Average Casualty Rates for War Games, Based on Historical Data,” 3 Volumes in 1, Dunn Loring, VA, February 1967.
E. P. Kerlin and R. H. Cole, “ATLAS: A Tactical, Logistical, and Air Simulation: Documentation and User’s Guide,” RAC-TP-338, Research Analysis Corporation, McLean, VA, April 1969 (AD 850 355).
E.P. Kerlin, L.A. Schmidt, A.J. Rolfe, M.J. Hutzler, and D,L. Moody, “The IDA Tactical Warfare Model: A Theater-Level Model of Conventional, Nuclear, and Chemical Warfare, Volume II- Detailed Description” R-21 1, Institute for Defense Analyses, Arlington, VA, October 1975 (AD B009 692L).
R. McQuie, “Military History and Mathematical Analysis,” Military Review 50, No, 5, 8-17 (1970).
S.M. Robinson, “Shadow Prices for Measures of Effectiveness, I: Linear Model,” Operations Research 41, 518-535 (1993).
J.G. Taylor, Lanchester Models of Warfare. Vols, I & II. Operations Research Society of America, Alexandria, VA, 1983. (a)
J.G. Taylor, “A Lanchester-Type Aggregated-Force Model of Conventional Ground Combat,” Naval Research Logistics Quarterly 30, 237-260 (1983). (b)
NOTES
[1] For example, see Taylor [1983a, Section 7.18], which contains a number of examples. The basic references given there may be more accessible through Robinson [I993].
[2] This term was apparently coined by L.B. Anderson [I974] (see also Kerlin et al. [1975, Chapter I, Section D.3]).
[3] The Tactical Warfare (TACWAR) model is a theater-level, joint-warfare, computer-based combat model that is currently used for decision support by the Joint Staff and essentially all CINC staffs. It was originally developed by the Institute for Defense Analyses in the mid-1970s (see Kerlin et al. [1975]), originally referred to as TACNUC, which has been continually upgraded until (and including) the present day.
[4] For example, see Kerlin and Cole [1969], GRC [1973, Fig. 6-6], or Taylor [1983b, Fig. 5] (also Taylor [1983a, Section 7.13]).
[5] The only apparent difference between Dupuy [1979] and Dupuy [1985] is the addition of an appendix (Appendix C “Modified Quantified Judgment Analysis of the Bekaa Valley Battle”) to the end of the latter (pp. 241-251). Hence, the page content is apparently the same for these two books for pp. 1-239.
[6] Technically speaking, one also has the engagement type and possibly several other descriptors (denoted in Fig. 1 as reduced list of operational circumstances) as other inputs to a historical battle.
[7] In Dupuy [1979, e.g. pp. 43-46] only environmental variables are mentioned, although basically the same formulas underlie both Dupuy [1979] and Dupuy [1987]. For simplicity, Fig. 1 and 2 follow this usage and employ the term “environmental circumstances.”
[8] In Dupuy [1979, e.g. pp. 46-47] only operational variables are mentioned, although basically the same formulas underlie both Dupuy [1979] and Dupuy [1987]. For simplicity, Fig. 1 and 2 follow this usage and employ the term “operational circumstances.”
[9] Chris Lawrence has kindly brought to my attention that since the same value for troop dispersion from an historical period (e.g. see Dupuy [1987, p. 84]) is used for both the attacker and also the defender, troop dispersion does not actually affect the determination of relative combat power PM/Pd.
[10] Eight different weapon types are considered, with three being classified as infantry weapons (e.g. see Dupuy [1979, pp, 43-44], [1981 pp. 85-86]).
[11] Chris Lawrence has kindly informed me that Dupuy‘s work on relating equipment losses to personnel losses goes back to the early 1970s and even earlier (e.g. see HERO [1966]). Moreover, Dupuy‘s [1992] book Future Wars gives some additional empirical evidence concerning the dependence of equipment losses on casualty rates.
[12] But actually going back much earlier as pointed out in the previous footnote.
[13] See Kerlin et al. [1975, Chapter I, Section D.l].
[14] See Footnote 4 above.
[15] See Kerlin et al. [1975, Chapter I, Section D.3]; see also Footnotes 1 and 2 above.
[16] The RAND Strategy Assessment System (RSAS) is a multi-theater aggregated combat model developed at RAND in the early l980s (for further details see Davis and Winnefeld [1983] and Bennett et al. [1992]). It evolved into the Joint Integrated Contingency Model (JICM), which is a post-Cold War redesign of the RSAS (starting in FY92).
[17] The Joint Integrated Contingency Model (JICM) is a game-structured computer-based combat model of major regional contingencies and higher-level conflicts, covering strategic mobility, regional conventional and nuclear warfare in multiple theaters, naval warfare, and strategic nuclear warfare (for further details, see Bennett et al. [1994]).
[18] RAND apparently replaced one weapon-scoring system by another (e.g. see Allen [1992, pp. 9, l5, and 87-89]) without making any other changes in their SFS System.
[19] For example, both Dupuy’s early HERO work (e.g. see Dupuy [1967]), reworks of these results by the Research Analysis Corporation (RAC) (e.g. see RAC [1973, Fig. 6-6]), and Dupuy’s later work (e.g. see Dupuy [1979]) considered daily fractional casualties for the attacker and also for the defender as basic casualty-outcome descriptors (see also Taylor [1983b]). However, RAND does not do this, but considers the defender’s loss rate and a casualty exchange ratio as being the basic casualty-production descriptors (Allen [1992, pp. 41-42]). The great value of using the former set of descriptors (i.e. attacker and defender fractional loss rates) is that not only is casualty assessment more straight forward (especially development of functional relationships from historical data) but also qualitative model behavior is readily deduced (see Taylor [1983b] for further details).
It appears that the Army has lowered its recruiting goals for 2018. In the first six months of the recruiting year, they brought in only 28,000 new soldiers. The goal for the year was 80,000. The overall goal is to grow the Army to 483,500. They have been able to maintain strength by retaining current soldiers (86% retention, compared to 81% in past years). Of course, the problem is the strong economy reduced recruits and “the declining quality of the youth market.”
What the article does not state is that there is a limit to how long they can maintain the force through retention. At some point, they need to recruit more.
There is one interesting statement towards the end of the article that gets my attention: “Defense officials have also complained that despite the last 16 years of war in Afghanistan, Iraq and Syria, the American public is increasingly disconnected from the military, and they say many people have misperceptions about serving and often don’t personally know any service members.”
Back in 2003-2005 we did some contracts for the state of Pennsylvania in preparation for the upcoming round of base closures. This was not our normal line of business, but some people who knew us contacted us and asked if we could help. It then got weird, because some people in Pennsylvania wondered why they were using a historical think-tank for this as opposed to all their politically connected lobbyists and consultants. So, they replaced us, except for Pittsburg, who independently maintained us as a contractor (The Military Affairs Council of Western Pennsylvania) . The end result was that all the bases targeted for closure in Eastern Pennsylvania were shut down, but Pittsburg managed to justify and keep their bases open (for the time being).
Anyhow, one of the arguments I was developing for Pennsylvania is that the U.S. military needed to maintain a presence in the Northeastern United States. As we pointed out in our first report we did in 2003 for the “Pennsylvania Department of Community and Economic Development Base Retention and Conversion-Pennsylvania Action Committee”:
As of 31 March 1943 New York, Pennsylvania and New Jersey were among the top ten states in terms of War Department civilian employment….By fiscal year 2001 no Northeastern Sates was in the top ten in terms of Army and Air Force civilian employment….One side effect of Department of Defense downsizing and the BRAC process has been a continued shrinkage of the presence of the United States Armed Forces in the Northeastern U.S. and concurrent with that, the representation of Northeasterners in the Armed Forces….The U.S. Armed Forces is in danger of being transformed from a truly national force to a force with unusually strong regional representations: with a significant portion of the U.S. Armed Forces based, oriented and recruited from the Southeast. The Dupuy Institute does not believe that these trends are healthy, either for the nation as a whole or for the Armed Forces themselves.
Just to drive home point:
The Northeast has always played a significant part in the defense of the United States. Some of America’s most famous military figures have come from the region…and yet, since World War II it appears that this pattern has shifted. For example an examination of the biographies of nineteen of the senior commanders in the U.S. military show that now only two are from the Northeast, and this from a region that constitutes one-fifth of the population of the United States….Currently (as of 2000) only 14.6 percent of all personnel recruited annually in the U.S. military are from the Northeast.
And listed under possible reasons for this shift in participation:
A stronger economy in the Northeast. U.S. military recruiting tends to be more successful in those area that have a lower per capita income. The Northeast has historically been one of the wealthiest areas of the U.S.
A lack of major U.S. military presence in the Northeast…..They all reduce the visibility of the military in the region relative to other regions of the U.S.
A lack of military families in the Northeast…..And since volunteers for military service often come from military families the reduced presence of the military in the Northeast has probably led to a decline in recruitment from the region…
Cultural differences. For a variety of personal, political and economic reasons the citizens of the Northeast may be less likely to join the military.
Anyhow, this is part of a larger concern that I have had with our all-volunteer military becoming increasing regionally based and not being representative of the United States population as a whole.
Allied force dispositions at the Battle of Anzio, on 1 February 1944. [U.S. Army/Wikipedia]
[The article below is reprinted from History, Numbers And War: A HERO Journal, Vol. 1, No. 1, Spring 1977, pp. 34-52]
The Lanchester Equations and Historical Warfare: An Analysis of Sixty World War II Land Engagements
By Janice B. Fain
Background and Objectives
The method by which combat losses are computed is one of the most critical parts of any combat model. The Lanchester equations, which state that a unit’s combat losses depend on the size of its opponent, are widely used for this purpose.
In addition to their use in complex dynamic simulations of warfare, the Lanchester equations have also sewed as simple mathematical models. In fact, during the last decade or so there has been an explosion of theoretical developments based on them. By now their variations and modifications are numerous, and “Lanchester theory” has become almost a separate branch of applied mathematics. However, compared with the effort devoted to theoretical developments, there has been relatively little empirical testing of the basic thesis that combat losses are related to force sizes.
One of the first empirical studies of the Lanchester equations was Engel’s classic work on the Iwo Jima campaign in which he found a reasonable fit between computed and actual U.S. casualties (Note 1). Later studies were somewhat less supportive (Notes 2 and 3), but an investigation of Korean war battles showed that, when the simulated combat units were constrained to follow the tactics of their historical counterparts, casualties during combat could be predicted to within 1 to 13 percent (Note 4).
Taken together, these various studies suggest that, while the Lanchester equations may be poor descriptors of large battles extending over periods during which the forces were not constantly in combat, they may be adequate for predicting losses while the forces are actually engaged in fighting. The purpose of the work reported here is to investigate 60 carefully selected World War II engagements. Since the durations of these battles were short (typically two to three days), it was expected that the Lanchester equations would show a closer fit than was found in studies of larger battles. In particular, one of the objectives was to repeat, in part, Willard’s work on battles of the historical past (Note 3).
The Data Base
Probably the most nearly complete and accurate collection of combat data is the data on World War II compiled by the Historical Evaluation and Research Organization (HERO). From their data HERO analysts selected, for quantitative analysis, the following 60 engagements from four major Italian campaigns:
Salerno, 9-18 Sep 1943, 9 engagements
Volturno, 12 Oct-8 Dec 1943, 20 engagements
Anzio, 22 Jan-29 Feb 1944, 11 engagements
Rome, 14 May-4 June 1944, 20 engagements
The complete data base is described in a HERO report (Note 5). The work described here is not the first analysis of these data. Statistical analyses of weapon effectiveness and the testing of a combat model (the Quantified Judgment Method, QJM) have been carried out (Note 6). The work discussed here examines these engagements from the viewpoint of the Lanchester equations to consider the question: “Are casualties during combat related to the numbers of men in the opposing forces?”
The variables chosen for this analysis are shown in Table 1. The “winners” of the engagements were specified by HERO on the basis of casualties suffered, distance advanced, and subjective estimates of the percentage of the commander’s objective achieved. Variable 12, the Combat Power Ratio, is based on the Operational Lethality Indices (OLI) of the units (Note 7).
The general characteristics of the engagements are briefly described. Of the 60, there were 19 attacks by British forces, 28 by U.S. forces, and 13 by German forces. The attacker was successful in 34 cases; the defender, in 23; and the outcomes of 3 were ambiguous. With respect to terrain, 19 engagements occurred in flat terrain; 24 in rolling, or intermediate, terrain; and 17 in rugged, or difficult, terrain. Clear weather prevailed in 40 cases; 13 engagements were fought in light or intermittent rain; and 7 in medium or heavy rain. There were 28 spring and summer engagements and 32 fall and winter engagements.
Comparison of World War II Engagements With Historical Battles
Since one purpose of this work is to repeat, in part, Willard’s analysis, comparison of these World War II engagements with the historical battles (1618-1905) studied by him will be useful. Table 2 shows a comparison of the distribution of battles by type. Willard’s cases were divided into two categories: I. meeting engagements, and II. sieges, attacks on forts, and similar operations. HERO’s World War II engagements were divided into four types based on the posture of the defender: 1. delay, 2. hasty defense, 3. prepared position, and 4. fortified position. If postures 1 and 2 are considered very roughly equivalent to Willard’s category I, then in both data sets the division into the two gross categories is approximately even.
The distribution of engagements across force ratios, given in Table 3, indicated some differences. Willard’s engagements tend to cluster at the lower end of the scale (1-2) and at the higher end (4 and above), while the majority of the World War II engagements were found in mid-range (1.5 – 4) (Note 8). The frequency with which the numerically inferior force achieved victory is shown in Table 4. It is seen that in neither data set are force ratios good predictors of success in battle (Note 9).
Table 3.
Results of the Analysis Willard’s Correlation Analysis
There are two forms of the Lanchester equations. One represents the case in which firing units on both sides know the locations of their opponents and can shift their fire to a new target when a “kill” is achieved. This leads to the “square” law where the loss rate is proportional to the opponent’s size. The second form represents that situation in which only the general location of the opponent is known. This leads to the “linear” law in which the loss rate is proportional to the product of both force sizes.
As Willard points out, large battles are made up of many smaller fights. Some of these obey one law while others obey the other, so that the overall result should be a combination of the two. Starting with a general formulation of Lanchester’s equations, where g is the exponent of the target unit’s size (that is, g is 0 for the square law and 1 for the linear law), he derives the following linear equation:
log (nc/mc) = log E + g log (mo/no) (1)
where nc and mc are the casualties, E is related to the exchange ratio, and mo and no are the initial force sizes. Linear regression produces a value for g. However, instead of lying between 0 and 1, as expected, the) g‘s range from -.27 to -.87, with the majority lying around -.5. (Willard obtains several values for g by dividing his data base in various ways—by force ratio, by casualty ratio, by historical period, and so forth.) A negative g value is unpleasant. As Willard notes:
Military theorists should be disconcerted to find g < 0, for in this range the results seem to imply that if the Lanchester formulation is valid, the casualty-producing power of troops increases as they suffer casualties (Note 3).
From his results, Willard concludes that his analysis does not justify the use of Lanchester equations in large-scale situations (Note 10).
Analysis of the World War II Engagements
Willard’s computations were repeated for the HERO data set. For these engagements, regression produced a value of -.594 for g (Note 11), in striking agreement with Willard’s results. Following his reasoning would lead to the conclusion that either the Lanchester equations do not represent these engagements, or that the casualty producing power of forces increases as their size decreases.
However, since the Lanchester equations are so convenient analytically and their use is so widespread, it appeared worthwhile to reconsider this conclusion. In deriving equation (1), Willard used binomial expansions in which he retained only the leading terms. It seemed possible that the poor results might he due, in part, to this approximation. If the first two terms of these expansions are retained, the following equation results:
log (nc/mc) = log E + log (Mo-mc)/(no-nc) (2)
Repeating this regression on the basis of this equation leads to g = -.413 (Note 12), hardly an improvement over the initial results.
A second attempt was made to salvage this approach. Starting with raw OLI scores (Note 7), HERO analysts have computed “combat potentials” for both sides in these engagements, taking into account the operational factors of posture, vulnerability, and mobility; environmental factors like weather, season, and terrain; and (when the record warrants) psychological factors like troop training, morale, and the quality of leadership. Replacing the factor (mo/no) in Equation (1) by the combat power ratio produces the result) g = .466 (Note 13).
While this is an apparent improvement in the value of g, it is achieved at the expense of somewhat distorting the Lanchester concept. It does preserve the functional form of the equations, but it requires a somewhat strange definition of “killing rates.”
Analysis Based on the Differential Lanchester Equations
Analysis of the type carried out by Willard appears to produce very poor results for these World War II engagements. Part of the reason for this is apparent from Figure 1, which shows the scatterplot of the dependent variable, log (nc/mc), against the independent variable, log (mo/no). It is clear that no straight line will fit these data very well, and one with a positive slope would not be much worse than the “best” line found by regression. To expect the exponent to account for the wide variation in these data seems unreasonable.
Here, a simpler approach will be taken. Rather than use the data to attempt to discriminate directly between the square and the linear laws, they will be used to estimate linear coefficients under each assumption in turn, starting with the differential formulation rather than the integrated equations used by Willard.
In their simplest differential form, the Lanchester equations may be written;
Square Law; dA/dt = -kdD and dD/dt = kaA (3)
Linear law: dA/dt = -k’dAD and dD/dt = k’aAD (4)
where
A(D) is the size of the attacker (defender)
dA/dt (dD/dt) is the attacker’s (defender’s) loss rate,
ka, k’a (kd, k’d) are the attacker’s (defender’s) killing rates
For this analysis, the day is taken as the basic time unit, and the loss rate per day is approximated by the casualties per day. Results of the linear regressions are given in Table 5. No conclusions should be drawn from the fact that the correlation coefficients are higher in the linear law case since this is expected for purely technical reasons (Note 14). A better picture of the relationships is again provided by the scatterplots in Figure 2. It is clear from these plots that, as in the case of the logarithmic forms, a single straight line will not fit the entire set of 60 engagements for either of the dependent variables.
To investigate ways in which the data set might profitably be subdivided for analysis, T-tests of the means of the dependent variable were made for several partitionings of the data set. The results, shown in Table 6, suggest that dividing the engagements by defense posture might prove worthwhile.
Results of the linear regressions by defense posture are shown in Table 7. For each posture, the equation that seemed to give a better fit to the data is underlined (Note 15). From this table, the following very tentative conclusions might be drawn:
In an attack on a fortified position, the attacker suffers casualties by the square law; the defender suffers casualties by the linear law. That is, the defender is aware of the attacker’s position, while the attacker knows only the general location of the defender. (This is similar to Deitchman’s guerrilla model. Note 16).
This situation is apparently reversed in the cases of attacks on prepared positions and hasty defenses.
Delaying situations seem to be treated better by the square law for both attacker and defender.
Table 8 summarizes the killing rates by defense posture. The defender has a much higher killing rate than the attacker (almost 3 to 1) in a fortified position. In a prepared position and hasty defense, the attacker appears to have the advantage. However, in a delaying action, the defender’s killing rate is again greater than the attacker’s (Note 17).
Figure 3 shows the scatterplots for these cases. Examination of these plots suggests that a tentative answer to the study question posed above might be: “Yes, casualties do appear to be related to the force sizes, but the relationship may not be a simple linear one.”
In several of these plots it appears that two or more functional forms may be involved. Consider, for example, the defender‘s casualties as a function of the attacker’s initial strength in the case of a hasty defense. This plot is repeated in Figure 4, where the points appear to fit the curves sketched there. It would appear that there are at least two, possibly three, separate relationships. Also on that plot, the individual engagements have been identified, and it is interesting to note that on the curve marked (1), five of the seven attacks were made by Germans—four of them from the Salerno campaign. It would appear from this that German attacks are associated with higher than average defender casualties for the attacking force size. Since there are so few data points, this cannot be more than a hint or interesting suggestion.
Future Research
This work suggests two conclusions that might have an impact on future lines of research on combat dynamics:
Tactics appear to be an important determinant of combat results. This conclusion, in itself, does not appear startling, at least not to the military. However, it does not always seem to have been the case that tactical questions have been considered seriously by analysts in their studies of the effects of varying force levels and force mixes.
Historical data of this type offer rich opportunities for studying the effects of tactics. For example, consideration of the narrative accounts of these battles might permit re-coding the engagements into a larger, more sensitive set of engagement categories. (It would, of course, then be highly desirable to add more engagements to the data set.)
While predictions of the future are always dangerous, I would nevertheless like to suggest what appears to be a possible trend. While military analysis of the past two decades has focused almost exclusively on the hardware of weapons systems, at least part of our future analysis will be devoted to the more behavioral aspects of combat.
Janice Bloom Fain, a Senior Associate of CACI, lnc., is a physicist whose special interests are in the applications of computer simulation techniques to industrial and military operations; she is the author of numerous reports and articles in this field. This paper was presented by Dr. Fain at the Military Operations Research Symposium at Fort Eustis, Virginia.
[5.] HERO, “A Study of the Relationship of Tactical Air Support Operations to Land Combat, Appendix B, Historical Data Base.” Historical Evaluation and Research Organization, report prepared for the Defense Operational Analysis Establishment, U.K.T.S.D., Contract D-4052 (1971).
[6.] T. N. Dupuy, The Quantified Judgment Method of Analysis of Historical Combat Data, HERO Monograph, (January 1973); HERO, “Statistical Inference in Analysis in Combat,” Annex F, Historical Data Research on Tactical Air Operations, prepared for Headquarters USAF, Assistant Chief of Staff for Studies and Analysis, Contract No. F-44620-70-C-0058 (1972).
[7.] The Operational Lethality Index (OLI) is a measure of weapon effectiveness developed by HERO.
[8.] Since Willard’s data did not indicate which side was the attacker, his force ratio is defined to be (larger force/smaller force). The HERO force ratio is (attacker/defender).
[9.] Since the criteria for success may have been rather different for the two sets of battles, this comparison may not be very meaningful.
[10.] This work includes more complex analysis in which the possibility that the two forces may be engaging in different types of combat is considered, leading to the use of two exponents rather than the single one, Stochastic combat processes are also treated.
[11.] Correlation coefficient = -.262;
Intercept = .00115; slope = -.594.
[12.] Correlation coefficient = -.184;
Intercept = .0539; slope = -,413.
[13.] Correlation coefficient = .303;
Intercept = -.638; slope = .466.
[14.] Correlation coefficients for the linear law are inflated with respect to the square law since the independent variable is a product of force sizes and, thus, has a higher variance than the single force size unit in the square law case.
[15.] This is a subjective judgment based on the following considerations Since the correlation coefficient is inflated for the linear law, when it is lower the square law case is chosen. When the linear law correlation coefficient is higher, the case with the intercept closer to 0 is chosen.
[17.] As pointed out by Mr. Alan Washburn, who prepared a critique on this paper, when comparing numerical values of the square law and linear law killing rates, the differences in units must be considered. (See footnotes to Table 7).
French retreat from Russia in 1812 by Illarion Mikhailovich Pryanishnikov (1812) [Wikipedia]
After discussing with Chris the series of recent posts on the subject of breakpoints, it seemed appropriate to provide a better definition of exactly what a breakpoint is.
Dorothy Kneeland Clark was the first to define the notion of a breakpoint in her study, Casualties as a Measure of the Loss of Combat Effectiveness of an Infantry Battalion (Operations Research Office, The Johns Hopkins University: Baltimore, 1954). She found it was not quite as clear-cut as it seemed and the working definition she arrived at was based on discussions and the specific combat outcomes she found in her data set [pp 9-12].
DETERMINATION OF BREAKPOINT
The following definitions were developed out of many discussions. A unit is considered to have lost its combat effectiveness when it is unable to carry out its mission. The onset of this inability constitutes a breakpoint. A unit’s mission is the objective assigned in the current operations order or any other instructional directive, written or verbal. The objective may be, for example, to attack in order to take certain positions, or to defend certain positions.
How does one determine when a unit is unable to carry out its mission? The obvious indication is a change in operational directive: the unit is ordered to stop short of its original goal, to hold instead of attack, to withdraw instead of hold. But one or more extraneous elements may cause the issue of such orders:
(1) Some other unit taking part in the operation may have lost its combat effectiveness, and its predicament may force changes, in the tactical plan. For example the inability of one infantry battalion to take a hill may require that the two adjoining battalions be stopped to prevent exposing their flanks by advancing beyond it.
(2) A unit may have been assigned an objective on the basis of a G-2 estimate of enemy weakness which, as the action proceeds, proves to have been over-optimistic. The operations plan may, therefore, be revised before the unit has carried out its orders to the point of losing combat effectiveness.
(3) The commanding officer, for reasons quite apart from the tactical attrition, may change his operations plan. For instance, General Ridgway in May 1951 was obliged to cancel his plans for a major offensive north of the 38th parallel in Korea in obedience to top level orders dictated by political considerations.
(4) Even if the supposed combat effectiveness of the unit is the determining factor in the issuance of a revised operations order, a serious difficulty in evaluating the situation remains. The commanding officer’s decision is necessarily made on the basis of information available to him plus his estimate of his unit’s capacities. Either or both of these bases may be faulty. The order may belatedly recognize a collapse which has in factor occurred hours earlier, or a commanding officer may withdraw a unit which could hold for a much longer time.
It was usually not hard to discover when changes in orders resulted from conditions such as the first three listed above, but it proved extremely difficult to distinguish between revised orders based on a correct appraisal of the unit’s combat effectiveness and those issued in error. It was concluded that the formal order for a change in mission cannot be taken as a definitive indication of the breakpoint of a unit. It seemed necessary to go one step farther and search the records to learn what a given battalion did regardless of provisions in formal orders…
CATEGORIES OF BREAKPOINTS SELECTED
In the engagements studied the following categories of breakpoint were finally selected:
Category of Breakpoint
No. Analyzed
I. Attack [Symbol] rapid reorganization [Symbol] attack
9
II. Attack [Symbol] defense (no longer able to attack without a few days of recuperation and reinforcement
21
III. Defense [Symbol] withdrawal by order to a secondary line
13
IV. Defense [Symbol] collapse
5
Disorganization and panic were taken as unquestionable evidence of loss of combat effectiveness. It appeared, however, that there were distinct degrees of magnitude in these experiences. In addition to the expected breakpoints at attack [Symbol] defense and defense [Symbol] collapse, a further category, I, seemed to be indicated to include situations in which an attacking battalion was ‘pinned down” or forced to withdraw in partial disorder but was able to reorganize in 4 to 24 hours and continue attacking successfully.
Category II includes (a) situations in which an attacking battalion was ordered into the defensive after severe fighting or temporary panic; (b) situations in which a battalion, after attacking successfully, failed to gain ground although still attempting to advance and was finally ordered into defense, the breakpoint being taken as occurring at the end of successful advance. In other words, the evident inability of the unit to fulfill its mission was used as the criterion for the breakpoint whether orders did or did not recognize its inability. Battalions after experiencing such a breakpoint might be able to recuperate in a few days to the point of renewing successful attack or might be able to continue for some time in defense.
The sample of breakpoints coming under category IV, defense [Symbol] collapse, proved to be very small (5) and unduly weighted in that four of the examples came from the same engagement. It was, therefore, discarded as probably not representative of the universe of category IV breakpoints,* and another category (III) was added: situations in which battalions on the defense were ordered withdrawn to a quieter sector. Because only those instances were included in which the withdrawal orders appeared to have been dictated by the condition of the unit itself, it is believed that casualty levels for this category can be regarded as but slightly lower than those associated with defense [Symbol] collapse.
In both categories II and III, “‘defense” represents an active situation in which the enemy is attacking aggressively.
* It had been expected that breakpoints in this category would be associated with very high losses. Such did not prove to be the case. In whatever way the data were approached, most of the casualty averages were only slightly higher than those associated with category II (attack [Symbol] defense), although the spread in data was wider. It is believed that factors other than casualties, such as bad weather, difficult terrain, and heavy enemy artillery fire undoubtedly played major roles in bringing about the collapse in the four units taking part in the same engagement. Furthermore, the casualty figures for the four units themselves is in question because, as the situation deteriorated, many of the men developed severe cases of trench foot and combat exhaustion, but were not evacuated, as they would have been in a less desperate situation, and did not appear in the casualty records until they had made their way to the rear after their units had collapsed.
In 1987-1988, Trevor Dupuy and colleagues at Data Memory Systems, Inc. (DMSi), Janice Fain, Rich Anderson, Gay Hammerman, and Chuck Hawkins sought to create a broader, more generally applicable definition for breakpoints for the study, Forced Changes of Combat Posture (DMSi, Fairfax, VA, 1988) [pp. I-2-3]
The combat posture of a military force is the immediate intention of its commander and troops toward the opposing enemy force, together with the preparations and deployment to carry out that intention. The chief combat postures are attack, defend, delay, and withdraw.
A change in combat posture (or posture change) is a shift from one posture to another, as, for example, from defend to attack or defend to withdraw. A posture change can be either voluntary or forced.
A forced posture change (FPC) is a change in combat posture by a military unit that is brought about, directly or indirectly, by enemy action. Forced posture changes are characteristically and almost always changes to a less aggressive posture. The most usual FPCs are from attack to defend and from defend to withdraw (or retrograde movement). A change from withdraw to combat ineffectiveness is also possible.
Breakpoint is a term sometimes used as synonymous with forced posture change, and sometimes used to mean the collapse of a unit into ineffectiveness or rout. The latter meaning is probably more common in general usage, while forced posture change is the more precise term for the subject of this study. However, for brevity and convenience, and because this study has been known informally since its inception as the “Breakpoints” study, the term breakpoint is sometimes used in this report. When it is used, it is synonymous with forced posture change.
Hopefully this will help clarify the previous discussions of breakpoints on the blog.
People do send me some damn interesting stuff. Someone just sent me a page clipped from U.S. Army FM 3-0 Operations, dated 6 October 2017. There is a discussion in Chapter 7 on “penetration.” This brief discussion on paragraph 7-115 states in part:
7-115. A penetration is a form of maneuver in which an attacking force seeks to rupture enemy defenses on a narrow front to disrupt the defensive system (FM 3-90-1) ….The First U.S. Army’s Operation Cobra (the breakout from the Normandy lodgment in July 1944) is a classic example of a penetration. Figure 7-10 illustrates potential correlation of forces or combat power for a penetration…..”
This is figure 7-10:
So:
Corps shaping operations: 3:1
Corps decisive operations: 9-1
Lead battalion: 18-1
Now, in contrast, let me pull some material from War by Numbers:
From page 10:
European Theater of Operations (ETO) Data, 1944
Force Ratio Result Percent Failure Number of cases
0.55 to 1.01-to-1.00 Attack Fails 100% 5
1.15 to 1.88-to-1.00 Attack usually succeeds 21% 48
1.95 to 2.56-to-1.00 Attack usually succeeds 10% 21
2.71-to-1.00 and higher Attacker Advances 0% 42
Note that these are division-level engagements. I guess I could assemble the same data for corps-level engagements, but I don’t think it would look much different.
From page 210:
Force Ratio…………Cases……Terrain…….Result
1.18 to 1.29 to 1 4 Nonurban Defender penetrated
1.51 to 1.64 3 Nonurban Defender penetrated
2.01 to 2.64 2 Nonurban Defender penetrated
3.03 to 4.28 2 Nonurban Defender penetrated
4.16 to 4.78 2 Urban Defender penetrated
6.98 to 8.20 2 Nonurban Defender penetrated
6.46 to 11.96 to 1 2 Urban Defender penetrated
These are also division-level engagements from the ETO. One will note that out of 17 cases where the defender was penetrated, only once was the force ratio as high as 9 to 1. The mean force ratio for these 17 cases is 3.77 and the median force ratio is 2.64.
Now the other relevant tables in this book are in Chapter 8: Outcome of Battles (page 60-71). There I have a set of tables looking at the loss rates based upon one of six outcomes. Outcome V is defender penetrated. Unfortunately, as the purpose of the project was to determine prisoner of war capture rates, we did not bother to calculate the average force ratio for each outcome. But, knowing the database well, the average force ratio for defender penetrated results may be less than 3-to-1 and is certainly is less than 9-to-1. Maybe I will take few days at some point and put together a force ratio by outcome table.
Now, the source of FM 3.0 data is not known to us and is not referenced in the manual. Why they don’t provide such a reference is a mystery to me, as I can point out several examples of this being an issue. On more than one occasion data has appeared in Army manuals that we can neither confirm or check, and which we could never find the source for. But…it is not referenced. I have not looked at the operation in depth, but don’t doubt that at some point during Cobra they had a 9:1 force ratio and achieved a penetration. But…..this is different than leaving the impression that a 9:1 force ratio is needed to achieve a penetration. I do not know it that was the author’s intent, but it is something that the casual reader might infer. This probably needs to be clarified.
Missile fire lit up the Damascus sky last week as the U.S. and allies launched an attack on chemical weapons sites. [Hassan Ammar, AP/USA Today]
Even as pundits and wonks debate the political and strategic impact of the 14 April combined U.S., British, and French cruise missile strike on Assad regime chemical warfare targets in Syria, it has become clear that effort was a notable tactical success.
Despite ample warning that the strike was coming, the Syrian regime’s Russian-made S-200 surface-to-air missile defense system failed to shoot down a single incoming missile. The U.S. Defense Department claimed that all 105 cruise missiles fired struck their targets. It also reported that the Syrians fired 40 interceptor missiles but nearly all launched after the incoming cruise missiles had already struck their targets.
Although cruise missiles are difficult to track and engage even with fully modernized air defense systems, the dismal performance of the Syrian network was a surprise to many analysts given the wary respect paid to it by U.S. military leaders in the recent past. Although the S-200 dates from the 1960s, many surmise an erosion in the combat effectiveness of the personnel manning the system is the real culprit.
[A] lack of training, command and control and other human factors are probably responsible for the failure, analysts said.
“It’s not just about the physical capability of the air defense system,” said David Deptula, a retired, three-star Air Force general. “It’s about the people who are operating the system.”
The Syrian regime has become dependent upon assistance from Russia and Iran to train, equip, and maintain its military forces. Russian forces in Syria have deployed the more sophisticated S-400 air defense system to protect their air and naval bases, which reportedly tracked but did not engage the cruise missile strike. The Assad regime is also believed to field the Russian-made Pantsir missile and air-defense artillery system, but it likely was not deployed near enough to the targeted facilities to help.
Despite the pervasive role technology plays in modern warfare, the human element remains the most important factor in determining combat effectiveness.
U.S. Marines from the The 11th MEU fire their M777 Lightweight 155mm Howitzer during Exercise Alligator Dagger, Dec. 18, 2016. (U.S. Marine Corps/Lance Cpl. Zachery C. Laning/Military.com)
According to Army historian Luke O’Brian, the Fiscal Year 2019 Defense budget includes funds to buy 28,737 XM1156 Precision Guided Kit (PGK) 155mm howitzer munitions, which includes replacements for the 6,269 rounds expended during Operation INHERENT RESOLVE. O’Brian also notes that the Army will also buy 2,162 M982 Excalibur 155mm rounds in 2019 and several hundred each in following years.
While the numbers appear large at first glance, data on U.S. artillery expenditures in Operation DESERT STORM and IRAQI FREEDOM (also via Luke O’Brian) shows just how much the volume of long-range fires has changed just since 1991. For the U.S. at least, precision fires have indeed replaced mass fires on the battlefield.
This map is from 2003.
